what is variable in a research study

Scale: Nominal and ordinal.

The confounding variable could be the   and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat.

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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• Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

• The independent variable is the cause. Its value is independent of other variables in your study.
• The dependent variable is the effect. Its value depends on changes in the independent variable.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Table of contents

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.

An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

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There are two main types of independent variables.

• Experimental independent variables can be directly manipulated by researchers.
• Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

• A low-dose experimental group
• A high-dose experimental group
• A placebo group (to research a possible placebo effect )

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.

In statistics , dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

• Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
• Does this variable come before the other variable in time?
• Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

• Is this variable measured as an outcome of the study?
• Is this variable dependent on another variable in the study?
• Does this variable get measured only after other variables are altered?

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Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .

The type of test is determined by:

• your variable types
• level of measurement
• number of independent variable levels.

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

• A bar chart is ideal when you have a categorical independent variable.
• A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

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Independent vs Dependent Variables: Definitions & Examples

A variable is an important element of research. It is a characteristic, number, or quantity of any category that can be measured or counted and whose value may change with time or other parameters.  

Variables are defined in different ways in different fields. For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value. In algebra, a variable represents an unknown entity, mostly denoted by a, b, c, x, y, z, etc. In statistics, variables represent real-world conditions or factors. Despite the differences in definitions, in all fields, variables represent the entity that changes and help us understand how one factor may or may not influence another factor.  

Variables in research and statistics are of different types—independent, dependent, quantitative (discrete or continuous), qualitative (nominal/categorical, ordinal), intervening, moderating, extraneous, confounding, control, and composite. In this article we compare the first two types— independent vs dependent variables .  

Table of Contents

What is a variable?  

Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments. They help in understanding how some factors influence others. Some examples of variables include age, gender, race, income, weight, etc.   

As mentioned earlier, different types of variables are used in research. Of these, we will compare the most common types— independent vs dependent variables . The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let’s explain this with an independent and dependent variable example : In a study to analyze the effect of antibiotic use on microbial resistance, antibiotic use is the independent variable and microbial resistance is the dependent variable because antibiotic use affects microbial resistance.( 1)  

What is an independent variable?  

Here is a list of the important characteristics of independent variables .( 2,3)  

• An independent variable is the factor that is being manipulated in an experiment.  
• In a research study, independent variables affect or influence dependent variables and cause them to change.  
• Independent variables help gather evidence and draw conclusions about the research subject.  
• They’re also called predictors, factors, treatment variables, explanatory variables, and input variables.  
• On graphs, independent variables are usually placed on the X-axis.  
• Example: In a study on the relationship between screen time and sleep problems, screen time is the independent variable because it influences sleep (the dependent variable).  
• In addition, some factors like age are independent variables because other variables such as a person’s income will not change their age.  

Types of independent variables  

Independent variables in research are of the following two types:( 4)  

Quantitative  

Quantitative independent variables differ in amounts or scales. They are numeric and answer questions like “how many” or “how often.”  

Here are a few quantitative independent variables examples :  

• Differences in treatment dosages and frequencies: Useful in determining the appropriate dosage to get the desired outcome.  
• Varying salinities: Useful in determining the range of salinity that organisms can tolerate.  

Qualitative  

Qualitative independent variables are non-numerical variables.  

A few qualitative independent variables examples are listed below:  

• Different strains of a species: Useful in identifying the strain of a crop that is most resistant to a specific disease.  
• Varying methods of how a treatment is administered—oral or intravenous.  

A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.  

What is a dependent variable ?  

Here are a few characteristics of dependent variables: ( 3)  

• A dependent variable represents a quantity whose value depends on the independent variable and how it is changed.  
• The dependent variable is influenced by the independent variable under various circumstances.  
• It is also known as the response variable and outcome variable.  
• On graphs, dependent variables are placed on the Y-axis.  

Here are a few dependent variable examples :  

• In a study on the effect of exercise on mood, the dependent variable is mood because it may change with exercise.  
• In a study on the effect of pH on enzyme activity, the enzyme activity is the dependent variable because it changes with changing pH.   

Types of dependent variables  

Dependent variables are of two types:( 5)  

Continuous dependent variables

These variables can take on any value within a given range and are measured on a continuous scale, for example, weight, height, temperature, time, distance, etc.  

Categorical or discrete dependent variables

These variables are divided into distinct categories. They are not measured on a continuous scale so only a limited number of values are possible, for example, gender, race, etc.  

Differences between independent and dependent variables  

The following table compares independent vs dependent variables .  

Independent and dependent variable examples  

Listed below are a few examples of research questions from various disciplines and their corresponding independent and dependent variables.( 6)

  Independent vs dependent variables in research  

Experiments usually have at least two variables—independent and dependent. The independent variable is the entity that is being tested and the dependent variable is the result. Classifying independent and dependent variables as discrete and continuous can help in determining the type of analysis that is appropriate in any given research experiment, as shown in the table below. ( 7)  

  Here are some more research questions and their corresponding independent and dependent variables. ( 6)  

  How to identify independent vs dependent variables  

In addition to all the characteristics of independent and dependent variables listed previously, here are few simple steps to identify the variable types in a research question.( 8)  

• Keep in mind that there are no specific words that will always describe dependent and independent variables.  
• If you’re given a paragraph, convert that into a question and identify specific words describing cause and effect.  
• The word representing the cause is the independent variable and that describing the effect is the dependent variable.  

Let’s try out these steps with an example.  

A researcher wants to conduct a study to see if his new weight loss medication performs better than two bestseller alternatives. He wants to randomly select 20 subjects from Richmond, Virginia, aged 20 to 30 years and weighing above 60 pounds. Each subject will be randomly assigned to three treatment groups.  

To identify the independent and dependent variables, we convert this paragraph into a question, as follows: Does the new medication perform better than the alternatives? Here, the medications are the independent variable and their performances or effect on the individuals are the dependent variable.  

Visualizing independent vs dependent variables  

Data visualization is the graphical representation of information by using charts, graphs, and maps. Visualizations help in making data more understandable by making it easier to compare elements, identify trends and relationships (among variables), among other functions.  

Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data. The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis.  

Figure 1 is a scatter plot that depicts the relationship between the number of household members and their monthly grocery expenses. 9 The number of household members is the independent variable and the expenses the dependent variable. The graph shows that as the number of members increases the expenditure also increases.  

Key takeaways   

Let’s summarize the key takeaways about independent vs dependent variables from this article:  

• A variable is any entity being measured in a study.  
• A dependent variable is often the focus of a research study and is the response or outcome. It depends on or varies with changes in other variables.  
• Independent variables cause changes in dependent variables and don’t depend on other variables.  
• An independent variable can influence a dependent variable, but a dependent variable cannot influence an independent variable.  
• An independent variable is the cause and dependent variable is the effect.  

Frequently asked questions  

• What are the different types of variables used in research?  

The following table lists the different types of variables used in research.( 10)  

 2. Why is it important to differentiate between independent vs dependent variables ?  

  Differentiating between independent vs dependent variables is important to ensure the correct application in your own research and also the correct understanding of other studies. An incorrectly framed research question can lead to confusion and inaccurate results. An easy way to differentiate is to identify the cause and effect.  

 3. How are independent and dependent variables used in non-experimental research?  

  So far in this article we talked about variables in relation to experimental research, wherein variables are manipulated or measured to test a hypothesis, that is, to observe the effect on dependent variables. Let’s examine non-experimental research and how variable are used. 11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable. Instead, the researcher would observe and measure the levels of each variable in the sample population. The level of control researchers have is the major difference between experimental and non-experimental research. Another difference is the causal relationship between the variables. In non-experimental research, it is not possible to establish a causal relationship because other variables may be influencing the outcome.  

  4. Are there any advantages and disadvantages of using independent vs dependent variables ?

  Here are a few advantages and disadvantages of both independent and dependent variables.( 12)

Advantages: 

• Dependent variables are not liable to any form of bias because they cannot be manipulated by researchers or other external factors.  
• Independent variables are easily obtainable and don’t require complex mathematical procedures to be observed, like dependent variables. This is because researchers can easily manipulate these variables or collect the data from respondents.  
• Some independent variables are natural factors and cannot be manipulated. They are also easily obtainable because less time is required for data collection.

Disadvantages: 

• Obtaining dependent variables is a very expensive and effort- and time-intensive process because these variables are obtained from longitudinal research by solving complex equations.  
• Independent variables are prone to researcher and respondent bias because they can be manipulated, and this may affect the study results.  

We hope this article has provided you with an insight into the use and importance of independent vs dependent variables , which can help you effectively use variables in your next research study.    

• Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size. Indian Dermatol Online J. 2019 Jan-Feb; 10(1): 82–86. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362742/  
• What Is an independent variable? (with uses and examples). Indeed website. Accessed March 11, 2024. https://www.indeed.com/career-advice/career-development/what-is-independent-variable  
• Independent and dependent variables: Differences & examples. Statistics by Jim website. Accessed March 10, 2024. https://statisticsbyjim.com/regression/independent-dependent-variables/  
• Independent variable. Biology online website. Accessed March 9, 2024. https://www.biologyonline.com/dictionary/independent-variable#:~:text=The%20independent%20variable%20in%20research,how%20many%20or%20how%20often .  
• Dependent variables: Definition and examples. Clubz Tutoring Services website. Accessed March 10, 2024. https://clubztutoring.com/ed-resources/math/dependent-variable-definitions-examples-6-7-2/  
• Research topics with independent and dependent variables. Good research topics website. Accessed March 12, 2024. https://goodresearchtopics.com/research-topics-with-independent-and-dependent-variables/  
• Levels of measurement and using the correct statistical test. Univariate quantitative methods. Accessed March 14, 2024. https://web.pdx.edu/~newsomj/uvclass/ho_levels.pdf  
• Easiest way to identify dependent and independent variables. Afidated website. Accessed March 15, 2024. https://www.afidated.com/2014/07/how-to-identify-dependent-and.html  
• Choosing data visualizations. Math for the people website. Accessed March 14, 2024. https://web.stevenson.edu/mbranson/m4tp/version1/environmental-racism-choosing-data-visualization.html  
• Trivedi C. Types of variables in scientific research. Concepts Hacked website. Accessed March 15, 2024. https://conceptshacked.com/variables-in-scientific-research/  
• Variables in experimental and non-experimental research. Statistics solutions website. Accessed March 14, 2024. https://www.statisticssolutions.com/variables-in-experimental-and-non-experimental-research/#:~:text=The%20independent%20variable%20would%20be,state%20instead%20of%20manipulating%20them .  
• Dependent vs independent variables: 11 key differences. Formplus website. Accessed March 15, 2024. https://www.formpl.us/blog/dependent-independent-variables

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Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative.  QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language:   Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable:  Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement  that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis:  There is no relationship between height and weight. Alternative Hypothesis:   There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better.  Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis:   Boys do not like reading more than girls. Alternative Hypothesis:   Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis:   There is no difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis:   There is a difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Types of variables and commonly used statistical designs.

Jacob Shreffler ; Martin R. Huecker .

Affiliations

Last Update: March 6, 2023 .

• Definition/Introduction

Suitable statistical design represents a critical factor in permitting inferences from any research or scientific study. [1]  Numerous statistical designs are implementable due to the advancement of software available for extensive data analysis. [1]  Healthcare providers must possess some statistical knowledge to interpret new studies and provide up-to-date patient care. We present an overview of the types of variables and commonly used designs to facilitate this understanding. [2]

• Issues of Concern

Individuals who attempt to conduct research and choose an inappropriate design could select a faulty test and make flawed conclusions. This decision could lead to work being rejected for publication or (worse) lead to erroneous clinical decision-making, resulting in unsafe practice. [1]  By understanding the types of variables and choosing tests that are appropriate to the data, individuals can draw appropriate conclusions and promote their work for an application. [3]

To determine which statistical design is appropriate for the data and research plan, one must first examine the scales of each measurement. [4]  Multiple types of variables determine the appropriate design.

Ordinal data (also sometimes referred to as discrete) provide ranks and thus levels of degree between the measurement. [5]  Likert items can serve as ordinal variables, but the Likert scale, the result of adding all the times, can be treated as a continuous variable. [6]  For example, on a 20-item scale with each item ranging from 1 to 5, the item itself can be an ordinal variable, whereas if you add up all items, it could result in a range from 20 to 100. A general guideline for determining if a variable is ordinal vs. continuous: if the variable has more than ten options, it can be treated as a continuous variable. [7]  The following examples are ordinal variables:

• Likert items
• Cancer stages
• Residency Year

Nominal, Categorical, Dichotomous, Binary

Other types of variables have interchangeable terms. Nominal and categorical variables describe samples in groups based on counts that fall within each category, have no quantitative relationships, and cannot be ranked. [8]  Examples of these variables include:

• Service (i.e., emergency, internal medicine, psychiatry, etc.)
• Mode of Arrival (ambulance, helicopter, car)

A dichotomous or a binary variable is in the same family as nominal/categorical, but this type has only two options. Binary logistic regression, which will be discussed below, has two options for the outcome of interest/analysis. Often used as (yes/no), examples of dichotomous or binary variables would be:

• Alive (yes vs. no)
• Insurance (yes vs. no)
• Readmitted (yes vs. no)

With this overview of the types of variables provided, we will present commonly used statistical designs for different scales of measurement. Importantly, before deciding on a statistical test, individuals should perform exploratory data analysis to ensure there are no issues with the data and consider type I, type II errors, and power analysis. Furthermore, investigators should ensure appropriate statistical assumptions. [9] [10]  For example, parametric tests, including some discussed below (t-tests, analysis of variance (ANOVA), correlation, and regression), require the data to have a normal distribution and that the variances within each group are similar. [6] [11]  After eliminating any issues based on exploratory data analysis and reducing the likelihood of committing type I and type II errors, a statistical test can be chosen. Below is a brief introduction to each of the commonly used statistical designs with examples of each type. An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs. 

Commonly Used Statistical Designs

Independent Samples T-test

An independent samples t-test allows a comparison of two groups of subjects on one (continuous) variable. Examples in biomedical research include comparing results of treatment vs. control group and comparing differences based on gender (male vs. female).

Example: Does adherence to the ketogenic diet (yes/no; two groups) have a differential effect on total sleep time (minutes; continuous)?

Paired T-test

A paired t-test analyzes one sample population, measuring the same variable on two different occasions; this is often useful for intervention and educational research.

Example :  Does participating in a research curriculum (one group with intervention) improve resident performance on a test to measure research competence (continuous)?

One-Way Analysis of Variance (ANOVA)

Analysis of variance (ANOVA), as an extension of the t-test, determines differences amongst more than two groups, or independent variables based on a dependent variable. [11]  ANOVA is preferable to conducting multiple t-tests as it reduces the likelihood of committing a type I error.

Example: Are there differences in length of stay in the hospital (continuous) based on the mode of arrival (car, ambulance, helicopter, three groups)?

Repeated Measures ANOVA

Another procedure commonly used if the data for individuals are recurrent (repeatedly measured) is a repeated-measures ANOVA. [1]  In these studies, multiple measurements of the dependent variable are collected from the study participants. [11]  A within-subjects repeated measures ANOVA determines effects based on the treatment variable alone, whereas mixed ANOVAs allow both between-group effects and within-subjects to be considered.

Within-Subjects Example: How does ketamine effect mean arterial pressure (continuous variable) over time (repeated measurement)?

Mixed Example: Does mean arterial pressure (continuous) differ between males and females (two groups; mixed) on ketamine throughout a surgical procedure (over time; repeated measurement)?  

Nonparametric Tests

Nonparametric tests, such as the Mann-Whitney U test (two groups; nonparametric t-test), Kruskal Wallis test (multiple groups; nonparametric ANOVA), Spearman’s rho (nonparametric correlation coefficient) can be used when data are ordinal or lack normality. [3] [5]  Not requiring normality means that these tests allow skewed data to be analyzed; they require the meeting of fewer assumptions. [11]

Example: Is there a relationship between insurance status (two groups) and cancer stage (ordinal)?  

A Chi-square test determines the effect of relationships between categorical variables, which determines frequencies and proportions into which these variables fall. [11]  Similar to other tests discussed, variants and extensions of the chi-square test (e.g., Fisher’s exact test, McNemar’s test) may be suitable depending on the variables. [8]

Example: Is there a relationship between individuals with methamphetamine in their system (yes vs. no; dichotomous) and gender (male or female; dichotomous)?

Correlation

Correlations (used interchangeably with ‘associations’) signal patterns in data between variables. [1]  A positive association occurs if values in one variable increase as values in another also increase. A negative association occurs if variables in one decrease while others increase. A correlation coefficient, expressed as r,  describes the strength of the relationship: a value of 0 means no relationship, and the relationship strengthens as r approaches 1 (positive relationship) or -1 (negative association). [5]

Example: Is there a relationship between age (continuous) and satisfaction with life survey scores (continuous)?

Linear Regression

Regression allows researchers to determine the degrees of relationships between a dependent variable and independent variables and results in an equation for prediction. [11]  A large number of variables are usable in regression methods.

Example: Which admission to the hospital metrics (multiple continuous) best predict the total length of stay (minutes; continuous)?

Binary Logistic Regression

This type of regression, which aims to predict an outcome, is appropriate when the dependent variable or outcome of interest is binary or dichotomous (yes/no; cured/not cured). [12]

Example: Which panel results (multiple of continuous, ordinal, categorical, dichotomous) best predict whether or not an individual will have a positive blood culture (dichotomous/binary)?

An example of one research focus, with each type of statistical design discussed, can be found in Table 1 to provide more examples of commonly used statistical designs.

(See Types of Variables and Statistical Designs Table 1)

• Clinical Significance

Though numerous other statistical designs and extensions of methods covered in this article exist, the above information provides a starting point for healthcare providers to become acquainted with variables and commonly used designs. Researchers should study types of variables before determining statistical tests to obtain relevant measures and valid study results. [6]  There is a recommendation to consult a statistician to ensure appropriate usage of the statistical design based on the variables and that the assumptions are upheld. [1]  With the variety of statistical software available, investigators must a priori understand the type of statistical tests when designing a study. [13]  All providers must interpret and scrutinize journal publications to make evidence-based clinical decisions, and this becomes enhanced by a limited but sound understanding of variables and commonly used study designs. [14]

• Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to be familiar with study design and the variables used in studies to accurately evaluate new data and studies as they are published and apply the latest data to patient care and drive optimal outcomes.

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Types of Variables and Statistical Designs Table 1 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Types of Variables and Commonly Used Statistical Designs. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Research Variables

The research variables, of any scientific experiment or research process, are factors that can be manipulated and measured.

This article is a part of the guide:

• Experimental Research
• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

Any factor that can take on different values is a scientific variable and influences the outcome of experimental research .

Most scientific experiments measure quantifiable factors, such as time or weight, but this is not essential for a component to be classed as a variable.

As an example, most of us have filled in surveys where a researcher asks questions and asks you to rate answers. These responses generally have a numerical range, from ‘1 - Strongly Agree’ through to ‘5 - Strongly Disagree’. This type of measurement allows opinions to be statistically analyzed and evaluated.

Dependent and Independent Variables

The key to designing any experiment is to look at what research variables could affect the outcome.

There are many types of variable but the most important, for the vast majority of research methods, are the independent and dependent variables.

The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design . For many physical experiments , isolating the independent variable and measuring the dependent is generally easy.

If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated independent variable is time and the dependent measured variable is temperature.

In other fields of science, the variables are often more difficult to determine and an experiment needs a robust design. Operationalization is a useful tool to measure fuzzy concepts which do not have one obvious variable.

The Difficulty of Isolating Variables

In biology , social science and geography, for example, isolating a single independent variable is more difficult and any experimental design must consider this.

For example, in a social research setting, you might wish to compare the effect of different foods upon hyperactivity in children. The initial research and inductive reasoning leads you to postulate that certain foods and additives are a contributor to increased hyperactivity. You decide to create a hypothesis and design an experiment , to establish if there is solid evidence behind the claim.

The type of food is an independent variable, as is the amount eaten, the period of time and the gender and age of the child. All of these factors must be accounted for during the experimental design stage. Randomization and controls are generally used to ensure that only one independent variable is manipulated.

To eradicate some of these research variables and isolate the process, it is essential to use various scientific measurements to nullify or negate them.

For example, if you wanted to isolate the different types of food as the manipulated variable, you should use children of the same age and gender.

The test groups should eat the same amount of the food at the same times and the children should be randomly assigned to groups. This will minimize the physiological differences between children. A control group , acting as a buffer against unknown research variables, might involve some children eating a food type with no known links to hyperactivity.

In this experiment, the dependent variable is the level of hyperactivity, with the resulting statistical tests easily highlighting any correlation . Depending upon the results , you could try to measure a different variable, such as gender, in a follow up experiment.

Converting Research Variables Into Constants

Ensuring that certain research variables are controlled increases the reliability and validity of the experiment, by ensuring that other causal effects are eliminated. This safeguard makes it easier for other researchers to repeat the experiment and comprehensively test the results.

What you are trying to do, in your scientific design, is to change most of the variables into constants, isolating the independent variable. Any scientific research does contain an element of compromise and inbuilt error , but eliminating other variables will ensure that the results are robust and valid .

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Martyn Shuttleworth (Aug 9, 2008). Research Variables. Retrieved Aug 16, 2024 from Explorable.com: https://explorable.com/research-variables

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The Different Types Of Variables Used In Research And Statistics

• APR Formula
• Total Variable Cost Formula
• How to Calculate Probability
• How To Find A Percentile
• How To Calculate Weighted Average
• What Is The Sample Mean?
• Hot To Calculate Growth Rate
• Hot To Calculate Inflation Rate
• How To Calculate Marginal Utility
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• How To Calculate Consumer Price Index
• How To Calculate Cost Of Goods Sold
• How To Calculate Correlation
• How To Calculate Confidence Interval
• How To Calculate Consumer Surplus
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• How To Calculate Depreciation
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• Types Of Variables

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Scientists and statisticians conduct experiments on a regular basis. Scientists use these experiments to identify cause and effect, while Statisticians use variables to represent the unknown or varied data in their experiments. Determining which variables to use is vital to the experiment. Also, choosing the right variables will lead to clearer analyses and more accurate results. What Is a Variable?

A variable is something you can control, manipulate, or measure when conducting research or experiments. They are characteristics, numbers, or quantities and can represent specific items, people, places, or an idea. The variables may be referred to as data items.

The variables in an experiment will vary depending on the desired outcome. All scientific experiments and statistical studies will analyze a variable.

They are referred to as variables because the values can vary. A variable’s value can change within a single experiment. Whether there is a change between the groups being studied or the value changes over time, it may not necessarily be a constant.

Designing Experiments

As noted, all scientific experiments and statistical studies will control, manipulate, or measure a variable. In fact, the experiments are usually designed to determine the effect one variable has on another variable, cause, and effect.

When designing experiments, it is extremely important to choose the right variables. Choosing incorrectly can skew the results and derail the experiment or study completely. Choosing right can help an experiment or study run much more smoothly and produce more accurate results.

It is not just the specific variable within the experiment that needs to be determined, but the variable type as well. Knowing the variable type will allow you to interpret the results of the experiment or study.

It should be noted, though, that categorizing variables is a little subjective. Scientists and statisticians have some wiggle room when they categorize their experiment variables.

Generally, you will need to know what data the variable represents and what part of the experiment the variable represents in order to determine the variable type.

Independent, Dependent, and Control Variables

Typically, there will be an independent variable, dependent variable, and control variable in every experiment or study conducted.

Independent variables. Independent variables are the variables in your experiment that are being manipulated. They are referred to as independent variables due to the fact that their value is independent of other variables, which means that the other variables cannot change the independent variable.

Dependent variables. Dependent variables are the variables in your experiment that rely on other variables and can be changed or manipulated by the other variables being measured.

Control variables. Control variables are the variables in your experiment that are constant. They do not change over the course of the experiment or study and will have no direct effect on the other variables being measured.

Qualitative Versus Quantitative Variables

Every single variable you include in your experiment will need to be categorized as either a qualitative variable or a quantitative variable.

Qualitative variables. Also referred to as categorical variables, qualitative variables are any variables that hold no numerical value. They are nominal labels. For example, eye color would be a qualitative variable. The data being recorded is not a number but a color.

These variables don’t necessarily measure, but they describe a characteristic of the data set. They can be broken down further as either ordinal variables or nominal variables (see below for definitions).

Quantitative variables. Quantitative variables, or numeric variables, are the variables in your experiment that hold a numerical value. Unsurprisingly, they will represent a measurable quantity and will be recorded as a number.

These variables will measure “how many” or “how much” of the data being collected. These can be broken down further as either continuous variables or discrete variables (see below for definitions).

30 Other Variable Types Used in Experiments

This is by no means a comprehensive list, as the list of all variable types would be difficult to document in one place.

Below are many of the common and some less common variable types used in scientific experiments and statistical studies. Included is a brief overview of what that variable type measures.

Active variable. An active variable is a variable that can be manipulated by those running the experiment.

Antecedent variable. Antecedent variables come before the independent and dependent variables. With “antecedent” meaning “preceding in time or order,” this is not surprising.

Attribute variable. An attribute variable also called a passive variable, is not manipulated during the experiment. It may be a fixed variable or simply a variable that is not manipulated for one experiment but could be for another.

Binary variable. Binary variables only have two values. Typically, this will be represented as a zero or one but can be yes/no or another two-value combination.

Categorical variable. Categorical variables are variables that can be divided into larger buckets or categories. Shoe brands, for instance, could include Nike, Reebok, or Adidas.

Composite variable. This variable type is a bit different from others. A composite variable is made up of two or more other variables. The individual variables that make up the composite variable will be closely related either conceptually or statistically.

Confounding variable. Confounding variables are not good. They can affect both independent and dependent variables and invalidate results. Sometimes referred to as a lurking variable, these variables are considered “extra” and were not accounted for during the designing phase.

Continuous variable. Continuous variables have an infinite number of values between the highest point and lowest point. Distance is a continuous variable.

Covariate variable. A covariate variable can affect the dependent variable in addition to the independent variable. It will not be of interest in the results of the experiment, though.

Criterion variable. This is a statistical variable only. It is another name for the dependent variable.

Dichotomous variable. This is another name for a binary variable. Dichotomous variables will have two values only.

Discrete variable. Discrete variables are the opposite of continuous variables. Where continuous variables have an infinite number of possible values, discrete variables have a finite number.

Endogenous variable. Endogenous variables are dependent on other variables and are used only in statistical studies, in econometrics specifically. The value of these variables is determined by the model.

Exogenous variable. An exogenous variable is the opposite of an endogenous variable. The value of this type of variable is determined outside of the model and will have an impact on other variables within the model.

Explanatory variable. This is a commonly used name for the independent variable or the variable that is being manipulated by those running the experiment.

Grouping variable. A grouping variable is used to sort, or split up, the data set into groups or categories.

Interval variable. Interval variables show the meaningful difference between the two values.

Intervening variable. Intervening variables, or mediator variables, explains the cause, connection, or relationship between two other variables being measured.

Manifest variable. A manifest variable is a variable that can be directly observed or measured within the experiment.

Moderating variable. A moderating variable can affect the relationship between the independent variable and dependent variable. It can either strengthen, diminish, or negate the relationship.

Nominal variable. This is another way of saying categorical value. Nominal values will have two or more categories.

Observed variable. Observed variables are variables that are being measured during the experiment.

Ordinal variable. Ordinal variables are similar to categorical or nominal variables but have a clear ordering of categories. Examples such as High to low and like to dislike would both be ordinal variables.

Polychotomous variable. Polychotomous variables have more than two possible categories or values. These can be either nominal or ordinal.

Ranked variable. Ranked variables are ordinal variables. The researcher may not know the exact value, but they will know the order in which the data points should fall.

Ratio variable. Ratio variables are similar to interval variables but have a clear definition of zero.

Responding variable. Responding variables are the effect or outcome of the experiment. Similar to dependent variables, responding variables will “respond” to changes being made in the experiment.

Scale variable. A scale variable is a variable that has a numeric value that can be ordered with a meaningful metric. It will be the amount or number of something.

Study variable. Often referred to as a research variable, a study variable is any variable used that has some kind of cause and effect relationship.

Test variable. A test variable also referred to as the dependent variable, is a variable that represents the outcome of the experiment.

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Chris Kolmar is a co-founder of Zippia and the editor-in-chief of the Zippia career advice blog. He has hired over 50 people in his career, been hired five times, and wants to help you land your next job. His research has been featured on the New York Times, Thrillist, VOX, The Atlantic, and a host of local news. More recently, he's been quoted on USA Today, BusinessInsider, and CNBC.

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What are Variables and Why are They Important in Research?

In research, variables are crucial components that help to define and measure the concepts and phenomena under investigation. Variables are defined as any characteristic or attribute that can vary or change in some way. They can be measured, manipulated, or controlled to investigate the relationship between different factors and their impact on the research outcomes. In this essay, I will discuss the importance of variables in research, highlighting their role in defining research questions, designing studies, analyzing data, and drawing conclusions.

Defining Research Questions

Variables play a critical role in defining research questions. Research questions are formulated based on the variables that are under investigation. These questions guide the entire research process, including the selection of research methods, data collection procedures, and data analysis techniques. Variables help researchers to identify the key concepts and phenomena that they wish to investigate, and to formulate research questions that are specific, measurable, and relevant to the research objectives.

For example, in a study on the relationship between exercise and stress, the variables would be exercise and stress. The research question might be: “What is the relationship between the frequency of exercise and the level of perceived stress among young adults?”

Designing Studies

Variables also play a crucial role in the design of research studies. The selection of variables determines the type of research design that will be used, as well as the methods and procedures for collecting and analyzing data. Variables can be independent, dependent, or moderator variables, depending on their role in the research design.

Independent variables are the variables that are manipulated or controlled by the researcher. They are used to determine the effect of a particular factor on the dependent variable. Dependent variables are the variables that are measured or observed to determine the impact of the independent variable. Moderator variables are the variables that influence the relationship between the independent and dependent variables.

For example, in a study on the effect of caffeine on athletic performance, the independent variable would be caffeine, and the dependent variable would be athletic performance. The moderator variables could include factors such as age, gender, and fitness level.

Analyzing Data

Variables are also essential in the analysis of research data. Statistical methods are used to analyze the data and determine the relationships between the variables. The type of statistical analysis that is used depends on the nature of the variables, their level of measurement, and the research design.

For example, if the variables are categorical or nominal, chi-square tests or contingency tables can be used to determine the relationships between them. If the variables are continuous, correlation analysis or regression analysis can be used to determine the strength and direction of the relationship between them.

Drawing Conclusions

Finally, variables are crucial in drawing conclusions from research studies. The results of the study are based on the relationship between the variables and the conclusions drawn depend on the validity and reliability of the research methods and the accuracy of the statistical analysis. Variables help to establish the cause-and-effect relationships between different factors and to make predictions about the outcomes of future events.

For example, in a study on the effect of smoking on lung cancer, the independent variable would be smoking, and the dependent variable would be lung cancer. The conclusion would be that smoking is a risk factor for lung cancer, based on the strength and direction of the relationship between the variables.

In conclusion, variables play a crucial role in research across different fields and disciplines. They help to define research questions, design studies, analyze data, and draw conclusions. By understanding the importance of variables in research, researchers can design studies that are relevant, accurate, and reliable, and can provide valuable insights into the phenomena under investigation. Therefore, it is essential to consider variables carefully when designing, conducting, and interpreting research studies.

The International Journal of Indian Psychȯlogy

Variables in Educational Research

| Published: August 13, 2024

A variable is a concept, construct, condition, or attribute that can have several values. For example, people’s IQ is measured on a scale from low to high. Gender is another variable that can have two values, such as ‘Male’ or ‘Female’. Kerlinger (1986), UNESCO (2005), and Kumar (2001) et al. classify the variable in various ways. However, most of them concentrate on independent, dependent, extraneous, confounding, intervening, and moderator variables. The majority of educational research studies are completely dependent on these variables. So, in any research study, these variables are referred to be key variables. These important variables are explored using four primary study designs: descriptive, causal comparative, co-relational, and experimental/quasi-experimental. (Korb, 2012). Variables other than the essential variables are not investigated in any research study. But these are used to carry out the research study.

Variable , Independent , Dependent , Extraneous , Confounding , Intervening , Moderator Variable

This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.

© 2024, Giri, C.K.

Received: August 02, 2024; Revision Received: August 10, 2024; Accepted: August 13, 2024

Dr. Chandra Kanta Giri @ [email protected]

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Published in   Volume 12, Issue 3, July-September, 2024

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Comparative lifespan and healthspan of nonhuman primate species common to biomedical research

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There is a critical need to generate age– and sex-specific survival curves to characterize chronological aging consistently across nonhuman primates (NHP) used in biomedical research. Accurate measures of chronological aging are essential for inferences into genetic, demographic, and physiological variables driving differences in NHP lifespan within and between species. Understanding NHP lifespans is relevant to public health because unraveling the demographic, molecular, and clinical bases of health across the life course in translationally relevant NHP species is fundamentally important to the study of human aging. Data from more than 110,000 captive individual NHP were contributed by 15 major research institutions to generate sex-specific Kaplan-Meier survival curves using uniform methods in 12 translational aging models: Callithrix jacchus (common marmoset), Chlorocebus aethiops sabaeus (vervet/African green), Macaca fascicularis (cynomolgus macaque), M. fuscata (Japanese macaque), M. mulatta (rhesus macaque), M. nemestrina (pigtail macaque), M. radiata (bonnet macaque), Pan troglodytes spp. (chimpanzee), Papio hamadryas spp. (baboon), Plecturocebus cupreus (coppery titi monkey), Saguinus oedipus (cotton-top tamarin), and Saimiri spp. (squirrel monkey). After employing strict inclusion criteria, primary analysis results are based on 12,269 NHP that survived to adulthood and died of natural/health-related causes. A secondary analysis was completed for 32,616 NHP that died of any cause. For the primary analyses, we report ages of 25 th , 50 th , 75 th , and 85 th percentiles of survival, maximum observed ages, rates of survivorship, and sex-based differences captured by quantile regression models and Kolmogorov-Smirnov tests. Our findings show a pattern of reduced male survival among catarrhines (African and Asian primates), especially macaques, but not platyrrhines (Central and South American primates). For many species, median lifespans were lower than previously reported. An important consideration is that these analyses may offer a better reflection of healthspan than lifespan. Captive NHP used in research are typically euthanized for humane welfare reasons before their natural end of life, often after diagnosis of their first major disease requiring long-term treatment with reduced quality of life (e.g., endometriosis, cancer, osteoarthritis).

Supporting the idea that these data are capturing healthspan, for several species typical age at onset of chronic disease is similar to the median lifespan estimates. This data resource represents the most comprehensive characterization of sex-specific lifespan and age-at-death distributions for 12 biomedically relevant species, to date. The results clarify the relationships among NHP ages and will provide a valuable resource for the aging research community, improving human-NHP age equivalencies, informing investigators of the expected survival rates of NHP assigned to studies, providing a metric for comparisons in future studies, and contributing to our understanding of the factors that drive lifespan differences within and among species.

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ORIGINAL RESEARCH article

Examining technical efficiency, prospects, and policies of farmers: data from a developing nation’s pineapple production.

• 1 Department of Agricultural Finance and Banking, Sylhet Agricultural University, Sylhet, Bangladesh
• 2 Department of Agricultural Finance and Banking, Bangladesh Agricultural University, Mymensingh, Bangladesh
• 3 Department of Agricultural Statistics, Sylhet Agricultural University, Sylhet, Bangladesh
• 4 Upazila Land Office (Golapganj), Sylhet, Bangladesh

Introduction: The unique characteristics of pineapples as a perennial plant, which guarantee their quick proliferation and adoption in both the tropics and subtropics, readily justify their economic significance. Although pineapple is a popular tropical fruit among Bangladeshi citizens, they continue to produce fewer pineapples than other international producers with limited export offerings. Hence, the study aimed to estimate the technological efficiency, prospects, and policies of pineapple growers in the northeastern district of Bangladesh.

Methods: One hundred respondent growers were surveyed directly to gather cross-sectional data using a multistage sampling technique. The technical efficiency scores of individual farms were calculated using the stochastic frontier model with the technical inefficiency model for identifying factors responsible for inefficiency.

Results: The technical efficiency scores range from about two-thirds to the absolute efficiency level, with a mean technical efficiency above the ninety percent level. The technical inefficiency effect model interpreted that farmers’ age and education had a significant positive impact, whereas credit, training, and family size had a significant negative impact on inefficiency.

Discussion: Findings indicated that sampled farmers may use inputs more efficiently and raise their yield by nearly one-twentieth. Therefore, the study suggests that the government should concentrate on strategies to attract young growers, as they are more capable of managing resources effectively and willing to accept technological breakthroughs. The study’s conclusions have significant policy ramifications specifically in the areas of finance, education and skills, and rural development that the Government should consider to increase farmer’s productivity and overcome various challenges while upholding national interests and ensuring the farming sector’s continued prosperity. To commercialize pineapple production and establish Bangladesh as a prominent production zone, more research and development are needed.

1 Introduction

Bangladesh’s agriculture industry is a vital economic pillar. Its GDP contribution immediately following liberation in 1971 was approximately 60 percent. We all know that it is the most significant industry in Bangladesh in terms of GDP contribution, employment opportunities, and support for people’s livelihoods. Notwithstanding its considerable potential for employment in the rural labor force, its percentage of the GDP has declined over the past 10 years, from 17 percent in 2010 to approximately 11.66 percent in 2020 ( BBS, 2022 ; BER, 2023 ). Improved agricultural productivity and food and nutrition security have been made possible by the adoption of agriculture-friendly policies and strategies, despite the effects of decreasing arable land, rising population demands for food and nutrition, climate change, the Russia-Ukraine crisis, and the coronavirus epidemic. This sector overwhelmingly impacts primary macroeconomic objectives like employment generation, poverty alleviation, human resources development, and food security. To meet the future needs of the expanding population, the government is working tirelessly to adopt short-term, medium-term, and long-term action plans. One such plan is to increase the export of high-value crops like pineapple ( Figure 1 ) to build sustainable, safe, and profitable agricultural systems that ensure food security ( Shakil, 2023 ).

Figure 1 . Safe and sustainable impacts of pineapple farming. This chart shows the favorable correlation between an economy’s overall development and pineapple production. Similar to many other fruits, pineapple production is important for the socioeconomic advancement of the populace, particularly for Bangladesh’s marginalized growers in the northeast.

Because of the success of commercial farming, fruit production in the nation has quietly undergone a revolution during the last 20 years. Bangladesh has maintained an average annual rise in fruit production of 11.5 percent over the previous 18 years, placing it among the top 10 tropical fruit-producing nations in the world ( FAO, 2018 ; BBS, 2019 ). Pineapple is one of Bangladesh’s most important commercial fruit crops. Among all the fruits produced in the country, pineapple ranks 3 rd in total garden area under fruit cultivation after banana and mango ( BBS, 2016 ). Because of its chemical composition, it is the primary raw material utilized by the confectionary industries to produce food additives and domestic fruit juices ( Akhilomen et al., 2015 ). As a significant fruit crop, pineapple is widely farmed in several districts, including Tangail, Rangamati, Mymensingh, Gazipur, Chattogram, Khagrachari, Bandarban, Moulvibazar, Sylhet, and Dhaka ( Hasan et al., 2011 ). These districts employ a large number of women and farmers. Bangladesh’s most crucial pineapple fruit-growing areas are the Madhupur upazila of Tangail and the Sreemangal upazila of Moulvibazar. Based on an estimated 14,164 hectares of land, the nation produced 208,000 tons of fruit in 2020–21. The number of pineapples that grew in 2021–2022 increased from 0.469 million tons to 0.538 million tons, according to Department of Agricultural Extension (DAE) data. Due to the more extended winter season in 2021–22, the honey queen kind of pineapple, also known locally as Joldubi, which is grown throughout Bangladesh, particularly in the central part and the north-eastern division, has produced a higher yield than it did last year. While the yield of pineapples in 2022 was more significant than in previous years, the cost of production is also higher because more labor, water, and fertilizers are needed. Policymakers consider the pineapple industry among the top priorities for growing exports ( Shakil, 2023 ). The agriculture department is addressing the issue of using chemicals to ripen and increase the size of pineapples.

In addition to utilizing chemical fertilizers, some avaricious growers and dealers treat immature pineapples with excessive growth hormones to get them onto the market sooner. Producers have not yet been able to lower their production risk due to the cultural practices used for pineapple growth up to that point. Nevertheless, pineapple output in Bangladesh is still far too low to meet export demands in the European Union (EU) and the subregion, despite the country’s potential for growing this fruit. There are some reasons for this low pineapple production, including the scarcity of good suckers, the producers’ inability to grasp basic principles like traceability and the crucial quality standard for fruit meant for export, and the high cost of specific inputs that have a detrimental effect on fruit quality and preservation. The fact that animals and birds consume naturally ripened crops while still in the fields presents another challenge for growers. However, animals and birds will not consume the fruits if ripening hormones are sprayed on them ( Datta et al., 2023 ).

Furthermore, it is unfortunate that Moulvibazar lacks standards for the amounts and mixes of mineral fertilizers appropriate for pineapple crops, which would better protect the environment’s components—particularly the soils and water resources vulnerable to pollution or degradation. These standards would also be economically beneficial. Because of this, Sreemangal pineapple growers use different doses and types of mineral fertilizers according to their needs. This impacts the production costs and the quality of pineapples ( Shakil, 2023 ) because confident growers overuse chemical fertilizers. Given that producers nowadays do not always have access to precise inputs and quality releases, knowing the efficiency level of producers allows for the definition of strategies to drive interventions ( Fassinou et al., 2012 ). Investigating production potentials is necessary to match demand and supply with worldwide standards for pineapple, which requires improving productivity and quality. To prevent resource waste and, more importantly, to focus guidance on increasing the output of pineapple growers, the technical efficiency of pineapple producers must be evaluated.

As we all know, efficiency is a comparative indicator of a company’s ability to use inputs in a production process relative to other companies in the same industry. It can encompass all pertinent production parameters and represents actual farm performance. Understanding how sound resources are being used and what opportunities there are to increase productivity with the resources and technology already in place is crucial ( Ahluwalia, 1996 ). Even though inputs are necessary for productive production, farmers in poor nations like Bangladesh confront significant obstacles when obtaining inputs. To help better comprehend the new demands of Bangladesh’s agricultural sector, it appears imperative to ascertain the study with two specific research questions: firstly, whether the farmers are producing in a technically efficient manner. Secondly, are there any factors or lack thereof for the technical inefficiency of pineapple growers?

However, several studies in the world and Bangladesh ( Bakh and Islam, 2005 ; Begum et al., 2010 ; Islam and Sumelius, 2011 ; Alam et al., 2012 ; Polas, 2013 ; Sarker and Alam, 2016 ; Razzaq et al., 2019 ; Phrommarat and Oonkasem, 2021 ) have investigated the overall efficiency along with technical efficiency in different fields. Akter et al. (2020) explored the technical efficiency of pineapple in the Tangail district. Nevertheless, the currently available literature ignores the technological efficiency level of pineapple growers in Bangladesh’s northeastern region. Studying the effectiveness of livelihood-focused interventions in that area is extremely important because of their unique ecological characteristics.

Furthermore, rice used to be the main focus of Bangladesh’s previous food policies, with the goal being rice self-sufficiency. While rice continues to play a significant role in society, it is now necessary to create regulations and other tools that encourage crop diversification to supply a variety of food products and encourage the consumption of balanced, nutrient-rich diets.

As one of the fastest-growing agricultural subsectors, pineapples ( Figure 1 ) are high-value commodities that need to be prioritized to ensure proper use of the nutrients ingested ( FAO, 2018 ).

Therefore, multi-sectoral interlinked interventions focused on enhancing nutritional outcomes can be designed with the assistance of a new policy that is holistic and encourages the use of a “nutrition lens” to evaluate and prioritize various choices. Furthermore, a recently implemented policy that crosses the purview of more than a dozen ministries can offer an institutional framework under a single roof for ensuring sustainable production-consumption systems (SDG 12 of United Nations global goals) in the agricultural sector. The National Food and Nutrition Security Policy (NFNSP) of Bangladesh will benefit from the study’s results, which will also likely serve as a reference for creating and carrying out the country’s eighth and ninth five-year plans ( MoF, 2022 ).

1.1 Background for identifying clear research gap

Despite extensive research on the Technical Efficiency of Pineapple, there is a research gap in classic literature and specific context. The contextual gap emerged when we could not explore any existing studies directly related to the topic-the understanding of the technological effectiveness of Pineapple in rural farm populations within the Sylhet Division. Hence, there was an opportunity for us to investigate a completely new geographic area with its high-value fruit crop.

The bulk of the studies were completed worldwide on the efficiency of different products including pineapple which helped us generate ideas regarding study design and analysis. For instance, Oladapo et al. (2007) examined the market margin and spatial pricing efficiency of pineapple in Nigeria. In addition, the technical efficiency and its determinants in Garden egg ( Solanum Spp.) production were determined by Okon et al. (2010) in Uyo Metropolis, Akwa Ibom State, while Trujillo and Iglesias (2013) and Ghimire et al. (2023) employed the stochastic frontier approach for measuring the small pineapple farmers and lentil producers’ technical efficiency in Colombia and Nepal, respectively.

However, no study has been documented on the technical efficiency of pineapple production in the northeastern part of Bangladesh to the best of the author’s knowledge. Although numerous published works were identified on the various contexts of pineapple cultivation in different areas of Bangladesh ( Table 1 ), our attempt could help mitigate this type of contextual gap in the advanced research arena.

Table 1 . Previous studies on the different aspects of pineapple.

To improve the economic, social, and consumer welfare of rural farmers, policies must be developed to increase their output through fruit farming. Hence, the aims of this study are twofold to mitigate the current research gaps: (1) To estimate the technical efficiency of pineapple production using the Stochastic Frontier Model (SFM) and (2) To identify the factors influencing the technical efficiency level of pineapple producers. The findings will add to the body of knowledge in light of Bangladesh’s shifting agricultural landscape, make it easier for the relevant authorities to pursue the right policies, and remove obstacles to the successful implementation of sustainable food systems through the productive production of pineapples in rural areas.

2 Materials and methods

2.1 study area: north-eastern district of bangladesh.

The northeastern part of Bangladesh (Sylhet basin; Figure 2 ) contains the most commercially and ecologically significant hillocks with evergreen pineapple orchards, which are referred to as pineapple villages ( Jahid, 2023 ). Sreemangal is situated southwest of the Moulvibazar district at 24.3083°N 91.7333°E ( Banglapedia, 2023 ). Agriculture is the communities’ primary industry as this sector occupies 30.90 percent compared to other sources of income in the study area. The Sreemangal lies 17 m above sea level where the climate was warm and temperate. In winter there is much more rainfall than in summer. The average annual temperature is 24.7°C/76.5°F in Sreemangal. The annual rainfall is 2,420 mm. Precisely, Sreemangal is notable for its extensive tea and pineapple gardens as well as its continuous rain. The presence of lush trees has enriched its dynamic vegetation. The terraced tea gardens, plantations, pineapple gardens, and evergreen hills of Sreemangal are popular tourist destinations ( Seema et al., 2023 ).

Figure 2 . Location of the study area. The research area, also known as “Pineapple Village,” is depicted on the map along with its physical borders and pertinent information. Because of the favorable biological circumstances in these areas, the fruit can be profitably grown on underutilized land, hillocks, and yards.

Since the Pakistani era, this region has been well-known for growing a variety of fruits, including pineapple, in the villages of Bishamoni, Bhunabir, Ashidron, Radhanagar, Ramnagar, Balishira, Noorjahan, Doluchhara, Satgaon, and Mohajerabad ( Deshwara, 2015 ). The district’s 1,210 hectares of land are used for pineapple cultivation. On the other hand, this season in Sreemangal, about 500 hectares of land have been planted with three different varieties of pineapples ( The Daily Tribunal, 2023 ). Nonetheless, this high-value cash crop can be grown in the northeastern region of Bangladesh on laterite soils on hillslopes and sandy, loamy soils high in humus. According to NHB (2015) , it favors soils with a pH range of 5.0–6.0. Since Sreemangal’s soil, environment, and warm, humid weather are ideal for growing pineapple, many pineapples are grown here in the upper hills. This region can help Bangladesh’s millions build a sustainable socioeconomic existence by providing jobs, wholesome food, fuel, and fodder. The study is focused on the Sreemangal Upazila ( Figure 2 ) due to its natural richness and biodiversity.

2.2 Variables of data collection, sampling technique, and sample size

Data on socioeconomic characteristics, pineapple production activities (such as input and output costs), and other farm-specific variables were collected from the farmers and analyzed using the stochastic frontier model, which was developed by Aigner et al. (1977) and adopted by Tadesse and Krishnamoorthy (1997) and Taylor and Shonkwiler (1986) were used to estimate the technical efficiency of pineapple farmers ( Balogun et al., 2018 ). In analyzing the efficiency or inefficiency of farmers, the greatest output that can be produced from a particular set of inputs rather than the average of the actual relationship between farmers’ inputs and output is of relevance. According to the statement, given the state of technology, not all producers can use the minimal inputs necessary to generate the desired output. Producers do not continuously optimize their production functions, according to theory. Technically efficient producers are those who operate above the frontier production curve, and technically inefficient producers are those who operate below the frontier production curve.

Various works on technical efficiency in developing countries were reviewed to choose appropriate variables that best suit our research objectives before preparing the questionnaire. Among others, age (in years), Household head gender (male = 0, female = 1), Educational status (years of schooling), Occupation (1 = Farming, 2 = Business, 3 = Service, 4 = Farming + Business, 5 = Farming + Service, 6 = Others), Farming experience (in years), Family size (in Numbers), Farm category (Marginal = 1, Small =2, Medium = 3 and Large = 4), Access to credit (if taken credit = 1, otherwise = 0), Training (if received any training = 1, otherwise = 0) and Advisory services (if received any advisory services = 1, otherwise = 0) were included for technical efficiency analysis.

2.2.1 Research design

A multistage sampling procedure was used to sample 100 farmers. The first stage was the purposive selection of the Sreemangal Upazila from the other six pineapple-producing upazilas of Moulvibazar District because of its production potentiality ( DS, 2011 ). The second stage was the random selection of 4 unions (Ashidron, Satgaon, Sindurkhan, and Sreemangal) of the upazila from 9 union parishads based on their bumper contribution to pineapple production using the lottery method. In contrast, the last stage was the random selection of a total of 100 farmers from the respective unions through the random number table method.

2.2.2 Method of data collection and sample size determination

This study collected raw data from 100 respondents employing a semi-structured questionnaire during on-farm production. The data were collected from sampled pineapple farmers about their production technology and output for 2019–20. The final questionnaire contained different study aspects, including general information, land-holding information, socio-economic aspects, problems, and probable solutions. Pre-testing was done before the final survey, which was conducted from February to April 2020. After that, a simple random sampling procedure was adopted to select the desired sample size and was calculated by using the method suggested by Cochran (1977) to calculate a representative sample for proportions as:

Here, n is the required sample size.

n 0 is Cochran’s sample size computed for the ideal sample size.

N is the size of the population.

Z α 2 2 is the selected critical value of the desired confidence level 95 percent = 1.96 2  = 3.8416.

p is the estimated proportion of an attribute that is present in the population = 0.5, q = p −1 = 0.5.

d is the error term 5% as we considered the confidence interval 95 percent (d = 0.05).

Therefore, n 0 = 384.14 and n = 200.

Thus, based on the Cochran formula the sample size required was 200. For some limitations, the total sample size for the study was maintained at 100.

2.3 Analytical framework

The previous literature illustrates that the evaluation of technical efficiency employs two methods: a parametric approach and a non-parametric approach. The parametric approach enables econometric techniques, but the nonparametric approach is based entirely on mathematical techniques of Data Envelopment Analysis (DEA) used by Pakravan-Charvadeh and Flora (2022) and Pakravan-Charvadeh et al. (2022) in their research work identify nutrition efficiency. Previous studies have discussed and explained the advantages and shortcomings of both approaches ( Battese and Coelli, 1992 ; Bravo-ureta and Evenson, 1994 ; Battese and Coelli, 1995 ). The econometric technique is stochastic and splits the impact of random error from the inefficiency effect. The non-parametric technique combines the errors and is known as combination inefficiency. The econometric technique is parametric and controls the impact of misspecification of practical form through inefficiency. The non-parametric technique is not so liable for this description error. However, the literature reveals that the econometric technique is commonly used to assess the technical efficiency of firms ( Tchale and Sauer, 2015 ; Ali et al., 2019 ; Ndubueze-Ogarak et al., 2021 ). Accordingly, the econometric techniques were used in our study for Stochastic Frontier Analysis (SFA).

2.3.1 Theoretical foundations

Formalizing the yields’ response to various inputs is the primary driving force behind measuring manufacturing processes’ technical efficiency. The primary causes of the observed variances in this responsiveness are changes in the technology employed by the enterprises, variations in the efficiency levels of the production processes, and variations in the production setting. As a result, technological efficiency determines economic efficiency ( Adegbite and Adeoye, 2015 ).

Farrell (1957) methodology was a precursor to technical efficiency modeling since it presumes the presence of an efficient production-possibility frontier (PPF). According to this concept, the PPF indicates the highest production that can be obtained from a specific combination of productive elements. The gap between each firm’s production level and the PPF’s peak level is used to calculate technical inefficiency. Consequently, it is possible to compute the technical efficiency as a percentage of the sample’s highest productive production unit.

Two different methods could be used to estimate this production function. First, there are the non-parametric approaches, which stand out for their adaptability. They are less constrictive when it comes to the parameters that are applied to the reference technology and when modeling manufacturing processes that involve many products ( Trujillo and Iglesias, 2013 ).

Second, one can econometrically estimate a production function using parametric methods, allowing one to draw statistical conclusions from the estimation’s findings.

To apply these techniques, the dataset must be in a functional form. Using the latter approach, Aigner et al. (1977) and Meeusen and Broeck (1977) develop a stochastic production function to separate mistakes resulting from model misspecification from those that productive inefficiencies can explain. Determining the precise distribution of the error component and the production function’s functional shape is necessary for this differentiation.

The primary empirical source on the factors influencing technical efficiency in agriculture is Battese and Coelli (1995) . The main hypothesis put out by these writers is the combined estimation of a model that incorporates the factors influencing agricultural production inefficiency and the efficient frontier of agricultural production.

As Coelli and Battese (1996) demonstrated Indian farmers tend to be more productive when they are older, have larger farms, and have more education. Similarly, Tian and Wan (2000) discovered that the efficiency of rice production in China is positively impacted by education, farm size, and the implementation of different cropping techniques. Similarly, Villano and Fleming (2006) found that factors including age, education level, the percentage of adults in the household, and the amount of money generated by non-farm activities influence technical efficiency in a sample of farmers in Central Luzon, the Philippines.

On the other hand, Amaza and Olayemi (2002) suggested that greater levels of technical efficiency are attained in Nigeria if education, technical support, and crop diversification all rise.

Within the same continent, Essilfie et al. (2011) measured maize growers in Ghana’s technical efficiency at the farm level; their findings indicated that variations in age, sex, years of education, family size, and farmers’ off-farm income impact technical efficiency. In Latin America, limited research was conducted on the estimation of the level of technical efficiency in agriculture using the stochastic frontier approach; however, it is noteworthy to mention the study of Benoit-Cattin and Mendez (1996) focused on a group of small coffee farmers in Guatemala for revealing their efficiency level at using the existing technology; these authors concluded that both technical assistance & credit support sustain Guatemalan coffee plantations. In Brazil, Conceicao and Araujo (2000) found the TE of a sample of commercial farmers and discovered that experience plays a significant role in explaining these farmers’ technical efficiency.

Furthermore, Richetti and Reis (2003) study looks at the economic effectiveness of the productive resources used in the State of Mato Grosso do Sul’s soybean farming. They demonstrated how the methods of transferring technology to growers of this grain are connected to technical inefficiency. According to Santos et al. (2006) , there is a positive correlation between technological efficiency and the following factors in Chile: the size of the property, the distance from the main road, the age of the head of the family, and membership in a technology transfer group. Moreira et al. (2006) worked on a highly unbalanced panel dataset for a sample of small dairy farms in Southern Chile in a related investigation carried out in the same nation.

Both Taylor and Shonkwiler (1986) and Tadesse and Krishnamoorthy (1997) used the stochastic frontier model created by Aigner et al. (1977) . When analyzing a farmer’s efficiency or inefficiency, what matters is not the mean of the actual relationship between the farmer’s inputs and output, but rather the highest output that can be produced with a specific set of inputs. The statement suggests that, given the state of technology, not all producers can use the minimal number of inputs needed to generate the desired quantity of output. Producers do not always optimize their production functions, according to theory. Technically efficient producers are those who operate on the production frontier; technically inefficient producers are those who operate below the frontier production curve.

Nevertheless, studies on the technical efficiency of production that employ the parametric method are scarce regarding pineapple production. Chen et al. (2001) conducted a remarkable study wherein the technical efficiency of 83 pineapple farms in China was measured. The study concluded that manpower is the most crucial aspect of production. In a similar vein, Adinya et al. (2010) established the inefficiency of Nigerian pineapple production. Their findings suggested that achieving higher production efficiency is significantly impacted by the farmers’ educational attainment.

Eventually, two types of functions namely: Cobb–Douglas and Translog dominate the technical efficiency literature. In the case of a lower sample size, the Translog specification might not be representative. The stochastic frontier production model is employed to determine respondents’ technical efficiency.

The Cobb–Douglas (CD) is the appropriate form of the frontier production function. The production technology is assumed to be characterized by the Cobb–Douglas production function since it has the advantage over other forms of production functions like the Linear and Semi-log production functions in that a logarithmic transformation provides a model that is linear in the log of input and hence, easily used for econometric studies ( Coelli, 1995 ). This production function gave the best fit to data compared to the linear, exponential, and semi-log functional forms ( Akhilomen et al., 2015 ).

In addition to the above, the CD functional form is mainly preferred because its coefficients directly represent the elasticity of production. It provides an adequate representation of the production process as we are interested in an efficiency measurement and not an analysis of the production structure ( Taylor and Shonkwiler, 1986 ). Further, the CD functional form has been widely used in farm efficiency analyses. It is an adequate representation of the data ( Abedullah and Bakhsh, 2006 ).

2.3.2 Stochastic frontier analysis

SFA, which is also known as a composed error model, was developed initially by Aigner et al. (1977) and Meeusen and Broeck (1977) . Supposing an appropriate production equation, we described the stochastic production frontier Eq. 1 below:

Where Y i , yield produced by i th pineapple grower; X i , inputs for the pineapple by i th growers; β , parameters of study; ε i , collected unsystematic errors; ε i  =  ν i – u i , ν i is symmetric (− ∞  <  ν i  <  ∞ ) and shows those random errors, such as climate change or other natural disasters, which are out of the farmer’s control in the Eq. 2 .

It is expected that ν i is identically and independently distributed as N (0, σ 2 v ; Gujarati, 2003 ). Farm-specific technical inefficiency is denoted by u i . On the other hand, it shows the gap of output ( Yi ) and its maximum possible output assumed by the SFA [f( X i , β ) +  ν i ] ( Aigner et al., 1977 ). u i arises from N (0, σ 2 v ) and is half normally distributed below 0 ( Kumbhakar and Lovell, 2000 ). The terms ν i and u i are always independent of the input factors X i .

2.3.3 Stochastic frontier model specification

The SFA model was used to estimate the technical efficiency of pineapple production. This technique specifies the effect of technical inefficiency that cannot be controlled by pineapple growers. The Cobb–Douglas Production function is suitable for estimating technical efficiency in our study, due to its advantages of easing interpretation and estimation. In addition, the elastic functional form solves the difficulty of multi-collinearity. We can express the SFA Eq. 3 for the analysis as below:

Where Y i , yield of pineapple in kilograms per ha; i th , Number of farmers up to 100; j th , Number of variables up to 8; X 1 , Area under pineapple cultivation (ha); X 2 , Quantity of seedling (piece/ha); X 3 , Lime (kg/ha); X 4 , Urea (kg/ha); X 5 , Triple Superphosphate (TSP) (kg/ha), X 6 , Muriate of Potash (MoP; kg/ha); X 7 , Hormone (ml/ha); X 8 , Human labor (man-days/ha); ε i , Error (composed error term); ln, natural logarithm; β 0 , Intercept of the model; β j , equation parameters.

2.3.4 Estimation of the stochastic frontier model

The Maximum Likelihood Estimation (MLE) technique was employed to estimate the SFA ( Greene, 2000 ). The basic idea of the maximum likelihood principle is to choose the parameter estimates (β, σ 2 ε ) to maximize the probability of obtaining the data:

Where σ 2 v and σ 2 u are the variances in the equation for v and u, respectively; further, σ 2 ε  = σ 2 v  + σ 2 u , and γ = σ u /σ v .

The MLEs of β, γ, and σ 2 ε at which the value of the likelihood function is the maximum were obtained by setting the first-order partial derivatives for β, γ, and σ 2 ε as equal to zero and solving these non-linear equations simultaneously in the Eqs. 4 , 5 . It can be estimated by using a non-linear optimization algorithm to find the optimal values of the parameters.

2.3.5 Equation of technical inefficiency estimation

In the model specification of technical efficiency estimation, it is expected that random v i is normally distributed as N (0, σ 2 v ), whereas u i is half normally distributed as N (0, σ 2 u ).

Where U i denotes the specific technical inefficiency of pineapple yield;

Z 1  = Age of the pineapple farmer (in years).

Z 2  = Education of the pineapple farmer (in years of schooling).

Z 3  = Dummy variable for credit taken from any source, e.g., Banks, NGOs (Non-governmental Organizations) only for cultivating pineapple (1 for yes and 0, otherwise).

Z 4  = Dummy variable for training on pineapple farming participated by the pineapple farmer (1 for yes and 0, otherwise).

Z 5  = Family size in number.

δ j  = Parameters of the respective technical inefficiency variable to be estimated.

(j = l, 2,............... 5)

W i  = Random error term that is defined by the truncation of the normal distribution (With zero mean and variance, σ w 2 ).

2.3.6 Estimation of technical efficiency and technical inefficiency of individual pineapple growers

The following formula Eq. 7 is applied to estimate the Technical Efficiency (TE) of pineapple growers:

Where Y i , observed yield of i th pineapple grower; Y i * , frontiers yield of i th pineapple grower that is obtained; TE i , technical inefficiency of i th pineapple grower in the range of 0 to 1.

To obtain the result of Technical Inefficiency (TI) of individual pineapple growers, the Eq. 8 below was employed

Where TI i  = 1- (Y i /Y i * ), TI i , technical inefficiency of i th pineapple growers in the ranges of 0 to 1 Eq. 8 .

2.3.7 Hypothesis testing

It would be reasonable to fit an inefficiency effect model like ( Eq. 6 ), if the inefficiency effects are significant, stochastic, and have a particular distribution specification.

Hence, according to Battese and Coelli (1995) , we ought to test the following null hypotheses:

The generalized likelihood ratio test was to be used to examine the null hypotheses. To get the log-likelihood values under the above three null hypotheses, i.e., from Eqs. (9–11) , three different models were fitted. The model under the null hypothesis Eq. (9) is the traditional mean response function where output or cost is regressed up on the respective predictor variables given in stochastic production or cost frontier assuming zero inefficiencies. That is, only statistical noise makes up the random component; the inefficiency effect model was discarded. The degrees of freedom under this null hypothesis was 7, since γ = 0, implying σ u 2  = 0 and δ j ’s (j = 0,1,2, …, 5) are equal to zero. Similarly, the model under the null hypothesis Eq. (10) is also a conventional mean response function where the farm-specific variables such as age, education, credit, training on farming, and family size were also included in the model as independent variables with the input variables. In this case, only the inefficiency component (i.e., u i ) is dropped from the original model which means that parameters γ, σ u 2 and δ 0 are equal to zero. Hence, the degree of freedom under this null hypothesis was 3. Again, the model under the null hypothesis Eq. (11) is the original stochastic frontier model as it is, but the inefficiency effect model will be spilled. That is, dropping δ j ’s parameters (where, j = 1, 2, …, 5), the values of γ, σ u 2 and δ 0 were to be calculated. Hence, the degrees of freedom under this null hypothesis are equal to the number of farm-specific variables dropped (i.e., 5 ).

Now the value of the log-likelihood function calculated at the model under H 0 needed to be close to the value calculated at the original model for the null hypothesis to be accepted. A measure of this familiarity was the likelihood ratio statistic under the above null hypothesis is

Where λ follows a chi-square distribution with degrees of freedom equal to the number of parameters assumed to be zero in the null hypothesis (H 0 ) provided that H 0 is correct and L(H o ) is the log-likelihood value under H 0 and L(H 1 ) is the log-likelihood value under H 1 in the Eq. (12) . Additionally, the renowned Student’s t-test was used to perform individual significance tests on the parameters in both the stochastic frontier model and the inefficiency effect model.

3 Results and discussion

3.1 stochastic production frontier estimates.

Table 2 displays the findings of maximum likelihood estimates for the pineapple technical inefficiency effect model and stochastic frontier production function. We discovered that area, lime and labor input would be sequentially correct based on their coefficients values all had statistically significant negative coefficients, meaning a negative impact on technical efficiency, with the values −0.011, −0.019, and −0.088, respectively. Small farms motivated family work and large farms’ tardiness in timely completing farm operations during crucial times may be responsible for the negative correlation between area and production. This conclusion coincides with those of Samarpitha et al. (2016) where the area under rice crops negatively impacted the technical efficiency (−0.0114), suggesting that small and marginal farms were technically more efficient than their bigger counterparts. So, the integration of rural industries is a crying need to improve agricultural productivity ( Ye et al., 2023 ). On the other hand, the case of labor input may be linked to ineffective labor management in the study area and a declining rate of return on labor. The negative coefficients of labor input indicate that every single unit increase in the amount of labor per hectare leads to the rise of farmers’ inefficiency level by 0.088 units. This might be due to the lack of workers’ specialization in pineapple farming with less labor productivity nature in the study area. To ensure the positive impact of labor input employed for certainly we ought to give more focus on labor productivity rather than the labor supplies.

Table 2 . Empirical results of the stochastic frontier production model.

Ghimire et al. (2023) also found the coefficient of labor was 0.066 and was negatively significant denoting possible negative change by 6.6% in aggregate output of lentils as a result of unit man-days increment in labor use. These unexpected outcomes would be overcome with suggestions provided by Hasan et al. (2022) and Alam et al. (2019) . The acreage of pineapple production explained the levels of technical efficiency to a significant degree as reported by Trujillo and Iglesias (2013) .

The primary input in the production of pineapples was seedlings (sucker), for which the elasticity was determined to be 0.998 at a 1 percent significant level. This suggests that a 1 percent increase in seedling pieces per hectare is positively connected to a 0.998 percent improvement in farm efficiency. This positive relationship with output conforms to a priori expectation that is supported by Ghimire et al. (2023) where they also observed the coefficient of seed input was positively significant indicating unit increase in seed quantity will lead to an increase in lentil production by 37.6 percent. According to Balogun et al. (2018) , as a management strategy for free movement and simple weeding, poor spacing of pineapple suckers after planting may have contributed to the positive coefficient of suckers’ substantial relationship with output. Leaders in the agriculture sector risk severe repercussions if they willfully ignore seed quality, such as increased produce prices, food insecurity, and a collapse in farm revenues. Considering the significant risks involved, it makes sense to spend money on premium seeds. Otherwise, this may be the reason that most of the farmers will use local seeds that may come up with poor germination and plant vigor ( Ghimire et al., 2023 ). Additionally, we should get ready for financial instability to cascade down the supply chain from the farm to the consumer. The implication is that this will shorten production times while improving the quality, yield, and consistency of the finished crop.

3.2 Determinants of technical inefficiency among pineapple farmers

The lower part of the table enlisted the parameters of the technical inefficiency model. A negative value of parameters of the technical inefficiency variable presages a negative impact on technical inefficiency and vice-versa . Thus, the sign of the ‘δ’ parameters in the inefficiency effect model was expected to be negative which will impact the technical efficiency positively. The sigma square (σ 2 ) is 0.025 of the estimated models was statistically significant at a 1 percent level of probability. This indicated a good fit of the distributional form assumed for the composite error term. The gamma (γ), which measures the dominance of the inefficiency effect over random error, with a value of 0.984 at a 1 percent level of significance implies that 0.984 percent of the variation in the output was attributed to technical inefficiency. The result shows that three variables credit, training, and family size had a significant impact on technical inefficiency with a negative coefficient. In the case of credit, the coefficient −0.066 at a 10 percent level of significance describes that technical inefficiency decreases with the increase of credit availability and utilization in pineapple production. This finding was consistent with the work of Balogun et al. (2018) , Haq (2013) and Amoah et al. (2014) , which showed a positive association between credit and input use and farm productivity. Again, at a 1 percent statistical level, farmers who received training conversely impact the technical inefficiency with the parameter coefficient of −1.034. It describes that farmer who had taken training tend to be less inefficient than their counterparts without having training. Trained farmers could implement improved production technology and had contact with the extension agents that led to efficient production for them. The result agrees with Balogun et al. (2018) and Akhilomen et al. (2015) . On the contrary, Mussa (2011) used family size in the inefficiency effect model and found a positive impact on technical efficiency that coincides with our result. The reason may be that more family members ensure the availability of more family labor and can carry out important agricultural practices timely thus improving efficiency. It follows that a farmer who interacts with extension agents more often will be able to recognize and implement new farming methods more successfully than a farmer who interacts with them less. Since efficiency and a nation’s economic development are directly correlated, over time, economic growth is increased by more efficient businesses, higher input productivity, and increased economic activity. Every gain in efficiency usually results in a decrease in the cost of manufacturing ( Naseer et al., 2020 ). Consumer prices for goods and services might go down when production costs are low. Technical efficiency is therefore necessary for long-term economic growth.

3.3 Farm efficiency level

The frequency distribution of the technical efficiency estimates for pineapple production is shown in both Table 3 and Figure 3 . It is observed that technical efficiency varies from 61.57 to 99.33 percent for pineapple growers. The mean technical efficiency of pineapple farming is 94.37 percent in the study area. It indicates that on average a pineapple farmer in the study area still had the capability of increasing technical efficiency by 5.63 percent, using the available resources and technology to achieve the frontier output level. The frequency distribution of the technical efficiency implies that 85 percent of farmers had TE above 0.9, and only 4 percent is less than 0.71 ( Figure 3 ). More precisely, in the study area, maximum farmers were found to operate at an 85 percent efficiency level. The result shows consistency with persistent heritage in the study area related to good agricultural practices, in particular for pineapple production. Eventually, a slight improvement in technical efficiency will lead to a good cost-efficient direction, which will help to enhance the farmer’s profit ( Ghimire et al., 2023 ).

Table 3 . Frequency distribution of technical efficiency estimates from C-D stochastic frontier production function.

Figure 3 . Technical efficiency scores of the individual pineapple farmers. The frequency distribution of technical efficiency estimates from the C-D stochastic frontier production function is demonstrated with percentages in this bar chart. Using the results, we may suggest suitable policies based on the nature of each farmer and their farm enterprise.

3.4 Tests of hypotheses on the parameters of the technical inefficiency model

Generalized likelihood-ratio tests of null hypotheses that the technical inefficiency effects are absent are presented in Table 4 . The first null hypothesis, which specified that the inefficiency effects were absent from the stochastic production frontier model, was strongly rejected. The second null hypothesis, which specified that the inefficiency effects were not stochastic for the production frontier model, was also strongly rejected. The third null hypothesis, considered in Table 4 , specifies that the inefficiency effects of stochastic production were not a linear function of age, education, credit, training, and family size. This null hypothesis was also strongly rejected at a 5 percent level of significance. This indicates that the joint effect of these five explanatory variables on the inefficiency of production was significant although the individual effect of one or more variables might not be statistically significant. The inefficiency effect in the stochastic production frontier was stochastic and was not uncorrelated to age, education, credit, training, and family size. Thus, it appears that, in this application, the proposed inefficiency stochastic production frontier model was a significant improvement over the corresponding stochastic frontiers which do not involve a model for the technical inefficiency effects.

Table 4 . Tests of hypotheses on the parameters of the technical inefficiency effect model.

3.5 Summary findings of the farm-level prospects

This study explored the future potentiality of fresh pineapples both in raw and processed form in Bangladesh ( Figure 4 ). We are well-informed that pineapple has huge demand both locally and internationally for its several types of uses. Its consumption is increasing day by day because of its different nutritional and medicinal values ( Shakil, 2023 ). Since exporting fresh pineapples is difficult because of their perishable nature, growers suggested that we focus on exporting canned pineapples. Besides, pineapple leaf fine fiber is already used for making different types of products (clothes, rope) in some countries due to its organic nature ( Pandit et al., 2020 ). In the study area, most of the pineapple cultivators are mainly small and marginal-level farmers, they sell their product through trade agents at the village level, and a major share of pineapple marketing is run by this system in Bangladesh. The farmers reported that if it can be marketed through proper marketing channels then these juicy and nutritious pineapples are sure to earn huge amounts of foreign currency through export.

Figure 4 . Potentials of a pineapple tree from growers’ perspectives. According to the farmers, there are both qualitative and quantitative advantages to a nation’s pineapple production. It raises farm families’ standards of life and produces long-term profit as it is a perennial crop. The figure depicts the direct contribution that pineapple cultivation and consumption have made to the development of a sustainable agricultural industry for the coming generations.

As pineapple production is increasing every year in Bangladesh, it will be easy to establish a potential export site by utilizing the huge amount of leaf wastage. This leaf is the main raw material for pineapple leaf fiber production. Despite having an efficiency in Bangladesh in cheap labor costs the government provides various incentives for export promotion so natural and organic pineapple leaf fiber can be used to make products rather than synthetic fiber.

It is already known that the European market is the main target of many pineapple exporting countries ( Untoro et al., 2021 ). As Bangladesh has been given different trade facilities by the European Union till now so entering that giant market is quite easy for us. Many Bangladeshis live in different countries of the Middle East and there is a demand for local fruits. Many of the local companies of Bangladesh have strong competitiveness in those markets, strongly similar to the findings of Untoro et al. (2021) . So, investing in the canned pineapple market will be beneficiary for Bangladesh. Based on the available raw materials and potential production with a low production cost, there is a high production possibility of canned pineapple and pineapple leaf fiber in the study area.

Moreover, pineapple waste which is rich in fiber can be used as an energy source as well as a good digestive feed for animals such as poultry, broilers, and cows ( Figure 4 ). Buliah et al. (2019) also found that feeding dairy cows with pineapple waste can increase milk production due to an increase in digestion rate.

In addition, the pineapple leaf fiber extraction and weaving industry along with the high demand for canned pineapple ensures the involved people can have income and employment opportunities needed to substantially improve their lives ( Figure 4 ). This will lead to huge potential and economic rewards for indigenous weavers. By improving the lives of their families and their communities, the country can benefit from this. Extraction of fiber, weaving, and embroidery jobs enable them to earn money that allows them to remain at home. By joining this sector, they do not need to join any hazardous and non-prestigious jobs (such as brick fields, rice mills, or domestic workers) to earn money. The qualitative field-level research findings are represented through the following chart:

Since Bangladesh has only a few actual and potential export sectors it is expected that this study will contribute to identifying another potential income and export-earning sector of Bangladesh. We hope academically that the findings of this study will encourage many entrepreneurs and established businessmen to engage in this business.

4 Conclusion and recommendations

Pineapple is one of the few fruits with adequate vitamins and improved productivity in Bangladesh. Efficiency in its production is crucial to food security, poverty abatement, and lessening vitamin-related deficiency among the masses (particularly children) in the state. The study measured the level of technical efficiency and identified the factors influencing the technical inefficiency of pineapple production using the Cobb–Douglas stochastic frontier approach. The study concludes that technical inefficiency was present in pineapple production. The average technical efficiency was estimated as just below 95% across the study area meaning that farmers had been operating their farms below the production frontier. So, the results indicated that there is still scope for a remarkable improvement in technical efficiency in pineapple production with the remaining level of input supply to obtain the optimum level of output efficiency. The efficiency model indicated that seedling is a significant primary input for pineapple farmers’ efficiency. Therefore, if maximum production is to be achieved in the pineapple industry, the quality and optimum quantity of seedlings must be ensured.

The technical inefficiency factors model revealed that the efficiency of farmers was significantly influenced by demographic and institutional factors, such as age, education, training, credit, and family size. The age and education level of farmers had a significant positive influence on technical inefficiency, this positive coefficient indicates that technical efficiencies were significantly higher for the younger farmers, compared to old age groups, and informally educated farmers. On the other hand, credit, training, and family size had a significant negative impact on technical inefficiency, which implies that technical inefficiency will be reduced significantly by increasing access to formal credit and training along with the intervention of productive family members.

Regarding the findings, the study makes the following recommendations: (a) to increase productivity and reduce unemployment and poverty in the district and the nation, the government should create an atmosphere that encourages more young people to work in the pineapple industry. This is due to the following reasons: (i) Results showed that more human work led to lower production; (ii) Youth recruitment into agriculture is crucial because they are likely to be able and willing to accept contemporary technical advancements and use resources perfectly. Farmers need to understand that increasing worker productivity is more important than limiting production with hired labor; (b) Since farmers are not receiving inputs at the government rate, public interventions may be necessary to guarantee that high-quality inputs are reasonably priced. Furthermore, farmers stated that contaminated fertilizers caused them harm. Therefore, it is important to increase public awareness of the need to maintain fertilizer quality at both the local and rural levels; (c) The government should also prioritize education and extension services by bolstering the Department of Agricultural Extension (DAE), establishing farmers’ training centers, formal and informal farmers’ education, and technical and vocational schools; (d) In addition, financial assistance on favorable terms must be given to the farmers who grow pineapples. Eventually, policymakers ought to make some necessary amendments to their policies to make production inputs available to respective farmers at the right time, in the right quantity, and at affordable prices.

It is undeniable that Bangladesh has demonstrated remarkable growth and development, particularly during periods of heightened global unpredictability. Bangladesh went from being one of the world’s poorest countries at birth in 1971 to having a lower middle class in 2015 ( The World Bank, 2023 ). Notwithstanding these successes, the economy continues to face significant obstacles, including growing inflationary pressure, energy scarcity, a deficit in the balance of payments, and a lack of revenue. The trade deficit shrank in Fiscal Year, FY23, while the balance of payments (BoP) deficit and foreign exchange reserves decreased as a result of a contraction in the financial account deficit. In a cutthroat commercial climate, technically proficient and prosperous pineapple growers may write a wonderful tale of poverty alleviation and job creation that would guarantee even a minor contribution to reaching upper-middle-class status by 2031. To maximize productivity given available resources and technological capabilities, it will be necessary to develop human capital, a skilled labor force, effective infrastructure, and a governmental climate that encourages private investment.

The results of this study, notwithstanding its focus on Bangladesh’s northeast, will significantly impact the 12th Sustainable Development Goal (SDG 12 of UNDP) by guaranteeing a more cost-effective and sustainable agri-food production and consumption system.

5 Limitations of the study and further research

Even though this study produced some important findings, it is important to recognize several limitations when evaluating the data. First off, the current study only looked at a small number of parameters to investigate their direct impact on pineapple growers’ technical inefficiency. Future studies in this area should take into account the effects of external variables on pineapple production, such as infrastructure, government regulations, market conditions, and climate change. Second, the paper only considers pineapple production in Bangladesh’s northeastern district, which might not be enough to allow the findings to be applied more broadly. Therefore, to strengthen the relevance and applicability of the study’s findings, future studies should be conducted in this direction including a wider variety of settings and circumstances. The use of cross-sectional data, which makes it difficult to capture the effects over time, and the reliance on farmers’ self-reported measures, which could lead to inaccurate predictions, are two other limitations. Time series data and panel data ( He et al., 2023 ) should be used in this field of study to provide an accurate picture.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the [patients/ participants OR patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

TD: Conceptualization, Resources, Investigation, Writing – original draft, Writing – review & editing. JKS: Supervision, Writing – review & editing. MAR: Supervision, Writing – review & editing. KMMA: Writing – review & editing. KA: Formal analysis, Writing – review & editing. AC: Writing – review & editing. MSA: Visualization, Writing – review & editing, Conceptualization.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research work was funded by the Ministry of Science and Technology (MoST), Government of the People’s Republic of Bangladesh. The funder has no specific role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

The authors would like to thank the data enumerators for their invaluable support throughout this study. We are also grateful to the study area’s agricultural extension personnel for their technical expertise and administrative assistance. This work would not have been possible without the generous financial support of the MoST. Finally, we would like to thank the study participants for their kind contributions of time and insights.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: Cobb–Douglas (C-D) production function, stochastic frontier analysis, technical efficiency, sustainable pineapple farming, future prospects and economic policies

Citation: Datta T, Saha JK, Rahman MA, Adnan KMM, Akter K, Chowdhury A and Alamgir MS (2024) Examining technical efficiency, prospects, and policies of farmers: data from a developing nation’s pineapple production. Front. Sustain. Food Syst . 8:1383948. doi: 10.3389/fsufs.2024.1383948

Received: 08 February 2024; Accepted: 03 July 2024; Published: 16 August 2024.

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*Correspondence: Md. Shah Alamgir, [email protected]

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