what is observation schedule in research

What Is an Observation Schedule?

Cari coleman.

Observation schedules are one of many essential analytical devices that scientists can use to turn multifaceted and complex visual observations into usable research data.

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An observation schedule is an analytical form, or coding sheet, filled out by researchers during structured observation. It carefully specifies beforehand the categories of behaviors or events under scrutiny and under what circumstances they should be assigned to those categories. Observations are then fragmented, or coded, into these more manageable pieces of information, which are later aggregated into usable, quantifiable data.

Observation schedules are utilized primarily in the fields of education, psychology, speech and language therapy, learning and behavioral therapy and market research. Schedules can range from exceedingly complex multiple-page examinations to simple tally sheets. Types of observation schedules include event sampling, time sampling, interval recording, rating scales and duration recording.

One of the most widely known and sophisticated observation schedules is the Autism Diagnostic Observation Schedule (ADOS), which systematically tests for telltale signs of autism in its subjects. Other notable examples include the Modified-Classroom Observation Schedule to Measure Intentional Communication (M-COSMIC) and the Flanders Interaction Analysis Categories.

About the Author

Cari Coleman has been a writer since 2004. She's written articles for the "Sinclair Lewis Society Newsletter," developed brochures for "Mid-Central Community Action" and produced a book for Elisavietta Ritchie. Coleman has a Master of Arts in English from Illinois State University. Currently she's working on a short-story collection entitled "Midnight Snacks."

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Observation

Observation, as the name implies, is a way of collecting data through observing. This data collection method is classified as a participatory study, because the researcher has to immerse herself in the setting where her respondents are, while taking notes and/or recording. Observation data collection method may involve watching, listening, reading, touching, and recording behavior and characteristics of phenomena.

Observation as a data collection method can be structured or unstructured. In structured or systematic observation, data collection is conducted using specific variables and according to a pre-defined schedule. Unstructured observation, on the other hand, is conducted in an open and free manner in a sense that there would be no pre-determined variables or objectives.

Moreover, this data collection method can be divided into overt or covert categories. In overt observation research subjects are aware that they are being observed. In covert observation, on the other hand, the observer is concealed and sample group members are not aware that they are being observed. Covert observation is considered to be more effective because in this case sample group members are likely to behave naturally with positive implications on the authenticity of research findings.

Advantages of observation data collection method include direct access to research phenomena, high levels of flexibility in terms of application and generating a permanent record of phenomena to be referred to later. At the same time, this method is disadvantaged with longer time requirements, high levels of observer bias, and impact of observer on primary data, in a way that presence of observer may influence the behaviour of sample group elements.

It is important to note that observation data collection method may be associated with certain ethical issues. As it is discussed further below in greater details, fully informed consent of research participant(s) is one of the basic ethical considerations to be adhered to by researchers. At the same time, the behaviour of sample group members may change with negative implications on the level of research validity if they are notified about the presence of the observer.

This delicate matter needs to be addressed by consulting with dissertation supervisor, and commencing the primary data collection process only after ethical aspects of the issue have been approved by the supervisor.

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• What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on 5 April 2022 by Tegan George . Revised on 20 March 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs experiment, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies.

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyse a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analysing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for ethical or practical reasons, or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

• Are you determining what you want to observe beforehand, or going in open-minded?
• Is there another research method that would make sense in tandem with an observational study?
• If yes, make sure you conduct a covert observation.
• If not, think about whether observing from afar or actively participating in your observation is a better fit.
• How can you preempt confounding variables that could impact your analysis?
• You could observe the children playing at the playground in a naturalistic observation.
• You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
• You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organised. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

• Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
• Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
• Get informed consent from your participants (or their parents) if you want to record:  Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or ‘lurking’ variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyse your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive or deductive approach :

• If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
• If you had specific hypotheses prior to conducting your observations, a deductive approach analyses whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis.

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

• Observational studies can provide information about difficult-to-analyse topics in a low-cost, efficient manner.
• They allow you to study subjects that cannot be randomised safely, efficiently, or ethically .
• They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilise preexisting data.
• They’re often invaluable in informing later, larger-scale clinical trials or experiments.

Disadvantages

• Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables.
• They lack conclusive results, typically are not externally valid or generalisable, and can usually only form a basis for further research.
• They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomise your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Tegan George

Effective Guidelines for Conducting Observation Research

Table of Contents

Have you ever wondered how psychologists gather authentic data about human behavior without influencing it? One key method they use is observation , a foundational approach that can yield deep insights when done correctly. In this post, we’ll walk through the essential guidelines for conducting effective observation research, ensuring that you can confidently plan, execute, and interpret your studies.

Understanding the Types of Observation

Before diving into data collection, it’s crucial to understand the different types of observation methods available. Each type serves a unique purpose and can affect the outcome of your study.

Structured vs. Unstructured Observation

Structured observation involves specific criteria and a systematic approach to recording behavior, while unstructured observation is more open-ended, allowing the observer to record all that occurs without a predetermined scheme.

Participant vs. Non-participant Observation

In participant observation, the researcher becomes a part of the group being observed, which can provide an inside view of the group’s dynamics. Non\-participant observation , on the other hand, requires the observer to remain detached from the group.

Covert vs. Overt Observation

When observers do not reveal their research intentions to the subjects, it is known as covert observation. This can help in capturing unaltered behavior but poses ethical questions. Overt observation is where the subjects are aware they are being observed, which can influence their behavior but is ethically transparent.

Preparing for Data Collection

Preparation is key to successful observation research. There are several steps you need to take before you begin collecting data.

Choosing the Right Setting and Subjects

Select a setting that is natural for the subjects and conducive to observing the behaviors of interest. The subjects should also be representative of the population you’re studying.

Developing the Observation Guide

Create an observation guide that details what behaviors or events are to be observed and recorded. This will ensure consistency and reliability in your data collection.

Training Observers

Ensure that all observers are thoroughly trained to use the observation guide and understand the nuances of the research. This reduces the risk of observer bias .

Executing the Observation Study

With preparation out of the way, it’s time to begin the actual observation. This phase is critical, as it’s where the data you’ll be analyzing is collected.

Recording Observations

Meticulous note-taking is a must. Use audio or video recording if possible, to capture details that might be missed in real-time.

Staying Unobtrusive

Whether you’re participating or not, staying unobtrusive helps in maintaining the integrity of the observed behaviors.

Being Ethical

Always adhere to ethical guidelines . If the observation is covert, ensure that it doesn’t intrude on privacy and is justified by the study’s potential benefits.

Analyzing and Interpreting Observational Data

Once you’ve gathered your data, the next step is to make sense of it. This phase is as important as the data collection itself.

Coding and Organizing Data

Develop a coding system to categorize the behaviors observed. This helps in organizing the data for analysis.

Identifying Patterns and Themes

Look for recurring behaviors or events that signify underlying patterns or themes in your data.

Maintaining Objectivity

It’s essential to remain objective during analysis. Avoid imposing your biases or expectations on the data.

Reporting Your Findings

Reporting your findings with clarity and honesty is the final step in the research process. Your report should be detailed enough to allow others to replicate the study if they wish.

Describing the Methodology

Detail the observation method used, the setting, and the subjects, so the context of the study is clear.

Presenting the Data

Present your data in a way that is both accessible and comprehensible, using visuals like charts or graphs where appropriate.

Discussing the Implications

Discuss what your findings mean in the broader context of the research question, and suggest areas for further study.

Observation research is a powerful method in psychology, allowing researchers to gather data in natural settings and gain insights that might otherwise be inaccessible. By following these guidelines, from choosing the right type of observation to effectively analyzing and reporting data, you can enhance the validity and reliability of your observational studies. It’s a meticulous process, but with careful planning and execution, your observations can lead to significant contributions in the field of psychology.

What do you think? How might the presence of an observer alter the behavior of the subjects, and how can this impact be minimized? Are there ethical considerations that might limit the use of observation research, and how should they be addressed? Share your thoughts and let’s delve into the complexities of observation research together.

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Research Methods in Psychology

1 Introduction to Psychological Research – Objectives and Goals, Problems, Hypothesis and Variables

• Nature of Psychological Research
• The Context of Discovery
• Context of Justification
• Characteristics of Psychological Research
• Goals and Objectives of Psychological Research

2 Introduction to Psychological Experiments and Tests

• Independent and Dependent Variables
• Extraneous Variables
• Experimental and Control Groups
• Introduction of Test
• Types of Psychological Test
• Uses of Psychological Tests

3 Steps in Research

• Research Process
• Identification of the Problem
• Review of Literature
• Formulating a Hypothesis
• Identifying Manipulating and Controlling Variables
• Formulating a Research Design
• Constructing Devices for Observation and Measurement
• Sample Selection and Data Collection
• Data Analysis and Interpretation
• Hypothesis Testing
• Drawing Conclusion

4 Types of Research and Methods of Research

• Historical Research
• Descriptive Research
• Correlational Research
• Qualitative Research
• Ex-Post Facto Research
• True Experimental Research
• Quasi-Experimental Research

5 Definition and Description Research Design, Quality of Research Design

• Research Design
• Purpose of Research Design
• Design Selection
• Criteria of Research Design
• Qualities of Research Design

6 Experimental Design (Control Group Design and Two Factor Design)

• Experimental Design
• Control Group Design
• Two Factor Design

7 Survey Design

• Survey Research Designs
• Steps in Survey Design
• Structuring and Designing the Questionnaire
• Interviewing Methodology
• Data Analysis
• Final Report

8 Single Subject Design

• Single Subject Design: Definition and Meaning
• Phases Within Single Subject Design
• Requirements of Single Subject Design
• Characteristics of Single Subject Design
• Types of Single Subject Design
• Advantages of Single Subject Design
• Disadvantages of Single Subject Design

9 Observation Method

• Definition and Meaning of Observation
• Characteristics of Observation
• Types of Observation
• Advantages and Disadvantages of Observation
• Guides for Observation Method

10 Interview and Interviewing

• Definition of Interview
• Types of Interview
• Aspects of Qualitative Research Interviews
• Interview Questions
• Convergent Interviewing as Action Research
• Research Team

11 Questionnaire Method

• Definition and Description of Questionnaires
• Types of Questionnaires
• Purpose of Questionnaire Studies
• Designing Research Questionnaires
• The Methods to Make a Questionnaire Efficient
• The Types of Questionnaire to be Included in the Questionnaire
• Advantages and Disadvantages of Questionnaire
• When to Use a Questionnaire?

12 Case Study

• Definition and Description of Case Study Method
• Historical Account of Case Study Method
• Designing Case Study
• Requirements for Case Studies
• Guideline to Follow in Case Study Method
• Other Important Measures in Case Study Method
• Case Reports

13 Report Writing

• Purpose of a Report
• Writing Style of the Report
• Report Writing – the Do’s and the Don’ts
• Format for Report in Psychology Area
• Major Sections in a Report

14 Review of Literature

• Purposes of Review of Literature
• Sources of Review of Literature
• Types of Literature
• Writing Process of the Review of Literature
• Preparation of Index Card for Reviewing and Abstracting

15 Methodology

• Definition and Purpose of Methodology
• Participants (Sample)
• Apparatus and Materials

16 Result, Analysis and Discussion of the Data

• Definition and Description of Results
• Statistical Presentation
• Tables and Figures

17 Summary and Conclusion

• Summary Definition and Description
• Guidelines for Writing a Summary
• Writing the Summary and Choosing Words
• A Process for Paraphrasing and Summarising
• Summary of a Report
• Writing Conclusions

18 References in Research Report

• Reference List (the Format)
• References (Process of Writing)
• Reference List and Print Sources
• Electronic Sources
• Book on CD Tape and Movie
• Reference Specifications
• General Guidelines to Write References

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• Privacy Policy

Home » Observational Research – Methods and Guide

Observational Research – Methods and Guide

Table of Contents

Observational Research

Definition:

Observational research is a type of research method where the researcher observes and records the behavior of individuals or groups in their natural environment. In other words, the researcher does not intervene or manipulate any variables but simply observes and describes what is happening.

Observation

Observation is the process of collecting and recording data by observing and noting events, behaviors, or phenomena in a systematic and objective manner. It is a fundamental method used in research, scientific inquiry, and everyday life to gain an understanding of the world around us.

Types of Observational Research

Observational research can be categorized into different types based on the level of control and the degree of involvement of the researcher in the study. Some of the common types of observational research are:

Naturalistic Observation

In naturalistic observation, the researcher observes and records the behavior of individuals or groups in their natural environment without any interference or manipulation of variables.

Controlled Observation

In controlled observation, the researcher controls the environment in which the observation is taking place. This type of observation is often used in laboratory settings.

Participant Observation

In participant observation, the researcher becomes an active participant in the group or situation being observed. The researcher may interact with the individuals being observed and gather data on their behavior, attitudes, and experiences.

Structured Observation

In structured observation, the researcher defines a set of behaviors or events to be observed and records their occurrence.

Unstructured Observation

In unstructured observation, the researcher observes and records any behaviors or events that occur without predetermined categories.

Cross-Sectional Observation

In cross-sectional observation, the researcher observes and records the behavior of different individuals or groups at a single point in time.

Longitudinal Observation

In longitudinal observation, the researcher observes and records the behavior of the same individuals or groups over an extended period of time.

Data Collection Methods

Observational research uses various data collection methods to gather information about the behaviors and experiences of individuals or groups being observed. Some common data collection methods used in observational research include:

Field Notes

This method involves recording detailed notes of the observed behavior, events, and interactions. These notes are usually written in real-time during the observation process.

Audio and Video Recordings

Audio and video recordings can be used to capture the observed behavior and interactions. These recordings can be later analyzed to extract relevant information.

Surveys and Questionnaires

Surveys and questionnaires can be used to gather additional information from the individuals or groups being observed. This method can be used to validate or supplement the observational data.

Time Sampling

This method involves taking a snapshot of the observed behavior at pre-determined time intervals. This method helps to identify the frequency and duration of the observed behavior.

Event Sampling

This method involves recording specific events or behaviors that are of interest to the researcher. This method helps to provide detailed information about specific behaviors or events.

Checklists and Rating Scales

Checklists and rating scales can be used to record the occurrence and frequency of specific behaviors or events. This method helps to simplify and standardize the data collection process.

Observational Data Analysis Methods

Observational Data Analysis Methods are:

Descriptive Statistics

This method involves using statistical techniques such as frequency distributions, means, and standard deviations to summarize the observed behaviors, events, or interactions.

Qualitative Analysis

Qualitative analysis involves identifying patterns and themes in the observed behaviors or interactions. This analysis can be done manually or with the help of software tools.

Content Analysis

Content analysis involves categorizing and counting the occurrences of specific behaviors or events. This analysis can be done manually or with the help of software tools.

Time-series Analysis

Time-series analysis involves analyzing the changes in behavior or interactions over time. This analysis can help identify trends and patterns in the observed data.

Inter-observer Reliability Analysis

Inter-observer reliability analysis involves comparing the observations made by multiple observers to ensure the consistency and reliability of the data.

Multivariate Analysis

Multivariate analysis involves analyzing multiple variables simultaneously to identify the relationships between the observed behaviors, events, or interactions.

Event Coding

This method involves coding observed behaviors or events into specific categories and then analyzing the frequency and duration of each category.

Cluster Analysis

Cluster analysis involves grouping similar behaviors or events into clusters based on their characteristics or patterns.

Latent Class Analysis

Latent class analysis involves identifying subgroups of individuals or groups based on their observed behaviors or interactions.

Social network Analysis

Social network analysis involves mapping the social relationships and interactions between individuals or groups based on their observed behaviors.

The choice of data analysis method depends on the research question, the type of data collected, and the available resources. Researchers should choose the appropriate method that best fits their research question and objectives. It is also important to ensure the validity and reliability of the data analysis by using appropriate statistical tests and measures.

Applications of Observational Research

Observational research is a versatile research method that can be used in a variety of fields to explore and understand human behavior, attitudes, and preferences. Here are some common applications of observational research:

• Psychology : Observational research is commonly used in psychology to study human behavior in natural settings. This can include observing children at play to understand their social development or observing people’s reactions to stress to better understand how stress affects behavior.
• Marketing : Observational research is used in marketing to understand consumer behavior and preferences. This can include observing shoppers in stores to understand how they make purchase decisions or observing how people interact with advertisements to determine their effectiveness.
• Education : Observational research is used in education to study teaching and learning in natural settings. This can include observing classrooms to understand how teachers interact with students or observing students to understand how they learn.
• Anthropology : Observational research is commonly used in anthropology to understand cultural practices and beliefs. This can include observing people’s daily routines to understand their culture or observing rituals and ceremonies to better understand their significance.
• Healthcare : Observational research is used in healthcare to understand patient behavior and preferences. This can include observing patients in hospitals to understand how they interact with healthcare professionals or observing patients with chronic illnesses to better understand their daily routines and needs.
• Sociology : Observational research is used in sociology to understand social interactions and relationships. This can include observing people in public spaces to understand how they interact with others or observing groups to understand how they function.
• Ecology : Observational research is used in ecology to understand the behavior and interactions of animals and plants in their natural habitats. This can include observing animal behavior to understand their social structures or observing plant growth to understand their response to environmental factors.
• Criminology : Observational research is used in criminology to understand criminal behavior and the factors that contribute to it. This can include observing criminal activity in a particular area to identify patterns or observing the behavior of inmates to understand their experience in the criminal justice system.

Observational Research Examples

Here are some real-time observational research examples:

• A researcher observes and records the behaviors of a group of children on a playground to study their social interactions and play patterns.
• A researcher observes the buying behaviors of customers in a retail store to study the impact of store layout and product placement on purchase decisions.
• A researcher observes the behavior of drivers at a busy intersection to study the effectiveness of traffic signs and signals.
• A researcher observes the behavior of patients in a hospital to study the impact of staff communication and interaction on patient satisfaction and recovery.
• A researcher observes the behavior of employees in a workplace to study the impact of the work environment on productivity and job satisfaction.
• A researcher observes the behavior of shoppers in a mall to study the impact of music and lighting on consumer behavior.
• A researcher observes the behavior of animals in their natural habitat to study their social and feeding behaviors.
• A researcher observes the behavior of students in a classroom to study the effectiveness of teaching methods and student engagement.
• A researcher observes the behavior of pedestrians and cyclists on a city street to study the impact of infrastructure and traffic regulations on safety.

How to Conduct Observational Research

Here are some general steps for conducting Observational Research:

• Define the Research Question: Determine the research question and objectives to guide the observational research study. The research question should be specific, clear, and relevant to the area of study.
• Choose the appropriate observational method: Choose the appropriate observational method based on the research question, the type of data required, and the available resources.
• Plan the observation: Plan the observation by selecting the observation location, duration, and sampling technique. Identify the population or sample to be observed and the characteristics to be recorded.
• Train observers: Train the observers on the observational method, data collection tools, and techniques. Ensure that the observers understand the research question and objectives and can accurately record the observed behaviors or events.
• Conduct the observation : Conduct the observation by recording the observed behaviors or events using the data collection tools and techniques. Ensure that the observation is conducted in a consistent and unbiased manner.
• Analyze the data: Analyze the observed data using appropriate data analysis methods such as descriptive statistics, qualitative analysis, or content analysis. Validate the data by checking the inter-observer reliability and conducting statistical tests.
• Interpret the results: Interpret the results by answering the research question and objectives. Identify the patterns, trends, or relationships in the observed data and draw conclusions based on the analysis.
• Report the findings: Report the findings in a clear and concise manner, using appropriate visual aids and tables. Discuss the implications of the results and the limitations of the study.

When to use Observational Research

Here are some situations where observational research can be useful:

• Exploratory Research: Observational research can be used in exploratory studies to gain insights into new phenomena or areas of interest.
• Hypothesis Generation: Observational research can be used to generate hypotheses about the relationships between variables, which can be tested using experimental research.
• Naturalistic Settings: Observational research is useful in naturalistic settings where it is difficult or unethical to manipulate the environment or variables.
• Human Behavior: Observational research is useful in studying human behavior, such as social interactions, decision-making, and communication patterns.
• Animal Behavior: Observational research is useful in studying animal behavior in their natural habitats, such as social and feeding behaviors.
• Longitudinal Studies: Observational research can be used in longitudinal studies to observe changes in behavior over time.
• Ethical Considerations: Observational research can be used in situations where manipulating the environment or variables would be unethical or impractical.

Purpose of Observational Research

Observational research is a method of collecting and analyzing data by observing individuals or phenomena in their natural settings, without manipulating them in any way. The purpose of observational research is to gain insights into human behavior, attitudes, and preferences, as well as to identify patterns, trends, and relationships that may exist between variables.

The primary purpose of observational research is to generate hypotheses that can be tested through more rigorous experimental methods. By observing behavior and identifying patterns, researchers can develop a better understanding of the factors that influence human behavior, and use this knowledge to design experiments that test specific hypotheses.

Observational research is also used to generate descriptive data about a population or phenomenon. For example, an observational study of shoppers in a grocery store might reveal that women are more likely than men to buy organic produce. This type of information can be useful for marketers or policy-makers who want to understand consumer preferences and behavior.

In addition, observational research can be used to monitor changes over time. By observing behavior at different points in time, researchers can identify trends and changes that may be indicative of broader social or cultural shifts.

Overall, the purpose of observational research is to provide insights into human behavior and to generate hypotheses that can be tested through further research.

Advantages of Observational Research

There are several advantages to using observational research in different fields, including:

• Naturalistic observation: Observational research allows researchers to observe behavior in a naturalistic setting, which means that people are observed in their natural environment without the constraints of a laboratory. This helps to ensure that the behavior observed is more representative of the real-world situation.
• Unobtrusive : Observational research is often unobtrusive, which means that the researcher does not interfere with the behavior being observed. This can reduce the likelihood of the research being affected by the observer’s presence or the Hawthorne effect, where people modify their behavior when they know they are being observed.
• Cost-effective : Observational research can be less expensive than other research methods, such as experiments or surveys. Researchers do not need to recruit participants or pay for expensive equipment, making it a more cost-effective research method.
• Flexibility: Observational research is a flexible research method that can be used in a variety of settings and for a range of research questions. Observational research can be used to generate hypotheses, to collect data on behavior, or to monitor changes over time.
• Rich data : Observational research provides rich data that can be analyzed to identify patterns and relationships between variables. It can also provide context for behaviors, helping to explain why people behave in a certain way.
• Validity : Observational research can provide high levels of validity, meaning that the results accurately reflect the behavior being studied. This is because the behavior is being observed in a natural setting without interference from the researcher.

Disadvantages of Observational Research

While observational research has many advantages, it also has some limitations and disadvantages. Here are some of the disadvantages of observational research:

• Observer bias: Observational research is prone to observer bias, which is when the observer’s own beliefs and assumptions affect the way they interpret and record behavior. This can lead to inaccurate or unreliable data.
• Limited generalizability: The behavior observed in a specific setting may not be representative of the behavior in other settings. This can limit the generalizability of the findings from observational research.
• Difficulty in establishing causality: Observational research is often correlational, which means that it identifies relationships between variables but does not establish causality. This can make it difficult to determine if a particular behavior is causing an outcome or if the relationship is due to other factors.
• Ethical concerns: Observational research can raise ethical concerns if the participants being observed are unaware that they are being observed or if the observations invade their privacy.
• Time-consuming: Observational research can be time-consuming, especially if the behavior being observed is infrequent or occurs over a long period of time. This can make it difficult to collect enough data to draw valid conclusions.
• Difficulty in measuring internal processes: Observational research may not be effective in measuring internal processes, such as thoughts, feelings, and attitudes. This can limit the ability to understand the reasons behind behavior.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Observation Method in Psychology: Naturalistic, Participant and Controlled

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Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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On This Page:

The observation method in psychology involves directly and systematically witnessing and recording measurable behaviors, actions, and responses in natural or contrived settings without attempting to intervene or manipulate what is being observed.

Used to describe phenomena, generate hypotheses, or validate self-reports, psychological observation can be either controlled or naturalistic with varying degrees of structure imposed by the researcher.

There are different types of observational methods, and distinctions need to be made between:

1. Controlled Observations 2. Naturalistic Observations 3. Participant Observations

In addition to the above categories, observations can also be either overt/disclosed (the participants know they are being studied) or covert/undisclosed (the researcher keeps their real identity a secret from the research subjects, acting as a genuine member of the group).

In general, conducting observational research is relatively inexpensive, but it remains highly time-consuming and resource-intensive in data processing and analysis.

The considerable investments needed in terms of coder time commitments for training, maintaining reliability, preventing drift, and coding complex dynamic interactions place practical barriers on observers with limited resources.

Controlled Observation

Controlled observation is a research method for studying behavior in a carefully controlled and structured environment.

The researcher sets specific conditions, variables, and procedures to systematically observe and measure behavior, allowing for greater control and comparison of different conditions or groups.

The researcher decides where the observation will occur, at what time, with which participants, and in what circumstances, and uses a standardized procedure. Participants are randomly allocated to each independent variable group.

Rather than writing a detailed description of all behavior observed, it is often easier to code behavior according to a previously agreed scale using a behavior schedule (i.e., conducting a structured observation).

The researcher systematically classifies the behavior they observe into distinct categories. Coding might involve numbers or letters to describe a characteristic or the use of a scale to measure behavior intensity.

The categories on the schedule are coded so that the data collected can be easily counted and turned into statistics.

For example, Mary Ainsworth used a behavior schedule to study how infants responded to brief periods of separation from their mothers. During the Strange Situation procedure, the infant’s interaction behaviors directed toward the mother were measured, e.g.,

• Proximity and contact-seeking
• Contact maintaining
• Avoidance of proximity and contact
• Resistance to contact and comforting

The observer noted down the behavior displayed during 15-second intervals and scored the behavior for intensity on a scale of 1 to 7.

Sometimes participants’ behavior is observed through a two-way mirror, or they are secretly filmed. Albert Bandura used this method to study aggression in children (the Bobo doll studies ).

A lot of research has been carried out in sleep laboratories as well. Here, electrodes are attached to the scalp of participants. What is observed are the changes in electrical activity in the brain during sleep ( the machine is called an EEG ).

Controlled observations are usually overt as the researcher explains the research aim to the group so the participants know they are being observed.

Controlled observations are also usually non-participant as the researcher avoids direct contact with the group and keeps a distance (e.g., observing behind a two-way mirror).

• Controlled observations can be easily replicated by other researchers by using the same observation schedule. This means it is easy to test for reliability .
• The data obtained from structured observations is easier and quicker to analyze as it is quantitative (i.e., numerical) – making this a less time-consuming method compared to naturalistic observations.
• Controlled observations are fairly quick to conduct which means that many observations can take place within a short amount of time. This means a large sample can be obtained, resulting in the findings being representative and having the ability to be generalized to a large population.

Limitations

• Controlled observations can lack validity due to the Hawthorne effect /demand characteristics. When participants know they are being watched, they may act differently.

Naturalistic Observation

Naturalistic observation is a research method in which the researcher studies behavior in its natural setting without intervention or manipulation.

It involves observing and recording behavior as it naturally occurs, providing insights into real-life behaviors and interactions in their natural context.

Naturalistic observation is a research method commonly used by psychologists and other social scientists.

This technique involves observing and studying the spontaneous behavior of participants in natural surroundings. The researcher simply records what they see in whatever way they can.

In unstructured observations, the researcher records all relevant behavior with a coding system. There may be too much to record, and the behaviors recorded may not necessarily be the most important, so the approach is usually used as a pilot study to see what type of behaviors would be recorded.

Compared with controlled observations, it is like the difference between studying wild animals in a zoo and studying them in their natural habitat.

With regard to human subjects, Margaret Mead used this method to research the way of life of different tribes living on islands in the South Pacific. Kathy Sylva used it to study children at play by observing their behavior in a playgroup in Oxfordshire.

Collecting Naturalistic Behavioral Data

Technological advances are enabling new, unobtrusive ways of collecting naturalistic behavioral data.

The Electronically Activated Recorder (EAR) is a digital recording device participants can wear to periodically sample ambient sounds, allowing representative sampling of daily experiences (Mehl et al., 2012).

Studies program EARs to record 30-50 second sound snippets multiple times per hour. Although coding the recordings requires extensive resources, EARs can capture spontaneous behaviors like arguments or laughter.

EARs minimize participant reactivity since sampling occurs outside of awareness. This reduces the Hawthorne effect, where people change behavior when observed.

The SenseCam is another wearable device that passively captures images documenting daily activities. Though primarily used in memory research currently (Smith et al., 2014), systematic sampling of environments and behaviors via the SenseCam could enable innovative psychological studies in the future.

• By being able to observe the flow of behavior in its own setting, studies have greater ecological validity.
• Like case studies , naturalistic observation is often used to generate new ideas. Because it gives the researcher the opportunity to study the total situation, it often suggests avenues of inquiry not thought of before.
• The ability to capture actual behaviors as they unfold in real-time, analyze sequential patterns of interactions, measure base rates of behaviors, and examine socially undesirable or complex behaviors that people may not self-report accurately.
• These observations are often conducted on a micro (small) scale and may lack a representative sample (biased in relation to age, gender, social class, or ethnicity). This may result in the findings lacking the ability to generalize to wider society.
• Natural observations are less reliable as other variables cannot be controlled. This makes it difficult for another researcher to repeat the study in exactly the same way.
• Highly time-consuming and resource-intensive during the data coding phase (e.g., training coders, maintaining inter-rater reliability, preventing judgment drift).
• With observations, we do not have manipulations of variables (or control over extraneous variables), meaning cause-and-effect relationships cannot be established.

Participant Observation

Participant observation is a variant of the above (natural observations) but here, the researcher joins in and becomes part of the group they are studying to get a deeper insight into their lives.

If it were research on animals , we would now not only be studying them in their natural habitat but be living alongside them as well!

Leon Festinger used this approach in a famous study into a religious cult that believed that the end of the world was about to occur. He joined the cult and studied how they reacted when the prophecy did not come true.

Participant observations can be either covert or overt. Covert is where the study is carried out “undercover.” The researcher’s real identity and purpose are kept concealed from the group being studied.

The researcher takes a false identity and role, usually posing as a genuine member of the group.

On the other hand, overt is where the researcher reveals his or her true identity and purpose to the group and asks permission to observe.

• It can be difficult to get time/privacy for recording. For example, researchers can’t take notes openly with covert observations as this would blow their cover. This means they must wait until they are alone and rely on their memory. This is a problem as they may forget details and are unlikely to remember direct quotations.
• If the researcher becomes too involved, they may lose objectivity and become biased. There is always the danger that we will “see” what we expect (or want) to see. This problem is because they could selectively report information instead of noting everything they observe. Thus reducing the validity of their data.

Recording of Data

With controlled/structured observation studies, an important decision the researcher has to make is how to classify and record the data. Usually, this will involve a method of sampling.

In most coding systems, codes or ratings are made either per behavioral event or per specified time interval (Bakeman & Quera, 2011).

The three main sampling methods are:

Event-based coding involves identifying and segmenting interactions into meaningful events rather than timed units.

For example, parent-child interactions may be segmented into control or teaching events to code. Interval recording involves dividing interactions into fixed time intervals (e.g., 6-15 seconds) and coding behaviors within each interval (Bakeman & Quera, 2011).

Event recording allows counting event frequency and sequencing while also potentially capturing event duration through timed-event recording. This provides information on time spent on behaviors.

• Interval recording is common in microanalytic coding to sample discrete behaviors in brief time samples across an interaction. The time unit can range from seconds to minutes to whole interactions. Interval recording requires segmenting interactions based on timing rather than events (Bakeman & Quera, 2011).
• Instantaneous sampling provides snapshot coding at certain moments rather than summarizing behavior within full intervals. This allows quicker coding but may miss behaviors in between target times.

Coding Systems

The coding system should focus on behaviors, patterns, individual characteristics, or relationship qualities that are relevant to the theory guiding the study (Wampler & Harper, 2014).

Codes vary in how much inference is required, from concrete observable behaviors like frequency of eye contact to more abstract concepts like degree of rapport between a therapist and client (Hill & Lambert, 2004). More inference may reduce reliability.

Coding schemes can vary in their level of detail or granularity. Micro-level schemes capture fine-grained behaviors, such as specific facial movements, while macro-level schemes might code broader behavioral states or interactions. The appropriate level of granularity depends on the research questions and the practical constraints of the study.

Another important consideration is the concreteness of the codes. Some schemes use physically based codes that are directly observable (e.g., “eyes closed”), while others use more socially based codes that require some level of inference (e.g., “showing empathy”). While physically based codes may be easier to apply consistently, socially based codes often capture more meaningful behavioral constructs.

Most coding schemes strive to create sets of codes that are mutually exclusive and exhaustive (ME&E). This means that for any given set of codes, only one code can apply at a time (mutual exclusivity), and there is always an applicable code (exhaustiveness). This property simplifies both the coding process and subsequent data analysis.

For example, a simple ME&E set for coding infant state might include: 1) Quiet alert, 2) Crying, 3) Fussy, 4) REM sleep, and 5) Deep sleep. At any given moment, an infant would be in one and only one of these states.

Macroanalytic coding systems

Macroanalytic coding systems involve rating or summarizing behaviors using larger coding units and broader categories that reflect patterns across longer periods of interaction rather than coding small or discrete behavioral acts. 

Macroanalytic coding systems focus on capturing overarching themes, global qualities, or general patterns of behavior rather than specific, discrete actions.

For example, a macroanalytic coding system may rate the overall degree of therapist warmth or level of client engagement globally for an entire therapy session, requiring the coders to summarize and infer these constructs across the interaction rather than coding smaller behavioral units.

These systems require observers to make more inferences (more time-consuming) but can better capture contextual factors, stability over time, and the interdependent nature of behaviors (Carlson & Grotevant, 1987).

Examples of Macroanalytic Coding Systems:

• Emotional Availability Scales (EAS) : This system assesses the quality of emotional connection between caregivers and children across dimensions like sensitivity, structuring, non-intrusiveness, and non-hostility.
• Classroom Assessment Scoring System (CLASS) : Evaluates the quality of teacher-student interactions in classrooms across domains like emotional support, classroom organization, and instructional support.

Microanalytic coding systems

Microanalytic coding systems involve rating behaviors using smaller, more discrete coding units and categories.

These systems focus on capturing specific, discrete behaviors or events as they occur moment-to-moment. Behaviors are often coded second-by-second or in very short time intervals.

For example, a microanalytic system may code each instance of eye contact or head nodding during a therapy session. These systems code specific, molecular behaviors as they occur moment-to-moment rather than summarizing actions over longer periods.

Microanalytic systems require less inference from coders and allow for analysis of behavioral contingencies and sequential interactions between therapist and client. However, they are more time-consuming and expensive to implement than macroanalytic approaches.

Examples of Microanalytic Coding Systems:

• Facial Action Coding System (FACS) : Codes minute facial muscle movements to analyze emotional expressions.
• Specific Affect Coding System (SPAFF) : Used in marital interaction research to code specific emotional behaviors.
• Noldus Observer XT : A software system that allows for detailed coding of behaviors in real-time or from video recordings.

Mesoanalytic coding systems

Mesoanalytic coding systems attempt to balance macro- and micro-analytic approaches.

In contrast to macroanalytic systems that summarize behaviors in larger chunks, mesoanalytic systems use medium-sized coding units that target more specific behaviors or interaction sequences (Bakeman & Quera, 2017).

For example, a mesoanalytic system may code each instance of a particular type of therapist statement or client emotional expression. However, mesoanalytic systems still use larger units than microanalytic approaches coding every speech onset/offset.

The goal of balancing specificity and feasibility makes mesoanalytic systems well-suited for many research questions (Morris et al., 2014). Mesoanalytic codes can preserve some sequential information while remaining efficient enough for studies with adequate but limited resources.

For instance, a mesoanalytic couple interaction coding system could target key behavior patterns like validation sequences without coding turn-by-turn speech.

In this way, mesoanalytic coding allows reasonable reliability and specificity without requiring extensive training or observation. The mid-level focus offers a pragmatic compromise between depth and breadth in analyzing interactions.

Examples of Mesoanalytic Coding Systems:

• Feeding Scale for Mother-Infant Interaction : Assesses feeding interactions in 5-minute episodes, coding specific behaviors and overall qualities.
• Couples Interaction Rating System (CIRS): Codes specific behaviors and rates overall qualities in segments of couple interactions.
• Teaching Styles Rating Scale : Combines frequency counts of specific teacher behaviors with global ratings of teaching style in classroom segments.

Preventing Coder Drift

Coder drift results in a measurement error caused by gradual shifts in how observations get rated according to operational definitions, especially when behavioral codes are not clearly specified.

This type of error creeps in when coders fail to regularly review what precise observations constitute or do not constitute the behaviors being measured.

Preventing drift refers to taking active steps to maintain consistency and minimize changes or deviations in how coders rate or evaluate behaviors over time. Specifically, some key ways to prevent coder drift include:

• Operationalize codes : It is essential that code definitions unambiguously distinguish what interactions represent instances of each coded behavior. 
• Ongoing training : Returning to those operational definitions through ongoing training serves to recalibrate coder interpretations and reinforce accurate recognition. Having regular “check-in” sessions where coders practice coding the same interactions allows monitoring that they continue applying codes reliably without gradual shifts in interpretation.
• Using reference videos : Coders periodically coding the same “gold standard” reference videos anchors their judgments and calibrate against original training. Without periodic anchoring to original specifications, coder decisions tend to drift from initial measurement reliability.
• Assessing inter-rater reliability : Statistical tracking that coders maintain high levels of agreement over the course of a study, not just at the start, flags any declines indicating drift. Sustaining inter-rater agreement requires mitigating this common tendency for observer judgment change during intensive, long-term coding tasks.
• Recalibrating through discussion : Having meetings for coders to discuss disagreements openly explores reasons judgment shifts may be occurring over time. Consensus on the application of codes is restored.
• Adjusting unclear codes : If reliability issues persist, revisiting and refining ambiguous code definitions or anchors can eliminate inconsistencies arising from coder confusion.

Essentially, the goal of preventing coder drift is maintaining standardization and minimizing unintentional biases that may slowly alter how observational data gets rated over periods of extensive coding.

Through the upkeep of skills, continuing calibration to benchmarks, and monitoring consistency, researchers can notice and correct for any creeping changes in coder decision-making over time.

Reducing Observer Bias

Observational research is prone to observer biases resulting from coders’ subjective perspectives shaping the interpretation of complex interactions (Burghardt et al., 2012). When coding, personal expectations may unconsciously influence judgments. However, rigorous methods exist to reduce such bias.

Coding Manual

A detailed coding manual minimizes subjectivity by clearly defining what behaviors and interaction dynamics observers should code (Bakeman & Quera, 2011).

High-quality manuals have strong theoretical and empirical grounding, laying out explicit coding procedures and providing rich behavioral examples to anchor code definitions (Lindahl, 2001).

Clear delineation of the frequency, intensity, duration, and type of behaviors constituting each code facilitates reliable judgments and reduces ambiguity for coders. Application risks inconsistency across raters without clarity on how codes translate to observable interaction.

Coder Training

Competent coders require both interpersonal perceptiveness and scientific rigor (Wampler & Harper, 2014). Training thoroughly reviews the theoretical basis for coded constructs and teaches the coding system itself.

Multiple “gold standard” criterion videos demonstrate code ranges that trainees independently apply. Coders then meet weekly to establish reliability of 80% or higher agreement both among themselves and with master criterion coding (Hill & Lambert, 2004).

Ongoing training manages coder drift over time. Revisions to unclear codes may also improve reliability. Both careful selection and investment in rigorous training increase quality control.

Blind Methods

To prevent bias, coders should remain unaware of specific study predictions or participant details (Burghardt et al., 2012). Separate data gathering versus coding teams helps maintain blinding.

Coders should be unaware of study details or participant identities that could bias coding (Burghardt et al., 2012).

Separate teams collecting data versus coding data can reduce bias.

In addition, scheduling procedures can prevent coders from rating data collected directly from participants with whom they have had personal contact. Maintaining coder independence and blinding enhances objectivity.

Data Analysis Approaches

Data analysis in behavioral observation aims to transform raw observational data into quantifiable measures that can be statistically analyzed.

It’s important to note that the choice of analysis approach is not arbitrary but should be guided by the research questions, study design, and nature of the data collected.

Interval data (where behavior is recorded at fixed time points), event data (where the occurrence of behaviors is noted as they happen), and timed-event data (where both the occurrence and duration of behaviors are recorded) may require different analytical approaches.

Similarly, the level of measurement (categorical, ordinal, or continuous) will influence the choice of statistical tests.

Researchers typically start with simple descriptive statistics to get a feel for their data before moving on to more complex analyses. This stepwise approach allows for a thorough understanding of the data and can often reveal unexpected patterns or relationships that merit further investigation.

simple descriptive statistics

Descriptive statistics give an overall picture of behavior patterns and are often the first step in analysis.

• Frequency counts tell us how often a particular behavior occurs, while rates express this frequency in relation to time (e.g., occurrences per minute).
• Duration measures how long behaviors last, offering insight into their persistence or intensity.
• Probability calculations indicate the likelihood of a behavior occurring under certain conditions, and relative frequency or duration statistics show the proportional occurrence of different behaviors within a session or across the study.

These simple statistics form the foundation of behavioral analysis, providing researchers with a broad picture of behavioral patterns. 

They can reveal which behaviors are most common, how long they typically last, and how they might vary across different conditions or subjects.

For instance, in a study of classroom behavior, these statistics might show how often students raise their hands, how long they typically stay focused on a task, or what proportion of time is spent on different activities.

contingency analyses

Contingency analyses help identify if certain behaviors tend to occur together or in sequence.

• Contingency tables , also known as cross-tabulations, display the co-occurrence of two or more behaviors, allowing researchers to see if certain behaviors tend to happen together.
• Odds ratios provide a measure of the strength of association between behaviors, indicating how much more likely one behavior is to occur in the presence of another.
• Adjusted residuals in these tables can reveal whether the observed co-occurrences are significantly different from what would be expected by chance.

For example, in a study of parent-child interactions, contingency analyses might reveal whether a parent’s praise is more likely to follow a child’s successful completion of a task, or whether a child’s tantrum is more likely to occur after a parent’s refusal of a request.

These analyses can uncover important patterns in social interactions, learning processes, or behavioral chains.

sequential analyses

Sequential analyses are crucial for understanding processes and temporal relationships between behaviors.

• Lag sequential analysis looks at the likelihood of one behavior following another within a specified number of events or time units.
• Time-window sequential analysis examines whether a target behavior occurs within a defined time frame after a given behavior.

These methods are particularly valuable for understanding processes that unfold over time, such as conversation patterns, problem-solving strategies, or the development of social skills.

observer agreement

Since human observers often code behaviors, it’s important to check reliability . This is typically done through measures of observer agreement.

• Cohen’s kappa is commonly used for categorical data, providing a measure of agreement between observers that accounts for chance agreement.
• Intraclass correlation coefficient (ICC) : Used for continuous data or ratings.

Good observer agreement is crucial for the validity of the study, as it demonstrates that the observed behaviors are consistently identified and coded across different observers or time points.

advanced statistical approaches

As researchers delve deeper into their data, they often employ more advanced statistical techniques.

• For instance, an ANOVA might reveal differences in the frequency of aggressive behaviors between children from different socioeconomic backgrounds or in different school settings.
• This approach allows researchers to account for dependencies in the data and to examine how behaviors might be influenced by factors at different levels (e.g., individual characteristics, group dynamics, and situational factors).
• This method can reveal trends, cycles, or patterns in behavior over time, which might not be apparent from simpler analyses. For instance, in a study of animal behavior, time series analysis might uncover daily or seasonal patterns in feeding, mating, or territorial behaviors.

representation techniques

Representation techniques help organize and visualize data:

• Many researchers use a code-unit grid, which represents the data as a matrix with behaviors as rows and time units as columns.
• This format facilitates many types of analyses and allows for easy visualization of behavioral patterns.
• Standardized formats like the Sequential Data Interchange Standard (SDIS) help ensure consistency in data representation across studies and facilitate the use of specialized analysis software.
• Indeed, the complexity of behavioral observation data often necessitates the use of specialized software tools. Programs like GSEQ, Observer, and INTERACT are designed specifically for the analysis of observational data and can perform many of the analyses described above efficiently and accurately.

Bakeman, R., & Quera, V. (2017). Sequential analysis and observational methods for the behavioral sciences. Cambridge University Press.

Burghardt, G. M., Bartmess-LeVasseur, J. N., Browning, S. A., Morrison, K. E., Stec, C. L., Zachau, C. E., & Freeberg, T. M. (2012). Minimizing observer bias in behavioral studies: A review and recommendations. Ethology, 118 (6), 511-517.

Hill, C. E., & Lambert, M. J. (2004). Methodological issues in studying psychotherapy processes and outcomes. In M. J. Lambert (Ed.), Bergin and Garfield’s handbook of psychotherapy and behavior change (5th ed., pp. 84–135). Wiley.

Lindahl, K. M. (2001). Methodological issues in family observational research. In P. K. Kerig & K. M. Lindahl (Eds.), Family observational coding systems: Resources for systemic research (pp. 23–32). Lawrence Erlbaum Associates.

Mehl, M. R., Robbins, M. L., & Deters, F. G. (2012). Naturalistic observation of health-relevant social processes: The electronically activated recorder methodology in psychosomatics. Psychosomatic Medicine, 74 (4), 410–417.

Morris, A. S., Robinson, L. R., & Eisenberg, N. (2014). Applying a multimethod perspective to the study of developmental psychology. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 103–123). Cambridge University Press.

Smith, J. A., Maxwell, S. D., & Johnson, G. (2014). The microstructure of everyday life: Analyzing the complex choreography of daily routines through the automatic capture and processing of wearable sensor data. In B. K. Wiederhold & G. Riva (Eds.), Annual Review of Cybertherapy and Telemedicine 2014: Positive Change with Technology (Vol. 199, pp. 62-64). IOS Press.

Traniello, J. F., & Bakker, T. C. (2015). The integrative study of behavioral interactions across the sciences. In T. K. Shackelford & R. D. Hansen (Eds.), The evolution of sexuality (pp. 119-147). Springer.

Wampler, K. S., & Harper, A. (2014). Observational methods in couple and family assessment. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (2nd ed., pp. 490–502). Cambridge University Press.

Qualitative Research: Observation

• Getting Started
• Focus Groups
• Observation
• Case Studies
• Data Collection
• Cleaning Text
• Analysis Tools
• Institutional Review

Participant Observation

Photo: https://slideplayer.com/slide/4599875/

Field Guide

• Participant Observation Field Guide

What is an observation?

A way to gather data by watching people, events, or noting physical characteristics in their natural setting. Observations can be overt (subjects know they are being observed) or covert (do not know they are being watched).

• Researcher becomes a participant in the culture or context being observed.
• Requires researcher to be accepted as part of culture being observed in order for success

Direct Observation

• Researcher strives to be as unobtrusive as possible so as not to bias the observations; more detached.
• Technology can be useful (i.e video, audiorecording).

Indirect Observation

• Results of an interaction, process or behavior are observed (for example, measuring the amount of plate waste left by students in a school cafeteria to determine whether a new food is acceptable to them).

Suggested Readings and Film

• Born into Brothels . (2004) Oscar winning documentary, an example of participatory observation, portrays the life of children born to prostitutes in Calcutta. New York-based photographer Zana Briski gave cameras to the children of prostitutes and taught them photography
• Davies, J. P., & Spencer, D. (2010).  Emotions in the field: The psychology and anthropology of fieldwork experience . Stanford, CA: Stanford University Press.
• DeWalt, K. M., & DeWalt, B. R. (2011).  Participant observation : A guide for fieldworkers .   Lanham, Md: Rowman & Littlefield.
• Reinharz, S. (2011).  Observing the observer: Understanding our selves in field research . NY: Oxford University Press.
• Schensul, J. J., & LeCompte, M. D. (2013).  Essential ethnographic methods: A mixed methods approach . Lanham, MD: AltaMira Press.
• Skinner, J. (2012).  The interview: An ethnographic approach . NY: Berg.
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• Last Updated: Mar 1, 2024 10:13 AM
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Observation schedule

Before commencing data collection, it is essential to create an observation schedule that outlines the features to be observed and recorded during observations, which can range from quantitative to qualitative. Observation schedules that are more quantitative in nature, often referred to as observation checklists, use predefined categories of variables that can be counted and analyzed statistically. They are employed in structured observational research. On the other hand, observation schedules that are more qualitative serve as flexible guidelines for data collection, listing topics of interest and providing space to record notes on new themes that emerge during observations. Highly structured observation schedules are more appropriate for situations where there is more knowledge about the topic of interest, whereas less structured observation schedules are more effective in situations where there is less information about the research questions.

Before implementation, it is recommended to pretest observation schedules and make necessary modifications (Given, 2008). Observation schedules should include relevant demographic information such as age and gender, the participants' roles in the research setting, the number of individuals present, and details of the physical setting. Later, predetermined categories or notes on observations can be added. Researchers are usually interested in what people say, the meanings they attribute to their words, and the relationships between participants. In more quantitative observation schedules, the categories are explicitly defined, exhaustive, and mutually exclusive, while subjective measures requiring judgment or inference are avoided whenever possible. In contrast, more qualitative observation schedules aim to identify as many emerging themes of interest to the project as possible (Given, 2008).

Research Design Review

A discussion of qualitative & quantitative research design, observation guide, facilitating reflexivity in observational research: the observation guide & grid.

Observational research is “successful” to the extent that it satisfies the research objectives by capturing relevant events and participants along with the constructs of interest.  Fortunately, there are two tools – the observation guide and the observation grid – that serve to keep the observer on track towards these objectives and generally facilitate the ethnographic data gathering process.

Not unlike the outlines interviewers and moderators use to help steer the course of their in-depth interviews and group discussions, the observation guide serves two important purposes: 1) It reminds the observer of the key points of observation as well as the topics of interest associated with each, and 2) It acts as the impetus for a reflexive exercise in which the observer can reflect on their own relationship and contribution to the observed at any moment in time (e.g., how the observer was affected by the observations).  An observation guide is an important tool regardless of the observer’s role.  For each of the five observer roles * – nonparticipant (off-site or on-site) and participant (passive, participant-observer, or complete) observation – the observation guide helps to maintain the observer’s focus while also giving the observer leeway to reflect on the particular context associated with each site.

* Roller & Lavrakas, 2015. Applied Qualitative Research Design: A Total Quality Framework Approach . New York: Guilford Press.

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• > Observational Research in U.S. Classrooms
• > The Uses of the Classroom Observation Schedule to Improve Classroom Instruction

Book contents

• Frontmatter
• Tables, Figures, and Appendixes
• Contributors
• 1 Introduction: Purposes and Perspectives on Classroom Observation Research
• 2 Using Multiple Perspectives in Observations of Diverse Classrooms: The Sheltered Instruction Observation Protocol (SIPO)
• 3 The Standards Performance Continuum: A Performance-Based Measure of the Standards for Effective Pedagogy
• 4 The Uses of the Classroom Observation Schedule to Improve Classroom Instruction
• 5 Development and Use of a Classroom Observation Instrument to Investigate Teaching for Meaning in Diverse Classrooms
• 6 Patterns of Language Arts Instructional Activity and Excellence in First– and Fourth–Grade Culturally and Linguistically Diverse Classrooms
• 7 Using Classroom Observation as a Research and Formative Evaluation Tool in Educational Reform: The School Observation Measure
• 8 Observing School Restructuring in Multilingual, Multicultural Classrooms: Balancing Ethnographic and Evaluative Approaches
• 9 Sociocultural Activity Settings in the Classroom: A Study of a Classroom Observation System
• 10 The Influence of School Reform on Classroom Instruction in Diverse Schools: Findings from an Observational Study of Success for All
• 11 Future Directions for Classroom Observation Research

4 - The Uses of the Classroom Observation Schedule to Improve Classroom Instruction

Published online by Cambridge University Press:  23 November 2009

Systematic classroom observation methods have been widely used in the past several decades to investigate effective teaching practices (Brophy & Good, 1986; Stallings & Mohlman, 1988; Waxman, 1995; Waxman & Huang, 1999). One of the most important uses of the method has been to determine which teaching practices improve student learning (Waxman & Huang, 1999). Most classroom observation instruments typically focus on the teacher as the unit of measurement or observation, and thus they describe a variety of instructional behaviors in which teachers engage. There are limitations, however, with teacher-based classroom observation instruments. First, teacher-focused instruments suggest that teaching practices directly impact student outcomes, without acknowledging that student behaviors impact teacher behaviors as well. Another concern with teacher-focused observation systems is that they often ignore student behaviors that have a greater impact on student outcomes than teacher behaviors.

Another limitation of teacher-based observation instruments is that they generally do not allow researchers to examine individual student behaviors, particularly differences by critical attributes such as student sex, ethnicity, or grouping classification (e.g., gifted/nongifted, resilient/nonresilient, monolingual/bilingual). A final concern with teacher-centered observation systems is that they are often very threatening to classroom teachers. Many teachers are reluctant to volunteer to participate in classroom observation research because they know the focus of attention is on the teachers and their instructional practices.

This chapter describes the uses of a systematic classroom observation instrument, the Classroom Observation Schedule (COS), that was designed to address some of the previous concerns of classroom observation by specifically focusing on individual students rather than the teacher (Waxman, Wang, Lindvall, & Anderson, 1990a, 1990b).

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• The Uses of the Classroom Observation Schedule to Improve Classroom Instruction
• By Hersh C. Waxman , Professor of Educational Leadership and Cultural Studies in the College of Education, University of Houston; Principal Researcher in the U.S. Department of Education, National Center for Research on Education, Diversity, and Excellence; Principal Investigator in the U.S. Department of Education, National Laboratory for Student Success, the Mid-Atlantic Regional Educational Laboratory, Yolanda N. Padrón , College of Education, University of Houston
• Edited by Hersh C. Waxman , University of Houston , Roland G. Tharp , University of California, Santa Cruz , R. Soleste Hilberg , University of California, Santa Cruz
• Book: Observational Research in U.S. Classrooms
• Online publication: 23 November 2009
• Chapter DOI: https://doi.org/10.1017/CBO9780511616419.004

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The Essential Guide to Doing Your Research Project

Student resources, checklist for observation, a: planning – thinking through ‘who’, ‘where’, ‘when’, ‘how’ and ‘what’. have you considered:.

☑ The type of observation study you will do – do your goals and context lend themselves to an observation study that is candid or covert; participant/nonparticipant; structured/unstructured – and of what duration?

☑ Population and sample/ respondent/ participants – who you plan to speak about (population) – and gather data from (sample)

☑ Access to the group/ situation/ activities you wish to observe

☑ How you will present yourself

☑ How you plan to control your biases

☑ How you might develop the skills/resources needed to carry out your observation

☑ Strategies for ensuring credibility

☑ The tools you w ill use, i.e.) an observation schedule or if unstructured, any relevant themes to explore

☑ Details – what timeframe will you be working towards? If you will observe on one occasion, multiple occasions, or will your study involve prolonged engagement?

☑ How you will record your data

☑ Ethics/ethics approval

☑ Contingencies – i.e.) having a back-up plan ready to go your original plan does not pan out

B: Implementing. Have you:

☑ Eased into the observation situation

☑ Prepared yourself to accept a range of sensory input – use all your senses, and possibly your intuition, to gather data

☑ Invested significant time in your observations

☑ Looked for saturation – try to ensure your observations no longer yield new knowledge before ending the process

C: Recording. Have you:

☑ Recorded your observation as soon as possible. If using schedules, they should be filled in while observations occur. If you are more immersed in your research context, you may want to record your observations when removed from the situation either on data sheets or in a journal

D: Reviewing. Have you:

☑ Reviewed the process and noted any difficulties encountered

☑ Reviewed your observation records

☑ Confirmed – checked with an insider, ask another observer to compare notes, or triangulate your observational data with other data types

E: Refining. Have you:

☑ Made modifications – based on your own review of the process; any confirmation strategies you have attempted; and the quality of the data generated

☑ Kept reviewing and refining – observation takes practice; keep refining until you are comfortable with the process and the data collected

☑ Considered major issues – if there are major issues, you will need to openly discuss with your supervisor and consider modifications

F: Managing and analysing. Have you:

☑ Organized/collated your data

☑ Analysed your data. Most data collected in observation can be quantitative(through the use of checklists) or can be much more qualitative (through the use of journaling)

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• Knowledge Base

Methodology

• What Is Qualitative Observation? | Definition & Examples

What Is Qualitative Observation? | Definition & Examples

Published on March 18, 2023 by Tegan George . Revised on June 22, 2023.

Qualitative observation is a research method where the characteristics or qualities of a phenomenon are described without using any quantitative measurements or data. Rather, the observation is based on the observer’s subjective interpretation of what they see, hear, smell, taste, or feel.

Qualitative observations can be done using various methods, including direct observation, interviews , focus groups , or case studies . They can provide rich and detailed information about the behavior, attitudes, perceptions, and experiences of individuals or groups.

Table of contents

When to use qualitative observation, examples of qualitative observation, types of qualitative observations, advantages and disadvantages of qualitative observations, other interesting articles, frequently asked questions.

Qualitative observation is a type of observational study , often used in conjunction with other types of research through triangulation . It is often used in fields like social sciences, education, healthcare, marketing, and design. This type of study is especially well suited for gaining rich and detailed insights into complex and/or subjective phenomena.

A qualitative observation could be a good fit for your research if:

• You are conducting exploratory research . If the goal of your research is to gain a better understanding of a phenomenon, object, or situation, qualitative observation is a good place to start.
• When your research topic is complex, subjective, or cannot be examined numerically. Qualitative observation is often able to capture the complexity and subjectivity of human behavior, particularly for topics like emotions, attitudes, perceptions, or cultural practices. These may not be quantifiable or measurable through other methods.
• You are relying on triangulation within your research approach. Qualitative observation is a solid addition to triangulation approaches, where multiple sources of data are used to validate and verify research findings.

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Qualitative observation is commonly used in marketing to study consumer behavior, preferences, and attitudes towards products or services.

During the focus group, you focus particularly on qualitative observations, taking note of the participants’ facial expressions, body language, word choice, and tone of voice.

Qualitative observation is often also used in design fields, to better understand user needs, preferences, and behaviors. This can aid in the development of products and services that better meet user needs.

You are particularly focused on any usability issues that could impact customer satisfaction. You run a series of testing sessions, focusing on reactions like facial expressions, body language, and verbal feedback.

There are several types of qualitative observation. Here are some of the most common types to help you choose the best one for your work.

Qualitative observations are a great choice of research method for some projects, but they definitely have their share of disadvantages to consider.

Advantages of qualitative observations

• Qualitative observations allow you to generate rich and nuanced qualitative data —aiding you in understanding a phenomenon or object and providing insights into the more complex and subjective aspects of human experience.
• Qualitative observation is a flexible research method that can be adjusted based on research goals and timeline. It also has the potential to be quite non-intrusive, allowing observation of participants in their natural settings without disrupting or influencing their behavior.
• Qualitative observation is often used in combination with other research methods, such as interviews or surveys , to provide a more complete picture of the phenomenon being studied. This triangulation can help improve the reliability and validity of the research findings.

Disadvantages of qualitative observations

• Like many observational studies, qualitative observations are at high risk for many research biases , particularly on the side of the researcher in the case of observer bias . These biases can also bleed over to the participant size, in the case of the Hawthorne effect or social desirability bias .
• Qualitative observations are typically based on a small sample size , which makes them very unlikely to be representative of the larger population. This greatly limits the generalizability of the findings if used as a standalone method, and the data collection process can be long and onerous.
• Like other human subject research, qualitative observation has its share of ethical considerations to keep in mind and protect, particularly informed consent, privacy, and confidentiality.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

Data analysis in qualitative observation often involves searching for any recurring patterns, themes, and categories in your data. This process may involve coding the data, developing conceptual frameworks or models, and conducting thematic analysis . This can help you generate strong hypotheses or theories based on your data.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

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• Research Methods
• Post last modified: 26 August 2021
• Reading time: 36 mins read
• Post category: Research Methodology

4 Research Methods

4 Major Research Methods are:

Observations

Schedule and questionnaire, case study method.

Table of Content

• 1.1.1 Types of Interview
• 1.1.2 Features of Interviews
• 1.1.3 Essentials for an Effective Interview
• 1.1.4 Advantages of Interviews
• 1.1.5 Disadvantages of Interviews
• 1.1.6 Interview Process
• 1.1.7 Problems Faced in an Interview
• 1.2.1 Characteristics of Observation Method
• 1.2.2 Types of Observation
• 1.2.3 Prerequisites of Observation
• 1.2.4 Advantages of observations
• 1.2.5 Disadvantages of observations
• 1.2.6 Use of observation in business research
• 1.3.1 Importance of questionnaires
• 1.3.2 Types of Questionnaire
• 1.3.3 Advantages of Questionnaires
• 1.3.4 Disadvantages of Questionnaires
• 1.3.5 Preparation of an Effective Questionnaire
• 1.3.6 Difference between schedule and questionnaire
• 1.4.1 Assumptions of case study method
• 1.4.2 Advantages of Case Study Method
• 1.4.3 Disadvantages of Case Study Method
• 1.4.4 Case study as a method of business research

Interviewing is a very effective method of data collection. It is a systematic and objective conversation between an investigator and respondent for collecting relevant data for a specific research study. Along with conversation, learning about the gestures, facial expressions and environmental conditions of a respondent are also very important.

Generally, interview collects a wide range of data from factual demographic data to highly personal and intimate information relating to a person’s opinions, attitudes, values and beliefs, past experience and future intentions.

The interview method is very important in the collection of data from the respondent who is less educated or illiterate. Personal interview is more feasible when the area covered for survey is compact. Probing is a very important part of an interview.

Types of Interview

The following are the various types of interviews:

Structured or directive interview

In this type of interview, the investigator goes to the respondent with a detailed schedule. Some questions in same sequence are asked from all respondents.

Unstructured or non-directive interview

In this type of interview, the respondent is encouraged to give his honest opinion on a given topic without or with minimum help from others.

Focused interview

This is a semi-structured interview where the respondent shares the effect of the experience to the given conditions with the researcher or investigator. It is conducted with those respondents only who have prior experience of conditions given by the investigator.

Analysis of the attitude, emotional feelings for the situations under study is main purpose behind conducting these interviews. A set of fix questions may not be required in this interview but a relevant topic is required which is known to the respondent.

Clinical interview

While a focussed interview is concerned with effects of specific experience, clinical interviews are concerned with broad underlying feelings or motivations or the course of the individual’s life experiences with reference to the research study. It encourages the interviewee to share his experience freely.

Depth interview

To analyse or study the respondent’s emotions, opinions, etc., depth interviews are conducted. This kind of interview aims to collect intensive data about individuals, especially their opinions.

It is a lengthy process to get unbiased data from the respondent. Interviewers should avoid advising or showing this agreement. Instead, the investigator has to motivate the respondent to answer the questions.

Features of Interviews

The following are some of the features of interviews

• The interviewer and the respondent are the participants in any interview. They both are unknown to each other and so it is important for an interviewer to introduce himself first to the respondent.
• An interview has a beginning and a termination point in the relationship between the participants.
• Interview is not a mere casual conversational exchange. It has a specific purpose of collecting data which is relevant to the study.
• Interview is a mode of obtaining a verbal response to questions to put verbally. It is not always face to face.
• Success of interview depends on the interviewer and respondent and how they perceive each other.
• It is not a standardized process.

Essentials for an Effective Interview

The following are the requirements for a successful interview:

• Data availability : The respondent should have complete knowledge of the information required for specific study.
• Role perception : The interviewer and the respondent should be aware of their roles in the interview process. The respondent should be clear about the topic or questions which have to be answered by him. Similarly, it is the responsibility of the interviewer to make the respondent comfortable by introducing himself first. The investigator should not affect the interview situation through subjective attitude and argumentation.
• Respondent’s motivation : The respondent can hesitate to answer the questions. In this case, the approach and skills of the interviewer are very important as he has to motivate the respondent to answer or express himself.

Advantages of Interviews

The following are the advantages of the interview method:

• In-depth and detailed information is collected.
• The interviewer tries to improve the responses and quality of data received.
• He can control the conditions in favour of the research study.
• Interviews help in gathering supplementary information which can be helpful to the study.
• Interviews use special scoring devices, visuals and materials to improve the quality of data or information collected.
• Interviews use observation and probing by the interviewer to see the accuracy and dependability of given data by the respondent.
• Interviews are flexible in nature.

Disadvantages of Interviews

The following are the disadvantages of interviews:

• Interviews consume more time and cost.
• The respondent’s responses can be affected by the way the interviewer asks the questions.
• The respondent may refuse to answer some personal questions which are relevant to the study.
• Recording and coding of data during the interview process may sometimes be difficult for the interviewer.
• The interviewer may not have good communication or interactive skills.

Interview Process

The following are the stages in an interview process:

Preparation

The interviewer needs to make certain preparations to make an interview successful. The interviewer should keep all the copies of the schedule or guide ready. They need to prepare the lists of respondents with their addresses, contact number and meeting time.

They should prepare themselves with all the approaches and skills required to conduct an interview. They should prepare themselves to face all adverse situations during the interview. If the interviewer is not doing such planning, they can fail to collect the right information from respondent.

Introduction

The interviewer is not known to the respondent. Therefore, the interviewer must introduce himself first to every respondent. In the introduction, the interviewer should tell about himself, his organization details and the purpose of his visit.

If the interviewer knows someone who the respondent is familiar with, then he can use that person’s reference to make the respondent more comfortable. The following are some steps which help in motivating the respondent:

• The interviewer should introduce himself with a smiling face and always greet the respondent.
• He should identify and call the respondent by name.
• He must describe how the respondent is selected.
• He should explain the purpose and usefulness of the study.
• He should focus on the value of the respondent’s cooperation.

Developing report

It is important for an interviewer to develop a rapport with the respondent before starting the interview. By doing this, a cordial relationship is established between them. It helps the interviewer understand the inherent nature of the respondent which helps in building a rapport and the discussion can be started with some general topic or with the help of a person who is commonly known to both of them.

Carrying the interview forward

After establishing a rapport, the skills of the interviewer are required to carry the interview forward. The following are some guidelines that should be followed:

• Start the interview in an informal and natural manner.
• Ask all the questions in the same sequence as in the schedule.
• Do not take an answer for granted. It is not necessary that an interviewee will know all answers or will give all answers. The interviewer has to create interest for answering questions.
• The objective of the question should be known to the interviewer to ensure that the correct information is collected for research study.
• Explain the question if it has not been understood properly by the respondent.
• Listen to the respondent carefully with patience.
• Never argue with the respondent.
• Show your concern and interest in the information given by the respondent.
• Do not express your own opinion for answers of any question in the schedule.
• Continue to motivate the respondent.
• If the respondent is unable to frame the right answer, the interviewer should help him by providing alternate questions.
• Ensure that the conversation does not go off track.
• If the respondent is unable to answer a particular question due to some reasons, drop the question at that moment. This question can be asked indirectly later on.

Recording the interview

Responses should be recorded in the same sequence as they are given by the respondent. The response should be recorded at the same time as it is generated. It may be very difficult to remember all the responses later for recording them.

Recording can be done in writing but there may be some problems if the writing skills of an interviewer are not good. Hence, the use of electronic devices like tape recorders can help in this purpose. The interviewer should also record all his probes and other comments on the schedule, but they should be in brackets to ensure that they are set off from response.

Closing the interview

After the interview is over, the interviewer must thank the respondent for his cooperation. He must collect all the papers before leaving the respondent. If the respondent wants to know the result of the survey, the interviewer must ensure that the results are mailed to him when they are ready.

At the end, the interviewer must edit the schedule to check that all the questions have been asked and recorded. Also, abbreviations in recording should be replaced by full words.

Problems Faced in an Interview

The following are some of the main problems faced in an interview:

Inadequate response

Kahn and Cannel laid down five principal symptoms of inadequate response. They are given as follows:

• Partial response in which the respondent gives a relevant but incomplete answer.
• Non-response in which the respondent remains silent or refuses to answer the questions.
• Irrelevant response in which the respondent’s answer is not relevant to the question asked.
• Inaccurate response in which the reply is biased.
• Verbalized response problem which arises because of the respondent’s failure to understand the question.

Interviewer’s biasness, refusal, incapability to understand questions

An interviewer can affect the performance of an interview with his own responses and suggestions. Such biasing factors can never be overcome fully, but their effect can be reduced by training and development techniques.

Non response

Some respondents out of the total respondents fail to respond to the schedule. The reasons for this non response can be non availability, refusal, incapability to understand questions, etc.

Non availability

Some respondents are not available at their places at the time of call. This could be because of odd timings or working hours.

Some respondents refuse to answer the questions. There can be many reasons for this, such as language, odd hours, sickness, no interest in such studies, etc.

Inaccessibility

Some respondents can be inaccessible because of various reasons such as migration, touring job, etc.

Observation can be defined as viewing or seeing. Observation means specific viewing with the purpose of gathering the data for a specific research study. Observation is a classical method of scientific study. It is very important in any research study as it is an effective method for data collection.

Characteristics of Observation Method

The following are the characteristics of the observation method of data collection:

• Physical and mental activity : Eyes observe so many things in our surroundings but our focus or attention is only on data which is relevant to research study.
• Observation is selective : It is very difficult for a researcher to observe everything in his surroundings. He only observes the data which is purposive for his research study and meets with the scope of his study. The researcher ignores all the data which is not relevant to the study.
• Observation is purposive and not casual : Observation is purposive as it is relevant to a particular study. The purpose of observation is to collect data for the research study. It focusses on human behaviour which occurs in a social phenomenon. It analyses the relationship of different variables in a specific context.
• Accuracy and standardization : Observation of pertinent data should be accurate and standardized for its applications.

Types of Observation

Different concepts define the classification of observations.

With respect to an investigator’s role, observation may be:

Participant observation

Non-participant observation

With respect to the method of observation, it can be classified into the following:

Direct observation

Indirect observation

With reference to the control on the system to be observed, observation can be classified into the following:

Controlled observation

Uncontrolled observation

In participant type of observation, the observer is an active participant of the group or process. He participates as well as observes as a part of a phenomenon;

For example, to study the behaviour of management students towards studying and understanding marketing management, the observer or researcher has to participate in the discussion with students without telling them about the observation or purpose. When respondents are unaware of observations, then only their natural interest can be studied.

In non-participant observation, the observer does not participate in the group process. He acknowledges the behaviour of the group without telling the respondents. It requires a lot of skills to record observations in an unnoticeable manner.

In direct observation, the observer and researcher personally observe all the happenings of a process or an event when the event is happening. In this method, the observer records all the relevant aspects of an event which are necessary for study.

He is free to change the locations and focus of the observation. One major limitation of the method is that the observer may not be able to cover all relevant events when they are happening.

Physical presence of an observer is not required and recording is done with the help of mechanical, photographic or electronic devices;

For example, close circuit TV (CCTV) cameras are used in many showrooms to observe the behaviour of customers. It provides a permanent record for an analysis of different aspects of the event.

All observations are done under pre-specified conditions over extrinsic and intrinsic variables by adopting experimental design and systematically recording observations. Controlled observations are carried out either in the laboratory or the field.

There is no control over extrinsic and intrinsic variables. It is mainly used for descriptive research. Participant observation is a typical uncontrolled one.

Prerequisites of Observation

The following are the prerequisites of observation:

• The conditions of observation must provide accurate results. An observer should be in a position to observe the object clearly.
• The right number of respondents should be selected as the sample size for the observation to produce the desired results.
• Accurate and complete recording of an event.
• If it is possible, two separate observers and sets of instruments can be used in all or some observations. Then the result can be compared to measure accuracy and completeness.

Advantages of observations

The following are the advantages of observations:

• It ensures the study of behaviour in accordance with the occurrence of events. The observer does not ask anything from the representatives, he just watches the doing and saying of the sample.
• The data collected by observation defines the observed phenomenon as they occur in their natural settings.
• When an object is not able to define the meaning of its behaviour, observation is best method for analysis; for example, animals, birds and children.
• Observation covers the entire happenings of an event.
• Observation is less biased as compared to questioning.
• It is easier to conduct disguised observation studies as opposed to disguised questioning.
• The use of mechanical devices can generate accurate results.

Disadvantages of observations

The following are the limitations of observation:

• Past studies and events are of no use to observation. For these events and study, one has to go through narrations, people and documents.
• It is difficult to understand attitudes with the help of observation.
• Observations cannot be performed by the choice of the observer. He has to wait for an event to occur.
• It is difficult to predict when and where the event will occur. Thus, it may not be possible for an observer to reach in every event.
• Observation requires more time and money.

Use of observation in business research

Observation is very useful in the following business research purposes:

• Buying behaviour of customer, lifestyles, customs, interpersonal relations, group dynamics, leadership styles, managerial style and actions.
• Physical characteristics of inanimate things like houses, factories, stores, etc.
• Movements in a production plant.
• Flow of traffic, crowd and parking on road.

Primary data can be collected with the help of emails and surveys. The respondents receive the questionnaires from the researcher and are asked to fill them completely and return them to the researcher. It can be performed only when the respondents are educated.

The mail questionnaire should be simple and easy to understand so that the respondents can answer all questions easily. In mail questionnaires, all the answers have to be given and recorded by the respondents and not by the researcher or investigator, as in the case of the personal interview method. There is no face-to-face interaction between the investigator and respondent and so the respondent is free to give answers of his own choice.

Importance of questionnaires

A questionnaire is a very effective method as well as research tool in any research study. It ensures the collection of a diversified and wide range of scientific data to complete the research objectives. The questionnaire provides all the inputs in the form of relevant data to all statistical methods used in a research study.

Types of Questionnaire

The following are the various categories of questionnaires:

• Structured or standard questionnaire Structured or standard questionnaires contain predefined questions in order to collect the required data for research study. These questions are the same for all the respondents. Questions are in the same language and in the same order for all the respondents.
• Unstructured questionnaire In unstructured questionnaires, the respondent has the freedom

Process of Data Collection

The researcher prepares the mailing list by collecting the addresses of all the respondents with the help of primary and secondary sources of data. A covering letter must accompany every questionnaire, indicating the purpose and importance of the research and importance of cooperation of the respondent for the success of the research study.

Advantages of Questionnaires

The following are the advantages of questionnaires:

• Wide reach and extensive coverage
• Easy to contact the person who is busy
• Respondent’s convenience in completion of questionnaire
• More impersonal, provides more anonymity
• No interviewer’s biasness

Disadvantages of Questionnaires

The following are the disadvantages of questionnaires:

• Low response by respondent
• Low scope in many societies where literary level is low
• More time requirement

Preparation of an Effective Questionnaire

While preparing a questionnaire, the researcher must focus on some key parameters to prepare it. These key parameters are as follows:

• Proper use of open and close probe
• Proper sequence of questions
• Use of simple language
• Asking no personal question in which the respondent is hesitating to answer
• Should not be time consuming
• Use of control questions indicating reliability of the respondent

Collecting Data through Schedule

This method is very similar to the collection of data through questionnaires. The only difference is that in schedule, enumerators are appointed. These enumerators go to the respondents, ask the stated questions in the same sequence as the schedule and record the reply of respondents.

Schedules may be given to the respondents and the enumerators should help them solve the problems faced while answering the question in the given schedule. Thus, enumerator selection is very important in data collection through schedules.

Difference between schedule and questionnaire

Both questionnaire and schedule are popular methods of data collection. The following are the main differences between questionnaire and schedule:

• A questionnaire is generally sent to the respondents through mail, but in case of schedule, it is sent through enumerators.
• Questionnaires are relatively cheaper mediums of data collection as compared to schedules. In the case of questionnaires, the cost is incurred in preparing it and mailing it to respondent, while in schedule, more money is required for hiring enumerators, training them and incurring their field expenses.
• The response rate in questionnaires is low as many people return it without filling. On the other hand, the response rate in schedules is high because they are filled by enumerators.
• In collecting data through questionnaires, the identity of the respondent may not be known, but this is not the case when it comes to schedules.
• Data collection through questionnaires requires a lot of time, which is comparatively very less in case of schedules.
• Generally, there is no personal contact in case of questionnaires, but in schedules, personal contact is always there.
• The literacy level of the respondent is very important while filling questionnaires, but in schedules, the literacy level of the respondent is not a major concern as the responses have to be recorded by enumerators.
• Wider distribution of questionnaires is possible but this is difficult with schedules.
• There is less accuracy and completeness of responses in questionnaires as compared to schedules.
• The success of questionnaires depends on the quality of questions but success of a schedule depends on the enumerators.
• The physical appearance of questionnaire matters a lot, which is less important in case of schedules.
• Observation method cannot be used along with questionnaires but it can be used along with schedule.

We explore and analyse the life of a social chapter or entity, whether it be a family, a person, an institution or a community, with the help of a case study. The purpose of case study method is to identify the factors and reasons that account for particular behaviour patterns of a sample chapter and its association with other social or environmental factors.

Generally social researchers use case study method to understand the complex social phenomenon and to identify the factors related to this phenomenon.

Case study provides the clues and ideas to a researcher for further research study. By adopting case study method, a researcher gets to know about happenings in the past, which could be related to the research studies and analyse the problem with better perspectives.

Assumptions of case study method

The assumptions made in a case study method are as follows:

• Case study depends on the imagination of the investigator who is analysing the case study. The investigator makes up his procedure as he goes along.
• History related to the case is complete and as coherent as it could be.
• It is advisable to supplement the case data by observational, statistical and historical data, since these provide standards for assessing the reliability and consistency of the case material.
• Efforts should be made to ascertain the reliability of life history data by examining the internal consistency of the material.
• A judicious combination of techniques of data collection is a prerequisite for securing data that is culturally meaningful and scientifically significant.

Advantages of Case Study Method

Key advantages of the case study method are as follows:

• Provides the basis for understanding complex social phenomenon and all related factors affecting the social phenomenon.
• Provides clues and ideas for exploratory research. When the researcher is not able to get a fair idea about the research, past happenings mentioned in a case study help the researcher get clues and ideas.
• Case study helps in generating objectives for exploratory research.
• It suggests the new courses of inquiry.
• Case study helps in formulating research hypothesis.

Disadvantages of Case Study Method

Some important disadvantages of case study method are as follows:

• Reliability : Data collected through case study may not be reliable or it can be difficult to verify the reliability of data in the current scenario.
• Adequacy : Data collected through case studies may not be adequate for research work as data is not pertinent to the research conditions.
• Representative : Data presented by case studies represents the happenings with unknown circumstances to a researcher. Hence, it cannot be the true representation of events to a researcher.

Case study as a method of business research

A detailed case study helps the researcher identify the reasons behind business related problems. As it can be possible that that particular incident has happened in past, so the current issues can be sorted out, by referring to the same case.

In depth analysis of selected cases is of particular value to business research when a complex set of variables may be at work in generating observed results and intensive study is needed to unravel the complexities.

The exploratory investigator should have an active curiosity and willingness to deviate from the initial plan, when the finding suggests a new course of enquiry, which might prove more productive. With the help of case study method, the risk can be minimized in any decision-making process.

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HOW TO CONDUCT OBSERVATIONS FOR RESEARCH

Observations are an important research method for managers, businesses, and researchers alike to determine how people interact and behave in different environments. Observations can help researchers better determine what people do given different scenarios and environmental factors.

Click on the diagram below to learn the five steps for conducting effective observations for research.

How to Conduct Observations for Research

• Identify Objective Determine what you want to observe and why. Are looking to see how students respond to a new environment? How customers interact with employees? How bosses interact with subordinates? When conducting observations, you are trying to learn habits, patterns, behaviors, reactions, and general information about people in a particular environment to better understand what they do and, hopefully eventually, why they do it (though observations alone often won’t tell you the “why”).
• Establish Recording Method To make observations most effective, it’s important that you minimize or eliminate any disruptive or unfamiliar devices into the environment you wish to observe. For example, it is often least effective to videorecord observations in situations where the people being observed know they are being filmed (but it’s usually unethical to film without telling them. Note-taking is the most common method, though in some public spaces you can take photographs, audio recordings, and other methods.
• Develop Questions and Techniques Determine whether you are conducting an informal or a formal observation (see explanations to the far right.) Knowing your objective, determine if there are specific questions you have or if you are going in completely open-minded. What you hope to learn will help you know what specifically to look for. Be prepared when entering an observation space by having a sound understanding of the type of information you are trying learn.
• Observe and Take Notes Visit the space you are hoping to get information from. Be as unobtrusive as possible, taking notes, photographs, audio, and film, only where it is allowed, you have permission, and it makes sense for the research without disrupting the environment. If you are doing formal observations, will you need to code certain behaviors, actions, words, visuals, and other observed data.
• Analyze Behaviors and Inferences Separate the difference between what you observed (which are factual behaviors) and why what you observed happened. Typically, to make some sense of your observed data, you will need to interview people in the environment you are observing, either during the observation itself, or afterwards. Make connections between interactions, responses, behaviors, and other phenomena.
• ← HOW TO CONDUCT INTERVIEWS FOR RESEARCH
• HOW TO CONDUCT SURVEYS →

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Components, Advantages and Disadvantages of Observation Schedule

Back to: Assessment for Learning

Observation schedule refers to a form that is prepared before the collection of data which is used for delineating the behaviour and situational features that need to be observed and recorded during the observation. Observation schedules may differ on a quantitative as well as a qualitative continuum. It is a method where data is collected by the observer in the field through observation. 

Types of Observation Schedule

There are various types of observation schedule which are as follows: 

1. Structured and unstructured observation 

2. Controlled and uncontrolled observation 

3. Participant and nonparticipant observation 

Components of Observation Schedule

The components of an observation schedule are as follows: 

1. An observation schedule must include items that are relevant to the objectives of the survey as well as the research questions. 

2. It must be appropriate for the environment and for the culture.

3. It should not require any effort other than observation.  

4. It can be completed within the deadline of the survey. 

5. It is clearly formatted and there is space to write observations. 

Advantages and Disadvantages of Observation Schedule

According to Waxman, & Padrón (2004), the Student Behavior Observation Schedule (COS) is,

“a low inference observation instrument that specifically focuses on the behaviors of individual students in classrooms.

Going beyond structured observations: looking at classroom practice through a mixed method lens

• Original Article
• Open access
• Published: 23 January 2018
• Volume 50 , pages 521–534, ( 2018 )

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You have full access to this open access article

• Ariel Lindorff   ORCID: orcid.org/0000-0001-6147-6289 1 &
• Pam Sammons 1  

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In this paper, we extend a mixed method (MM) approach to lesson observation and analysis used in previous research in England, combining multiple structured observation instruments and qualitative field notes, to provide a framework for studying three videotaped lessons from 3rd-grade US mathematics classrooms. Two structured observation schedules are employed, one subject-specific and research-oriented and the other generic and inspection-oriented. Both instruments were previously developed based on evidence from the teacher effectiveness research (TER) knowledge base. Qualitative field notes, in addition to structured observation schedules, provide detailed narratives for each lesson video. Separate findings from each instrument and approach are presented, followed by an integrated analysis and synthesis of results. Although previous studies used similar methods to analyze teaching practice within broader research designs incorporating additional methods and perspectives (e.g. teacher interviews, pupil assessments, pupil questionnaires), this paper explicitly examines the strengths and limitations of the multi-instrument, mixed method approach to lesson observation. Using multiple observation instruments allows for triangulation as well as consideration of complementary foci (i.e. a content-specific instrument measures fine-grained aspects of practice not emphasized in a more generic instrument, and vice versa). Field notes facilitate rich descriptions and more thorough contextualization and illumination of teaching practice than structured observation ratings alone. Further, the MM approach allows for consideration of lesson features beyond those established in TER literature as sufficient to characterize ‘effective’ practice.

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1 Introduction

The use of standardized observation instruments is common practice in studies of teacher effectiveness and classroom practice. While such instruments are useful in large-scale studies, their utility for in-depth studies of smaller samples or for exploratory purposes is more limited. Any individual instrument is inherently bounded by the context(s) in which it was developed, the theoretical framework underpinning it, and its intended purpose (e.g. to study relationships between teaching practice and pupil outcomes; to compare teaching practice in different countries; to evaluate practice in a specific subject area; to inform professional development). Previous studies have used multiple observation instruments, as well as qualitative field notes, to mitigate the limitations of any single instrument while providing thorough descriptions of teaching practice (e.g. Hall et al. 2016 ; Kington et al. 2014 ; Sammons et al. 2014 ). The emphasis in these previous studies, however, was not on investigating strengths and weaknesses of the overall mixed methods (MM) approach to lesson observation, nor of its components. The novel contribution of this paper, therefore, is to illustrate and discuss the benefits and challenges of using a MM approach to lesson observation and analysis, as well as the strengths and weaknesses of each aspect of this approach (two quantitative observation schedules, and qualitative field notes), based on our analysis of three videotaped lessons in 3rd-grade US mathematics classrooms.

Below, the theoretical perspective framing our approach to observation and analysis is introduced, followed by an overview of the instruments and approaches employed and empirical support for the use of these to observe and evaluate lessons. Next we present quantitative, qualitative and integrated findings based on three videotaped lessons, with particular emphasis on how qualitative findings elaborate, extend and diverge from quantitative findings. Finally, we discuss strengths and weaknesses of the structured observation schedules and field notes, as well as of the combined MM approach as a whole, and draw implications for research, professional development, and teacher evaluation. We focus our analysis on features and practices in the three focal lessons rather than global judgments of teachers’ effectiveness, as one lesson is not necessarily a typical or full representation of a teacher’s classroom practice.

Overall, the purpose of this paper is illustrate this multi-instrument mixed methods approach to lesson observation and analysis, and to address the following questions:

What does each aspect of the approach tell us about the focal lessons, and what do we learn from integrating and synthesising findings across all aspects?

What are the strengths and weaknesses of each aspect of the approach, and of the approach as a whole?

2 Theoretical perspective

The overarching theoretical perspective framing our MM approach to lesson observation and analysis is grounded in the teacher effectiveness research (TER) knowledge base. More specifically, we place an emphasis on those practices which empirical evidence from previous research has shown to promote pupil attainment (see Teddlie et al. 2006 ; Muijs et al. 2014 ). Theoretical frameworks for studying teacher effectiveness can be traced back to early studies that took “student learning in classrooms as schools as a point of departure” (Creemers, 1994 , p24), developing models including instructional factors such as Carroll’s ( 1963 ) ratio of time spent to time needed for learning. Models for effective instruction have been further refined since via empirical studies to establish features of teaching that predict improved students’ outcomes (academic and socio-emotional), and variously involve aspects including lesson structure, content delivery, behaviour management, interaction, focus, questioning, pupil involvement, and emotive/cognitive feedback (Ko, Sammons, & Bakkum, 2013 ).

Previous research has shown, however, that an effectiveness perspective may not fully characterise teacher practice. An exploratory MM study of “inspirational” teaching in England found that what constituted “inspirational” practice—across several themes emerging across the teacher perspectives, pupil responses, and lesson observations, relevant core features including: positive relationships, good classroom management, positive and supportive classroom climate, formative feedback, enjoyment, and a high quality learning experience overall—overlapped with but also extended understanding beyond those constituting effective practice (Sammons et al., 2014 , 2016 ). For example, although secondary teachers in the sample were rarely observed using formal methods of differentiation (by providing different tasks or using distinctly different teaching approaches for different pupils), they met individual needs through informal approaches to personalizing learning experiences and by capitalizing on strong rapport and personal relationships with individual pupils. A similar approach to observation used in another study evaluating a mathematics textbook and mastery-oriented teaching approach in England also demonstrated that qualitative evidence illuminated features of teaching practice not covered in systematic observation schedules based only on TER literature (Hall et al. 2016 ). For example, findings illustrated how teachers were consistently using mixed-ability grouping, a key feature of the specific teaching approach being evaluated, but were adopting a variety of approaches to doing so according to their perceptions of the needs of their pupils. This ability grouping was not an aspect of classroom practice covered in the pre-existing quantitative observation schedules used, so that qualitative data played an important role in informing understandings of classroom practice in the context of a mastery-oriented teaching approach.

In keeping with a theoretical perspective framed by TER, the approach to lesson observation and analysis presented below employs two existing systematic observation schedules both explicitly designed based on evidence from the TER literature. The mathematics enhancement classroom observation recording system (MECORS; Schaffer et al. 1998 ), is specific to mathematics and was originally used to evaluate a mathematics intervention programme in the UK. The other is the quality of teaching (QoT) lesson observation form (van de Grift et al. 2004 ; 2007 ), also based on a review of TER and designed for use in primary schools with an orientation towards evaluation of quality based on school inspection frameworks from the Netherlands and the UK (but tested in several European countries). We combine these instruments with qualitative field notes for two reasons: first, to provide richer and deeper descriptions of practice, and second, to help contextualise findings because lessons were conducted in US classrooms while observation schedules were designed in non-US settings. The use of multiple quantitative instruments that were designed for different purposes avoids an overly narrow characterisation of teaching practice driven by the nature of a particular scale (e.g. based on frequency or high-judgment quality ratings). The use of quantitative instruments alongside qualitative field notes helps to ensure that ratings of teaching practice are contextualised and underscored by vignettes, quotes and examples, and that the approach to observation and analysis is flexible enough to account for aspects of practice not emphasized in the quantitative instruments.

3 Framework, instruments and approaches

Below, we provide details of the measures and approaches for each of the three aspects of our design. While this paper is intended to illustrate the combined mixed methods approach and is based on only three lessons, it is important to note that implementation in a larger study would include multiple raters to allow for calculations of inter-rater reliability and to avoid possibilities of bias arising from individual raters’ use of multiple instruments. It should be noted that both the systematic instruments have been used in previous studies and have published details of inter-rater reliability.

3.1 The mathematics enhancement classroom observation recording system (MECORS)

As described by the authors of the instrument, the MECORS was used in two stages. First, the observer recorded:

type of activity (whole-class interactive teaching, lecture, group/pair work, individual practice, assessment, or management of resources/materials/physical space), coded each time this changed;

detailed notes on the activity,

number of pupils on-/off-task at 5-min intervals (time-sampling).

Because this instrument was originally designed for use by an observer physically present in the classroom, we modified the approach for video lesson observation. In particular, field notes and time-sampling were done in multiple passes (once per camera angle) to ensure the fullest possible information and alignment by time code across the notes from different camera angles in the same lesson.

The observer then filled out a rating sheet comprised of eight dimensions and a total of 57 items, each rated on a Likert scale from 1=“behavior rarely observed” to 5=“behavior consistently observed”. Table 1 shows the eight domains in the order in which they appear on the observation schedule, and the number of items contributing to each.

Most of the categories above are relatively self-explanatory; “Classroom management” might be less so. Here, this refers to management of physical space and resources, distinct from behavior management. “Mathematics Enhancement Project (MEP) strategies” is a phrase from the original study for which the instrument was designed, and refers to teaching features relevant to: connecting new content to prior content, other areas of mathematics, and real-world context; use/promotion of correct mathematical language; teaching/encouragement of (a variety of) problem-solving strategies; and rapid-fire mental questioning. This category is the most specific to mathematics in the MECORS instrument (Schaffer et al. 1998 ).

MECORS items are framed in terms of teacher behaviors or practices, and rated on a frequency scale rather than judgments of the quality of a given practice. Items span a range of aspects of teachers’ practice and provide a tool to identify general areas of strength/weakness (Muijs & Reynolds 2011 ). Appendix 1 contains the full list of items included in the MECORS observation schedule, with appropriate reference to the original authors of the instrument.

The rationale for using the MECORS to illustrate our approach to lesson observation and analysis is threefold. First, the inclusion of eight mathematics-specific items (such as ‘The teacher uses correct mathematical language’ and ‘The teacher allows pupils to use their own problem-solving strategies’ under the dimension of ‘Demonstrates MEP strategies’) and items related to established features of effective teaching specifically in mathematics (such as ‘The teacher asks pupils to explain how they reached their solution’ and ‘Pupils are asked for more than one solution’ under the dimension of ‘Demonstrated skills in questioning’) made the MECORS well-suited for the observation of the three mathematics lesson videos on which this paper focuses. Second, the research team was familiar with the instrument and experienced in its use from a previous research project in England. Third, the MECORS instrument also covered more general features of effective practice allowing for triangulation and comparison with the QoT instrument described below.

3.2 The quality of teaching (QoT) lesson observation form

The QoT instrument included nine dimensions of classroom practice, with each dimension measured with 2–4 indicators, and each indicator rated from 1=“predominantly weak” to 4=“predominantly strong” and supported by 1–5 “Good practice examples” rated either 0 (“no, I didn’t observe this”) or 1 (“yes, I have observed this”). We included an additional option of “not applicable” for these, as we found this to be appropriate in some instances. Table 2 shows the nine dimensions of the QoT and the number of items contributing to each.

A few of the below descriptors merit clarification. “Stimulating learning climate” with items such as, “The teacher stimulates the independence of pupils”, relates to the teacher’s facilitation of cohesion, cooperation, independence, and individual involvement on the part of pupils, whereas “Safe and orderly climate” items (such as, “The teacher promotes mutual respect”) focus on fostering a relaxed atmosphere, mutual respect between pupils, pupils’ self-confidence, and demonstration of respect for pupils in the teacher’s language and behavior. “Effective classroom organisation” includes items relating to lesson structure, organisation, and orderly progression, as well as effective management of time and lesson materials and resources, rather than physical space. “Effective classroom layout,” on the other hand, refers to physical space/décor. The “final judgement” is an overall rating of lesson quality, so is not fundamentally separate from ratings across the other categories. Appendix 2 contains the full list of items included in the QoT observation schedule, with appropriate reference to the original authors of the instrument.

In contrast to the MECORS, items on the QoT are rated first in terms of whether “good practice” examples (e.g. “The teacher allows pupils to finish speaking” for the item, “The teacher shows respect for pupils in behavior and language use” under the dimension, “Safe and orderly climate”) are observed or not, then items in terms of the observers’ perception of the extent to which a teacher displays strength or weakness for a particular indicator. While both the MECORS and QoT involve some degree of observer inference, the QoT levels of measurement may be seen as involving more subjective (high inference) judgement due to a focus on quality rather than frequency. Like the MECORS, QoT covers a broad range of classroom practice features and is intended to identify general areas of strength or weakness.

The rationale for using the QoT instrument to illustrate our approach was that it had been internationally validated and used in previous published research (more detail on this is given below in Sect. 4.2 ), and was developed from a combination of inspection- and research-based perspectives and evidence. Additionally, our research team was trained on this instrument and experienced in its implementation, having used it for multiple prior studies in England.

Using the MECORS and QoT in combination for observation of the same lessons affords opportunities for triangulation where overlap exists between the two instruments, and complementarity where items on one schedule cover aspects absent from the other. As noted elsewhere, we use these instruments to illustrate our approach here with ratings from a single researcher for three lesson videos; in a larger study we would either randomly assign different instruments to multiple raters for the same lessons, or randomly assign the order in which different instruments were used by raters if using only one rater per lesson.

3.3 Qualitative field notes

Qualitative field notes are used to provide rich descriptions of classroom activities and tasks, teacher and pupil behaviors, classroom interactions, and classroom environment (including decorations and physical space) in these example lessons. Using a loose framework informed by previous research (e.g. Kington et al., 2014 ), notes focused on: Descriptions of tasks and activities, what pupils were doing, and what teachers were doing, with quotes recorded to illustrate specific interactions and teacher feedback, and descriptions of the physical environment (this last would normally be addressed before the lesson started, for a “live” rather than videotaped lesson). As such, these field notes were not strictly structured, but included time codes as a reference point to allow for alignment with video transcripts along with descriptions covering the aspects mentioned above as the observed lesson progressed. Researcher memos were also recorded alongside descriptions of what was happening in the classroom; these included any questions or comments that arose in relation to what was directly observed, for example, classroom routines not made explicit but apparent in teacher and student interactions or behaviors. A major benefit of field notes as part of an overall MM approach is the potential to provide a greater degree of specificity regarding particular features of a teacher’s behaviors and practices during an observed lesson, in contrast to the more general categories of behavior and practice offered by the structured observation schedules.

The field notes provide the opportunity both to give rich vignettes illustrating specific examples of classroom practice to support ratings on structured observation schedules, and to generate further understandings of specific teaching contexts and initiatives through the use of an inductive approach to analysis. The latter is characteristic of a grounded-theory approach to analysis, and allows themes to emerge from the qualitative data from a particular study. The qualitative analytical approach is described in more detail below in Sect. 5 .

4 Empirical support for the framework and approach

There is precedent in previous empirical research literature for the use of each observation instrument described above, as well as for their integration with qualitative field notes in a mixed methods approach to lesson observation and analysis.

4.1 Empirical support for the MECORS instrument

The MECORS instrument was developed to evaluate a mathematics intervention in primary schools in England. It drew on instruments designed previously in the US and shown to be reliable, including the Special Strategies Observation System (SSOS; see Schaffer et al. 1994 ) and the Virgilio Teacher Observation Instrument (Teddlie et al. 1990 ). Findings from the study in England using the MECORS, including observations of 78 teachers, found that direct instruction was strongly and positively related to effective teaching scales. Internal consistency was above Cronbach’s Alpha α = 0.8 for all scales in the schedule (Muijs & Reynolds 2000 ).

The MECORS has also been used outside England. A study in Malta used the instrument but omitted some items in consultation with local teachers (Said 2013 ), suggesting that a larger-scale study of US lessons than this paper allows might adopt such consultation or consideration of whether items showing little variation should be candidates for exclusion. This study showed high reliability in terms of overall inter-rater agreement ( k = 0.89, p < 0.01) based on 25 mathematics lessons rated by two observers, but some items had substantially lower inter-rater agreement (particularly, “The teacher uses a brisk pace”, with k = 0.67, p < 0.01). Implications for scale validity and reliability would need to be tested, however, for a larger study than the present illustrative analysis of three lesson observations, if using the MECORS in a new setting.

4.2 Empirical support for the QoT instrument

The QoT instrument was piloted in England, Belgium, Germany, and the Netherlands; except in England, participating schools constituted within-country random samples, with 854 primary mathematics lessons observed in total (van de Grift 2007 ). Findings from the pilot demonstrated scales were internally consistent (based on Cronbach’s Alpha) with values above α = 0.7. For dual observations (two observers from two countries), overall inter-rater reliability was high (consensus above 83%) (Ibid.). Sufficient construct validity for international comparisons was assessed both by computing correlations to related concepts on a separate instrument (about 0.70 across “teaching” and “learning” categories on the alternative instrument) and correlations between QoT constructs and the overall judgment of teaching quality (between 0.59 and 0.72; Ibid.). A subsequent study investigated QoT measurement invariance across Flanders (Belgium), Lower Saxony (Germany), the Slovak Republic and The Netherlands, and the relationship of cultural differences to measurement differences; findings supported the reliability of QoT measures but indicated that some (particularly classroom management and adaptation of teaching) were differentially sensitive to student background and school/classroom characteristics across different countries (van de Grift 2014 ). In particular, measures related to clear instruction and activating pupils were reliable and fully scalar equivalent across the four countries, the measure of safe and stimulating climate was only partially scalar equivalent, and measures of classroom management and adaptation of teaching were only metrically equivalent and interacted differently across countries with variables related to student background and school and class features, suggesting caution in making multi-country comparisons using this instrument with regard to these latter measures (Ibid.).

4.3 Empirical support for the overall MM approach

In a MM framework, concepts of validity and reliability are extended to both individual methods and the combination of methods (Teddlie & Sammons 2010 ; Sammons & Davis 2016 ). For the quantitative instruments, these are addressed in the sections above. In qualitative inquiry, we consider trustworthiness and dependability as analogs of the more quantitatively-oriented terms “validity” and “reliability,” respectively (Lincoln & Guba 1985 ). The use of both the qualitative approach to taking and analysing field notes, and the mixed methods approach overall, has precedent in previous studies (Hall et al. 2016 ; Kington et al. 2014 ; Sammons et al. 2014 ); participant practitioners’ positive responses to both the approaches and findings of those studies supports the trustworthiness of the overall approach, and the engagement of multiple researchers in the process of observation and analysis in at least one study (Sammons et al. 2014 ) has supported the dependability of the MM approach and findings arising out of it.

5 Methods and analytical approach

The approach to analyzing quantitative data were limited by the very small number of teachers, focus on only one videotaped lesson per teacher rated by a single researcher with training and experience using both observation schedules and qualitative field notes. This is sufficient for the purposes of this paper to illustrate the multiple-instrument, mixed methods approach. In a larger study, it would be ideal to have multiple researchers trained on different instruments to avoid potential bias; alternatively, this could be accomplished by having multiple researchers observe each lesson but randomly altering the order in which they complete their observation ratings.

In order to obtain results that could be discussed/compared across teachers and instruments, we calculated each teacher’s mean scores for the items within each component of each instrument. We report means rather than sums because on both the QoT and the MECORS, the number of items per component varies considerably. Thus, a mean score is more straightforward to interpret in terms of meanings of original item scales.

Qualitative analysis was undertaken in several stages. First, field notes were coded using a grounded approach to allow themes to emerge from what was observed/recorded (Glaser 1992 ). This was followed by more fine-grained coding of data coded according to broader categories (e.g. “feedback” or “assessment”) to more fully and specifically characterize patterns in the data (e.g. variations of practice within a particular category, or similarities across lessons) emulating an approach to analysis used in previous studies (Hall et al. 2016 ; Sammons et al. 2014 ).

6 Analysis of the three focal lessons

We begin with a brief overview of results from each structured observation schedule, followed by a summary of themes emerging from the thematic coding of qualitative field notes and how these are illustrated in each observed lesson.

6.1 Structured observation findings: mathematics enhancement classroom observation record

Table 3 shows the mean scores for the three teachers on each of the components of the MECORS instrument.

In Mr. Smith’s lesson the highest-rated aspect was “focusing and maintaining attention on lesson”. There was little off-task behavior at any of the time-sampling occasions, although exceptions to this are discussed below from qualitative findings. Items contributing to “providing pupils with review and practice” and “demonstrating skills in questioning” had average ratings of “often observed” (3.0). However, within these categories the ratings had a wide range across different items; for example, within the 14 items related to questioning skills, “pupils are asked for more than one solution” was rated 1 (“rarely observed”), while items specific to “high frequency of questions” and “academic questions” were rated 5 (“consistently observed”). Similarly, within the 6 items related to review and practice, the item specific to “clearly explains tasks” was rated 4 (“often observed”) while another item specific to “offers effective assistance to individuals/groups” was rated 1 (“rarely observed”).

In Ms. Young’s lesson, the mean scores for components relevant to focusing and maintaining attention on the lesson, providing pupils with review and practice, and demonstrating questioning skills were all between “often” and “frequently” observed (i.e. slightly above the middle of the scale); however, like Mr. Smith’s lesson, the ratings for individual items varied considerably. The lowest ratings for this lesson related to classroom and behavior management.

The mean scores for Ms. Jones’s lesson, compared to the two above, showed less variation across categories, with all mean scores above 3 (“often observed”). The highest ratings of this lesson corresponded to classroom management, focusing and maintaining attention on the lesson, review and practice, and behavior management. The lowest scores were for using MEP strategies and demonstrating a variety of teaching methods, but these were still “often observed”.

Based on MECORS scales, the three lessons were most similar in terms of teachers’ “use of MEP strategies”, and least similar with regard to classroom and behavior management. Mr. Smith’s and Ms. Young’s lessons were most similar to one another in their mean scores across categories.

6.2 Structured observation results: quality of teaching

Table 4 presents the mean scores for the three teachers on each dimension of the QoT instrument.

Mr. Smith’s lesson was rated roughly in the middle of the QoT scales according to mean scores for safe and orderly climate, clear objectives, clear instruction, and effective classroom layout. Adaptation of teaching and teaching learning strategies were the lowest-rated categories, as shown in Table 4 ; there was no apparent adaptation of materials, activities, or teaching approach to individual needs, little to no interaction between pupils, only one apparent solution or strategy for any of the problems, and little context given for problems and solutions. All other categories had mean scores reflecting “more weaknesses than strengths” (i.e. two on the QoT scale). According to the QoT’s underlying framework for assessing quality of instruction, this lesson had more observed weaknesses than strengths overall, and two general areas of particular weakness that would potentially warrant further consideration (for research or professional development purposes).

From the QoT ratings, Ms. Young’s lesson had wider-ranging mean scores across categories. “Clear instruction” was the highest-rated (close to 4, “predominantly strong”), while “adaptation of teaching” and “safe and orderly climate” were the lowest-rated categories. All of the other categories ranged from 2.0 to 2.8 (somewhere between 2, “more weaknesses than strengths,” and 3, “more strengths than weaknesses”). Thus, the QoT clearly suggests a balance of strengths and weakness in this lesson.

The mean scores across categories for Ms. Jones’s lesson were almost all high (between 3, “more strengths than weaknesses,” and 4, “predominantly strong”). The only exceptions were “activating pupils” (rated 2, “more weaknesses than strengths”) and “adaptation of teaching” (rated 1.5, between “predominantly weak” and “more weaknesses than strengths”). The teaching observed in this lesson was predominantly strong except for these two specific areas, so that a further focus on adapting instruction and assignments to individual learning needs and using teaching and questioning strategies that involve all pupils throughout a lesson might be areas for professional development or further inquiry.

According to the QoT scales, these three lessons were most similar across the (overall, generally weaker) areas of adapting for individual pupil differences and learning needs, and using methods/approaches that “activate” pupils. Ms. Jones’s lesson was rated highest across the remaining categories, except for clear instruction, in which Ms. Young’s lesson was rated equally to Ms. Jones’s. For the most part, Ms. Jones’s lesson had higher ratings across the other categories than Mr. Smith’s, with the exception of “effective classroom layout” (for which the two lessons were rated equally) and “safe and orderly climate”, for which Mr. Smith’s lesson had a higher mean score.

6.3 Qualitative observation results: themes

Several themes emerged from the grounded initial approach to coding qualitative field notes. These fell into five broad categories: lesson structure and activities, teacher interaction and feedback, behavior management, pupil involvement and participation, and assessment.

6.3.1 Lesson structure and activities

Within this broad category, three thematic areas emerged from the coding for the three lessons. These included, in order of their frequency in the field notes: format of lesson activities (e.g. direct instruction, individual table work), timing/transitions, variety of activities, and differentiation/adaptation for individual needs. These are addressed in this section with respect to each lesson.

In Mr. Smith’s lesson, almost the entire class period was spent on direct instruction. There were clear introductory and closure parts of the lesson; the teacher asked about prior knowledge during the introduction before proceeding with the lesson, and for closure pupils were given a problem set answer individually on paper. Transitions were quick and relatively smooth, perhaps in part because pupils remained in their seats throughout and few materials were distributed except protractors and printed closure exercises. Activities largely involved the teacher projecting angle images on the smart board and asking questions to the whole class. There was no evidence of differentiation or adaptation.Pupils were questioned collectively, an aspect of practice which would not have been obvious from the quantitative ratings alone.

In Ms. Young’s lesson, the introduction involved the teacher asking about the previous mathematics lesson content, presenting pupils with two related multiplication problems, and using these to explicitly present the objectives of the lesson (i.e. to use doubling and halving strategies to solve problems). Transitions were not well organised; it took several attempts and approximately 4 min for children to assemble on the carpet when called, and the first few announcements of this transition (including ringing a bell) got limited pupil response. The lesson task involved one focal problem, but the teaching approach varied (direct instruction at the beginning and end and individual work with teacher circulation in between). Much of the lesson time involved pupils working at tables to justify the equivalence of two multiplication problems; this use of class time on a single problem is an aspect that emerged only from the qualitative analysis, but may well be an important consideration in analysing a mathematics lesson. Even during this time the teacher employed some direct instruction, repeating questions and key points to the whole class. Although there was little evidence of adaptation for individual needs, the teacher’s questioning discussion with individuals and groups while circulating may have served as an informal approach to adapting instruction.

In Ms Jones’s lesson, the introduction included explicit framing of the lesson objectives (“multiple ways to multiply a whole number times a fraction”) and specific instructions for pupils to set up activity materials (writing the title; organising three sections on construction paper). There was a defined closure, but it was also clear that some planned lesson activities had not been completed (two of the planned three approaches had been addressed, with no time for pupils to record the second). Transitions were smooth, but there was little movement of pupils around the room. There was some variety in lesson activities, with the teacher telling stories to engage pupils with core concepts, asking them to write processes in a structured way, and finally working with manipulatives in small groups to demonstrate one solving strategy. With the exception of a few minutes spent on group work, the teaching approach mainly consisted of direct instruction. There was little formal adaptation for individual learning needs, but the teacher circulated regularly and occasionally spent time scaffolding task instructions for individual pupils, which suggests—as noted in Ms. Young’s class—of an informal approach to differentiation/adaptation, an aspect for which having detailed field notes was useful to more fully characterise the teacher’s practice.

Thematic patterns are apparent across these lessons. All three teachers emphasised direct instruction and used some formal lesson structure (i.e. introduction and conclusion). Cohesion and whole-class activities were prioritised, and there was little evidence of adaptation of tasks for individual learning needs (although two of the three teachers appeared to use informal approaches to adapt instruction for individuals/groups).

6.3.2 Teacher interaction and feedback

Four apparent aspects of teacher interaction and feedback stood out across the three lessons. These included the extent and nature of teachers’ interactions with individual pupils, positive/negative feedback to pupils’, and evidence of building/maintaining relationships with pupils.

In Mr. Smith’s lesson, most teacher–pupil interaction was between the teacher and the whole class group, with the teacher asking frequent questions and allowing the class to respond simultaneously. Less frequently, the teacher called on volunteers to answer his questions, and when teaching how to properly use protractors, volunteers were invited to demonstrate on the smart board while the class observed. Occasionally, the teacher spoke to pupils individuals beyond the above. On one occasion he responded to a pupil’s erroneous response, giving examples of when approximate angle measures were insufficient (e.g. “if you make your building 91° everybody’s going to be walking slanted a little bit”). Feedback was also largely directed towards the whole class, an important distinction that would not have been apparent through the use of only the quantitative instruments, and was not expressed in strongly positive or negative terms. When the responses were correct (or mostly correct) he frequently said “Okay” and repeated correct answers, and used relatively neutral wording to correct wrong answers (e.g. “Actually, it’s D”). Feedback focused mainly on whether answers were correct or incorrect, but sometimes the teacher strove to explain solution processes (e.g. how a protractor should be used and what mistakes to watch out for; explaining that pupils may have read the wrong line of numbers). There was some evidence of the teacher’s ongoing attempts to build and maintain relationships, however, with little individual pupil-teacher interaction, it was difficult to gauge the extent to which Mr. Smith had established relationships with pupils.

Ms. Young interacted frequently with pupils in her classroom, speaking to individuals while circulating, using their names, and spending more time with some pupils to support their work. Ms. Young frequently used both positive and negative language (e.g. “Oh, that’s nice!”; “You made the mistake, you fix it”) in giving feedback to pupils. There was evidence of the teacher’s relationships with individuals, but in the observed lesson this was largely negative, with the teacher making some global comments to individuals about their behavior or participation before this lesson, and commenting sarcastically at the end to two pupils, “Thank you, Student A and Student B, for disrupting the lesson throughout the day…” While quantitative instruments included categories relevant to many of the above behaviors, having specific examples from the field notes illustrates reasons why some of the quantitative ratings indicating less effective practice in this lesson.

Two main forms of teacher–pupil interactions were visible in Ms. Jones’s lesson. The first involved Ms. Jones’s feedback to answers during direct instruction, frequently confirming whether a response was correct and repeating the response to the class. The second involved Ms. Jones having quiet conversations with individuals when circulating, sometimes prompting pupils to reflect if they had not followed or understood instructions (e.g. “Look at your fraction. Do you have a denominator of four?”). The teacher gave succinct and frequent positive feedback (e.g. “Exactly”, “Love it!”), and occasional, brief negations to incorrect answers (“No, it can’t be zero ‘cause it won’t work”). There was positive feedback to the whole class at the end of the lesson (“Very good job, guys, today. I’m very proud of you,”) and to individual groups at other times (“Thank you, yellow table. And thank you, pink table”). There was evidence that the teacher had previously built relationships with pupils, as reflected in her use of (and their excited engagement with) personal stories about her daughter, and the relaxed affect of both pupils and teacher. For example, the teacher’s calling pupils “friends,” and her laughter with them when one belched intentionally in response to a story reinforcing the meaning of “times,” suggested a relaxed learning environment supported by a positive teacher–pupil dynamic. Here again, the qualitative field notes help to illustrate some of the reasons behind quantitative ratings confirming and extending findings on certain features of effective practice in this lesson.

To summarise, there was considerable variation across lessons in terms of the nature and extent of teachers’ feedback to and interactions with pupils, and in the extent to which positive teacher–pupil relationships were evident.

6.3.3 Behavior management

Qualitative thematic coding revealed several aspects relevant to behavior management: frequency and seriousness of disruptions, response and responsiveness of the teacher to pupil behavior, and evidence of established classroom routines and norms.

Mr. Smith’s class appeared largely appropriately-behaved and quiet throughout the lesson. There were few disruptions, and those that were evident were fairly minor. One boy appeared to look at something under his table for much of the lesson, made faces at the camera, and engaged a seatmate in off-task conversation. The teacher did not seem to see or respond to this. Classroom routines and norms were not explicit, but it appeared that these had been previously established, given the majority of pupils’ ready engagement in whole class question-and-response processes and quiet waiting when the teacher was speaking or a classmate was demonstrating at the board.

In Ms. Young’s class, there were numerous times when children were out of their seats and walking around the class, particularly when they were supposed to be working at tables. In a few instances, it was clear that this behavior was task-oriented; several children approached Ms. Young to check their work, and a few others went to get graph paper or other supplies. Some, however, appeared off-task. One boy approached another table to chat, another ran across the back of the room out of the camera’s field of view. One of these pupils also appeared to spend the beginning of the lesson reading an unrelated book. The teacher sometimes responded quickly to redirect disruptive or off- task behaviour (e.g. “Sit down. You need to be part of this discussion or I’ll put you in that room, too,” after sending a child out of the classroom). On the other hand, some behaviors noted above got no apparent teacher response. There was some evidence of established rules and norms (e.g. “You’re not supposed to leave your seat during the class discussion”), but this was less apparent with regard to when, how and why pupils could move around the room. Getting the class to quiet down, pay attention and come to the carpet appeared to be a challenge. Overall, behavior management was inconsistent, which may have been partly due to the high level of activity when the lesson required pupils to work at tables and use a variety of materials to support their work.

Pupils in Ms. Jones’s lesson were mostly seated quietly when the teacher or another pupil was speaking, and there was little overt disruption. What disruptions did occur were minor and did not disrupt the flow of the lesson or continue for long (for example, one boy was seen trying to touch his seatmate’s hair with a pencil, but stopped when his seatmate looked at him and both raised their hands to participate). The teacher overtly redirect pupil behavior often, but divided her time circulating around the room and stood in different locations during direct instruction so that she did appear to be monitoring the whole class. The teacher sometimes referenced classroom norms explicitly, usually framed in positive statements (e.g. “We raise our hands”). Evidence of established routines and norms was suggested by generally consistent pupil behavior, raising hands to answer questions, and visible shared work habits (e.g. pupils having notes handy at the start of the lesson or turning to a partner to share notes).

Although each lesson had a distinct pattern of observed behavior and behavior management, both pupils’ behaviors and teachers’ responses to disruption were more consistent (though distinct) in the lessons that involved less pupil movement around the room. This suggests that the nature of lesson activities can affect teachers’ behavior management, pupils’ behavior, and an observer’s ability to see clear patterns in both (within the limits of a single lesson).

6.3.4 Pupil involvement/participation

Notes on pupil involvement and participation fell into two main categories. These pertained to the extent to which pupils showed individual engagement in activities, and the extent to which they participated in discussions.

In Mr. Smith’s lesson, most pupils were involved in calling out whole-class responses. One or two did not respond throughout the lesson, but even those who remained silent appeared to look and listen, with the exception of the one child involved in disruptive/off-task behavior described above. Only a few children volunteered to answer/demonstrate individually, however, and no apparent strategy or approach was used by the teacher to encourage broader participation. The only opportunity for individual work occurred at the end of the lesson, and it was impossible to gauge individual involvement as the video ended with instructions for this activity.

In Ms. Young’s lesson, there were a few eager volunteers during class discussion. There was no evidence of specific strategies to elicit responses from non-volunteers. However, much time was spent working at tables, and during this time most pupils worked on their own or discussed in their groups. Only a few (seated at the back of the room) were apparently off-task. At the end of the lesson, most pupils listened quietly as classmates presented.

In Ms. Jones’s lesson, most pupils in view of the camera raised hands to volunteer answers, so there was a high level of individual involvement in class discussion. The teacher did not use strategies to elicit answers from non-volunteers, however, she did ask for nonverbal signals when pupils completed a task, such as different gestures to indicate readiness to proceed at different times (e.g. making “bull horns” with their fingers, putting their thumbs up on the table, putting their hands on their heads), and children seemed almost universally to engage with these instructions (with some turning to neighbors to remind them of instructions). Every pupil seen on screen followed the instructions for the written and individual components of the lesson (e.g. cutting circles into fourths), and many called the teacher over to confirm that they were on track or debated with peers about how to complete a task, suggesting a high degree of individual involvement.

In short, despite variations in behavior and behavior management, there were common patterns of pupil involvement and participation. The three lessons were characterised by high levels of apparent attention and engagement with tasks, but participation in discussions was mostly limited to pupils who volunteered.

6.3.5 Assessment

Two aspects of assessment were identified in the field notes: informal and formal assessment of progress towards learning objectives. Here, we define formal assessment as a task completed individually by pupils and marked by the teacher to measure pupils’ knowledge/skills in relation to learning objectives. We define informal assessment as any process/task/interaction providing the teacher with information about pupils’ knowledge and skills.

At the end of Mr. Smith’s lesson, pupils were asked to individually written questions; this may have constituted formal assessment, if papers were marked subsequently. There was little informal assessment during the lesson, at least on an individual level, as questions were posed to the whole class and most were answered in chorus. Mr. Smith appeared to get information informally about what the whole class knew, but not about individual learning, based on pupils’ responses in chorus.

Ms. Young implemented no formal assessment, but engaged in some informal assessment. While pupils worked at their tables, she asked questions about what they were doing or corrected their work. In some instances she instructed pupils to share resources, and they were allowed to speak to one another while working, so there was no empirical evidence that Ms. Young was be able to gauge an individual child’s knowledge and skills during the lesson. Additionally, the teacher sometimes asked questions but interrupted a pupil’s response to offer her own phrasing, so that evidence of the individual pupil’s understanding was limited.

Ms. Jones’s lesson similarly included no formal assessment but some informal assessment. The teacher circulated frequently when pupils were completing tasks, looking at and responding to their work. However, there was little evidence that the teacher had any means to accurately assess individual knowledge and skills, because tasks were undertaken as a group and guided by the teacher rather than completed independently.

It should be apparent from these descriptions that there were strong similarities across the three lessons with regard to assessment, with overarching themes consisting of a lack of both formal and informal assessment of individual pupil progress.

7 Integration and synthesis using the MM lens

Here we discuss the integrated findings across the various instruments and approaches. Emphasis is placed on the ways in which findings from the observation instruments and field notes elaborate on, extend, or contradict one another. This is followed by comments on the strengths and weaknesses of each of the structured observation instruments and the qualitative approach, and on the benefits and challenges of the MM approach as a whole.

7.1 Integration of results from observation schedules and field notes

Categories of the QoT and MECORS instruments do not have a one-to-one correspondence. Because these instruments are focused somewhat differently, and items are phrased differently and rated on different scales, we do not attempt a category-by-category comparison. Taking the two (or three, given a tie) highest rated categories for a particular lesson as areas of greatest strength, and the lowest two (or three, given a tie) as the areas of greatest weakness, we can establish for each observation instrument the main messages about the effectiveness of the teaching observed in each lesson. We examine how particular strengths and weaknesses in the three lessons compare as measured by these two instruments, then integrate these with themes from the qualitative findings.

7.1.1 Triangulation

First, we examine of how results from the different instruments and the field notes converge or diverge.

Some weaknesses and strengths highlighted by the two observation schedules appear to confirm one another. Ratings on both instruments suggest relative weaknesses in classroom climate and behavior management in Mr. Smith’s and Ms. Young’s lessons, and more effective practice in these areas in Ms. Jones’s lesson. On the other hand, the two schedules lead to divergent conclusions in some cases. All three lessons have relatively high ratings on the MECORS scale for “Focuses and maintains attention on the lesson”, and are likewise rated as having “More weaknesses than strengths” for “Activating pupils” (which may be seen as overlapping with though not having a one-to-one correspondence to the MECORS category mentioned) based on scores from the QoT. This may arise from the fact that the MECORS ratings are frequency-based and QoT scales are based on judgements of relative strength/weakness, or from the different phrasing of specific items. We suggest that the use of both schedules is more robust, as it is apparent that using one or the other might mask important information about observed teaching practice.

The qualitative findings enable further triangulation with the quantitative findings to see where they confirm or diverge. With regard to behavior management, the qualitative thematic analysis informs similar conclusions to the scores from the MECORS and QoT instruments, indicating that in Mr. Smith’s and Ms. Young’s lessons there were some noticeable off-task or disruptive behaviors and little or inconsistent responses from teachers, while in Ms. Jones’s class there was very little observed disruption and some positive redirection from the teacher. In such instances where findings from both quantitative instruments and the qualitative field notes converge, conclusions about relative effectiveness of teaching practices are well supported. On the other hand, the MECORS instrument suggested that all lessons were strong in “Focusing and maintaining attention on the lesson,” but qualitative findings highlighted considerable variety across lessons. Although all teachers stated objectives and checked for prior knowledge, the pacing of lessons differed greatly with many problems completed in Mr. Smith’s class and few in Ms. Young’s and Ms. Jones’s lessons. Where the QoT indicated equal weakness in “Activating pupils” across all three lessons, the qualitative coding again revealed variation across lessons. In Ms. Jones’s lesson, pupils were engaged and involved, but not specifically according to QoT items. Moreover, there was evidence of engagement in Ms. Young’s lesson when pupils worked at tables, and less individual involvement in Mr. Smith’s class but a use of technology that raised his lesson’s mean QoT score for “Activating pupils.” This echoes findings from previous studies using a similar mixed methods approach to lesson observation; in one study, qualitative field notes suggested variety in the use of mixed ability grouping (Hall et al., 2016 ), while in another study, it was noted that despite low ratings on quantitative scales relevant to adapting instruction and tasks, teachers were in fact engaging in informal but observable forms of differentiation that did not fit with the phrasing of the structured observation items (Sammons et al. 2014 ).

From this, we would suggest that the examination of results from multiple observation instruments alongside detailed qualitative notes is an important part of observing and analysing lessons. The scheduled observation approach provides a distillation of strengths and weaknesses within a particular framework, and the comparison of these across different frameworks and alongside detailed narrative field notes allows for more robust conclusions about the effectiveness of teaching practice that are less driven by the specific wording of a particular instrument or the nature of its item scales (e.g. frequency-based ratings on the MECORS and strength-to-weakness ratings on the QoT), and that are reinforced by contextualised accounts of specific practices from the qualitative field notes. This also allows the observer (or, for the sake of teacher professional learning, an evaluator, supervisor or colleague) to engage in careful reflection before highlighting possible areas of strength/weakness or goals for professional development.

7.1.2 Elaboration and extension

The qualitative field notes not only allow for triangulation with the quantitative findings from two scheduled observation instruments, but also elaborate upon and extend those findings through detailed description of lesson features and teacher behaviors. We highlight here some key aspects of lessons for which qualitative notes provide information beyond that provided by MECORS and QoT ratings.

The MECORS and QoT instruments both provide indications of the quality or effectiveness of a teacher’s feedback to pupils. In the MECORS instrument, this falls within the broader category of “Demonstrates skills in questioning”; in the QoT, under “Clear instruction”. However, qualitative field notes provide greater detail regarding teachers’ feedback on aspects that are missed by the quantitative instruments alone. Neither observation instrument clarifies whether feedback includes teacher responses to groups, individuals, or the whole class, whereas qualitative field notes reflect variety across the three observed lessons in this regard (Mr. Smith typically gave whole class; Ms. Young gave feedback to individuals, some behavior-focused and some including prompting before pupils had finished speaking; Ms. Jones used a range of whole-class, individual and group feedback). Depending on the purpose of the analysis, the quantitative data from observation schedules may not be specific enough to explore, evaluate, or differentiate between teachers’ practice with regard to using feedback to enhance pupil learning.

Similarly, while both of the quantitative observation schedules include items relevant to behaviour management, and this is sufficient to provide an indication of general strength or weakness in this regard, qualitative field notes provide more detail to explain why ratings are high/low for a given lesson. Results from both quantitative instruments suggest a more positive climate for learning and stronger behavior management in Ms. Jones’s lesson than in the other two, but findings from field notes bolster these quantitative findings by illustrating specific practices in this lesson that differed from the others: Ms. Jones tended to positively and consistently reinforce routines norms such as hand-raising, used more praise, and notes suggested she had developed positive relationships with pupils, all of which may have contributed to pupils’ engagement and also proactively minimised disruptive behavior by creating a positive environment and classroom community.

A third key aspect for qualitative elaboration extending of quantitative findings concerns assessment. Although there are items on both quantitative instruments relating to formal and informal assessment, these are very general (both instruments have items phrased in terms of “checking for understanding,” under “Provides pupils with review and practice” on the MECORS schedule and “Clear instruction” on the QoT; the QoT also has an item more closely linked to what we define as formal assessment, phrased as “checks the pupils’ achievements”, under the heading “Clear objectives”). Teachers’ mean scores for the three lessons on relevant categories are middling to high across both instruments, but the qualitative findings revealed little individual assessment, informal or formal, in any of the three lessons. The field notes instead reveal descriptions of the sorts of informal assessment that took place (e.g. Mr. Smith’s listening for majority responses and correcting when these were inaccurate, or Ms. Young’s and Ms. Jones’s circulation to look at pupils’ work but not necessarily strictly independent work).

In short, the qualitative notes can add nuance to our understandings of teachers’ practices and behaviors in the observed lessons above and beyond the general categories covered in structured observation schedules, and they can also help to explain the reasons behind quantitative ratings by providing contextualised descriptions of the activities, behaviors and interactions in observed lessons. This has been similarly demonstrated by prior attempts aimed at exploring features of teaching beyond those defined as effective (Sammons et al., 2014 ), in which it was useful to describe aspects not covered in quantitative instruments, and evaluating a particular approach to teaching mathematics (Hall et al., 2016 ), in which it was essential to understand how different teachers were using the approach in their classrooms and what this looked like (e.g. ways of structuring mixed-ability groups).

7.2 Strengths and weaknesses of each instrument/approach

Benefits of the MECORS instrument include its subject-specific mathematics orientation, detailed notes as part of the rating process so that ratings are more likely to be driven by evidence than if they relied on the observer’s memory, and guidelines for structured coding (types of teaching and time-sampling of off-task or on-task pupils) that help to organise the note-taking process. Challenges include the use of a frequency scale (so that ratings are driven by whether and how often a practice is observed rather than the quality of the practice), and some ambiguity about scale steps [i.e. the distinction between “often” (3) and “frequently” (4) is not obvious].

Strengths of the QoT include the use of “good practice” examples to illustrate items, relatively straightforward scales emphasising strength/weakness on each item, and minimal time to complete. Challenges of using the QoT include some poorly-defined items that may not translate well between contexts (e.g. “Effective classroom layout” for multi-subject or shared classrooms), and difficulty rating practices as observed or not observed without gradations of quality for “good practice” examples.

Strengths of the qualitative field notes include rich detail, and contextualised accounts of teaching practice, and the provision of a record to support quantitative ratings. Challenges include observer subjectivity, as including every detail of all aspects of a lesson is not feasible, so there are inherent selection processes that might involve bias when taking such field notes.

Across all of these instruments and methods, challenges are mitigated somewhat by observer training and experience, as well as careful attention to reflexivity both during and after a lesson observation. A trained mathematician and/or mathematics teacher may pick up details that a differently qualified observer might not.

7.3 Benefits and challenges of the overall mixed methods approach

A benefit of combining multiple instruments and qualitative and qualitative strands in lesson observation and analysis are that this capitalises on the strengths of each, while minimising the weaknesses of each. Quantitative observation ratings (on the MECORS and QoT) provide limited information with regard to assessment and teacher–pupil relationships, while field notes provided rich description of these features of teaching. The more content-specific MECORS informs a more detailed assessment of the use of questioning and instructional strategies, while the more generic QoT informs broader judgments of lesson quality. Drawing on multiple sources of evidence allows for triangulation and elaboration; although the MECORS and QoT emphasise some different features of a lesson they also overlap in many aspects, and qualitative field notes provide a narrative and descriptive vignettes to confirm, challenge and extend the information gleaned using quantitative instruments. As found in previous research on “inspirational” teaching (Sammons et al. 2014 ), qualitative field notes suggested that some of the features differentiating Ms. Jones’s lesson from the other two, such as telling personal stories and laughing with pupils, went beyond descriptors of effective practice covered in quantitative instruments. For example, the use of a variety of strategies for eliciting pupil responses, both verbal and nonverbal, allowed pupils in Ms. Jones’s class to participate and engage with the lesson in different ways, even during direct instruction; while quantitative findings suggested that teaching practice in this lesson showed many effective features, the qualitative field notes allowed for a more detailed account of the specific practices in context and how these worked for the pupils. Further, while in Ms. Jones’s lesson there was little formal adaptation of teaching or activities for individual needs, but pupils were engaged and on task. Quantitative instruments alone provide little insight into informal strategies to adapting for individual learning needs, so that the description provided by qualitative field notes adds an important dimension to our understanding of a lesson, and makes a case for extending this informal adaptation aspect of the current knowledge base on effective teaching.

Ultimately, this mixing of different qualitative and quantitative evidence can support more complete explanation and understanding of effective and high quality practice.

8 Discussion and conclusions

Findings in this paper have implications for research, teacher evaluation, and professional development. In all of these applications, quantitative observation schedules provide an organised and tightly structured way of highlighting strengths and weaknesses of teaching practice, and qualitative field notes provide rich evidence of teacher behaviors, classroom climate and lesson flow. However, field notes are less useful for generalisation or comparisons with norms generated from past research based on large samples, a strength of using systematic schedules. The use of multiple observation schedules and qualitative field notes together can provide a robust and well-rounded analysis of teaching practice, whether this is intended to drive teacher evaluation, frame professional development goals, or extend research findings on effective practice to enhance the quality of teaching and learning in primary mathematics lessons.

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Acknowledgements

We thank the authors of the MECORS and QoT instruments for the permission to use their observation schedules in the approach we present in this paper. This work was not supported by any external funding body.

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Lindorff, A., Sammons, P. Going beyond structured observations: looking at classroom practice through a mixed method lens. ZDM Mathematics Education 50 , 521–534 (2018). https://doi.org/10.1007/s11858-018-0915-7

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Using the observation schedules

• The observation schedules are designed to reveal patterns of either teacher or pupil behaviour that may be significant to the promotion of pupil learning
• The examples of observation schedules include a wide range of instruments designed to help you observe specific aspects of teaching
• The observation schedules are usually completed by a non-participant observer; that is, not the teacher of the class being taught. This could be yourself observing your tutor, another teacher or another student teacher; or by your tutor, a teacher or another student teacher when observing you
• In those cases when an observation schedule is being used to gather data about your own teaching, it is likely that after a period of time youwill want to reuse the observation schedule to chart your progress
• Some of these observation schedules are simple, others are complex
• You may want to modify an observation schedule so that it suits your own particular need
• You can use these examples of observation schedules in combination, e.g. if combinations of skills are being observed
• You can use these examples of observation schedules to help you develop your own observation schedules for a specific purpose
• It is always wise to practise using an observation schedule before you begin to collect specific data
• You may need to acknowledge the source of the observation schedule if this is to be stored in your PDP and used in evidence of your progress.

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• Personal Finance

New Survey Shows: Top 6 Ways That Money Is Hurting Your Mental Health

Published on July 27, 2024

By: Ben Gran

• A new survey from Jenius Bank found that Americans are suffering from money-related mental health problems: headaches, lost sleep, depression, and more.
• 54% of unmarried people might never get married because of money concerns.
• Budgeting apps and automatic transfers to a high-yield savings account could help relieve your money stress.

The amount of money in your bank account affects every part of life -- from where you can go on vacation to what car you can buy, to how much you can spend on clothes and restaurants and everyday fun. But money also affects people's mental health.

A lack of money can be stressful. It's like running out of fuel for your car -- having too little money feels like running out of gas and getting stranded on the road of life. Unfortunately, too many Americans are feeling like they're running out of money and losing confidence in their future.

Top six biggest money impacts on mental health

A new survey from Jenius Bank ( The Mind-Money Connection ) found some concerning connections between how people feel about money and their mental health. Money stress is causing a wide range of impacts on people's emotional wellness.

Here are a few findings from the Jenius Bank survey:

• Over 50% of people feel stressed, anxious, and/or depressed about their personal finances
• 52.8% of people have lost sleep because of money concerns
• 42.5% of people say financial stress has hurt their relationships with romantic partners
• 41.4% feel guilt or hopelessness about personal finances
• 32% of people have headaches regularly because of money stress
• 30.1% of people have had strained relationships with family or friends because of finances

Money problems hinder people's life plans and long-term goals

If you're living paycheck to paycheck, struggling to pay the bills, and don't have enough money in your savings account, it's hard to feel happy in everyday life or hopeful about the future. The Jenius Bank survey also found that financial stress is making it harder for people to make big plans and achieve traditional life milestones:

• About 54% of unmarried people said that money stress made it less likely for them to ever get married.
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This survey result doesn't mean that marriage and children are the only way to have a good life as an adult! Lots of people build happy, successful lives while being single and childfree. But if you want to find a life partner and have kids it's unfortunate that financial stress seems to be holding so many people back from achieving these dreams.

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Money stress is all around us. Many Americans feel overstretched financially -- underpaid at work and overcharged at the grocery store. But there are many things you can do to regain control of your financial life.

Jenius Bank offers a few tips for how people can achieve a healthier relationship with their personal finances.

Get money smart

Boost your financial literacy. Understanding the ins and outs of credit card debt, bank account fees, and interest rates can help you get a better deal on your financial life.

Schedule face time with your finances

Track your spending. The best budgeting apps (and some bank accounts) can do this automatically for you. Most banks offer helpful mobile apps with online banking tools so you can easily check your account balance, set up online bill pay and automatic transfers, and more.

Address debt strategically

Being debt free was ranked No. 1 in the Jenius Bank survey for people's definition of what it means to be rich. Debt payoff apps can help you visualize a plan for how to get out of debt faster, and track your progress along the way.

Make saving routine

Every month, every payday, you should try to put some money into a savings account. The best savings accounts can help you make saving automatic by setting up transfers in advance, and by paying higher yields on your cash. Don't think about it, don't decide, just do it.

Saving money automatically out of every paycheck can become the most powerful financial move you make. And it happens quietly in the background of your financial life, with your savings growing month after month, year after year.

Bottom line

Financial stress is becoming unbearable for many Americans. But even if you aren't experiencing money-related headaches or on the verge of breaking up with your romantic partner because of money, everyone can use some tips to reduce financial worries.

Start with a budgeting app to get a clear view of your spending. And consider opening a high-yield savings account to give you extra motivation to save -- and watch your savings grow.

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Ben Gran is a freelance writer based in Des Moines, Iowa. He has written for regional banks, fintechs, and major financial services companies. Ben is a graduate of Rice University.

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Soft Matter

Observation of ferroelectric behaviour in non-symmetrical cholesterol-based bent-shaped dimers.

Non-symmetrical cholesterol-based dimers have emerged as crucial materials in the field of liquid crystal research, owing to their remarkable ability to stabilize various exotic mesophases, including the cholesteric nematic (N*) phase, blue phases, twist grain boundary phase, smectic blue phase, smectic A/smectic A* phase, and smectic C/smectic C* (SmC/SmC*) phase. These mesophases have garnered considerable attention due to their diverse applications in spatial light modulation, chiro-optical devices, optical switching, thermochromic materials, and more. In this study, we present the synthesis and comprehensive characterization of a series of non-symmetrical cholesterol-based bent-shaped dimers (1/12, 1/14, 1/16) in which the cholesterol unit is intricately linked to an aromatic mesogenic core through a flexible spacer. These novel materials exhibit the intriguing ability to stabilize a variety of mesophases, including the N*, TGBA, SmA, and SmC* phases. The chiro-optical properties of the helical SmC* phase have been meticulously investigated through temperature-dependent chiro-optical measurements, shedding light on their potential for advanced optoelectronic applications. Additionally, we have conducted a thorough examination of the physical characteristics of these cholesterol-based dimers, including static permittivity measurements, dielectric spectroscopy, and electro-optical performance analysis. Remarkably, both homologues (1/14, 1/16) exhibit negative dielectric anisotropy, a crucial parameter for liquid crystal devices. Furthermore, investigation reveals that these materials exhibit ferroelectric behaviour in the SmC* phase, with compounds 1/14 and 1/16 demonstrating substantial spontaneous polarization values of approximately 132 nC cm-2 and 149 nC cm-2, respectively. These findings underscore the potential of non-symmetrical cholesterol-based dimers as versatile components for the development of innovative electro-optical devices.

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V. Punjani, G. Mohiuddin, S. Chakraborty, P. Barman, A. Baghla, M. B. Kanakala, M. K. Das, C. V. Yelamaggad and S. K. Pal, Soft Matter , 2024, Accepted Manuscript , DOI: 10.1039/D4SM00496E

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• AI and machine learning

Connect with Microsoft Security at Black Hat USA 2024​​

• By Dasha Zenkovich, Senior Product Marketing Manager
• Security operations
• Threat intelligence

Black Hat USA 2024 is packed with timely, relevant information for today’s security professionals. During the conference this August, we’ll share our deep expertise in AI-first end-to-end security and extensive threat intelligence research. Join us as we present our main stage speaker Ann Johnson, Corporate Vice President and Deputy Chief Information Security Officer (CISO) of Microsoft Security, as she shares threat intelligence insights and best practices from the Office of the CISO in her conversation with Sherrod DeGrippo, Director of Threat Intelligence Strategy at Microsoft Threat Intelligence Center (MSTIC).  

Also at Black Hat, our Microsoft AI Red Team will be onsite holding training sessions, briefings, and panel discussions. And today, we’re releasing a white paper to demonstrate the impact of red teaming in practice when incorporated in the AI development life cycle. The paper details our innovative “Break-Fix” approach to red teaming AI systems and our close collaboration with Microsoft’s Phi-3 team, which allowed us to reduce the harms by 75% in Microsoft’s state-of-the-art small language models. 1   

As a proud sponsor of the inaugural AI Summit at Black Hat, we’re further investing in the community by sharing our learnings in both AI for Security and Securing AI. We’ll be participating in a panel discussion titled “Balancing Security and Innovation—Risks and Rewards in AI-Driven Cybersecurity,” where we’ll debate the trade-offs between innovation in AI and security risks and share strategies to foster innovation while maintaining robust security postures.  

There’s also a sponsored session titled “Moonstone Sleet: A Deep Dive into their TTPs,” presented by Greg Schloemer, Threat Intelligence Analyst at Microsoft, that takes a deep dive into cyber threat actors associated with the Democratic People’s Republic of Korea (DPRK), as well as educational and engaging theater sessions in our Microsoft booth #1240 . With a ton of critical security content to catch—all detailed below—we hope you’ll make time to connect with us at Black Hat 2024. 

Plan your schedule with our standout sessions  

Join us for core Black Hat sessions, submitted for consideration by Microsoft subject matter experts and selected by the Black Hat content committee to be included in its main agenda.  

Stop by our booth (1240) to connect with Microsoft security experts  

At Black Hat 2024, Microsoft Security is here with security leaders and resources that include:   

• Threat researchers and security experts from Microsoft Security, here to connect with the community and share insights.  
• Live demos of Microsoft Copilot for Security , informed by the 78 trillion signals Microsoft processes daily, to help security pros be up to 22% faster.  2
• Theater presentations of Microsoft’s unified security operations experience, which brings together extended detection and response (XDR) and security information and event management (SIEM), so you get full visibility into cyberthreats across your multicloud, multiplatform environment.  
• Hands-on experience with Microsoft Security solutions to help you adopt AI safely.  

Connect with Microsoft leaders and representatives to learn about our AI-first end-to-end security for all. Additionally, you’ll be able to view multiple demonstrations on a wide range of topics including threat protection, securing AI, multicloud security, Copilot for Security, data security, and advanced identity. You’ll also be able to connect with our Microsoft Intelligent Security Association (MISA) partners during your visit—the top experts from across the cybersecurity industry with the shared goal of improving customer security worldwide. And if you have specific questions to ask, sign up for a one-on-one chat with Microsoft Security leaders. 

Partner presence at the Microsoft booth

At the Theater in the Microsoft booth, watch our series of presentations and panels featuring Microsoft Threat Intelligence Center (MSTIC) experts and Microsoft Researchers. Half of the sessions will be presented by the MSTIC Team. The Microsoft booth will also feature sessions from select partners from the Microsoft Intelligent Security Association (MISA). MISA is an ecosystem of leading Security companies that have integrated their solutions with Microsoft Security technology with a goal of protecting our mutual customers from cybersecurity threats. Seven partners will showcase their solutions at our MISA demo station and five partners will be presenting their solutions in our mini-theater. We would love to see you there. Click here to view our full theater session schedule. 

Reserve your spot at the Microsoft Security VIP Mixer  

The event will be co-hosted by Ann Johnson, Corporate Vice President and Deputy CISO of Microsoft Security, and Aarti Borkar, Vice President of Microsoft Security, Customer Success and Microsoft Incident Response, and, we are thrilled to have five MISA partners— Avertium , BlueVoyant , NCC Group , Trustwave , and Quorum Cyber —sponsoring our Microsoft Security VIP Mixer. The mixer is a great time to connect and network with fellow industry experts, and grab a copy of Security Mixology , a threat intelligence-themed cocktail and appetizer cookbook—you’ll be able to meet some of the contributors! Drinks and appetizers will be provided. Reserve your spot to join us at this exclusive event.

Don’t miss the AI Summit at Black Hat  

On Tuesday, August 6, 2024, from 11:10 AM PT to 11:50 AM PT, we’ll be part of a panel discussion titled “Balancing Security and Innovation—Risks and Rewards in AI-Driven Cybersecurity.” Microsoft is honored to be a VisionAIre sponsor for this event. Brandon Dixon, Partner Product Manager, Security AI Strategy will debate the trade-offs between innovation in AI and security risks, share strategies to foster innovation while maintaining robust security, and more. Note: The AI Summit is a separate, one-day event featuring technical experts, industry leaders, and security tsars, designed to give attendees a comprehensive understanding of the potential risks, challenges, and opportunities associated with AI and cybersecurity.   

Microsoft’s Most Valuable Researchers 

Security researchers are a critical part of the defender community, on the front lines of security response evolution, working to protect customers and the broader ecosystem. On Thursday, August 8, 2024, we’ll host our invite-only Microsoft Researcher Celebration . And on August 6, 2024, Microsoft Security Response Center (MSRC) will announce the annual top 100 Most Valuable Researchers (MVRs) who help protect our customers through surfacing and reporting security vulnerabilities under Coordinated Vulnerability Disclosure (CVD). Follow @msftsecresponse on X and Microsoft Security Response Center on LinkedIn for the MVR reveal. 

Secure your future with Microsoft global-scale threat intelligence  

In the hands of security professionals and teams, AI can deliver the greatest advantage to organizations of every size, across every industry, tipping the scales in favor of defenders. Microsoft is bringing together every part of the company in a collective mission to advance cybersecurity protection to help our customers and the security community. We offer four powerful advantages to drive security innovation: large-scale data and threat intelligence; the most complete end-to-end protection; industry leading, responsible AI; and the best tools to secure and govern the use of AI. Together we can propel innovation and create a safer world. We’re excited to share the latest product news and Microsoft Security innovations during Black Hat 2024 and we hope to see you there.  

Join us at the Microsoft Security VIP Mixer

Don’t miss this opportunity to connect with Microsoft Security experts and fellow industry leaders—and pick up your copy of Security Mixology!

For more threat intelligence guidance and insights from Microsoft security experts, visit Security Insider . 

To learn more about Microsoft Security solutions, visit our  website . Bookmark the  Security blog  to keep up with our expert coverage on security matters. Also, follow us on LinkedIn ( Microsoft Security ) and X ( @MSFTSecurity ) for the latest news and updates on cybersecurity. 

1 Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone , Microsoft. April 2024.

2 Microsoft Copilot for Security is generally available on April 1, 2024, with new capabilities , Vasu Jakkal. March 13, 2024.

NASA’s Webb Images Cold Exoplanet 12 Light-Years Away

An international team of astronomers using NASA’s James Webb Space Telescope has directly imaged an exoplanet roughly 12 light-years from Earth. The planet, Epsilon Indi Ab, is one of the coldest exoplanets observed to date.

The planet is several times the mass of Jupiter and orbits the K-type star Epsilon Indi A (Eps Ind A), which is around the age of our Sun, but slightly cooler. The team observed Epsilon Indi Ab using the coronagraph on Webb’s MIRI (Mid-Infrared Instrument). Only a few tens of exoplanets have been directly imaged previously by space- and ground-based observatories.

Image A: Exoplanet Epsilon Indi Ab

“Our prior observations of this system have been more indirect measurements of the star, which actually allowed us to see ahead of time that there was likely a giant planet in this system tugging on the star,” said team member Caroline Morley of the University of Texas at Austin. “That's why our team chose this system to observe first with Webb.”

“This discovery is exciting because the planet is quite similar to Jupiter — it is a little warmer and is more massive, but is more similar to Jupiter than any other planet that has been imaged so far,” added lead author Elisabeth Matthews of the Max Planck Institute for Astronomy in Germany.

Previously imaged exoplanets tend to be the youngest, hottest exoplanets that are still radiating much of the energy from when they first formed. As planets cool and contract over their lifetime, they become significantly fainter and therefore harder to image.

A Solar System Analog

“Cold planets are very faint, and most of their emission is in the mid-infrared,” explained Matthews. “Webb is ideally suited to conduct mid-infrared imaging, which is extremely hard to do from the ground. We also needed good spatial resolution to separate the planet and the star in our images, and the large Webb mirror is extremely helpful in this aspect.”

Epsilon Indi Ab is one of the coldest exoplanets to be directly detected, with an estimated temperature of 35 degrees Fahrenheit (2 degrees Celsius) — colder than any other imaged planet beyond our solar system, and colder than all but one free-floating brown dwarf . The planet is only around 180 degrees Fahrenheit (100 degrees Celsius) warmer than gas giants in our solar system. This provides a rare opportunity for astronomers to study the atmospheric composition of true solar system analogs.

“Astronomers have been imagining planets in this system for decades; fictional planets orbiting Epsilon Indi have been the sites of Star Trek episodes, novels, and video games like Halo,” added Morley. “It's exciting to actually see a planet there ourselves, and begin to measure its properties.”

Not Quite As Predicted

Epsilon Indi Ab is the twelfth closest exoplanet to Earth known to date and the closest planet more massive than Jupiter. The science team chose to study Eps Ind A because the system showed hints of a possible planetary body using a technique called radial velocity , which measures the back-and-forth wobbles of the host star along our line of sight.

“While we expected to image a planet in this system, because there were radial velocity indications of its presence, the planet we found isn’t what we had predicted,” shared Matthews. “It’s about twice as massive, a little farther from its star, and has a different orbit than we expected. The cause of this discrepancy remains an open question. The atmosphere of the planet also appears to be a little different than the model predictions. So far we only have a few photometric measurements of the atmosphere, meaning that it is hard to draw conclusions, but the planet is fainter than expected at shorter wavelengths.”

The team believes this may mean there is significant methane, carbon monoxide, and carbon dioxide in the planet’s atmosphere that are absorbing the shorter wavelengths of light. It might also suggest a very cloudy atmosphere.

The direct imaging of exoplanets is particularly valuable for characterization. Scientists can directly collect light from the observed planet and compare its brightness at different wavelengths. So far, the science team has only detected Epsilon Indi Ab at a few wavelengths, but they hope to revisit the planet with Webb to conduct both photometric and spectroscopic observations in the future. They also hope to detect other similar planets with Webb to find possible trends about their atmospheres and how these objects form.

NASA's upcoming Nancy Grace Roman Space Telescope will use a coronagraph to demonstrate direct imaging technology by photographing Jupiter-like worlds orbiting Sun-like stars – something that has never been done before. These results will pave the way for future missions to study worlds that are even more Earth-like.

These results were taken with Webb’s Cycle 1 General Observer program 2243 and have been published in the journal Nature .

The James Webb Space Telescope is the world’s premier space science observatory. Webb is solving mysteries in our solar system, looking beyond to distant worlds around other stars, and probing the mysterious structures and origins of our universe and our place in it. Webb is an international program led by NASA with its partners, ESA (European Space Agency) and CSA (Canadian Space Agency).

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View/Download all image products at all resolutions for this article from the Space Telescope Science Institute.

Media Contacts

Laura Betz  -  [email protected] , Rob Gutro - [email protected] NASA’s Goddard Space Flight Center , Greenbelt, Md.

Christine Pulliam - [email protected] , Hannah Braun [email protected] Space Telescope Science Institute , Baltimore, Md.

Related Information

Animation : Eclipse/Coronagraph Animation

Webb Blog : NASA’s Webb Takes Its First-Ever Direct Image of Distant World

Webb Blog : How Webb’s Coronagraphs Reveal Exoplanets in the Infrared

Article : Webb's Impact on Exoplanet Research

NASA's Exoplanet Website

More Webb News

More Webb Images

Webb Mission Page

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Related Topics

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Related Terms

• Astrophysics
• Exoplanet Science
• Gas Giant Exoplanets
• Goddard Space Flight Center
• James Webb Space Telescope (JWST)
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Apple research lab opening, focusing on boosting quality and reliability.

A new Apple research lab is about to open in China , focusing on boosting the quality and reliability of iPhone , iPad , and Apple Vision products.

The company chose the city of Shenzhen because many of its suppliers are based there, and it will be working closely with them to test new materials and production techniques …

Apple first announced plans for the lab back in March.

Apple Inc said that it is expanding in China with new applied research labs, in its latest move to tap into the country’s manufacturing and research and development prowess to produce the best products. The US company will […] establish a new applied research lab in Shenzhen, Guangdong province, providing stronger support for employees in the region and deepening collaboration with local suppliers. 

A local report said at the time that Apple wanted to work on making its devices better able to cope with tougher conditions, from intense physical activity to extreme temperatures.

Shenzhen Daily tweeted that the lab is now about to open.

According to Cailian Press, Apple’s newly established applied research laboratory in Shenzhen is set to open. The lab will focus on reliability, quality, and materials analysis testing for products such as the iPhone, iPad, and Apple Vision Pro.

South China Morning Post (via Yahoo ) reports that Apple COO Jeff Williams was in the city yesterday.

According to official statements from Shenzhen, Apple chief operating officer Jeff Williams met on Wednesday with Meng Fanli, the city’s Communist Party secretary. Williams reportedly told Meng that Shenzhen is an important market for Apple, which intends to deepen its cooperation with the city.

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