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Research is formalized curiosity. It is poking and prying with a purpose. - Zora Neale Hurston

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

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Last Updated: Dec 21, 2023 3:49 PM
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Article contents

Job and work design.

Anja Van den Broeck Anja Van den Broeck KU Leuven
and Sharon K. Parker Sharon K. Parker University of Western Australia
https://doi.org/10.1093/acrefore/9780190236557.013.15
Published online: 24 May 2017

Job design or work design refers to the content, structure, and organization of tasks and activities. It is mostly studied in terms of job characteristics, such as autonomy, workload, role problems, and feedback. Throughout history, job design has moved away from a sole focus on efficiency and productivity to more motivational job designs, including the social approach toward work, Herzberg’s two-factor model, Hackman and Oldham’s job characteristics model, the job demand control model of Karasek, Warr’s vitamin model, and the job demands resources model of Bakker and Demerouti. The models make it clear that a variety of job characteristics make up the quality of job design that benefits employees and employers alike. Job design is crucial for a whole range of outcomes, including (a) employee health and well-being, (b) attitudes like job satisfaction and commitment, (c) employee cognitions and learning, and (d) behaviors like productivity, absenteeism, proactivity, and innovation. Employee personal characteristics play an important role in job design. They influence how employees themselves perceive and seek out particular job characteristics, help in understanding how job design exerts its influence, and have the potential to change the impact of job design.

work design
job characteristics
satisfaction
performance
proactivity

“It is about a search, too, for daily meaning as well as daily bread, for recognition as well as cash, for astonishment rather than torpor; in short, for a sort of life rather than a Monday through Friday sort of dying” (Terkel, 1974 , p. xi).

Billions of people spend most of their waking lives at work, so it is fortunate that work can be a positive feature of living. Obviously, the associated salary helps to pay the bills and provides a means for a certain standard of living. But good work also structures one’s time, builds identity, allows for social contact, and enables engagement in meaningful activities (Jahoda, 1982 ). Nevertheless, although work can serve these important functions, it can also be a threat to people’s well-being, cause alienation, and result in burnout. As an extreme example of its negative effects, Chinese and French telecom workers have been reported committing suicide because of work-related issues.

Whether work is beneficial or detrimental is largely dependent upon how it is designed. Work design is defined as the content, structure, and organization of one’s task and activities (Parker, 2014 ). It is mostly studied in terms of job characteristics, such as job autonomy and workload, which are like the building blocks of work design. Meta-analytical results show that these job characteristics predict employees’ health and well-being, their cognitions and learning, and their attitudes and behavior (Humphrey, Nahrgang, & Morgeson, 2007 ; Nahrgang, Morgeson, & Hofmann, 2011 ). There is no doubt that work design is important, so it is not surprising that it has received considerable research attention (Parker, Morgeson, & Johns, 2017 ).

Throughout the 20th century , several authors developed different job-design models, which have been expanded into various contemporary perspectives. The net effect is that the literature on work design is somewhat fragmented. Rather than providing one overall framework to study the design of jobs—similar to the Big 5 framework in personality, for example—job-design models consider the topic from different angles. This diversity may be an advantage in understanding the complexity of job design, but an overview—let alone on overarching model—is lacking, which inhibits the sharing of knowledge and ultimately our understanding of job design.

Against this background, it is necessary to review the approaches that have dominated the literature around the world and the contemporary models that have emerged from them. This overview reveals the basic principles that guide views on job design: how work is conceptualized, how job characteristics relate to important outcomes, and the roles personal aspects play in job design–outcome relationships.

This article makes several contributions to the literature. First, by providing an overview of important job-design models that have dominated the work-design literature around the globe, the article introduces job-design scholars working in one research tradition to other traditions. Second, the article explicates the most important assumptions about the impact of job design across the different models and brings the assumptions together in one integrative work design (IWD) model. Finally, the article supplies an overview of fruitful avenues for future research that might stimulate future research on the important topic of job design. Although previously it had been argued that “we know all there is to know” about job design (Ambrose & Kulik, 1999 ), one in three employees in Europe still has a job of poor intrinsic quality (Lorenz & Valeyre, 2005 ) and different influences put pressure on the quality of jobs (Parker, Holman, & Van den Broeck, 2017 ) . Building on this overall model, different pathways emerge for the rejuvenation of the literature (Parker et al., 2017 ) and for fostering knowledge on how jobs can be designed so that work brings out the best in people.

Historical Overview of Models Around the Globe

Job design has a long history. Ever since people organized themselves to hunt and gather for food, or even to build the Aztec temples, people identified activities, tasks, and roles and distributed them among collaborators. The scientific study of work design, however, started with the work of Adam Smith, who described in his book The Wealth of Nations how the division of labor could increase productivity. Previously, the timing and location of work fitted seamlessly in with everyday activities, and many industries were characterized by craftsmen, who developed a product from the beginning to the end (Barley & Kunda, 2001 ). A blacksmith would craft pins starting from iron ore, while a carpenter made cupboards out of trees. But Adam Smith advocated the dissection of labor into different tasks, and the division of these task among employees, so that each would repetitively execute small tasks: One employee would cut the metal plate, while another one would polish the pins.

Scientific Management

The principle of the division of labor was further developed into scientific management (Taylor, 2004 ). Adopting a scientific approach to work in order to increase efficiency, Taylor argued that ideal jobs included single, highly simplified, and specialized activities that were repeated throughout the working day, with little time to waste in between (Campion & Thayer, 1988 ). Taylor developed his ideas in the realm of the Industrial Revolution, which made it possible to automate many of the activities in people’s jobs. Employees were essentially considered parts of the machinery, with the idea they could easily be replaced. While previously employees inherited or chose their trades and learned on the job by trial and error, with Taylorism, employees were selected and trained to execute specific tasks according to prescribed procedures and standards. Supervisors were tasked with monitoring employees’ actions, leading to the division between unskilled manual labor and skilled managerial tasks, and to the rewarding of employees according to their performance (e.g., via piecework) so that the goals of the employees (i.e., making money) would be aligned with the goal of the company (i.e., making profit).

Essential principles of Taylorism thus include simplification and specialization, but also the selection and training of employees to achieve a fit between demands of the job and employees’ abilities. Because of these principles, efficiency rocketed, and Taylorism was soon also adopted for office jobs. Today, Taylorism still inspires the design of both manufacturing and service jobs in many organizations (Parker et al., 2017 ).

Despite its positive consequences in terms of productivity, one downside of this mechanical approach to job design was that employee morale dropped. For instance, in the Midvale Steel plant, where Taylorism was implemented, employees experienced mental and physical fatigue and boredom, resulting in sabotage and absenteeism (Walker & Guest, 1952 ). The negative effects of Taylorism eventually led to the development of several less mechanistic and more motivational work designs, including social and psychological approaches.

Social Approaches to Work

In further exploring the effects of Taylorism, Mayo and his colleagues uncovered the importance of individual attitudes toward work and teams. In the famous “Hawthorne studies,” focusing on a team of Western Electric Company workers, Mayo and colleagues aimed to improve employees’ working conditions, but they failed to find strong effects of interventions like increasing or decreasing illumination, or shortening or lengthening the working day, on individual employee performance, even though such effects would be expected based on Taylorism. Rather, production went up over the course of the period in which the employees were involved and consulted in the experiment. The free expression of ideas and feelings to the management, and sustained cooperation in teams, increased employee morale and ultimately efficiency. Group norms were shown to have a strong effect on employee attitudes and behavior and were more effective in generating employee productivity than individual rewards, potentially because being part of a group increased feelings of security. In a Taylorism model, people were seen as a part of a machine, but according to Mayo, employees should be regarded as part of a social group.

The focus on groups was further developed into sociotechnical systems theory by human relations scholars at the Tavistock Institute in the United Kingdom (Pasmore, 1995 ). The scholars aimed to optimize the alignment of technical systems and employees. To make optimal use of the available technology, the scholars were convinced that teams of employees should have the autonomy to organize themselves (without too much supervision) and to manage technological problems and to suggest improvements, thereby breaking with the previous division between manual labor and managerial tasks. Furthermore, rather than advocating specialization, human relations scholars argued that, within the teams, employees should work on a meaningful and relatively broad set of tasks and that team members should be allowed to rotate, so that they would have some variety and become multi-skilled (Pasmore, 1995 ).

Sociotechnical systems theory gave rise to the use of autonomous working groups, later labeled self-managing teams . Several studies provided evidence for the positive effects of autonomous working groups on job satisfaction and performance, but the positive effects were not always found Some have therefore argued that autonomous work teams need to be implemented with care and may be most effective in uncertain contexts, where individuals can make a difference (Wageman, 1997 ; Wright & Cordery, 1999 ).

Herzberg’s Two-Factor Theory

Building on the importance of employees’ attitudes, the first major model that made an explicit link between job design and employee motivation is the two-factor theory of Herzberg ( 1968 ). Herzberg started from Maslow’s need pyramid ( 1954 ) and argued that, while some job aspects caused job satisfaction, other were responsible for employee dissatisfaction. Satisfaction and dissatisfaction were thus considered independent states, with different antecedents. Dissatisfaction was said to occur when employees feel deprived of their physical, animal needs, due to a lack of “hygiene factors,” such as a decent salary, security, safe working conditions, status, good relationships at work, and attention to one’s personal life. Satisfaction, in contrast, was said to be intertwined with growth-oriented human needs and is influenced by the availability of motivators like achievement, recognition, responsibility, and growth.

Although Herzberg’s model has been criticized and has received little empirical support (Wall & Stephenson, 1970 ), it has had a vast impact on the literature on job design. First, Herzberg provided the building blocks for the meta-theory underlying job design. In building on the differentiation between basic animal needs and more human, higher order growth needs, Herzberg inspired McGregor ( 1960 ) to develop Theory X and Theory Y, two views on humankind that managers may hold that have implications for how jobs should be designed. Theory X assumes that employees are passive and lazy and need to be pushed (i.e., the stick approach to motivation) or pulled (i.e., the carrot approach to motivation) using the principles of Taylorism. In contrast, managers who hold Theory Y see employees as active and growth-oriented human beings who like to interact with their environment. Adopting this theory likely stimulates managers to design highly satisfying and motivational jobs that make optimal use of the interest and energy of employees.

Second, Herzberg was the first to develop a well-defined job-design model and to advocate the empirical study of people’s jobs, thereby paving the way for the tradition of job-design research that we know today. Moreover, Herzberg pointed out the importance of a fair wage and good working conditions, similar to Taylorism and social relations at work, as did the human relations movement. In addition, he called for attention to opportunities to learn and develop oneself, which were to be found in the content of the job. As such, Herzberg was arguably the first to advance that the true motivational potential of work is linked to the content of one’s job. He further advanced that jobs could become more motivating by job enlargement (i.e., adding additional tasks of similar difficulty) and—most importantly—job enrichment (i.e., by adding more complex task and decision authority). As in the case of autonomous teams, these practices can lead to beneficial outcomes, although the effects ultimately depend on the context and manner in which they are implemented (Axtell & Parker, 2003 ; Campion, Mumford, Morgeson, & Nahrgang, 2005 )

Hackman and Oldham’s Job Characteristics Model

Hackman and Oldham followed through on the idea of motivational jobs and exclusively focused on job content in their job characteristics model (JCM; Hackman & Oldham, 1976 ). Specifically, they argued that the motivating potential of jobs could be determined by assessing the degree of task significance, task identity, and variety, as well as the autonomy and feedback directly from the job. Moving beyond mere job satisfaction, these job characteristics were argued to lead to an expanded set of outcomes of job design, including internal motivation, performance, absenteeism, and turnover. Furthermore, where previous models were silent about the psychological process through which job design may have its impact, Hackman and Oldham proposed three psychological states as mediating mechanism: having knowledge of results, feelings responsible, and experiencing meaningfulness in the job. These states were expected to be influenced by feedback, autonomy, and the combination of task identity, task significance, and variety, respectively. While both autonomy and feedback are essential for jobs to be motivating, as each of the latter aspects relate to the same critical psychological state, task identity, task significance, and variety were considered to be interchangeable, which introduced the possibility that particular job aspects can compensate for each other, so that low task identity wouldn’t be problematic when employees experience high levels of variety.

An important contribution from Hackman and Oldham is that they acknowledged the importance of individual differences. They assumed that peoples’ skills, knowledge, and ability, as well as general satisfaction with the work context, may impact the strength of the relations between the job characteristics and the critical psychological states, and between the latter and the work outcomes (Oldham, Hackman, & Pearce, 1976 ). Perhaps the most important moderator in the JCM is peoples’ growth-need strength, which is defined as the degree to which employees want to develop in the context of work. Highly growth-oriented employees may benefit more from job enrichment.

In addition to developing the JCM, Hackman and Oldham also contributed to the job-design literature by presenting a measure (Hackman & Oldham, 1976 ), which spurred empirical research. Meta-analysis supported the basic tenets of the model, showing that motivational characteristics lead to favorable attitudinal and behavioral outcomes, via some of the critical psychological states (Fried & Ferris, 1987 ; Humphrey et al., 2007 ; Johns, Xie, & Gang, 1992 ). However, criticism has been directed at the inclusion of only a limited set of job characteristics, mediating mechanisms, and behavioral outcomes, as well as at the model’s focus only on the motivational aspects of work, while ignoring the stressful aspects (Parker, Wall, & Cordery, 2001 ) proposed an elaborated version of the JCM that identified an expanded set of work characteristics (including those more important in contemporary work, such as emotional demands and performance-monitoring demands), elaborated moderators (including, for example, operational uncertainty), and outcomes (including creativity, proactivity, and safety). This model also proposed antecedents of work design.

Karasek’s Job Demand Control Model

Karasek ( 1979 ) built on the criticisms of the JCM. In his job demand control model, he synthesized the traditions on detrimental aspects of work design (i.e., demands, including workload and role stressors) and the beneficial aspects (i.e., job control, including autonomy and skill variety) mentioned in the literature following the development of the Michigan Model (Caplan, Cobb, French, Harrison, & Pinneau, 1975 ) and the JCM, respectively. Rather than considering both aspects separately, seeing all structural work aspects as a demand, Karasek argued that job demands and job control have to be examined in combination, as the effects of each may be fundamentally different depending on the level of the other.

Specifically, Karasek built his theory on four types of jobs: Passive jobs are characterized by low demands and low control, while high-strain jobs include high job demands and low job control. Low-strain jobs are characterized by low demands and high job control, while active jobs include both high demands and high job control. These four types of jobs fall along two continua. Low- and high-strain jobs are modeled on a continuum from low strain to high strain, which over time may result in stress and health problems, while passive and active jobs are modeled on a growth-related continuum ranging from low to high activation, fostering motivation, learning, and development. Following up on the assumptions of the Michigan Model, Karasek proposed that job design not only may have short-term effects, but also in the long run, may affect employee personality: Continuous exposure to stressful jobs leads to accumulated strain, which then causes long-term anxiety that inhibits learning. Continuous exposure to active jobs, in contrast, builds experiences of mastery, which then buffers the perception of strain (Theorell & Karasek, 1996 ).

Apart from an expanded focus on the content of work in terms of job demands and job control, Karasek expanded his model by reintroducing social support, as a beneficial aspect of job design, and more specifically as an antidote to job demands. The role of social relations at work was acknowledged by Herzberg (although only as a hygiene factor) but was not included in the JCM, which continued to dominate the job-design literature in the United States.

Karasek’s model spurred research on job stress, as well as on health-related outcomes, such mortality and cardiovascular diseases (Van der Doef & Maes, 1998 ). The additive effects of job demands and job control are often found, but more cross-sectionally than over time, which suggests that reciprocal or reversed effects may also occur, with well-being, motivation, and learning also predicting job design (Hausser, Mojzisch, Niesel, & Schulz-Hardt, 2010 ). Results for the interaction between job demands and job control are limited (Van der Doef & Maes, 1999 ), even among high-quality studies (de Lange, Taris, Kompier, Houtman, & Bongers, 2003 ).

Warr’s Vitamin Model

Warr ( 1987 ) further expanded on the number of job characteristics that may influence people’s well-being. Going beyond the design of jobs, per se, Warr examined environmental aspects that may serve as vitamins for people’s well-being, in or outside the context of work. Well-being is herein broadly defined, including affective well-being, which is arranged around three axes—pleasure and displeasure, anxiety versus comfort, and depression versus enthusiasm—as well as competence, aspiration, autonomy, and integrated functioning of feeling harmonious. In total, Warr discerned nine different broad environmental factors that affect aspects of well-being, including:

Availability of money or a decent salary.

Physical security (good working conditions and working material).

Environmental clarity (low job insecurity, high role clarity, predictable outcomes, and task feedback).

A valued social position associated with, for example, task significance and the possibility to contribute to society.

Contact with others or the possibility of having (good) social relations at work, being able to depend on others, and working on a nice team.

Variety or having changes in one’s task context and social relations.

Externally generated goals or a challenging workload, with low levels of role conflict and conflict or competition with others.

Opportunity for skill use and acquisition or the potential to apply and extend one’s skills.

Opportunities for personal control or having autonomy, discretion, and opportunities to participate (Warr, 1987 ).

Intriguingly, Warr was the first to recognize that these job characteristics are not necessarily linearly related to employee well-being. Some job characteristics, and more specifically money, safety, and a valued social position, are the vitamins C and E. First, they affect employee well-being linearly, but only a certain amount, with their effects plateaus maintaining a constant effect (CE). The other job characteristics, however, are vitamins A and D and affect employee well-being in a curvilinear way: both low and high levels are detrimental, with any addition beyond a certain level leading to decrease in well-being (AD). In assuming these relations, Warr captured the widely held assumption that there can be too much of a good thing (Pierce & Aguinis, 2011 ). For example, while some amount of workload can be beneficial, too much workload may be detrimental for employees’ well-being. Similarly, too much job stimulation may contribute to negative health outcomes (Fried et al., 2013 ).

Furthermore, in line with Hackman and Oldham, Warr proposed that some employees are more susceptible to the impact of particular job characteristics than others, because their personal values or abilities fit better with particular job characteristics. For example, employees with low preference for independence benefit less from autonomy, while employees having a high tolerance for ambiguity suffer less when their environment provides less clear guidelines (Warr, 1987 ).

Research has provided some support for the vitamin model, showing that externally generated goals, autonomy, and social support may indeed have curvilinear relations with employee well-being (De Jonge & Schaufeli, 1998 ; Xie & Johns, 1995 ), but these results are not always replicated, especially not longitudinally (Mäkikangas, Feldt, & Kinnunen, 2007 ) or when general, rather than job-related, well-being is assessed (Rydstedt, Ferrie, & Head, 2006 ). One of the merits of the vitamin model is, however, that it broadened researchers’ horizons in terms of which job characteristics could influence employee well-being.

The Job Demands Resources Model of Bakker, Demerouti, and Schaufeli

The job demands resources model (JD-R model; Bakker, Demerouti, & Sanz-Vergel, 2014 ; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001 ) aimed to provide an integrative view of job characteristics. At the core of the model lies the various job characteristics that may impact employees, which can be meaningfully classified as job demands and job resources. Job demands are defined as “those physical, psychological, social, or organizational aspects of the job that require sustained physical and/or psychological (cognitive and emotional) effort or skills and are therefore associated with certain physiological and/or psychological costs” (Bakker & Demerouti, 2007 , p. 312). They are not necessarily negative, but turn into job stressors when they exceed workers’ capacities, which makes it hard for them to recover. Job resources are defined as the “physical, psychological, social, or organizational aspects of the job that … (1) [are] functional in achieving work goals, (2) reduce job demands and the associated physiological and psychological costs, [or] (3) stimulate personal growth, learning, and development” (Bakker & Demerouti, 2007 , p. 312).

Just like the vitamin model, the JD-R model focuses on employee well-being as a crucial outcome. Following the positive psychology movement advocating the balanced study of the bright side of employees’ functioning (Seligman & Csikszentmihalyi, 2000 ) along with the dark side, both negative (i.e., burnout) and positive (i.e., work engagement) aspects of well-being are considered as the crucial pathways through which job demands and job resources relate to a host of other outcomes, including employee physical health and well-being, job satisfaction, organizational commitment, and different types of behaviors, including in-role and extra-role performance, as well as counterproductive behavior (for an overview, see Van den Broeck, Van Ruysseveldt, Vanbelle, & De Witte, 2013 ).

Job demands are considered the main cause of burnout. In being continuously confronted with job demands, employees can become emotionally exhausted because they put all their energy into the job. Under particular situations, such as when all their effort is in vain, they likely start withdrawing from their job as a means to protect themselves and become cynical, which is part of the burnout response. Job resources can also have a (limited) direct negative relationship with burnout (Schaufeli & Bakker, 2004 ), but they are most crucial for the development of vigor and dedication, the main components of work engagement. Job demands and job resources are also assumed to interact, so that high levels of resources may attenuate (i.e., buffer) the association between job demands and burnout, while job demands are said to strengthen (i.e., boost) the association between job resources and work engagement.

Within the JD-R model, individual factors are modeled as personal resources, which are defined as malleable lower-order, cognitive-affective personal aspects reflecting a positive belief in oneself or the world (van den Heuvel, Demerouti, Bakker, & Schaufeli, 2010 ). As in the job characteristics model, personal resources can represent the underlying process through which job resources prevent burnout and foster work engagement (Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2007 ), moderate, and—more specifically—buffer the health-impairing impact of job demands, as job resources do, and they may serve as antecedents of the job characteristics, preventing the occurrence of job demands and increasing the (perceived) availability of job resources.

Evidence supporting the JD-R model is abundant, but the model is used mostly in the European literature. Job demands and job resources are convincingly shown to relate to burnout and work engagement (Nahrgang et al., 2011 ), while some evidence is provided for their interactions and the role of personal resources (Van den Broeck et al., 2013 ).

Contemporary Job-Design Models

Over the years, various other models have been developed. They may range from slightly different perspectives on job characteristics and their roles in the prediction of employee functioning to more fundamental changes in how we could perceive job design.

For example, some scholars suggested that not all job demands are equal, but need to be differentiated into challenging and hindering job demands. While challenges are obstacles that can be overcome and hold the potential for learning, hindrances are threatening obstacles that drain people’s energy and prevent goal achievement (Lepine, Podsakoff, & Lepine, 2005 ). Some authors suggested that job demands can be either challenging, or hindering, or both, depending on the appraisal of the individual employee (Rodríguez, Kozusnik, & Peiro, 2013 ; Webster, Beehr, & Christiansen, 2010 ). Others, in contrast, argued that employees generally categorize particular job demands as challenging (e.g., workload and time pressure) or hindering (e.g., red tape and role conflict), in relatively clear-cut categorizations (Cavanaugh, Boswell, Roehling, & Boudreau, 2000 ; Van den Broeck, De Cuyper, De Witte, & Vansteenkiste, 2010 ).

Morgeson and Humphrey ( 2006 ) aimed to integrate the various job characteristics that have been examined in the literature and encouraged job-design scholars not only to focus on task characteristics, such as autonomy and variety, and social characteristics, such as social support and interdependence, but also to pick up on the work context and consider ergonomics, equipment use, and work conditions. This call aligns with the observations of Campion and Thayer ( 1985 ). They noticed that job-design scholars seem to have specialized in either the biological (e.g., concern with noise and lifting), the ergonomic (e.g., lighting, information input), the motivational (e.g., autonomy, variety), or the mechanistic (e.g., specialization, simplification) job characteristics, while Taylor, for example, considered each of these aspects in designing jobs. The more integrative view may be more beneficial, because motivational job characteristics may also have an impact on biological functioning (e.g., heart disease), and the best results may be achieved when ergonomic and motivational factors are jointly considered. For example, Das, Shikdar, and Winters ( 2007 ) found that drill press operators who had the most ergonomic tools and received training were more satisfied and performed better than their counterparts who also could use the ergonomic tools but didn’t receive any training.

New developments in the job-design literature also focused on the relations between the job characteristics and outcomes. The Demand-Induced Strain Compensation model (DISC model; de Jonge & Dormann, 2003 ), for example, further refined job-design theory by qualifying the interaction between job demands and job resources. Specifically, the DISC model assumes that job resources have more potential to buffer the negative effect of job demands on employee well-being when the demands, resources, and outcomes are all physical, cognitive, or emotional. That is, emotional resources, such as social support, may best buffer the impact of emotional demands on emotional stability (Van de Ven, De Jonge, & Vlerick, 2014 ).

More profound changes in job-design theory have been launched. For example, building on the notions of role conflict and role ambiguity (Kahn et al., 1964 ), Ilgen and Hollenbeck ( 1992 ) argued for the study of work roles, which are generally broader than people’s prescribed jobs because they also include emergent and self-imitated tasks. The focus on work roles led to a flourishing literature on role breadth self-efficacy (Parker, 1998 ), personal initiative (Frese, Garst, & Fay, 2007 ), and proactive work behavior (Parker, Williams, & Turner, 2006 ), with the argument being that work design is an especially important facilitator of these outcomes.

The relational perspective of Grant and colleagues is also a novel extension (e.g., Grant, 2007 ). In this approach, a powerful way to design work is to ensure that employees are connected with those that benefit from the work. Such an approach enhances task significance, and thereby promotes greater prosocial motivation amongst employees, which in turn benefits employee performance (for a review, see Grant & Parker, 2009 ).

Another development has been to recognize the role of work design in promoting learning. As Parker ( 2014 , p. 671) argued: “Motivational theories of work design have dominated psychological approaches to work design. However, we need to expand the criterion space beyond motivation, not just by adding extra dependent variables to empirical studies but by exploring when, why, and how work design can help to achieve different purposes.” Parker outlined existing theory and research that suggest work design might be a powerful—yet currently rather neglected—intervention for promoting learning outcomes, such as the accelerated acquisition of expert knowledge, as well as for promoting developmental outcomes over the lifespan (such as the development of cognitive complexity, or even moral development).

Nevertheless, despite, or perhaps because of, the different perspectives, the current job-design literature can be fragmented, with different job-design models offering insights to different parts of the puzzle but not necessarily the whole puzzle. To move the job-design literature forward, a more synthesized mental model of the literature has been developed that describes what can already be considered established knowledge and that highlights fruitful ways forward.

The Integrative Work Design Model

Figure 1. The Integrated Work Design (IWD) Model.

Building from the job-design models featured in the literature and the principles they put forward, an integrative work design model can be developed. The model may stimulate scholars to think broadly when studying job design and to develop new areas for research. It may equally assist managers to consider various aspects of people’s jobs when assessing the adequateness of the jobs they design. The model includes job characteristics as antecedents, and their possible relations with employee outcomes, including employee well-being, cognitions, attitudes, and behaviors. Finally, personal characteristics are taken into account as intervening variables in these relationships. The core aspects of the integrative work design (IWD) model are outlined in Figure 1 .

Job Characteristics as Antecedents

In keeping with the approach of the JD-R, the differentiation between job resources and job demands is maintained as a valuable framework for grouping job characteristics. This approach is not without criticism (Van den Broeck et al., 2013 ). For example, not all job characteristics can be easily classified as either a job demand or a job resource (e.g., job security could be a resource, while job insecurity could be a demand). However, within the IWD model it is maintained that various positive and negative events are not simply opposite ends of the spectrum (e.g., the absence of aggressive or troublesome patients doesn’t necessarily turn patient contacts into positive experiences; Hakanen, Bakker, & Demerouti, 2005 ). The difference between positive and negative reflects the universal differentiation between the positive and the negative, which is rooted in our neurophysiology and how we appraise each encounter with the environment (Barrett, Mesquita, Ochsner, & Gross, 2007 ). Negatives typically loom larger than positives and have a stronger impact on negative aspects of employee functioning, while positive aspects are more predictive of positive outcomes (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001 ). This suggests that having a mindset that looks at both job demands and job resources allows scholars and managers alike to take a balanced perspective on the beneficial and detrimental characteristics of a job.

Although Warr, as well as the JD-R model, start from the assumption that several job characteristics may have an impact on employee functioning, by far the most empirical attention has been paid to the restricted list of job characteristics proposed by Karasek: autonomy, workload, and social support (Humphrey et al., 2007 ), and/or the five job characteristics covered in the JCM. To overcome this issue, a broader view on job design seems necessary. People may be inspired by new developments in the job-design literature differentiating between job challenges (e.g., responsibility) and job hindrances (e.g., red tape), as was done in the development of a model including job hindrances, challenges, and resources (Crawford, Lepine, & Rich, 2010 ; Van den Broeck et al., 2010 ). Second, the classification by Campion and Thayer ( 1985 , 1988 ) may be a source of inspiration to also include job characteristics related to human factors (e.g., equipment) and biological factors (e.g., noise, temperature), along with the mechanical (e.g., repetition) and motivational (e.g., promotion, task significance) job characteristics. Job-design scholars may consider the inclusion of context-specific job-specific hindrances, challenges, and resources, such as student aggression for teachers, number and duration of interventions for firefighters, or having contact with the patients’ families for nurses (for an overview, see Van den Broeck et al., 2013 ).

The study of specific and general job characteristics may also take into account recent developments in the labor market, such as the digital revolution, and the changes in demographics. Few studies have included the consequences of these changes in the study of contemporary jobs, although they have caused dramatic changes in job design (Cordery & Parker, 2012 ). For example, due to technological advances, jobs have undergone profound changes. While some jobs are disappearing due to automation and digitalization, the remaining jobs—for example, in supporting, maintaining, and repairing technology—have become more analytical and problem-solving in nature. Recent job-design scales therefore include concentration and precision as cognitive or mental demands (Van Veldhoven, Prins, Van der Laken, & Dijkstra, 2016 ), which may become extremely relevant for older workers who experience a decline in fluid intelligence (Krings, Sczesny, & Kluge, 2011 ). Due to the technological revolution, employees also become increasingly dependent on technology, leading to techno-stress (Tarafdar, D’Arcy, Turel, & Gupta, 2015 ). The growing body of research on this issue, however, developed outside of I/O psychology, with the leading publications in information and computer sciences. Similarly, the use of digital technology has made it possible to work from home. This increased the degree to which work-related activities intruded into private life and had implications for job design in terms of autonomy and social support (Allen, Golden, & Shockley, 2015 ; Gajendran & Harrison, 2007 ). Research interest on the impact of telework is growing, but—again—mostly as a separate field, rather than as an aspect of job design (Bailey & Kurland, 2002 ).

Finally, and again in line with the JD-R model and the recent work of Morgeson and Humphrey ( 2006 ), job demands and job resources can be found at the level of one’s task and job in general—relating to the content of work—but also at the level of the social relations at work (e.g., conflict vs. social support), which includes the social support component of the Karasek model and the call for more research on the role of social influences at work (Grant & Parker, 2009 ). Apart from the social aspects, attention could also be paid to job characteristics at the team level. In 2012 , Hollenbeck, Beersma, and Schouten noted that up to 80% of all Fortune organizations rely on teamwork to achieve their goals. Team characteristics, such as interdependence and team autonomy, have an impact on employee task characteristics, such as autonomy and well-being and performance (Langfred, 2007 ; Van Mierlo, Rutte, Vermunt, Kompier, & Doorewaard, 2007 ) and could therefore be taken into account.

Furthermore, scholars may want to go one step further and incorporate aspects at the level of the organization, such as HR-related demands and resources (e.g., strategic impact; De Cooman, Stynen, Van den Broeck, Sels, & De Witte, 2013 ) and organizational climate (e.g., safety climate; Dollard & Bakker, 2010 ). Apart from examining the direct impact of these characteristics on employee functioning, job-design scholars could also examine their interplay, in terms of how organizational and team-level variables influence social and task characteristics, as well as how the different levels may buffer, amplify, or boost each other’s impact (Parker, Van den Broeck, & Holman, 2017 ). Furthermore, scholars could examine the interplay between job characteristics through profile analyses (Van den Broeck, De Cuyper, Luyckx, & De Witte, 2012 ).

The Relations of Job Characteristics with Outcomes

The previous models have outlined that job characteristics may influence employee outcomes in many different ways. While most models assume linear relations, Warr argued for curvilinear relations, where both too little and too much of a job characteristic, such as workload, would lead to lower levels of employee well-being, an assumption that also seems to be implicit in the definition of job demands in the JD-R model. Others have argued that such curvilinear relationships are nothing less than “urban myths,” as they are difficult to establish empirically (Taris, 2006 ). Although the lack of empirical support for curvilinear relations may also be attributable to methodological shortcomings, this challenging statement has encouraged other scholars to argue that not the amount, but the type, of job demands matters for how they relate to employee functioning (Lepine et al., 2005 ; Van den Broeck et al., 2010 ). This approach ties in with the appraisal theory (Folkman & Lazarus, 1985 ), which states that the interpretation of an event as challenging or threatening determines how people react to it. Future studies may follow through on these potential curvilinear or differentiated results.

Moreover, it would be interesting to see more research on the differentiated results of particular job characteristics. While Hackman and Oldham argued that all motivational job characteristics would have an impact on employee motivation, performance, and turnover, other frameworks (e.g., Herzberg’s two-factor theory, Karasek’s job demand control model, the JD-R, and the DISC model) propose that some job characteristics would be more strongly related to particular outcomes. This is in line with the meta-analysis findings that motivational characteristics may, for example, explain more variance in performance than social characteristics, but the latter seemed to be most important in the prediction of turnover intentions (Humphrey et al., 2007 ). Furthermore, this meta-analysis showed that work-scheduling autonomy is less predictive of job satisfaction than decision-making autonomy, which shows that it is worthwhile to examine the different effects of different job characteristics.

Different Outcomes of Job Design

Within the job-design literature, different outcomes of job design have been put to the fore. Whereas Taylor mostly focused on performance, most of the motivational and health-oriented job-design models focused on aspects of employee well-being and attitudes. But none of the existing job-design models does full justice to the rich amount of consequences that have been empirically studied. The immediate, i.e., individual-level, outcomes of job design are here grouped in terms of health and well-being, cognitions and learning, attitudes, and behaviors (Cordery & Parker, 2012 ; Humphrey et al., 2007 ). The IWD model thus goes beyond mere well-being, core task performance, absenteeism, and turnover.

Health and Well-being

First, following through on their importance in job-design models, employee health and well-being have arguably been the most studied outcomes of job design. Meta-analytic results convincingly show that job characteristics like autonomy, feedback, and social support increase employee engagement and prevent employees from feeling anxious, stressed, or burned out (Humphrey et al., 2007 ; Nahrgang et al., 2011 ). In line with these results, many countries developed policies that urge employers to take care of the psychosocial risk factors—including mostly quantitative and qualitative demands, job control, and opportunities for skill development—to prevent these outcomes (Formazin et al., 2014 ).

A policy-oriented focus on the improvement of job design also has the potential to prevent more injuries and somatic health problems related to job design. For example, job characteristics are also important precursors of accidents, injuries, and unsafe behavior (Nahrgang et al., 2011 ), because high job demands and low job resources might cause employees to routinely violate safety rules (Hansez & Chmiel, 2010 ). People working in jobs of low quality also have higher risk of stroke and the development of heart disease (Backé, Seidler, Latza, Rossnagel, & Schumann, 2012 ; Eller et al., 2009 ). Similar results have been found for the experience of low back pain, pain in the shoulders or knees (Bernal et al., 2015 ), or obesity (Fried et al., 2013 ; Kim & Han, 2015 ). Interestingly, these results are mostly reported in journals featuring biomedical and human factors research (Parker et al., 2017 ), leaving these far-reaching consequences of job design relatively unnoticed in I/O psychology.

A considerable body of research established the importance of job design for employee attitudes toward work, such as organizational commitment, job involvement, and job satisfaction (Humphrey et al., 2007 ). Meta-analytic results, for example, show that job demands explain 28% of the variance in job design, while job resources can explain no less than 62% to 85% (Humphrey et al., 2007 ; Nahrgang et al., 2011 ). Results are inconclusive whether high levels of job satisfaction should be attributed primarily to good social relations at work or to the motivational characteristics defined by Hackman and Oldham.

Cognitions and Learning

While much attention has been devoted to health and well-being, research interest in cognitions as outcomes of job design has only been emerging recently and is a promising avenue for the future (Parker, 2014 ). Although more complex jobs may be challenging (Van Veldhoven et al., 2016 ), in their systematic review, Then et al. ( 2014 ) demonstrated that this might also have positive consequences, as high work complexity—together with high job control—has a protective effect against the decline of cognitive functions later in life and dementia. Increasing cognitive demands may thus start a process in which cognitive processes are maintained, if not increased. Similarly, work pressure may reduce daytime intuitive decision making, but enhance analytical thinking (Gordon, Demerouti, Bipp, & Le Blanc, 2015 ) and foster learning (De Witte, Verhofstadt, & Omey, 2007 ), as could be expected based on Karasek’s model and German action theory (Frese & Zapf, 1994 ). For example, Holman et al. ( 2012 ) showed that blue collar workers in a vehicle manufacturer improved their learning strategies when being allotted job control, while solving complex problems. Similar results were found in a diary study (Niessen, Sonnentag, & Friederike, 2012 ), where job resources, such as having meaning on one’s job, allowed employees to maintain focus and explore new information, which then led to employees’ thriving, defined as a combination of learning and high levels of energy.

In the work context, different behaviors are valued, ranging from performance and adaptivity to proactivity (Griffin, Neal, & Parker, 2007 ). Taylor’s primary aim was to design jobs to increase job performance according to the scientific standards he established. Although performance has received less attention throughout the different classic job-design models, it is still considered crucial in job design research. Results are somewhat mixed. While meta-analyses show that a range of job resources (e.g., autonomy, skill or task variety, task significance, feedback) all relate positively to self-rated performance, only autonomy seems to relate to objective performance (Humphrey et al., 2007 ). Motivational and social factors like autonomy, task identity, feedback from the job, and social support are also important predictor of behaviors like absenteeism, while social aspects of work design, such as social support, feedback from others, and interdependence, prove to be most important for turnover intentions (Humphrey et al., 2007 ). Job resources like feedback and intrinsically motivating tasks also predict extra role behaviors, such as altruism, courtesy, conscientiousness, and civic virtue, while job demands like role ambiguity, role conflict, and task routinization are negatively related to these behaviors (Podskaoff, Mackenzie, Paine, & Bachrach, 2000 ).

Job design also affects adaptivity, or the degree employees cope well with the ongoing change and adversities in organizations (Griffin et al., 2007 ). For example, sportsmanship, or tolerating work-related inconveniences without complaining, is fostered when employees find their jobs inherently satisfying and receive feedback, while role problems likely forestall sportsmanship (Podskaoff et al., 2000 ).

More than just adapting to the rapid changes and the insecurity characterizing the contemporary labor market, employees are also required to proactively anticipate and act upon potential future opportunities (Griffin et al., 2007 ). Job design had not always been considered essential for employee proactivity (Anderson, De Dreu, & Nijstad, 2004 ), but recent views see job design—and most importantly autonomy and social support—as an important antecedent (Parker et al., 2006 ), which is confirmed by meta-analytic results (Tornau & Frese, 2013 ). Employees in enriched jobs are more inclined to be proactive than employees in jobs characterized by routinization and formalization (Marinova, Peng, Lorinkova, Van Dyne, & Chiaburu, 2015 ).

Job design is also an important moderator for proactivity, allowing proactive motivations to materialize in proactive behavior (Parker, Bindl, & Strauss, 2010 ). Meta-analyses, for example, suggest that employees don’t need autonomy to generate ideas, but require autonomy for idea implementation (Hammond, Neff, Farr, Schwall, & Zhao, 2011 ). As for job demands, the relations may be complex: while uncertainty relates negatively to feedback seeking (Anseel, Beatty, Shen, Lievens, & Sackett, 2015 ), job demands like complexity associate positively with proactive innovation (Hammond et al., 2011 ). Similar results are found at the within-person level. For example, civil servants are more likely to proactively try to improve procedures and introduce new ways of working when they are challenged by time pressure and situational constraints (Fritz & Sonnentag, 2007 ). Proactive behavior may also become a challenge in itself, because one has to plan his actions and invest additional hours or effort in the proactive behavior (Podsakoff, Podsakoff, MacKenzie, Maynes, & Spoelma, 2014 ). Job characteristics like job insecurity may also lead to counterproductive behaviors toward the organization (Van den Broeck et al., 2014 ) and also toward other employees. For example, Van den Broeck, Baillien, and De Witte ( 2011 ) found that job demands like workload are a risk factor for bullying behavior, while job resources like autonomy and supervisory support seem to reduce this risk. However, employees having both high job demands and high job resources were most at risk of becoming bullies at work.

Employee health and well-being, cognitions, attitudes, and behavior are treated as independent elements in the IWD model. They are, however, most likely to influence each other. High levels of well-being have, for example, been shown to relate to commitment and behavioral outcomes (Nahrgang et al., 2011 ). Moreover, thus far, mostly short-term outcomes at the level of the employee were mentioned, leaving outcomes that evolve only over time or develop at the organizational unexplored. Job design is, however, also related to several such outcomes, potentially through its impact on well-being, cognitions, attitudes, and behaviors. For example, job design also has an effect on one’s self-definition (Parker, Wall, & Jackson, 2016 ) and careers (Fried, Grant, Levi, Hadani, & Slowik, 2007 ). Longitudinal studies among more than 2,000 employees show that having high job demands and few opportunities for skill development or social support cause employees to retire early, even above and beyond their impact on mental and physical health (de Wind et al., 2014 ; de Wind, Geuskens, Ybema, Bongers, & van der Beek, 2015 ). Job design also leads to the financial success of the organization. High levels of job resources during one’s shift, for example, increase the financial returns in the fast food industry, as they contributed to the work engagement of the employees (Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2009 ). High job resources equally increase the service climate within the hospitality sector, which then associates with customer loyalty (Salanova, Agut, & Peiró, 2005 ). These outcomes not only may be caused by job characteristics, but also feed into job characteristics (i.e., they have reciprocal relationships), and they may be dependent on employees’ personal characteristics.

Personal Characteristics

Several of the job-characteristics models have considered the role of personal characteristics within job design. Rightly so, as employee functioning is likely to be a function of both situation (i.e., to be job related) and person factors. Most attention with regard to the role of person factors has been paid to personal resources, which are defined as highly valued aspects, relating to resilience and contributing to individuals’ potential to successfully control and influence the environment (Hobfoll, Johnson, Ennis, & Jackson, 2003 ).

Several personal characteristics have been considered personal resources. Within the JCM, for example, growth-need strength can be seen as a personal resource, as are the critical psychological states of meaning, knowledge of results, and responsibility. Within Warr’s vitamin model, employees’ values are considered essential to how employees respond to certain contexts, while a host of personal resources have been studied in the realm of the JD-R model, ranging from hope and optimism to the core self-evaluations of self-esteem, generalized self-efficacy, emotional stability, and locus of control (Van den Broeck et al., 2013 ).

In our view, personal resources may play at least three different roles in the job-design literature. First, they may moderate the impact of job characteristics on employee functioning (i.e., job resources as moderators). Conservation of resources Theory (COR), for example, assumes that having resources allows people to cope with demanding circumstances, so that personal resources may buffer the negative consequences (Hobfoll, 1989 ). In line with this view, employees endowed with self-esteem and optimism were found to experience less psychological distress when confronted with job demands like time pressure (Mäkangas & Kinnunen, 2003 ), while customer orientation buffers the association between job demands and burnout (Babakus, Yavas, & Ashill, 2009 ).

In addition to the buffering effect, job resources can also amplify the positive effects of resourceful job characteristics, as was also mentioned by Hackman and Oldham, as well as by Warr. Again following COR, employees holding high levels of personal resources in a resourceful environment may build resource caravans, which may then lead to low levels of stress and high performance (Hobfoll, 2002 ). More specifically, personal resources may boost the impact of job resources, because a fit between the personal resources and the job characteristics causes employees to pay more attention to the availability of the job characteristics, but also because they have more adaptive ways to act upon the job characteristics (Kristof-brown, Zimmerman, & Johnson, 2005 ). For example, employees who aim to develop themselves see more opportunities for development and may make better use of such opportunities, which then increases their well-being (Van den Broeck, Schreurs, Guenter, & van Emmerik, 2015 ; Van den Broeck, Van Ruysseveldt, Smulders, & De Witte, 2011 ).

Apart from the relatively straightforward moderating effects, personal resources may also influence the impact of job characteristics in a more complex way. Because of their boosting effect on job resources, personal resources may also enable employees to use their job resources better to offset the negative effects of job demands, leading to a three-way interaction between personal and job resources and job demands. For example, employees having an internal locus of control may make optimal use of the job control at their disposal to attenuate the effects of daily stressors, while for employees having an external locus of control, job control may not be put in practice and in some research actually predicted poorer well-being and health (Meier, Semmer, Elfering, & Jacobshagen, 2008 ; for a similar study, see Parker & Sprigg, 1999 ). Considering the curvilinear effects, personal resources may affect the tipping point at which increases in job characteristics stop being positive or even start to have negative consequences, so that employees with a high need for security may appreciate higher levels of role clarity than employees who are more adventurous (Warr, 1987 ). Overall, personal resources may allow employees to make better use of job resources, while dealing better with job demands, leading to different outcomes for employees working in the same jobs.

A second role of personal resources may be in explaining the relationships between job characteristics and their outcomes (i.e., personal resources as mediators). Hackman and Oldham suggested that the environment could influence employees’ psychological states, which then explains why job characteristics affect, for example, employee motivation and performance. According to JD-R scholars, this is true not only for the critical psychological states, but also for various personal resources. An important addition to the assumption is that not only may job resources add to psychological states, but also job demands can be assumed to take away employees’ energy and hinder their goal orientation, thereby decreasing employees’ personal resources (Hobfoll, 1989 ). In support of this, research shows that the same psychological resources may indeed explain the effects of both motivational and demanding job characteristics: While job resources lead to high levels of engagement and low levels of burnout through increased satisfaction of SDT’s basic needs for autonomy, competence, and relatedness, job demands hinder the experience of basic need satisfaction and therefore lead to higher levels of burnout and more counterproductive behavior (Van den Broeck, Suela, Vander Elst, Fischmann, Iliescu, & De Witte, 2014 ; Van den Broeck, Vansteenkiste, De Witte, & Lens, 2008 ). In exploring the mediating role of personal resources, future research may answer the call for more attention to the processes underpinning the relations between job characteristics and outcomes (Parker et al., 2001 ).

Finally, personal resources may serve as antecedents of job demands and job resources. This may be because managers provide more favorable job conditions to highly motivated employees (Rousseau, 2001 ), because such employees craft their job to include more motivational and less demanding characteristics (Wrzesniewski & Dutton, 2001 ), or because resourceful employees appraise their job situation as more benign or challenging and less threatening (Folkman & Lazarus, 1985 ).

Notably, thus far, the job-design literature focuses on relatively changeable and positive personal characteristics. However, more stable personal characteristics may also play a role, as they may shape employees’ directedness to particular goals and thereby equally serve as antecedents and moderators of job characteristics (Barrick, Mount, & Li, 2013 ). The personality trait of neuroticism may, for example, cause employees to report higher job demands, while extroverted employees experience more job resources (Bakker et al., 2010 ). Moreover, recently, it was also shown that job characteristics may change employees’ personality (Wu, 2016 ). A final consideration is that particular personal aspects may also make employees more vulnerable to the negative impact of job demands or make it more difficult to benefit from positive aspects.

The job-design literature has a long history and continues to grow. Although some job-design scholars have argued there is nothing left to know about job design, new aspects are still unraveling and many aspects of the nature of job characteristics and their relationship with various outcomes, as well as the role of personal characteristics, remain underexplored. Because job design strongly associates with a host of outcomes and various jobs are still of low quality, job design still deserves scholarly and managerial attention. The integrative work design (IWD) model may assist in the process.

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

Identify potential and new customers
Understand existing customers
Set pragmatic goals
Develop productive market strategies
Address business challenges
Put together a business expansion plan
Identify new business opportunities
Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
The analysis is based on logical reasoning and involves both inductive and deductive methods.
Real-time data and knowledge is derived from actual observations in natural settings.
There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
It creates a path for generating new questions. Existing data helps create more research opportunities.
It is analytical and uses all the available data so that there is no ambiguity in inference.
Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

One-to-one Interview
Focus Groups
Ethnographic studies
Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

Survey research
Descriptive research
Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

Valid – founded, logical, rigorous, and impartial.
Accurate – free of errors and including required details.
Reliable – other people who investigate in the same way can produce similar results.
Timely – current and collected within an appropriate time frame.
Complete – includes all the data you need to support your business decisions.

Gather insights

Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
Keep track of the frequency with which each of the main findings appears.
Make a list of your findings from the most common to the least common.
Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
Prepare conclusions and recommendations about your study.
Act on your strategies
Look for gaps in the information, and consider doing additional inquiry if necessary
Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Psychology: Research and Review

Open access
Published: 04 January 2021

What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire

Anne Pignault ORCID: orcid.org/0000-0001-7946-3793 1 &
Claude Houssemand 2

Psicologia: Reflexão e Crítica volume 34 , Article number: 2 ( 2021 ) Cite this article

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Considering the recent and current evolution of work and the work context, the meaning of work is becoming an increasingly relevant topic in research in the social sciences and humanities, particularly in psychology. In order to understand and measure what contributes to the meaning of work, Morin constructed a 30-item questionnaire that has become predominant and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, it has been validated only in part.

Meaning of work questionnaire was conducted in French with 366 people (51.3% of women; age: ( M = 39.11, SD = 11.25); 99.2% of whom were employed with the remainder retired). Three sets of statistical analyses were run on the data. Exploratory and confirmatory factor analysis were conducted on independent samples.

The questionnaire described a five-factor structure. These dimensions (Success and Recognition at work and of work, α = .90; Usefulness, α = .88; Respect for work, α = .88; Value from and through work, α = .83; Remuneration, α = .85) are all attached to a general second-order latent meaning of work factor (α = .96).

Conclusions

Validation of the scale, and implications for health in the workplace and career counseling practices, are discussed.

Introduction

Since the end of the 1980s, many studies have been conducted to explore the meaning of work, particularly in psychology (Rosso, Dekas, & Wrzesniewski, 2010 ). A review of the bibliographical data in PsychInfo shows that between 1974 and 2006, 183 studies addressed this topic (Morin, 2006 ). This scholarly interest was primarily triggered by Sverko and Vizek-Vidovic’s ( 1995 ) article, which identified the approaches and models that have been used and their main results.

Whereas early studies on the meaning of work introduced the concept and its theoretical underpinnings (e.g., Harpaz, 1986 ; Harpaz & Fu, 2002 ; Morin, 2003 ; MOW International Research team, 1987 ), later research tried to connect this aspect of work with other psychological dimensions or individual perceptions of the work context (e.g., Harpaz & Meshoulam, 2010 ; Morin, 2008 ; Morin, Archambault, & Giroux, 2001 ; Rosso et al., 2010 ; Wrzesniewski, Dutton, & Debebe, 2003 ). Nevertheless, scholars, particularly those in organizational and occupational psychology, soon found it difficult to precisely identify the meaning of work because it changes in accordance with the conceptualizations of different researchers, the theoretical models used to describe it, and the tools that are available to measure it for individuals and for groups.

This article first seeks to clarify the concept of the meaning of work (definitions and models) before bringing up certain problems involved in its measurement and the diversity in how the concept has been used. Then the paper focuses on a particular meaning of work measurement tool developed in Canada, which is now widely used in French-speaking countries. At the beginning of the twenty-first century, Morin et al. ( 2001 ) developed a 30-item questionnaire to better determine the dimensions that give meaning to a person’s work. The statistical analyses needed to determine the reliability and validity of Morin et al.’s meaning of work questionnaire have never been completed. Indeed, some changes were made to the initial scale, and the analyses only based on homogenous samples of workers in different professional sectors. Thus and even though the meaning of work scale is used quite frequently, both researchers and practitioners have been unsure about whether or not to trust its results. The main objective of the present study was thus to provide a psychometric validation of Morin et al.’s meaning of work scale and to uncover its latent psychological structure.

Meaning of work: from definition to measurement

Meaning of work: what is it.

As many scholars have found, the concept of the meaning of work is not easy to define (e.g., Rosso et al., 2010 ). In terms of theory, it has been defined differently in different academic fields. In psychology, it refers to an individual’s interpretations of his/her actual experiences and interactions at work (Ros, Schwartz, & Surkiss, 1999 ). From a sociological point of view, it involves assessing meaning in reference to a system of values (Rosso et al., 2010 ). In this case, its definition depends on cultural or social differences, which make explaining this concept even more complex (e.g., Morse & Weiss, 1955 ; MOW International Research team, 1987 ; Steers & Porter, 1979 ; Sverko & Vizek-Vidovic, 1995 ).

At a conceptual level, the meaning of work has been defined in three different ways (Morin, 2003 ). First, it can refer to the meaning of work attached to an individual’s representations of work and the values he/she attributes to that work (Morse & Weiss, 1955 ; MOW International Research team, 1987 ). Second, it can refer to a personal preference for work as defined by the intentions that guide personal action (Super & Sverko, 1995 ). Third, it can be understood as consistency between oneself and one’s work, similar to a balance in one’s personal relationship with work (Morin & Cherré, 2004 ).

With respect to terms, some differences exist because the meaning of work is considered an individual’s interpretation of what work means or of the role it plays in one’s life (Pratt & Ashforth, 2003 ). Yet this individual perception is also influenced by the environment and the social context (Wrzesniewski et al., 2003 ). The psychological literature on the meaning of work has primarily examined its positive aspects, even though work experiences can be negative or neutral. This partiality about the nature of the meaning of work in research has led to some confusion in the literature between this concept and that of meaningful , which refers to the extent to which work has personal significance (a quantity) and seems to depend on positive elements (Steger, Dik, & Duffy, 2012 ). A clearer demarcation should be made between these terms in order to specify the exact sense of the meaning of work: “This would reserve ‘meaning’ for instances in which authors are referring to what work signifies (the type of meaning), rather than the amount of significance attached to the work” (Rosso et al., 2010 , p. 95).

The original idea of the meaning of work refers to the central importance of work for people, beyond the simple behavioral activity through which it occurs. Drawing on various historical references, certain authors present work as an essential driver of human life; these scholars then seek to understand how work is fundamental (e.g., Morin, 2006 ; Sverko & Vizek-Vidovic, 1995 ). The concept of the meaning of work is connected to the centrality of work for the individual and consequently fulfills four different important functions: economic (to earn a living), social (to interact with others), prestige (social position), and psychological (identity and recognition). In this view, the centrality of work is based on an ensemble of personal and social values that differ between individuals as well as between cultures, economic climates, and occupations (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ).

Meaning of work: which theoretical model?

The first theoretical model for the meaning of work was based on research in the MOW project (MOW International Research team, 1987 ), considered the “most empirically rigorous research ever undertaken to understand, both within and between countries, the meanings people attach to their work roles” (Brief, 1991 , p. 176). This view suggests that the meaning of work is based on five principal theoretical dimensions: work centrality as a life role, societal norms regarding work, valued work outcomes, importance of work goals, and work-role identification. A series of studies on this theory was conducted in Israel (Harpaz, 1986 ; Harpaz & Fu, 2002 ; Harpaz & Meshoulam, 2010 ), complementing the work of the MOW project (MOW International Research team, 1987 ). Harpaz ( 1986 ) empirically identified six latent factors that represent the meaning of work: work centrality, entitlement norm, obligation norm, economic orientation, interpersonal relations, and expressive orientation.

Another theoretical model on the importance of work in a person’s life was created by Sverko in 1989 . This approach takes into account the interactions among certain work values (the importance of these values and the perception of possible achievements through work), which depend on a process of socialization. The ensemble is then moderated by an individual’s personal experiences with work. In the same vein, Rosso et al. ( 2010 ) tried to create an exhaustive model of the sources that influence the meaning of work. This model is built around two major dimensions: Self-Others (individual vs. other individuals, groups, collectives, organizations, and higher powers) and Agency-Communion (the drives to differentiate, separate, assert, expand, master, and create vs. the drives to contact, attach, connect, and unite). This theoretical framework describes four major pathways to the meaning of work: individuation (autonomy, competence, and self-esteem), contribution (perceived impact, significance, interconnection, and self-abnegation), self-connection (self-concordance, identity affirmation, and personal engagement), and unification (value systems, social identification, and connectedness).

Lastly, a more recent model (Lips-Wiersma & Wright, 2012 ) converges with the theory suggested by Rosso et al. ( 2010 ) but distinguishes two dimensions: Self-Others versus Being-Doing. This model describes four pathways to meaningful work: developing the inner self, unity with others, service to others, and expressing one’s full potential.

Without claiming to be exhaustive, this brief presentation of the theoretical models of the meaning of work underscores the difficulty in precisely defining this concept, the diversity of possible approaches to identifying its contours, and therefore implicitly addresses the various tools designed to measure it.

Measuring the meaning of work

Various methodologies have been used to better determine the concept of the meaning of work and to grasp what it involves in practice. The tools examined below have been chosen because of their different methodological approaches.

One of the first kinds of measurements was developed by the international MOW project (MOW International Research team, 1987 ). In this study, England and Harpaz ( 1990 ) and Ruiz-Quintanilla and England ( 1994 ) used 14 defining elements to assess agreement on the perception of work of 11 different sample groups questioned between 1989 and 1992. These elements, resulting from the definition of work given by the MOW project and studied by applying multivariate analyses and textual content analyses ( When do you consider an activity as working ? Choose four statements from the list below which best define when an activity is “ working,” MOW International Research team, 1987 ), can be grouped into four distinct heuristic categories (Table 1 ).

Similarly, England ( 1991 ) studied changes in the meaning of work in the USA between 1982 and 1989. He used four different methodological approaches to the meaning of work: societal norms about work, importance of work goals, work centrality, and definition of work by the labor force. In the wake of these studies, others developed scales to measure the centrality of work in people’s lives, either for the general population (e.g., Warr, 2008 ) or for specific subpopulations such as unemployed people, on the basis of a rather similar conceptualization of the meaning of work (McKee-Ryan, Song, Wanberg, & Kinicki, 2005 ; Wanberg, 2012 ).

Finally, Wrzesniewski, McCauley, Rozin, and Schwartz ( 1997 ) developed a rather unusual method for evaluating people’s relationships with their work. Although not directly connected to research on the meaning of work, this study and the questionnaire they used ( University of Pennsylvania Work-Life Questionnaire ) addressed some of the same concepts. Above all, they employed the concepts in a very particular way that combined psychological scales, scenarios, and sociodemographic questions. Through these scenarios (Table 2 ) and the extent to which the respondents felt like the described characters, their relationship to work was described as either a Job, a Career, or a Calling.

This presentation of certain tools for measuring the meaning of work reveals a variety of methodological approaches. Nevertheless, whereas certain methods have adopted a rather traditional psychological approach, others are often difficult to use for various reasons such as their psychometrics (e.g., the use of only one item to measure a concept; England, 1991 ; Wrzesniewski et al., 1997 ) or for practical reasons (e.g., the participants were asked questions that pertained not only to their individual assessment of work but also to various other parts of their lives; England, 1991 ; Warr, 2008 ). This diversity in the possible uses of the meaning of work makes it difficult to select a tool to measure it.

In French-speaking countries (Canada and Europe primarily), the previously mentioned scale created by Morin et al. ( 2001 ) has predominated and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, there has not been a complete validation of the scale (i.e., different forms of the same tool, only the use of exploratory factor analyses, and no similar structures found) that was the motivation for the current study.

The present study

The present article conceives of the meaning of work as representing a certain consistency between what an individual wants out of work and the individual’s perception, lived or imagined, of his/her work. It thus corresponds to the third definition of the meaning of work presented above—consistency between oneself and one's work (Morin & Cherré, 2004 ). This definition is strictly limited to the meaning given to work and the personal significance of this work from the activities that the work implies. Within this conceptual framework, some older studies adopted a slightly different cognitive conception, in which individuals constantly seek a balance between themselves and their environment, and any imbalance triggers a readjustment through which the person attempts to stabilize his/her cognitive state (e.g., Heider, 1946 ; Osgood & Tannenbaum, 1955 ). Here, the meaning of work must be considered a means for maintaining psychological harmony despite the destabilizing events that work might involve. In this view, meaning is viewed as an effect or a product of the activity (Brief & Nord, 1990 ) and not as a permanent or fixed state. It then becomes a result of person-environment fit and falls within the theory of work adjustment (Dawis, Lofquist, & Weiss, 1968 ).

Within this framework, a series of recurring and interdependent studies should be noted (e.g., Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ) because they have attempted to measure the coherence that a person finds in the relation between the person’s self and his/her work and thus implicitly the meaning of that work. Therefore, these studies make it possible to understand the meaning of work in greater detail, meaning that it could be used in practice through a self-evaluation questionnaire. The level of coherence is considered the degree of similarity between the characteristics of work that the person attributes meaning to and the characteristics that he/she perceives in his/her present work (Aronsson, Bejerot, & Häremstam, 1999 ; Morin & Cherré, 2004 ). Based on semi-structured interviews and on older research related to the quality of life at work (Hackman & Oldham, 1976 ; Ketchum & Trist, 1992 ), a model involving 14 characteristics was developed: the usefulness of work, the social contribution of work, rationalization of the tasks, workload, cooperation, salary, the use of skills, learning opportunities, autonomy, responsibilities, rectitude of social and organizational practices, the spirit of service, working conditions, and, finally, recognition and appreciation (Morin, 2006 ; Morin & Cherré, 1999 ). Then, based on this model, a 30-item questionnaire was developed to offer more precise descriptions of these dimensions. Table 3 presents the items, which were designed and administered to the participants in French.

Some studies for structurally validating this questionnaire have been conducted over the years (e.g., Morin, 2003 , 2006 , 2008 ; Morin & Cherré, 2004 ). However, their results were not very precise or comparable. For example, the number of latent factors in the meaning of work scale structure varied (e.g., six or eight factors: Morin, 2003 ; six factors: Morin, 2006 ; Morin & Cherré, 2004 ), the sample groups were not completely comparable (especially with respect to occupations), and finally, items were added or removed or their phrasing was changed (e.g., 30 and 33 items: Morin, 2003 ; 30 items: Morin, 2006 ; 26 items: Morin, 2008 ). Yet the most prominent methodological problem was that only exploratory analyses (most often a principal component analysis with varimax rotation) had been applied. This scale was entirely relevant from a theoretical point of view because it offered a more specific definition of the meaning of work than other scales and, mainly, because some subdimensions appeared to be linked with anxiety, depression, irritability, cognitive problems, psychological distress, and subjective well-being (Morin et al., 2001 ). It was also relevant from a practical point of view because it was short and did not take much time to complete. However, its use was questionable because it had never been validated psychometrically, and a consistent latent psychological structure had not been identified across studies.

As an example, two models representing the structure of the 30-item scale are presented in Table 3 (Morin et al., 2001 ; Morin, 2003 , for the first model; Morin & Cherré, 2004 , for the second one). This table presents the items, the meaning of work dimensions they are theoretically related to, and the solution from the principal component analysis in each study. These analyses revealed that the empirical and theoretical structures of this tool are not stable and that the latent structure suffers from the insufficient use of statistical methods. In particular, there was an important difference found between the two models in previous studies (Morin et al., 2001 ; Morin & Cherré, 2004 ). Only the “usefulness of work” dimension was found to be identical, comprised of the same items in both models. Other dimensions had a maximum of only three items in common. Therefore, it is very difficult to utilize this tool both in practice and diagnostically, and complementary studies must be conducted. Even though there are techniques for replicating explanatory analyses (e.g., Osborne, 2012 ), such techniques could not be used here because not all the necessary information was given (e.g., all factor loadings, communalities). This is why collecting new data appeared to be the only way to analyze the scale.

More recently, two studies (which applied a new 25-item meaningful work questionnaire ) were developed on the basis of Morin’s scale (Bendassolli & Borges-Andrade, 2013 ; Bendassolli, Borges-Andrade, Coelho Alves, & de Lucena Torres, 2015 ). Even though the concepts of the “meaning of work” and “meaningful work” are close, the two scales are formally and theoretically different and do not evaluate the same construct.

The purpose of the present study was thus to determine the structure of original Morin’s 30-item scale (Morin, 2003 ; Morin & Cherré, 2004 ) by using an exploratory approach as well as confirmatory statistical methods (structural equation modeling) and in so doing, to address the lacunae in previous research discussed above. The end goal was thus to identify the structure of the scale statistically so that it can be used empirically in both academic and professional fields. Indeed, as mentioned previously, this scale is of particular interest to researchers because its design is not limited to measuring a general meaning of work for each individual; it can also be used to evaluate discrepancies or a convergence between a person’s own personal meaning of work and a specific work context (e.g., tasks, relations with others, autonomy). Finally, and with respect to previous results, the scale could be a potential predictor of professional well-being and psychological distress at work (Morin et al., 2001 ).

Participants

The questionnaire was conducted with 366 people who were mainly resident in Paris and the surrounding regions in France. The gender distribution was almost equal; 51.3% of the respondents were women. The respondents’ ages ranged from 19 to 76 years ( M = 39.11, SD = 11.25). The large majority of people were employed (99.2%). Twenty percent worked in medical and paramedical fields, 26% in retail and sales, and 17% in human resources (the other respondents worked in education, law, communication, reception, banking, and transportation). Seventy percent had fewer than 10 years of seniority in their current job ( M = 8.64, SD = 9.65). Only three people were retired (0.8%).

Morin’s 30-item meaning of work questionnaire (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ) along with sociodemographic questions (i.e., sex, age, job activities, and seniority at work) were conducted in French through an online platform. Answers to the meaning of work questionnaire were given on a 5-point Likert scale ranging from 1 ( strongly disagree ) to 5 ( strongly agree ).

Participants were recruited through various professional online social networks. This method does not provide for a true random sample but, owing to it resulting in a potentially larger range of respondents, it enlarges the heterogeneousness of the participants, even if it cannot ensure representativeness (Barberá & Zeitzoff, 2018 ; Hoblingre Klein, 2018 ). This point seems important because very homogenous samples were used in previous studies, especially with regard to professions.

Participants were volunteers, and were given the option of being able to stop the survey at any time. They received no compensation and no individual feedback. Participants were informed of these conditions before filling out the questionnaire. Oral and informed consent was obtained from all participants. Moreover, the Luxembourg Agency for Research Integrity (LARI on which the researchers in this study depend) specified that according to Code de la santé publique—Article L1123-7, it appears that France does not require research ethics committee [Les Comités de Protection des Personnes (CPP)] approval if the research is non-biomedical, non-interventional, observational, and does not collect personal health information, and thus CNR approval was not required.

Participants had to answer each question in order to submit the questionnaire: If one item was not answered, the respondent was not allowed to proceed to the next question. Thus, the database has no missing data. An introduction presented the subject of the study and its goals and guaranteed the participant’s anonymity. Researchers’ e-mail addresses were given, and participants were informed that they could contact the researchers for more information.

Data analyses

Three sets of statistical analyses were run on the data:

Analysis of the items, using traditional true score theory and item response theory, for verifying the psychometric qualities (using mainly R package “psych”). The main objectives of this part of analysis were to better understand the variability of respondents’ answers, to compute the discriminatory power of items, and to verify the distribution of items by using every classical descriptive indicator (mean, standard-deviation, skewness, and kurtosis), corrected item-total correlations, and functions of responses for distributions.

An exploratory factor analysis (EFA) with an oblimin rotation in order to define the latent structure of the meaning of work questionnaire, performed with the R packages “psych” and “GPArotation”. The structure we retained was based on adequation fits of various solutions (TLI, RMSEA and SRMR, see “List of abbreviations” section at the end of the article), and the use of R package “EFAtools” which helps to determine the adequate number of factors to retain for the EFA solution. Finally, this part of the analysis was concluded using calculations of internal consistency for each factor found in the scale.

A confirmatory factor analysis using the R package Lavaan and based on the results of the EFA, in order to verify that the latent structure revealed in Step c was valid and relevant for this meaning of work scale. The adequation between data and latent structure was appreciated on the basis of CFI, TLI, RMSEA, and SRMR (see “Abbreviations” section).

For step a, the responses of the complete sample were considered. For steps b and c, 183 subjects were selected randomly for each analysis from the total study sample. Thus, two subsamples comprised of completely different participants were used, one for the EFA in step b and one for the CFA in step c.

Because of the ordinal measurement of the responses and its small number of categories (5-point Likert), none of the items can be normally distributed. This point was verified in step a of the analyses. Thus, the data did not meet the necessary assumptions for applying factor analyses with conventional estimators such as maximum likelihood (Li, 2015 ; Lubke & Muthén, 2004 ). Therefore, because the variables were measured on ordinal scales, it was most appropriate to apply the EFA and CFA analyses to the polychoric correlation matrix (Carroll, 1961 ). Then, to reduce the effects of the specific item distributions of the variables used in the factor analyses, a minimum residuals extraction (MINRES; Harman, 1960 ; Jöreskog, 2003 ) was used for the EFA, and a weighted least squares estimator with degrees of freedom adjusted for means and variances (WLSMV) was used for the CFA as recommended psychometric studies (Li, 2015 ; Muthén, 1984 ; Muthén & Kaplan, 1985 ; Muthén & Muthén, 2010 ; Yang, Nay, & Hoyle, 2010 ; Yu, 2002 ).

The size of samples for the different analyses has been taken into consideration. A model structure analysis with 30 observed variables needs a recommended minimum sample of 100 participants for 6 latent variables, and 200 for 5 latent variables (Soper, 2019 ). The samples used in the present research corresponded to these a priori calculations.

Finally, according to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), acceptable and excellent model fits are indicated by CFI and TLI values greater than .90 and .95, respectively, by RMSEA values smaller than .08 (acceptable) and .06 (excellent), respectively, and SRMR values smaller than .08.

Item analyses

The main finding was the limited amount of variability in the answers to each item. Indeed, as Table 4 shows, respondents usually and mainly chose the answers agree and strongly agree , as indicated by the column of cumulated percentages of these response modalities (%). Thus, for all items, the average answer was higher than 4, except for item 11, the median was 4, and skewness and kurtosis indicators confirmed a systematic skewed on the left leptokurtic distribution. This lack of variability in the participants’ responses and the high average scores indicate nearly unanimous agreement with the propositions made about the meaning of work in the questionnaire.

Table 4 also shows that the items had good discriminatory power, expressed by corrected item-total correlations (calculated with all items) which were above .40 for all items. Finally, item analyses were concluded through the application of item response theory (Excel tools using the eirt add in; Valois, Houssemand, Germain, & Belkacem, 2011 ) which confirmed, by analyses of item characteristic curves (taking into account that item response theory models are parametric and assume that the item responses distributions follow a logistic function, Rasch, 1980 ; Streiner, Norman, & Cairney, 2015 , p. 297), the psychometric quality of each item and their link to an identical latent dimension. These different results confirmed the interest in keeping all items of the questionnaire in order to measure the work-meaning construct.

Exploratory analyses of the scale

A five-factor solution was identified. This solution explained 58% of the total variance in the responses of the scale items; the TLI was .885, the RMSEA was .074, and the SRMR was .04. The structure revealed by this analysis was relatively simple (saturation of one main factor for each item; Thurstone, 1947 ), and the communality of each item was high, except for item 11. The solution we retained presented the best adequation fits and the most conceptual explanation concerning the latent factors. Additionally, the “EFAtools” R package confirmed the appropriateness of the chosen solution. Table 5 shows the EFA results, which described a five-factor structure.

Nevertheless, the correlation matrix for the latent factors obtained by the EFA (see Table 6 ) suggested the existence of a general second-order meaning of work factor, because the five factors were significantly correlated each with others. This result could be described as the existence of a general meaning of work factor, which alone would explain 44% of the total variance in the responses.

Internal consistency of latent factors of the scale

The internal consistency of each latent factor, estimated by Cronbach alpha and McDonald omega, was high (above .80) and very high for the entire scale (α = .96 and ω = .97). Thus, for S uccess and Recognition at work and from work ’ s factor ω was .93, for Usefulness ’s factor ω was .92, for Respect ’s factor ω was .91, for Value from and through work ’s factor ω was slightly lower and equal to .85, and finally for Remuneration ’ s factor for which ω was .87.

Confirmatory factor analyses of the scale

In order to improve the questionnaire, we applied a CFA to this five-factor model to improve the model fit and refine the latent dimensions of the questionnaire. We used CFA to (a) determine the relevance of this latent five-factor structure and (b) confirm the relevance of a general second-order meaning-of-work factor. Although this procedure might appear redundant at first glance, it enabled us to select a definitive latent structure in which each item represents only one latent factor (simple structure; Thurstone, 1947 ), whereas the EFA that was computed in the previous step showed that certain items loaded on several factors. The CFA also easily verified the existence of a second-order latent meaning of work factor (the first-order loadings were .894, .920, .873, .892, and .918, respectively). Thus, this CFA was computed to complement the previous analyses by refining the latent model proposed for the questionnaire.

According to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), although the RMSEA value for the five-factor model was somewhat too high, the CFI and TLI values were excellent (χ 2 = 864.72, df = 400, RMSEA = .080, CFI = .989, TLI = .988). Table 7 presents the adequation fits for both solutions: a model with 5 first-order factors (as EFA suggests), and a model with 5 first-order factors and 1 second-order factor.

Figure 1 shows the model after the confirmatory test. This analysis confirmed the existence of a simple structure with five factors for the meaning of work scale and with a general, second-order factor of the meaning of work as suggested by the previous EFA.

Standardized solution of the structural model of the Meaning of Work Scale

The objective of this study was to verify the theoretical and psychometric structure of the meaning of work scale developed by Morin in recent years (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ). This scale has the advantages of being rather short, of proposing a multidimensional structure for the meaning of work, and of making it possible to assess the coherence between the aspects of work that are personally valued and the actual characteristics of the work environment. Thus, it can be used diagnostically or to guide individuals. To establish the structure of this scale, we analyzed deeply the items, and we implemented exploratory and confirmatory factor analyses, which we believe the scale’s authors had not carried out sufficiently. Moreover, we used a broad range of psychometric evaluation methods (traditional true score theory, item response theory, EFA, and structural equation modeling) to test the validity of the scale.

Item analyses confirmed results found in previous studies in which the meaning-of-work scale was administered. The majority of respondents agreed with the proposals of the questionnaire. Thus, this lack of variability is not specific to the present research and its sample (e.g., Morin & Cherré, 2004 ). Nevertheless, this finding can be explained by different reasons (which could be studied by other research) such as social desirability and the importance of work norms in industrial societies, or a lack of control regarding response bias.

The various versions of the latent structure of the scale proposed by the authors were not confirmed by the statistical analyses seen here. It nevertheless appears that this tool for assessing the meaning of work can describe and measure five different dimensions, all attached to a general factor. The first factor (F1), composed of nine items, is a dimension of recognition and success (e.g., item 17: work where your skills are recognized ; item 19: work where your results are recognized ; item 24: work that enables you to achieve the goals that you set for yourself ). It should thus be named Success and Recognition at work and from work and is comparable to dimensions from previous studies (personal success, Morin et al., 2001 ; social influence, Morin & Cherré, 2004 ). The second factor (F2), composed of seven items, is a dimension that represents the usefulness of work for an individual, whether that usefulness is social (e.g., Item 22: work that gives you the opportunity to serve others ) or personal (e.g., Item 28: work that enables you to be fulfilled ). It can be interpreted in terms of the Usefulness of work and generally corresponds to dimensions of the same name in earlier models (Morin, 2003 ; Morin & Cherré, 2004 ), although the definition used here is more precise. The third factor (F3), described by four items, refers to the Respect dimension of work (e.g., Item 5: work that respects human values ) and corresponds in part to the factors highlighted in prior studies (respect and rationalization of work, Morin, 2003 ; Morin & Cherré, 2004 ). The fourth factor (F4), composed of four items, refers to the personal development dimension and Value from and through work (e.g., Item 2: work that enables you to learn or to improve ). It is in some ways similar to autonomy and effectiveness, described by the authors of the scale (Morin, 2003 ; Morin & Cherré, 2004 ). Finally, the fifth and final factor (F5), with six items, highlights the financial and, more important, personal benefits sought or received from work. This includes physical and material safety and the enjoyment of work (e.g., item 14: work you enjoy doing ). This dimension of Remuneration partially converges with the aspects of personal values related to work described in previous research (Morin et al., 2001 ). Although the structure of the scale highlighted here differed from previous studies, some theoretical elements were nevertheless consistent with each other. To be convinced of this, the Table 8 highlights possible overlaps.

A second important result of this study is the highlighting of a second-order factor by the statistical analyses carried out. This latent second-level factor refers to the existence of a general meaning of work dimension. This unitary conception of the meaning of work, subdivided into different linked facets, is not in contradiction with the different theories related to this construct. Thus, Ros et al. ( 1999 ) defined the meaning of work as a personal interpretation of experiences and interaction at work. This view of meaning of work can confer it a unitary functionality for maintaining psychological harmony, despite the destabilizing events that are often a feature of work. It must be considered as a permanent process of work adjustment or work adaptation. In order to be effective, this adjustment needs to remain consistent and to be globally oriented toward the cognitive balance between the reality of work and the meaning attributed to it. Thus, it has to keep a certain coherence which would explain the unitary conception of the meaning of work.

In addition to the purely statistical results of this study, whereas some partial overlap was found between the structural model in this study and structural models from previous work, this paper provides a much-needed updating and improvement of these dimensions, as we examined several theoretical meaning of work models in order to explain them psychologically. Indeed, the dimensions defined here as Success and Recognition , Usefulness , Respect , Value , and Remuneration from the meaning of work scale by Morin et al. ( 2001 ) have some strong similarities to other theoretical models on the meaning of work, even though the authors of the scale referred to these models only briefly. For example, the dimensions work centrality as a life role , societal norms regarding work , valued work outcomes , importance of work goals , and work-role identification (MOW International Research team, 1987 ) concur with the model described in the present study. In the same manner, the model by Rosso et al. ( 2010 ) has some similarities to the present structure, and there is a conceptual correspondence between the five dimensions found here and those from their study ( individuation , contribution , self-connection , and unification ). Finally, Baumeister’s ( 1991 ), Morin and Cherré’s ( 2004 ), and Sommer, Baumeister, and Stillman ( 2012 ) studies presented similar findings on the meaning of important life experiences for individuals; they described four essential needs that make such experiences coherent and reasonable ( purpose , efficacy - control , rectitude , and self - worth ). It is obvious that the parallels noted here were fostered by the conceptual breadth of the dimensions as defined in these models. In future research, much more precise definitions are needed. To do so, it will be essential to continue running analyses to test for construct validity by establishing convergent validity between the dimensions of the various existing meaning of work scales.

It is also interesting to note the proximity between the dimensions described here and those examined in studies on the dimensions that characterize the work context (Pignault & Houssemand, 2016 ) or in Karasek’s ( 1979 ) and Siegrist’s ( 1996 ) well-known models, for example, which determined the impact of work on health, stress, and well-being. These studies were able to clearly show how dimensions related to autonomy, support, remuneration, and esteem either contribute to health or harm it. These dimensions, which give meaning to work in a manner that is similar to the dimensions highlighted in the current study (Recognition, Value, and Remuneration in particular), are also involved in health. Thus, it would be interesting to verify the relations between these dimensions and measures of work health.

Thus, the conceptual dimensions of the meaning of work, as defined by Morin ( 2003 ) and Morin and Cherré ( 1999 ), remained of strong theoretical importance even if, at the empirical level, the scale created on this basis did not correspond exactly. The present study has had the modest merit of showing this interest, and also of proposing a new structure of the facets of this general dimension. One of the major interests of this research can be found in the possible better interpretations that this scale will enable to make. As mentioned above, the Morin’s scale is very frequently used in practice (e.g., in state employment agencies or by Human Resources departments), and the divergent models of previous studies could lead to individual assessments of the meaning of work diverging, depending on the reading grid chosen. Showing that a certain similarity in the structures of the meaning of work exists, and that a general factor of the meaning of work could be considered, the results of the current research can contribute to more precise use of this tool.

At this stage and in conclusion, it may be interesting to consider the reasons for the variations between the structures of the scale highlighted by the different studies. There were obviously the different changes applied to the different versions of the scale, but beyond that, three types of explanation could emerge. At the level of methods, the statistics used by the studies varied greatly, and could explain the variations observed. At the level of the respondents, work remains one of the most important elements of life in our societies. A certain temptation to overvalue its importance and purposes could be at the origin of the broad acceptance of all the proposals of the questionnaire, and the strong interactions between the sub-dimensions. Finally, at the theoretical level, if, as our study showed, a general dimension of meaning of work seems to exist, all the items, all the facets and all the first order factors of the scale, are strongly interrelated at each respective level. As well, small variations in the distribution of responses could lead to variations of the structure.

The principal contribution of this study is undoubtedly the use of confirmatory methods to test the descriptive models that were based on Morin’s scale (Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ). The principal results confirm that the great amount of interest in this scale is not without merit and suggest its validity for use in research, both by practitioners (e.g., career counselors and Human Resources departments) and diagnostically. The results show a tool that assesses a general dimension and five subdimensions of the meaning of work with a 30-item questionnaire that has strong psychometric qualities. Conceptual differences from previous exploratory studies were brought to light, even though there were also certain similarities. Thus, the objectives of this study were met.

Limitations

As with any research, this study also has a certain number of limitations. The first is the sample size used for statistical analyses. Even if the research design respected the general criteria for these kind of analyses (Soper, 2019 ), it will be necessary to repeat the study with larger samples. The second is the cultural and social character of the meaning of work, which was not addressed in this study because the sample was comprised of people working in France. They can thus be compared with those in Morin’s studies ( 2003 ) because of the linguistic proximity (French) of the samples, but differences in the structure of the scale could be due to cultural differences between America and Europe. Nevertheless, other different international populations should be questioned about their conception of the meaning of work in order to measure the impact of cultural and social aspects (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ). In the same vein, a third limitation involves the homogeneity of the respondents’ answers. Indeed, there was quasi-unanimous agreement with all of the items describing work (see Table 4 and previous results, Morin & Cherré, 2004 ). It is worth examining whether this lack of variance results from a work norm that is central and promoted in industrialized countries as it might mask broader interindividual differences. Thus, this study’s protocol should be repeated with other samples from different cultures. Finally, a fourth limitation that was mentioned previously involves the validity of the scale. Concerning the content validity and because some items loaded similarly different factors, it could be interesting to verify the wording content of the items, and potentially modify or replace some of them. The purpose of the present study was not to change the content of the scale but to suggest how future studies could analyze this point. Concerning the construct validity, this first phase of validation needs to be followed by other phases that involve tests of convergent validity between the existing meaning of work scales as well as tests of discriminant validity in order to confirm the existence of the meaning of work construct examined here. In such studies, the centrality of work (Warr, 2008 ; Warr, Cook, & Wall, 1979 ) should be used to confirm the validity of the meaning of work scale. Other differential, individual, and psychological variables related to work (e.g., performance, motivation, well-being) should also be introduced in order to expand the understanding of whether relations exist between the set of psychological concepts involved in work and individuals’ jobs.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author.

Abbreviations

Confirmatory factor analyses

Comparative Fit Index

Exploratory factor analyses

Luxembourg Agency for Research Integrity

Meaning of work

Tucker Lewis Index of factoring reliability

Root mean square error of approximation

Standardized root mean square residual

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Pignault, A., Houssemand, C. What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire. Psicol. Refl. Crít. 34 , 2 (2021). https://doi.org/10.1186/s41155-020-00167-4

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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

Observational
Experimental
Descriptive
Retrospective
Prospective
Cross-sectional
Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

Case series
Surveillance studies

II. Analytical research

Observational studies: cohort, case control and cross- sectional research
Interventional research: quasi-experimental and clinical research
Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

Type-I (α) and type-II (β) error levels
Difference between groups (d-difference) and effect size (ES)
Distribution ratio of groups
Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site www.clinicaltrials.gov for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( www.iku.com ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.

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Meaning of research in English

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He has dedicated his life to scientific research.
He emphasized that all the people taking part in the research were volunteers .
The state of Michigan has endowed three institutes to do research for industry .
I'd like to see the research that these recommendations are founded on.
It took months of painstaking research to write the book .
absorptive capacity
dream something up
modularization
nanotechnology
non-imitative
operational research
think outside the box idiom
think something up
uninventive
study What do you plan on studying at university?
major US She majored in philosophy at Harvard.
cram She's cramming for her history exam.
revise UK I'm revising for tomorrow's test.
review US We're going to review for the test tomorrow night.
research Scientists are researching possible new treatments for cancer.
The amount of time and money being spent on researching this disease is pitiful .
We are researching the reproduction of elephants .
She researched a wide variety of jobs before deciding on law .
He researches heart disease .
The internet has reduced the amount of time it takes to research these subjects .
adjudication
interpretable
interpretive
interpretively
investigate
reinvestigate
reinvestigation
risk assessment
run over/through something
run through something

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customer support

help and advice that a company makes available to customers when they have bought something

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Circular economy introduction

What is a circular economy?

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Circular economy explained

The circular economy is a system where materials never become waste and nature is regenerated. In a circular economy, products and materials are kept in circulation through processes like maintenance, reuse, refurbishment, remanufacture, recycling, and composting. The circular economy tackles climate change and other global challenges, like biodiversity loss, waste, and pollution, by decoupling economic activity from the consumption of finite resources.

The circular economy is based on three principles, driven by design:

Eliminate waste and pollution.

Circulate products and materials (at their highest value)

Regenerate nature

In our current economy, we take materials from the Earth, make products from them, and eventually throw them away as waste – the process is linear. In a circular economy, by contrast, we stop waste being produced in the first place.

We must transform every element of our take-make-waste system: how we manage resources, how we make and use products, and what we do with the materials afterwards. Only then can we create a thriving circular economy that can benefit everyone within the limits of our planet.

A way to transform our system

What will it take to transform our throwaway economy into one where waste is eliminated, resources are circulated, and nature is regenerated?

The circular economy gives us the tools to tackle climate change and biodiversity loss together, while addressing important social needs.

It gives us the power to grow prosperity, jobs, and resilience while cutting greenhouse gas emissions, waste, and pollution.

How the circular economy works

Get the basics or explore the circular economy in detail.

The butterfly diagram: visualising the circular economy

The 'butterfly diagram' shows the continuous flow of materials in a circular economy.

Explaining the circular economy: re-thinking progress

Watch our beginner's guide to understanding how a circular economy works.

Circular economy key ideas

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Circular economy principles

A circular economy is based on three principles, all driven by design.

The first principle of the circular economy is to eliminate waste and pollution....

Circulate products and materials

The second principle of the circular economy is to circulate products and materials at their...

By shifting our economy from linear to circular, we shift the focus from extraction to...

Circular economy examples

See the circular economy in action with these case studies from brands, businesses and policy makers.

Natura Brazil

Working with nature to make food last longer

Apeel is a company that has come up with an innovative way to eliminate single-use shrink wrap plastic packaging on fresh fruit and veg, while at the same time tackling food waste.

Apeel is a layer of edible, plant-based coating applied to fresh products that mimics and enhances the natural defences of fruit and vegetables. This slows down the two main things that cause spoilage – water loss and oxidation.

Case studies

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Ellen MacArthur Foundation FILMS

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Circular Economy: The Big Idea

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What If We Don’t Buy Products and We Buy Service? Circular Economy Explained

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Biodiversity

Biodiversity and the circular economy

This topic area examines how the circular economy can help shape a nature-positive future.

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Fashion and the circular economy

This topic area explores how the circular economy works for the fashion industry.

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Food and the circular economy

This topic area shows how moving to a circular economy for food will help people and nature thrive.

Plastics and the circular economy

This topic area shows how the circular economy can help keep plastic in the economy and out of the...

Government and policy for a circular economy

The circular economy provides a framework which allows governments and cities to realise many of...

Education and learning towards a circular economy

We support circular economy learning across a growing global community of schools, colleges and...

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News and updates from The Ellen MacArthur Foundation

The Ellen MacArthur Foundation works to accelerate the transition to a circular economy. We develop and promote the idea of a circular economy, and work with business, academia, policymakers, and institutions to mobilise systems solutions at scale, globally.

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Female labor force participation

Across the globe, women face inferior income opportunities compared with men. Women are less likely to work for income or actively seek work. The global labor force participation rate for women is just over 50% compared to 80% for men. Women are less likely to work in formal employment and have fewer opportunities for business expansion or career progression. When women do work, they earn less. Emerging evidence from recent household survey data suggests that these gender gaps are heightened due to the COVID-19 pandemic.

Women’s work and GDP

Women’s work is posited to be related to development through the process of economic transformation.

Levels of female labor force participation are high for the poorest economies generally, where agriculture is the dominant sector and women often participate in small-holder agricultural work. Women’s participation in the workforce is lower in middle-income economies which have much smaller shares of agricultural activities. Finally, among high-income economies, female labor force participation is again higher, accompanied by a shift towards a service sector-based economy and higher education levels among women.

This describes the posited U-shaped relationship between development (proxied by GDP per capita) and female labor force participation where women’s work participation is high for the poorest economies, lower for middle income economies, and then rises again among high income economies.

This theory of the U-shape is observed globally across economies of different income levels. But this global picture may be misleading. As more recent studies have found, this pattern does not hold within regions or when looking within a specific economy over time as their income levels rise.

In no region do we observe a U-shape pattern in female participation and GDP per capita over the past three decades.

Structural transformation, declining fertility, and increasing female education in many parts of the world have not resulted in significant increases in women’s participation as was theorized. Rather, rigid historic, economic, and social structures and norms factor into stagnant female labor force participation.

Historical view of women’s participation and GDP

Taking a historical view of female participation and GDP, we ask another question: Do lower income economies today have levels of participation that mirror levels that high-income economies had decades earlier?

The answer is no.

This suggests that the relationship of female labor force participation to GDP for lower-income economies today is different than was the case decades past. This could be driven by numerous factors -- changing social norms, demographics, technology, urbanization, to name a few possible drivers.

Gendered patterns in type of employment

Gender equality is not just about equal access to jobs but also equal access for men and women to good jobs. The type of work that women do can be very different from the type of work that men do. Here we divide work into two broad categories: vulnerable work and wage work.

The Gender gap in vulnerable and wage work by GDP per capita

Vulnerable employment is closely related to GDP per capita. Economies with high rates of vulnerable employment are low-income contexts with a large agricultural sector. In these economies, women tend to make up the higher share of the vulnerably employed. As economy income levels rise, the gender gap also flips, with men being more likely to be in vulnerable work when they have a job than women.

From COVID-19 crisis to recovery

The COVID-19 crisis has exacerbated these gender gaps in employment. Although comprehensive official statistics from labor force surveys are not yet available for all economies, emerging studies have consistently documented that working women are taking a harder hit from the crisis. Different patterns by sector and vulnerable work do not explain this. That is, this result is not driven by the sectors in which women work or their higher rates of vulnerable work—within specific work categories, women fared worse than men in terms of COVID-19 impacts on jobs.

Among other explanations is that women have borne the brunt of the increase in the demand for care work (especially for children). A strong and inclusive recovery will require efforts which address this and other underlying drivers of gender gaps in employment opportunities.

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Are You Taking on Too Many Non-Promotable Tasks?

Linda Babcock,
Brenda Peyser,
Lise Vesterlund,
Laurie Weingart

Learn to weigh your opportunities and say “no” more often.

Though non-promotable tasks (NPTs) are often crucial to an organization’s success, they rarely contribute to an employee’s career progression. Women are not only 48% more likely to volunteer for these jobs, but they are disproportionately assigned them. Next time you’re asked to do an NPT, give yourself some time, and use it to carefully evaluate the consequences of taking on the work.

Consider the “implicit no” of saying yes. When you agree to help another team streamline their workflow, for example, you are implicitly saying no to another, potentially more visible, project or activity.
Weigh the urgency of the task. A task with a short deadline will trump a task with a longer one, no matter how insignificant it is. Taking on too many NPTs with with short time horizons, however, will likely distract you from longer-term initiatives that are more valued by your organization.
Evaluate the indirect benefits of the NPT. Some NPTs are good to take on, as they might help you gain knowledge, develop skills, or connections that you can leverage later on.

Where your work meets your life. See more from Ascend here .

Francesca, a sixth-year associate at a prestigious law firm (and a young woman we know), loved her job. When her boss asked her to help run the summer intern program, she immediately said yes. It was a chance for her to learn about different departments, meet partners, and showcase her organizational skills. She put a lot of time and energy into it. But once performance reviews rolled around, her efforts were never mentioned. Instead, her boss warned Francesca that her billable hours had fallen behind. She was baffled and disappointed — what she thought would benefit her career didn’t seem to matter at all.

Linda Babcock is a professor of economics at Carnegie Mellon University. She is the author of Women Don’t Ask and Ask for It. A behavioral economist, she is the founder and director of PROGRESS, which pursues positive social change for women and girls through education, partnerships, and research.
Brenda Peyser has held leadership positions in the corporate world and academia for over thirty years. Most recently, she was a professor of communications at Carnegie Mellon, where she also served as associate dean of the School of Public Policy and Management and was the founding Executive Director of Carnegie Mellon University Australia
Lise Vesterlund is a professor of economics at the University of Pittsburgh and director of the Pittsburgh Experimental Economics Laboratory. She founded and directs the Behavioral Economic Design Initiative. Published in leading economic journals, her research has been covered by NPR, The New York Times , The Washington Post , ABC, The Economist , The Atlantic , The Guardian , Chicago Tribune , and Forbes .
Laurie R. Weingart is a management professor at Carnegie Mellon University. She has served as CMU’s Interim Provost and Chief Academic Officer and as a Senior Associate Dean and Director of the Accelerate Leadership Center. Her award-winning research has been covered by The New York Times and Business Insider , and published in top management and psychology journals.

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Weight loss

Exercise for weight loss: Calories burned in 1 hour

Being active can help you lose weight and keep it off. Find out how much you need.

Being active is vital to losing weight and keeping it off. When active, the body uses more energy in the form of calories. And burning more calories than you take in leads to weight loss.

To lose weight, most people need to cut the number of calories they eat and move more. This is according to the 2020-2025 Dietary Guidelines for Americans. Most often, that means cutting daily calories by 500 to 750 to lose 1 1/2 pounds (0.7 kilograms) a week.

Other factors might be involved in losing weight. Because of changes to the body over time, you might need to cut calories more as you age to keep losing weight or to stay at the same weight.

Diet or exercise: Does one matter more?

Both are important. Diet affects weight loss more than physical activity does. Physical activity, including exercise, has a stronger effect in keeping weight from coming back after weight loss.

Losing weight with diet alone and without physical activity can make people weaker. This is because of age-related losses in bone density and muscle mass. Adding resistance training and aerobic exercise to a weight-loss program helps prevent the loss of bone and muscle.

These are the exercise guidelines for most healthy adults from the U.S. Department of Health and Human Services:

Aerobic activity. Get at least 150 minutes of moderate aerobic activity a week. Or get 75 minutes of vigorous aerobic activity a week. You also can get an equal mix of the two types.

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Physical Activity Guidelines for Americans. 2nd ed. U.S. Department of Health and Human Services. https://health.gov/paguidelines/second-edition. Accessed March 13, 2024.
Physical activity for a healthy weight. Centers for Disease Control and Prevention. https://www.cdc.gov/healthyweight/physical_activity/index.html. Accessed March 13, 2024.
Ainsworth BE, et al. 2011 compendium of physical activities: A second update of codes and MET values. Medicine & Science in Sports & Exercise. 2011;43:1575.
2020-2025 Dietary Guidelines for Americans. U.S. Department of Health and Human Services and U.S. Department of Agriculture. https://www.dietaryguidelines.gov. Accessed March 13, 2024.
Perreault L, et al. Obesity in adults: Role of physical activity and exercise. https://www.uptodate.com/contents/search. Accessed March 13, 2024.
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Parameters of the best fitting lunar ellipsoid based on GRAIL’s selenoid model

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Published: 27 June 2023
Volume 58 , pages 139–147, ( 2023 )

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Kamilla Cziráki ORCID: orcid.org/0009-0005-4162-4500 1 &
Gábor Timár ORCID: orcid.org/0000-0001-9675-6192 1

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Since the Moon is less flattened than the Earth, most lunar GIS applications use a spherical datum. However, with the renaissance of lunar missions, it seems worthwhile to define an ellipsoid of revolution that better fits the selenoid. The main long-term benefit of this might be to make the lunar adaptation of methods already implemented in terrestrial GNSS and gravimetry easier and somewhat more accurate. In our work, we used the GRGM 1200A Lunar Geoid (Goossens et al. in A global degree and order 1200 model of the lunar gravity field using GRAIL mission data. In: Lunar and planetary science conference, Houston, TX, Abstract #1484, 2016; Lemoine et al. in Geophys Res Lett 41:3382–3389. http://dx.doi.org/10.1002/2014GL060027 , 2014), a 660th degree and order potential surface, developed in the frame of the GRAIL project. Samples were taken from the potential surface along a mesh that represents equal area pieces of the surface, using a Fibonacci sphere. We tried Fibonacci spheres with several numbers of points and also separately examined the effect of rotating the network for a given number of points on the estimated parameters. We estimated the best-fitting rotation ellipsoid’s semi-major axis and flatness data by minimizing the selenoid undulation values at the network points, which were obtained for a = 1,737,576.6 m and f = 0.000305. This parameter pair is already obtained for a 10,000 point grid, while the case of reducing the points of the mesh to 3000 does not cause a deviation in the axis data of more than 10 cm. As expected, the absolute value of the selenoid undulations have decreased compared to the values taken with respect to the spherical basal surface, but significant extreme values still remained as well.

How to Calculate Bouguer Gravity Data in Planetary Studies

Optimal Combination of Satellite and Terrestrial Gravity Data for Regional Geoid Determination Using Stokes-Helmert’s Method, the Auvergne Test Case

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1 Introduction

The theoretical shape of a celestial body is defined by Gauss ( 1828 ) as the level surface of its gravitational field for a specific potential value. On the Earth, this potential surface is aligned with the mean sea level, but on other celestial bodies, such as the Moon, in the absence of sea, a potential value is chosen, compared to which about half of the Moon's surface is higher and half is lower. The resulting W = 2,821,713.3 m 2 * s −2 (Martinec & Pěč 1988 ) is the potential value for the selenoid surface. In addition to the theoretical definition of the shape of a celestial body, it is also essential to have a reference surface (a sphere or rotation ellipsoid in general) that can be described easily, with a few parameters, whose centre lies at the centre of mass of the body and which deviates as little as possible from the potential surface.

In our work, we have created a lunar rotation ellipsoid fitting this criteria using the GRGM 1200A Lunar Geoid from the GRAIL mission. To do this, we sampled the selenoid with a mesh of points representing the same areas and then used a program to find the semi-major axis-semi-minor axis pair that gave the best fit.

Such a rotating ellipsoid has significant practical benefits. It can be used well as a geodetic datum, and thus has a significant role as a base surface for various Geographic Informaton System (GIS) applications. What makes the problem of selenodetical datums relevant now is that lunar exploration is experiencing a renaissance, with not only the US but also the EU (in cooperation with the US) and China planning to send a series of space probes and landers to the Moon. NASA's Artemis program began this year, and its goal is to land on the Moon again, and perform research there. The Chinese Chang'e program has been going on for more than a decade, and its ultimate goal is to land a man on the far side of the Moon. During these missions, more time than ever before is planned to be spent on the lunar surface, during which positioning, and hence the lunar equivalent of Global Navigation Satellite System (GNSS), will play a major role. Since the basic data for GNSS applications is always the ellipsoid of revolution that best approximates the geometric shape on Earth, the simplest lunar application of these requires the parameters of the ellipsoidal approximation of the solenoid.

For LunaNet (NASA 2022 ), which NASA plans to create for GIS applications, the reference surface is currently planned to be the Lunar Reference Frame Standard, a sphere with a radius of 1737.4 km (NASA 2008 ). However, in the future, mainly in the process of the migration of gravimetric applications developed on Earth and the equations of the terrestrial GNSS, it is conceivable that a rotational ellipsoid will replace it.

For the estimation of the ellipsoidal parameters, we used the latest and highest resolution selenoid-model, the GRGM 1200A Lunar Geoid, based on GRAIL (Fig. 1 ). This includes the coefficients of the spherical harmonical functions up to 660th degree and order (Goossens et al. 2016 ; Lemoine et al. 2014 ) and also provides direct data from the far side of the Moon.

GRGM 1200A above the r = 1737.4 km reference sphere displayed in QGIS Source : Goossens et al. ( 2016 ), Lemoine et al. ( 2014 ), QGIS

Besides the selenoid model, sampling points were needed to perform the estimation. The aim was to define points that represent equal areas on a spherical surface.

The points were determined using the Fibonacci sphere (Fig. 2 ). This is a set of points that maps the Fibonacci spiral onto a sphere. To create this, the z-axis was first divided into as many parts as the number of sample points defined. This gives the latitudes, and the longitudes were determined by rotations calculated with the Fibonacci number (Appendix A).

Display of the 3000-point Fibonacci sphere in QGIS

This was then used to sample the selenoid model. The resulting database contains the coordinates of the points and the altitude of the selenoid above a reference surface (a sphere of radius 1737.4 km). From this, the ellipsoidal approximation was made in a Python program. The idea is to find a semi-major axis-semi-minor axis pair for which the sum of the squares of the undulations of the selenoid at the sample points is minimal.

Equation 1 : Method to calculate the best-fitting ellipsoid for an N-point Fibonacci sphere.

Since the semi-major axis-major axis pair cannot be any value, both must be in the vicinity of the lunar reference sphere, all calculations were only performed between 1734 and 39 kms in the program. In addition, several iterative steps were used, first determining the minimum at a resolution of only 100 m, then at a higher resolution in the vicinity of the resulting minimum ± 100 m, and so on down to a resolution of 10 cm, saving a lot of time.

This program was not only used once on a single grid, but we also tested how the number of points on the Fibonacci sphere affected the results, using 100, 300, 1000, 3000, 5000, 10,000 and 100,000 point spheres, performing the steps of the calculation described above.

The semi-major axes, semi-minor axes and flatnesses of the ellipsoids thus obtained are shown in the table below. The seven results are based on samples of 100, 300, 1000, 3000, 5000, 10,000 and 100,000 points, with an accuracy of 10 cm (Table 1 ).

The parameters estimated from 100,000 sampled points have the highest accuracy (this result is the same as the parameters obtained for 10,000 points), so the half major axis of the rotation ellipsoid optimally fitting the Moon has a major axis of 1,737,576.6 m, a minor axis of 1,737,046.8 m and a flatness of 3.05 * 10 –4 (Cziráki & Timár 2023 ).

5 Discussion

5.1 comparison of the most accurate result with other reference surfaces.

As shown in Table 2 , the parameters of the rotation ellipsoid we have estimated differ by 176.6 and 353.2 m from the best fitting sphere. The flattening is 3/10,000, which is quite small, the same value on Earth is roughly 33/10,000 (World Geodetic System, WGS84), a significantly larger deviation from the sphere. This is due to the Earth's much faster axial rotation, which not only affects its gravitational field but also distorts its physical shape.

Our results were also compared to the triaxial ellipsoid created in 2010. This is based on the Chang'e 1 and Lunar Prospector (Konopliv et al. 2001 ) data CE-1-LAM-LEVEL (Wang et al. 2010 ). As can be seen from Table 2 , the differences are 125.9, − 12.8 and − 112.8 ms respectively. These may be due to the fact that the two models describe different geometric bodies. Another important difference is that the selenoid models used to calculate the parameters of the two ellipsoids are not the same. In the case of CE-1-LAM-LEVEL, the primary source of the selenoid model is the 180 degree and order model of the Lunar Prospector mission (Wang et al. 2010 ), which is lower in resolution than the GRGM 1200A model we used, and the Lunar Prospector model does not have direct data from the far side of the Moon, while GRAIL provided data from the far side with the same resolution as the near side. The differences between the two ellipsoids could therefore be due to these factors.

5.2 Variation of results as a function of Fibonacci spheres with different numbers of points

A central element of our research was that we carried out the estimation with meshes containing different numbers of points. These also provide information on the minimum number of points at which the result does not differ or differs only marginally from the most accurate estimate.

This was not expected for the samples with few points, as they represented too large areas (e.g., 379,323 km 2 for the 100-point sphere). Nevertheless, no deviation of more than 1 m was observed in our original sample. In order to determine the uncertainties, the 100-point sphere was rotated above the selenoid model every 30 degrees, the results of which are presented in Sect. 5.3 .

The resolution of the GRGM 1200A selenoid model is roughly 16.54 km, so the aim was to perform the calculation with a Fibonacci sphere of similar resolution. The 100,000-point Fibonacci sphere is the closest to achieving this, with 1 point representing 379 km 2 . Of course, the two resolutions are not exactly the same, as the selenoid model does not provide data in equal areas, but they are still similar in magnitude.

An important result, however, is that the result obtained with a 100,000 point sphere is already obtained with a sample with a tenth of a point, and from the 3000 point sample onwards, there is no difference of more than 10 cm.

5.3 Effect of rotating the Fibonacci sphere on the results

The results obtained are influenced to some extent by the position of the sampling points relative to the selenoid model. The significance of this decreases as the number of points increases and becomes negligible. In order to quantify this effect, the 100- and 5000-point Fibonacci spheres were rotated every 30 degrees above the selenoid and the approximation was also performed with these samples.

As expected, there are significant variations (Figs. 3 , 4 ), especially among the 100-point samples, with a deviation of 5.56 m for the semi-major axis and 7.54 m for the semi-minor axis, and differences of more than 10 m. At 5000 points, the differences are almost negligible, with a standard deviation of 0.09 m for the semi-major axis and 0.14 m for the semi-minor axis, and all differences are within 0.5 m.

Length of the semi-major axis of the fitted ellipsoid for spheres with different positions

Length of the semi-minor axis of the fitted ellipsoid for spheres with different positions

5.4 Histogram of selenoid undulations on the resulting ellipsoid

The histogram of the selenoid undulations is of interest for the resulting ellipsoid, and for the selenoid model as well. This is shown with 5 m intervals in Fig. 5 .

Histogram of the selenoid undulations at the points of a Fibonacci sphere of 100,000 points, and the density function of the normal distribution ( m = −0.0147 m, σ = 110 m) fitted to it

Fortunately, the expected value of the fitted normal distribution is 0, but there are significant outliers. These are mainly indicative of inequalities in the potential surface of the selenoid. For example, mass concentrations, mascons (Muller & Sjorgen 1968 ), which cause a variation of several hundred mgal, have such an effect on the shape of the potential surfaces, and it is therefore not surprising that such excursions are observed.

It is also interesting to note that the extremes are much larger than, for example, on the Earth, where the geoidundulation values do not exceed ± 110 m. And for the Moon, these values reach as high as ± 450 m. This is mainly due to the absence of active planeto-dynamics, which does not allow the formation of mass accretions of this magnitude on Earth. Without these, anomalies can persist on the Moon, which can affect the shape of the potential surfaces to such a large extent.

5.5 Map representation of the selenoid undulations

As can be seen in Fig. 6 , the outliers are localised, mainly in the vicinity of certain craters. The positive anomalies mainly occur near the two craters on the Moon's near side, Mare Serenitatis and Mare Imbrium. The largest negative anomalies are found at the South Pole Aitken and at the Mare Orientale craters.

The undulations of the selenoid visualised in QGIS

On the map representation of the undulations, the outliers characterise the selenoid model, not the ellipsoid, since the localised large anomalies are not significantly reduced by just one extra parameter. More information about the effect of the ellipsoid can be obtained by comparing Fig. 6 with Fig. 1 , which is basically a map of the selenoidal undulations with respect to the reference sphere. This shows that the ellipsoid has reduced the undulations somewhat in general, but most strikingly, it has almost completely eliminated the anomalies at the poles. This was expected, as the rotation ellipsoid should show the greatest improvement in this area compared to the sphere.

5.6 Definition of the ideal ellipsoid on Earth

Using the methods described in Chapter 3, and with some minor modifications, the ideally fitting ellipsoid of the geoid was also calculated. This is, of course, a very widely used existing ellipsoid, WGS84 (DMA 1984 ). The primary aim of the calculation is therefore not to create a "new" ellipsoid, but rather to see how well our method can reproduce the parameters of WGS84, essentially to check the calculation method used for the Moon.

The input data used was the geoid model grid of EGM96 (Lemoine et al. 1996 ), downloaded from https://www.agisoft.com/downloads/geoids/ . This takes into account the coefficients of the spherical harmonic functions up to 360 degrees and order. The geoid was sampled using the 100,000-point Fibonacci sphere.

The ideal ellipsoid for the geoid was also calculated to an accuracy of 10 cm. This, as expected, differs only minimally from the parameters of WGS 84. Its semi-major axis is 6,378,137.0 m and its semi-minor axis is 6,356,752.3142 m. For the ellipsoid we obtained, the former is 6,378,136.4 m and the latter 6,356,751.7 m.

6 Conclusion

In our research, we performed an ellipsoidal approximation of the lunar gravitational field, we searched for the rotational ellipsoid that deviates least from the Moon's specific potential surface, which defines the selenoid. In doing so, we sampled the GRGM1200A selenoid model using a Fibonacci sphere with points representing equal areas, and then with this database, we estimated the parameters of the ellipsoid using a program based on a least-squares approximation of the selenoid undulations.

The resulting ellipsoid has its centre at the centre of mass of the Moon, a semi-major axis of 1,737,576.6 m, a semi-minor axis of 1,737,046.8 m and a flattening of 0.000305. The ellipsoidal parameters were determined with an accuracy of 10 cm, using multiple samples.

In our research, we also performed the calculation for the Earth geoid, with the aim of showing that this method can give a good estimate of the ideally fitting ellipsoid. Since the parameters of this ellipsoid on Earth are known, we compared our results to it, and they were very close, with a deviation of only 60 cm.

In the future, we would like to extend our research to the Earth, and investigate the differences in best fitting ellipsoids using varying geoid models.

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Acknowledgements

Supported by the ÚNKP-22-6 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

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Cziráki, K., Timár, G. Parameters of the best fitting lunar ellipsoid based on GRAIL’s selenoid model. Acta Geod Geophys 58 , 139–147 (2023). https://doi.org/10.1007/s40328-023-00415-w

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Research Basics

So What Do We Mean By “Formal Research?”

Article contents

Historical Overview of Models Around the Globe

Scientific Management

Social Approaches to Work

Herzberg’s Two-Factor Theory

Hackman and Oldham’s Job Characteristics Model

Karasek’s Job Demand Control Model

Warr’s Vitamin Model

The Job Demands Resources Model of Bakker, Demerouti, and Schaufeli

Contemporary Job-Design Models

The Integrative Work Design Model

Job Characteristics as Antecedents

The Relations of Job Characteristics with Outcomes

Different Outcomes of Job Design

Health and Well-being

Cognitions and Learning

Personal Characteristics

Further Reading

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What is Research: Definition, Methods, Types & Examples

What is Research?

Qualitative methods

What is the purpose of research?

Types of research methods and Examples

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Other categories

What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire

Conclusions

Introduction

Meaning of work: from definition to measurement

Meaning of work: which theoretical model?

Measuring the meaning of work

The present study

Participants

Data analyses

Item analyses

Exploratory analyses of the scale

Internal consistency of latent factors of the scale

Confirmatory factor analyses of the scale

Limitations

Availability of data and materials

Abbreviations

Acknowledgements

Author information

Contributions

Corresponding author

Ethics declarations

Competing interests

Additional information

Rights and permissions

About this article

Share this article

What is Scientific Research and How Can it be Done?

Classification of Scientific Research

I. Descriptive research

II. Analytical research

Clinical Research

I. Calculation of the Number of Subjects

a. Type-I (α) and Type-II (β) Error (β) Levels

b. Difference between Groups and ES

c. Allocation Ratio of Groups

d. Direction of Hypothesis (H1)

II. Blinding (Masking) and Randomisation Methods

Blinding Method

Randomisation Method

Bias and Chicanery

Meaning of research in English

Related word

Translations of research

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Circular economy introduction

What is a circular economy?

The circular economy is based on three principles, driven by design:

Regenerate nature