What We Know About Safety Climate and Workplace Injuries: A Summary of Beus, Payne, Bergman, & Arthur (2010)

What is the relationship between workplace injuries and the perceptions employees have of an organization’s safety policies, procedures, and practices? Beus and colleagues attempted to examine this relationship between injuries and safety climate by meta-analyzing the existing literature. The approach that Beus and colleagues took was particularly interesting because they highlighted a number of important but vastly underappreciated distinctions. They strongly emphasized the theoretical motivation for contrasting directional relationships (i.e., whether injury leads to changes in safety climate, or whether safety climate leads to changes in injuries have theoretical differences) and the separation of climate levels (i.e., we could expect the idiosyncrasies of individual-level psychological climate and the group-level organizational safety climate to have non-overlapping variance to be explained). From a research perspective, the emphasis that Beus and colleagues place on these theoretical and analytical distinctions is strengthened by the fact that they are building on clear limitations of previous reviews on the relationship between injuries and safety climate (Clarke, 2006; Christian et al., 2009).

First, the direction of the safety climate and injury relationship contains two different meanings. In one direction, it is expected that safety climate fosters expectations as to what type of behaviours will lead to certain outcomes. An organization marked by a high safety climate will be one where employees believe that unsafe behaviour is highly frowned upon and that management is committed to ensure that employees maintain a safe work environment. As such, the safety climate-to-injuries relationship will be dictated by expectations fostered by the safety climate. In the other direction, observing or experiencing injuries at work will signal important information with regards to the importance of safety to the organization. As such, the presence or absence of injuries will influence perceptions of safety climate.

Second, Beus and colleagues note how climate can be conceptualized at both the individual- and group-level. At the individual-level we have psychological safety climate, and at the group-level we have organizational safety climate. As Beus and colleagues rightfully point out, very similar phenomena can be quite different at different levels. The idiosyncrasies of individuals, such as their unique experience and worldviews, will distinguish them from the group. However, group norms will shape individual behaviours to make group members more similar in comparison to those in other groups. As such, both individual- and group-level safety climate should have unique relationships with injuries.

Finally, Beus and colleagues note a number of conditional factors on the proposed safety climate and injury relationship. In particular, they suggest that the length of time over which injuries are assessed, the contamination and deficiency of safety climate measures, and the operationalization of injuries will shape the strength of relationship between safety climate and injuries. Length of time over which injuries are assessed was tested as an exploratory moderator, while safety climate contamination (i.e., incorrectly adding modifications) and deficiency (i.e., insufficiently measuring safety climate) should attenuate the climate-injury relationship. Meanwhile, stricter injury operationalization should result in stronger injury-to-safety climate relationships, while broader injury operationalization should produce stronger safety climate-to-injury relationships.

What were the results? Fortunately, there were enough studies to test the injury to psychological (r = -.16) and organizational safety climate (r = -.29) and the organizational safety climate to injury (r = -.24) relationships, but not the psychological safety climate to injury relationship. These results suggest that there is a relationship in both directions, and that the group-level safety climate appears to have a stronger relationship with injuries than the individual-level safety climate.

What about the moderators? Length of time over which injuries were assessed only explained some variance for the organizational safety climate to injury relationship, with longer times of measurement producing smaller effect sizes. Contamination appeared to inflate the prospective relationship between injury to both psychological and organizational safety climates (contrary to expectations), while deficiency weakened these same relationships (as expected). Finally, the operationalization of injuries had a pattern to suggest that stricter operationalization was found to have a stronger relationship in the injury-to-climate relationships, and the opposite for climate-to-injury relationships. However, the confidence intervals overlapped, suggesting that the statistical difference between them is weak.

What is something that managers and organizations can do to reduce injuries and improve safety climate based on the findings of this paper? One finding is particularly important to answering this question. The most common dimension of safety climate is management’s commitment to safety. Beus and colleagues found that this was a much stronger predictor for reducing injuries than vice versa (although the opposite relationship was still significant) and that this was the strongest predictor in reducing injuries than any other safety climate measure. This means that the most important thing that managers and organizations can do is effectively communicate their commitment to employee safety. This can be done in numerous ways, such as providing engaging methods of safety training, safety programs and initiatives, and encouraging the reporting and discussion of safety, accidents, and injuries.  There is little else that can compare to a sincere and invested effort on behalf of organizations to improve the health and safety of employees.

References

Beus, J. M., Payne, S. C., Bergman, M. E., Arthur, W. (2010). Safety climate and injuries: An examination of theoretical and empirical relationships. Journal of Applied Psychology, 95, 713-727.

Christian, M. S., Bradley, J. C., Wallace, J. C., & Burke, M. J. (2009). Workplace safety: A meta-analysis of the roles of person and situation factors. Journal of Applied Psychology, 94, 1103-1127.

Clark, S. (2006). The relationship between safety climate and safety performance. A meta-analytic review. Journal of Occupational Health Psychology, 11, 315-327.

Consider Dread – A Review of Burke, Salvador, Smith-Crowe, Chan-Serafin, Smith, & Sonesh (2011)

Does safety training work?  It sure does.  But that’s not the interesting question.  Does the type of training matter?  Now things are starting to get a bit more interesting.  The answer to this second question has important implications for both safety as well as the all-mighty dollar.  Are training methods that are highly engaging worth the time and resources they consume?  Or should companies take advantage of economies of scale by letting their employees suffer through poorly made safety videos or e-tutorials?  Previous reviews on this question provide mixed results.  While one paper found evidence that engaging training methods had a much stronger impact in comparison to less engaging methods (Burke et al., 2006), another paper found little evidence for a difference (Robson et al., 2012).

Now what question is typically elicited when faced with mixed results?  It depends – figuratively speaking.  We add to the second question above to make it a bit more interesting: Under what conditions does the type of safety training matter?  This is the question that Burke and colleagues answer effectively in their paper The Dread Factor: How Hazards and Safety Training Influence Learning and Performance (2011).

In this paper, Burke and colleagues readdress the importance of engagement.  However, this time they also provide an interesting boundary condition to the relative importance of engagement.  This is the “realization of the dangers in the work context and associated negative affect” (p. 49) – otherwise known as dread.  The rationale underlying dread as an important feature to consider in safety training has roots in learning through social construction.  A person comes to understand one’s work context and the risks it entails through communication and social interaction with others already engrained in this context.  If there are few hazards in the work context, then the necessity to communicate these risks in such a way as to shape what people perceive to be risks will be less important.  In other words, pre-conceived notions of risk will require less implicit knowledge that can only be shared through interacting with others.

Burke and colleagues maintained that more engaging training methods will still be more important to acquisition of safety knowledge and safety performance.  However, they also proposed that under hazardous work conditions, highly engaged training will show stronger training effects on the outcomes.  Burke and colleagues then set out to conduct a meta-analysis of all the research conducted on this topic to date.

So, what did they find?

First, to cautiously reiterate, safety training matters!  No matter the engagement, safety training has important implications towards the acquisition of safety knowledge and exhibition of safety performance.  Second, highly engaging methods of safety training showed much better outcomes for both the acquisition of safety knowledge, as well as better safety performance.  Third, and most importantly, the level of hazard had a conditional effect on both outcomes.  When hazards were high, engaging methods were by far more important than less engaging methods.  On the other hand, when hazards were low, there was actually no statistically significant difference between the method of training and the outcomes.

So, what are the implications? Investing more into engaging methods of training is well worth it if you operate in hazardous conditions or if the job entails relatively higher levels of hazards to employee safety.  Meanwhile, for less hazardous jobs, it may not be necessary to invest as much resources into more engaging methods of training – at least in predicting safety performance and safety knowledge.  However, keep in mind that there may be other consequences to support the idea of emphasizing more engaging methods of training – even if it is to make something as important as safety a little less boring.

References

Burke, M. J., Salvador, R. O., Smith-Crowe, K., Chan-Serafin, S., Smith, A., & Sonesh, S. (2011). The dread factor: How hazards and safety training influence learning and performance. Journal of Applied Psychology96(1), 46-70.

Burke, M. J., Sarpy, S. A., Smith-Crowe, K., Chan-Serafin, S., Salvador, R. O., & Islam, G. (2006). Relative effectiveness of worker safety and health training methods. American Journal of Public Health96(2), 315-324.

Robson, L. S., Stephenson, C. M., Schulte, P. A., Amick III, B. C., Irvin, E. L., Eggerth, D. E., … & Peters, R. H. (2012). A systematic review of the effectiveness of occupational health and safety training. Scandinavian Journal of Work, Environment & Health, 193-208.

Re-thinking the Role of Perceptual Acuity – A Review and Commentary on Veazie, Landen, Bender, & Amandus (1994)

This article is a review of the epidemiological literature on occupational injuries spanning 1970 to 1992. While there are brief sections on worker populations (i.e., industries represented in the studies they reviewed), and outcomes (i.e., injuries, ranging from minor to severe), the most interesting and potentially insightful section is on risk factors. The remainder of the article focuses on where research efforts should be directed, such as studying specific risk factors, as well as a thorough consideration of methodological issues related to this line of inquiry.

The worker populations found within the studies Veazie and colleagues reviewed were predominantely from industries that are known to be hazardous or simply accessibile to researchers but not particularly hazardous. Consider manufacturing with regards to the latter.  Manufacturing is an industry commonly examined in research because of the relative practicality of conducting research in this industry and not because of any particular unique hazardous conditions. As such, our understanding of occupational injuries, be it outcomes or risk factors, is potentially industry biased when aggregated.

Meanwhile, Veazie and colleagues contrast the practical motivations with motivations driven by actual hazards, such as the focus on transportation and mining. Both of these industries are recognized as relatively high-risk industries. However, as hinted at above, whether an industry is hazardous does not mean it is more likely to be the target of researchers, as many extremely hazardous industries are underrepresented (at least when this paper was published) as they are less accessible, such as agriculture, logging, construction, and fishing. Veazie and colleagues suggest this is largely due to the “transient and independent nature of their workers” (p. 205). Fair enough.

The next notable section is on the outcomes found in the literature they reviewed. The authors note that most non-injury mishaps are excluded and that most studies focus on accidents. Alas, this causes tremendous grief to other safety researchers as the recording and measurement of accidents does not allow us to separate accidents that result in injuries and those that do not. This inability to isolate injuries from accidents is still a problem for those of us joining the field of occupational safety and searching for empirical precision. Another notable shortcoming of the literature on outcomes that Veazie and colleagues pointed out, and which caused my head to nod incessantly in agreement, was that it has been rare for researchers to isolate severity in their measures of accidents and injuries – an idea that still requires empirical attention.

The most insightful section in this paper is on risk factors. Veazie and colleagues classify three categories of risk factors: human, job content, and environment. Human risk factors include things such as demographics, experience, stress reactions, knowledge, and attitudes (p. 206), job content includes factors such as work design and scheduling, while environmental factors include social and organizational features such as physical stressors and hazards.

Veazie and colleagues outline risk factors found in 32 studies that meet their standard of quality to infer an existing relationship. While these specific factors can be found outlined in the three tables on page 207 onwards, the most insightful comment about human risk factors, and I think potentially overlooked theme for all risk factors, is that these factors in some way influence perceptual acuity. While the authors do not expand on what they mean by this, I think the summary of factors found in one study they mention is worth expanding upon.

Veazie and colleagues use the term perceptual acuity when discussing one study where it was found that noise exposure, hearing loss, and alcohol use (among others) were related to injury in shipyard workers (Moll van Charante & Mulder, 1990). They summarized these and the other factors as those which influence perceptual acuity and left it at that. However, I think this idea could be expanded to explain the connection between not only majority of the human factors, but also the job content and environmental factors.  It can be inferred that human factors infringe upon or dampen perceptual acuity. In turn, this leaves individuals vulnerable to hazards that, under better conditions, they would be fully attentive towards and better able to avoid.

The idea that under better conditions individuals would be better prepared for and able to prevent injuries is only part of the story. Other human factors, such as experience, tell us something else.  For one, less experience is typically related to a higher likelihood of injury at work. This lack of experience plays a role on what people perceive to be hazards in their work place, making them more vulnerable to injury. Perceptual acuity is still an accurate mechanism, but instead of it being hampered attention or focus, it is the nature of the perception. As such, a lack of experience fully well influences perceptual acuity, but not in such a way that under better conditions, fully attentive individuals would be prepared for and able to prevent injuries. Instead, individuals who lack experience would still overlook hazards or engage in work activities that carry job specific risks. How can someone prevent an injury if they are not aware of the hazards or risks or the circumstances that increase the likelihood of increasing these very hazards or risks.

The next sections do not at all discuss the effects that job content nor environmental factors have on perceptual acuity. But again, I argue that these can be roughly described as factors that either infringe upon or shape motivations to allocate attention towards hazards in the workplace. In some sense, this may just me forcibly imposing a way of connecting the factors described in these sections. I argue that job content, represented by aspects of job design and layout (e.g., job difficulty, workload, shift work, and so forth), is largely that which can infringe upon or limit perceptual acuity. Meanwhile I would place environmental factors as those which direct or explain how perceptual acuity is devised and divided, such as physical environmental obstacles, structural incentives, and dealing with other human beings.

While I may be over-simplifying things by stretching the idea of perceptual acuity as something that connects the categories provided by Veazie and colleagues, I found it to be an insightful and fun exercise. Reading through much of the literature on workplace safety has left me thinking there is a lot more room for theoretical improvement. As such, part of my approach in familiarizing myself with the literature has been to take that which we know in different directions – even if I know the odds suggest it will be a dead end.

References

Moll van Charante, A. W., & Mulder, p. G. (1990). Perceptual acuity and the risk of industrial accidents. American Journal of Epidemiology131(4), 652-663.

Veazie, M. A., Landen, D. D., Bender, T. R., & Amandus, H. E. (1994). Epidemiologic research on the etiology of injuries at work. Annual Review of Public Health15(1), 203-221.

The Past, Present, and Future of Workplace Safety Research – A Review of Beus, McCord, & Zohar (2016)

Beus and colleagues (2016) introduce the integrative safety model to provide a much needed comprehensive and coherent narrative behind research on workplace safety.  This includes thinking which has been supported by research and that which is currently attracting the attention of researchers. The integrative safety model (see figure below) is not an attempt at providing an overarching theory but is simply a way of organizing the most current approaches to workplace safety. The conceptual frame combines the three most dominant theories in workplace safety literature: job performance theory (Campbell, McCloy, Oppler, & Sager, 1993), job demands-resources theory (Bakker & Demerouti, 2007), and organizational climate theory (Zohar, 1980).

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An important advance in safety research was to start thinking about safety as a performance behaviour. To perform well, a person requires knowledge, motivation, and skill, and these three are largely determined by individual differences (such as personality) and contextual factors (such as leadership and training). The theory also suggests that history and experience will also shape knowledge, motivation, and skills. Together, job performance theory suggests that individual and contextual factors influence the safety triad of knowledge, motivation, and skill, which in turn influence safety behaviour and outcomes. Outcomes are then said to loop back and have a role in further shaping this safety triad of knowledge, motivation, and skill.

The literature largely, albeit in a rather scattered fashion, supports the proposed links between individual and contextual factors on knowledge, motivation, and skill. These in turn are related to safety-related behaviour and outcomes. However, no actual path or mediation models were reviewed, so it is unclear whether the actual indirect effects have been supported. In addition, the authors repeatedly mention safety skills as a feature with knowledge and motivation, but has this actually been developed? What does safety skill actually look like? The authors acknowledge that the idea of safety skill requires more work, but even the article they cite as an example to have used safety skill (Eklöf & Törner, 2002) really only measured knowledge despite calling their measure knowledge and skill. As such, safety skill is something worth thinking about and potentially developing as a construct, even just to show that it has no effect on safety behaviour.

Another advancement in the workplace safety literature was the adoption of the job demands-resources theory. This theory has proven to be extremely useful because it focuses on an array of job characteristics and contextual factors that either contribute to a person’s ability to do their job (i.e., resources) or contribute to the pressure people face do their job (i.e., demands).  In other words, contextual and job-related demands and resources influence personal resources, which in turn are related to safe and unsafe behaviour.

Again, the literature largely supports this theory. As research on the job demands-resources theory has been fairly substantial, there is a fuller picture of the relationship. Not only do demands and resources rooted in job characteristics and contextual factors indirectly impact safety behaviour through personal resources, they also have a direct relationship with safety behaviour. However, generalizability of the job demands-resources theory is also its weakness. There is very little consensus on how demands and resources interact with each other and what the most important types of demands and resources are to safety behaviour and outcomes. This necessary theoretical contribution will, when it occurs, have important implications for workplace safety research and will have a considerable contribution to practice.

Finally, the most prominent theory in workplace safety research is the application of organizational climate theory in the form of safety climate.  The broader theory suggests that an organization’s collective expectations of how people behave will shape individual- and group-level safety related behaviours.  These expectations typically represent the belief that certain behaviours will be reinforced or punished, and in turn motivate people to behave accordingly. Then, in typical topic specific fashion, the appropriate adjective of safety gets tacked onto climate and we have shared perceptions about the value of safety in the workplace.

The literature on safety climate has turned out to be one of the most productive approaches to explaining and predicting safety-related behaviour.  This includes both levels of safety climate: individual and collective perceptions of safety. However, climate is not the only contextual factor shaping expectations about safety and safety-related behaviour. Other factors include transformational leadership, safety norms, and organizational goal-setting and feedback. While the evidence for these features toward safety-related behaviour is strong, there is disagreement about the intermediate behaviour-outcome expectancy of individuals and the nature of the consequential motivation. The authors argue that safety motivation and behaviour-outcome expectancy produce different types of motivation, the former is a matter of valence (i.e., there is value attached to safety), and the latter is a matter of instrumentality (i.e., the connection between behaviour and outcome is a strategy to achieve or retrieve desired outcomes). Theoretically this makes sense, but empirically I can imagine this would be difficult to separate and is something that will need to be solved to contribute to this argument.

Combining the three theories together, we get the natural tail end of the conceptual model linking individual- and group-level safety-related behaviour to accidents. These accidents in turn have consequences for contextual factors such as policy surrounding workplace safety and perceptions of safety climate. Unlike the previous set of variables, there is no theoretical narrative given to weave these variables together. However, this is arguably unnecessary as it is only one step removed from the previous three theories and can be argued to be a natural consequence of the causal sequence for all three theories.

Nonetheless, the presence of a theoretical explanation for the link between safety-related behaviour and accidents may be warranted.  As much as it is no surprise that safety-related behaviour is related to injuries and accidents, the actual effect size is smaller than would be expected, both at the individual and group level. The authors suggest that part of the story is missing, and other factors outside employee safety-related behaviour play an important role in determining the likelihood of accidents. Therefore, the introduction of a broader narrative encompassing employee safety-related behaviour and accidents will be necessary to fully appreciate the predictors of workplace accidents.

Overall, I found the integrative safety model to be a useful narrative for thinking about workplace safety from a distance. Beus and colleagues provide a good overview of what management and occupational health research has uncovered about workplace safety, what researchers are thinking now, and some speculation as to where we should focus our efforts next. Ultimately, I found this paper to be a helpful exercise to also speculate as to what the future of workplace safety research will look like.

References:

Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology22(3), 309-328.

Beus, J. M., McCord, M. A., & Zohar, D. (2016). Workplace safety: A review and research synthesis. Organizational Psychology Review6(4), 352-381.

Campbell, J. P., McCloy, R. A., Oppler, S. H., & Sager, C. E. (1993). A theory of performance. Personnel Selection in Organizations3570, 35-70.

Eklöf, M. & Törner, M. (2002). Perception and control of occupational injury risks in fishery–a pilot study. Work & Stress16(1), 58-69.

Zohar, D. (1980). Safety climate in industrial organizations: theoretical and applied implications. Journal of Applied Psychology65(1), 96-102.

Chronic motivational state interacts with task reward structure in dynamic decision-making

This paper is about motivation. Cooper and colleagues (2015) claim that the definition of motivation (i.e., “a simple increase in effortful cognitive processing”) is due for a revision.  The authors suggest that motivation is instead better thought of as something more dynamic – an interacting multilevel variable if you will.  This is exemplified in the theoretical lens that they adopted.

The theoretical lens through which Cooper & co. approached motivation is called regulatory fit. This regulatory fit is “achieved when the individual’s global motivational state (chronic or situational) aligns with the local motivational task framing” (p. 41).  When there is “fit”, there should be an increase in effortful cognitive processing and a decreased reliance on habitual cognitive processing. When there is a misfit, the opposite occurs.

To clarify, the global motivational states that Cooper & co. are speaking of are called promotion-focused (i.e., these individuals are more sensitive to potential gains) and prevention-focused (i.e., these individuals are more sensitive to potential losses).

Without overcomplicating things, people who a chronically promotion-focused will engage in effortful cognitive processing if a task is framed as promotion-focused (i.e., they are asked to maximize gains), while individuals who are chronically prevention-focused will engage in effortful cognitive processing if a task is framed as prevention-focused (they are asked to minimize losses).  They call this effortful cognitive processing goal-directed or the model-based system.  Meanwhile, if there is a misfit (e.g., a chronically promotion-focused person is asked to complete a prevention-focused task), people will opt towards the less costly habitual reward-based or model-free system of cognitive processing.

To test this motivational regulatory fit model, the authors recruited participants who were either chronically promotion or prevention focused to repeatedly (250 times) choose between two rewarding options for extracting a valuable resource: one will always provide larger immediate reward but decrease future rewards (called the decreasing option) and the other will always provide lower immediate reward but causes future rewards to increase (called the increasing option).  Meanwhile, participants were randomly assigned to either a gain-maximization condition (the extraction procedures produce varying gains of the resource that needs to be maximized) or loss-minimization condition (the extraction procedures produce a varying output of a dangerous by-product that needs to be minimized). See figure below for how this was shown to participants (gain-maximization on the left, loss-minimization on the right).

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What were the most important results?  In the gain-maximization condition, promotion-focused folks performed better than the prevention-focused folks, and in the loss-minimization condition, prevention-focused folks performed better than the promotion-focused folks.  Even within the regulatory focus groups, the alignment of regulatory focus proved beneficial.  Promotion-focused folk performed better in the gain-maximization condition and prevention-focused folk performed better (albeit non-significantly) in the loss-minimization condition.  The regulatory fit hypothesis of motivation was thus supported.  Additional regression analyses reinforced these findings by showing that that relatively promotion-focused folk performed better in gain-maximization and worse at loss-minimization.

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References

Cooper, J. A. Worthy, D. A. & Maddox, W. T. (2015). Chronic motivational state interacts with task reward structure in dynamic decision-making. Cognitive Psychology, 83, 40-53.

Hand or foot?

homonculus

Photo via Dr. Joe Kiff

If you could only keep one, which would you choose: Hand or foot? Eyesight or hearing? Arm or leg? Choices like this luckily come to most of us in the form of morbid games of imagination we play with our friends. But for an unfortunate population, the choice is made for them at work.

In an article by Elsie Cheung and colleagues (2003), they drew on an observation of many clinicians: employees who experience severe injuries or amputations to their upper-extremity (i.e., fingers, hands, arms) at work seem to be particularly vulnerable to psychological maladjustment. While anecdotes may serve their purpose, Cheung and co. wanted to test whether those who experienced upper-extremity injuries were in fact psychologically worse-off than others who experienced severe injuries and amputations elsewhere. This clearly had implications for treatment and rehabilitation.

Diving into the library at the Workers Compensation Board of British Columbia, Cheung and colleagues pulled out files for individuals who 1) experienced upper extremity amputations or lower extremity amputations, 2) who were assessed by a clinical psychologist at the outpatient rehabilitation center, and 3) were psychologically healthy prior to the injury.

Statistical comparisons of the two groups revealed some interesting results in line with the observations of clinicians.  Workers who had injuries to their upper extremities had substantially more symptoms of posttraumatic stress disorder (e.g., distressing flashbacks, emotional numbness) and slightly elevated signs of depression. When considering pain, however, both groups experienced similar levels.

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So, what is the take away? Why do severe injuries and amputations to our fingers, hands, and arms leave us more vulnerable to psychological maladjustment? Cheung and co. align with Grunert and colleagues (1988), who made the argument that it comes down to functional loss, self-image, and social acceptance. So much of what we do on a day-to-day basis depends on using our hands (like typing this very sentence). What we do is important in shaping who we are, and who we are is who people have come to accept. All of this comes crashing down when that choice is made for the unfortunate few.

References

Cheung, E., Alvaro, R., & Colotla, V. A. (2003). Psychological distress in workers with traumatic upper or lower limb amputations following industrial injuries. Rehabilitation Psychology, 48(2), 109-112.  

Grunert, B. K., Smith, C. J., Devine, C. A., Fehring, B. A., Matloub, H. S., Sanger, J. R., & Yousif, N. J. (1988). Early psychological aspects of severe hand injury. Journal of Hand Surgery, 13B, 177–180.

Get [M]oving in Mplus – part 5: Define subcommands

Despite having to import a dataset into Mplus from another stats program, you can conduct most of the variable manipulation you need in Mplus. This is good news as you’ll often find yourself in a position of having to transform exisiting variables (e.g., log transformations) or creating new variables (e.g., mean scores).

In any case, it can be very annoying having to go back to SPSS to do all of this stuff. Fret not, Mplus has your back with the DEFINE command.

There are a few notes to make before summarizing the most used operations under the DEFINE command.

  • Operations with the DEFINE command can be done on all observations or a selection of some based on conditional statements (e.g., IF(gender EQ 1) THEN…)
  • Transformations do not alter the original data (phew) but hold the alterations in memory only during analysis (unless you use the SAVEDATA command, then the transformed values are saved)
  • All statements in the DEFINE command are done in order (so if you create a mean score and want to transform it, it must be done in this order and not the opposite)
  • Any new variables you create for use in analysis must be listed after original variables being used in analysis within the USEVARIABLES subcommand.
  • The following logical operators, arithmetic operators, and functions can be used in the DEFINE command:

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And here are some of the common operations (although not an exhaustive list) you’ll likely find yourself using at one point or another:

Create mean score variables:

Love = Mean(intimate passion commit); 

or

Love = intimate+passion+commit/3;

Create summative score variables:

Love = Sum(intimate passion commit);

Create other variables (e.g., interaction terms or convert units such as kilos to pound)

Lust = intimate*passion;
Pounds = .454*kgs;

Grand- or group-mean center a variable or variables:

CENTER Love (GRANDMEAN);

or

CENTER Love (GROUPMEAN);

Standardize a variable or variables:

STANDARDIZE Love;

Transform variables:

Lovelog = log10(Love);
Lovesqrt = sqrt(Love);

Conditional statements:

IF (sex EQ 0 AND relstat EQ 1) THEN group = 1;
IF (sex EQ 0 AND relstat EQ 2) THEN group = 2;
IF (sex EQ 1 AND relstat EQ 1) THEN group = 3;
IF (sex EQ 1 AND relstat EQ 2) THEN group = 4;

If there are other operations that you need to do and are possible in the DEFINE command but I haven’t covered here, please let me know. If there are other operations I ever use along the way, I’ll be sure to update this post!