Author Archives: Steve Granger

About Steve Granger

I’m currently a PhD candidate in Organizational Behaviour at the Haskayne School of Business, University of Calgary. My research primarily focuses on occupational health psychology. In particular, I’m interested in studying mental health & safety (e.g., the experience and consequences of workplace injury), leadership & followership (e.g., social support and interpersonal knowledge as resources), and proactivity & resilience (e.g., predicting the anticipation and adaptation to adversity). When I am not working on research or teaching, I can typically be found reading a book, riding my bike, or spending time with my dog.

Variable and Value Labels

Let’s face it, a well prepped SPSS dataset has informative and accurate labels for each variable and their respective values. However, it’s all too easy to plow ahead and think you’ll remember what each obscure acronym you create in the moment and the values assigned to them will mean some years down the road. Maybe, maybe not, but what I know is that if you spend a little extra time prepping your dataset, you can save your colleagues or yourself a great deal of time that would be spent trying to understand what your past-self was thinking.

Luckily, the business of renaming variable and value labels is fairly straightforward, yet there are still some tips and tricks that you can use in special cases that I will mention below.

But first, I’ll quickly go over the basics.

Syntax for Labeling or Relabeling Variable Labels

Labeling one variable

VARIABLE LABELS varname ‘Type your variable label here’.

e.g.,
VARIABLE LABELS FPK ‘MEAN SCALE SCORE: Follower’s political knowledge’.

Labeling more than one variable

VARIABLE LABELS varname ‘Type your variable label here’
/varname2 ‘Type your variable label 2 here’
/varname3 ‘Type your variable label 3 here’.

e.g.,
VARIABLE LABELS FPK ‘MEAN SCALE SCORE: Follower’s political knowledge’
/FPS ‘MEAN SCALE SCORE: Follower’s political skill’
/FPW ‘MEAN SCALE SCORE: Follower’s political will’.

Syntax for Labeling or Relabeling Value Labels

Labeling the values for one variable

VALUE LABELS varname #’Type your value number here’.

e.g.,
VALUE LABELS FPK 1’Strongly disagree’ 2’Somewhat disagree’ 3’Neither agree nor disagree’ 4’Somewhat agree’ 5’Strongly agree’

Labeling the values for more than on consecutive variable

VALUE LABELS varname1 to varname9 #’Type your value number here’.

e.g.,
VALUE LABELS FPK1 to FPK9 1’Strongly disagree’ 2’Somewhat disagree’ 3’Neither agree nor disagree’ 4’Somewhat agree’ 5’Strongly agree’

Labeling the values for more than one non-consecutive variable

VALUE LABELS varname1 #’Type your value number here’
/varname6 #’Type your value number here’.

e.g.,
VALUE LABELS FPK1 1’Strongly disagree’ 2’Somewhat disagree’ 3’Neither agree nor disagree’ 4’Somewhat agree’ 5’Strongly agree’
/ABSENCE 0’No’ 1’Yes’.

Tips and Tricks for Renaming Variable Labels

The most important thing to remember when labeling or relabeling variable labels is that you have something for each variable. The idea is that you should understand what each variable is without having to open any other file or going back to your original survey or source material.

Often times, you will have special variables that you created solely to conduct analyses on, such as mean scale scores, clinical cut-off scores, and so forth. I find it helpful to make these important variables pop out by beginning their label with an all-caps description (e.g., MEAN SCALE SCORE: Follower’s political knowledge; CLINICAL CUTOFF SCORE: HADS depression).

Tips and Tricks for Renaming Value Labels

The same general informative tip applies to value labels. It’s easy to leave these blank, but you can make your life easier by labeling these where appropriate.

Occasionally your source material will have or produce wonky values and value labels for you that you want to change (recoding variables is another related but separate topic that I will write about soon). After recoding the variable values, there is a very easy method of removing the old value labels and replacing them with ones that match your updated values.

Here is the syntax:
VALUE LABELS varname.
VALUE LABELS varname #’Type your value label here’.

e.g.,
VALUE LABELS FPK.
VALUE LABELS FPK 1’Strongly disagree’ 2’Somewhat disagree’ 3’Neither agree nor disagree’ 4’Somewhat agree’ 5’Strongly agree’

Here, the first VALUE LABELS command will remove the existing value labels and the second VALUE LABELS command will produce new value labels for your variable.

Ride into the storm

Time has come to finally end my summer blogging hiatus as the fall winds sweep in. A lot has happened over the last two months in and outside of graduate school — from my very first first-author publication to countless cycling adventures (photo above being from the latest ride northwest of Calgary). While it’s sad to see summer wind down, I’m excited to embrace the beginning of the fall and the challenges it will offer. It’s time to ride into the storm.

Project WIMH: Post #6

One of the arguments I’ve been working on in my proposal to explain the link between prior issues with mental health and subsequent work injuries is the role of cognitive resources (memory, attention, acuity, etc.).

The basic argument goes something like this: cognitive resources that are negatively impacted by mental health problems are the same resources that reduce the likelihood of experiencing a work injury.

While I was working through papers on the meta-analysis, I came across one that brought this idea to the forefront.

Arlinghaus and colleagues (2012) assessed the intermediary role of fatigue as a result of inadequate sleep in predicting work injuries. One of the core predictive variables of inadequate sleep that they assessed was psychological distress.

They find that psychological distress was not only directly related to an increased chance of experiencing a serious work injury, but that it was also indirectly related to experiencing a serious work injury through obtaining less sleep.

The implication of this is that the effect of mental health on cognitive resources is also complex, potentially reducing a persons day-to-day acuity and functioning by influencing other factors such as the amount of sleep they get the night before.

References

Arlinghaus, A., Lombardi, D. A., Willetts, J. L., Folkard, S., & Christiani, D. C. (2012). A structural equation modeling approach to fatigue-related risk factors for occupational injury. Am J Epidemiol, 176(7), 597-607. doi:10.1093/aje/kws219

Bicycle helmets

Do they matter?

The answer is an overwhelming yes.

Here are just a few numbers from a meta-analysis (i.e., a summary of all existing quantitative research) by Oliver & Creighton (2017) assessing the effectiveness of bicycle helmets in crashes and falls:

51% less likely to experience a head injury
69% less likely to experience a serious head injury
33% less likely to experience a facial injury
And 65% less likely to experience a fatal injury

To boot, other meta-analyses find relatively similar results (Attewell et al., 2001; Elvik, 2011; Høye, 2018).

So yes, bicycle helmets matter.

But recent innovations in bicycle helmet tech have improved their effectiveness a considerable amount.

Here I’m talking about WAVECEL and MIPS (Multi-Directional Impact Protection System).

While these two helmet technologies work in slightly different ways, they essentially soften the impact on the head by separating the helmet and your head from the initial shock.

With a traditional helmet, there is essentially a plastic and foam barrier between your head and what it hits, but your head rotates with the helmet at the same speed (and it’s this initial rotation and acceleration that leads to most head injuries, such as concussions and traumatic brain injuries).

With MIPS and WAVECEL, there is within the helmet a moving liner or collapsible structure, respectively, that decreases this rotation, and ultimately the chance of head injuries (Bliven et al., 2019).

So if you’re in the market for a helmet, I would highly recommend looking out for either MIPS or WAVECEL, with MIPS helmets tending to come in at slightly lower costs because the tech has been around for quite a bit longer.

If you’d like more information about bicycle helmet testing, check out the website for Virginia Tech’s helmet testing lab. They run comprehensive third-party testing on helmets for various sports, including cycling.

References

Attewell, R. G., Glase, K., & McFadden, M. (2001). Bicycle helmet efficacy: a meta-analysis. Accident Analysis & Prevention33(3), 345-352.Chicago

Bliven, E., Rouhier, A., Tsai, S., Willinger, R., Bourdet, N., Deck, C., … & Bottlang, M. (2019). Evaluation of a novel bicycle helmet concept in oblique impact testing. Accident Analysis & Prevention124, 58-65.

Elvik, R. (2011). Publication bias and time-trend bias in meta-analysis of bicycle helmet efficacy: a re-analysis of Attewell, Glase and McFadden, 2001. Accident Analysis & Prevention43(3), 1245-1251.

Høye, A. (2018). Bicycle helmets–To wear or not to wear? A meta-analyses of the effects of bicycle helmets on injuries. Accident Analysis & Prevention117, 85-97.

Olivier, J., & Creighton, P. (2017). Bicycle injuries and helmet use: a systematic review and meta-analysis. International Journal of Epidemiology46(1), 278-292.Chicago

Two wish list destinations, one (110km) ride

This summer I wanted to find the cycling path to and visit Chestermere, as well as visit and ride through Fish Creek provinicial park in southern Calgary. I never thought I’d do this in a single ride, but that’s what ends up happening when you get slightly lost. Besides, it’s never really getting lost or going in the wrong direction when you’re on a bike, it’s called exploration. Kudos to my buddy Kevin for the company and hammering through. Ride on.

First ride in the mountains

Had the wonderful opportunity to ride out in Kananaskis Country on a highway that is closed to cars for half of the year (how awesome is that?!). Apparently Highwood Pass is the highest paved road in Canada at 2206 meters. Definitely one of my favourite rides so far. Shout out to my buddy Vaarun for letting me know about this and inviting me out. Ride on!

Project WIMH: Post #5

To what extent could mental health explain the underreporting of work injuries?

A study by Zadow and colleagues (2017) examined whether emotional exhaustion, a core aspect of burnout and a common sign of mental health problems, predicted both reported and unreported injuries among hospital personnel.

They found that reported injuries were not statistically related to emotional exhaustion but unreported injuries were – and the difference between the correlational effect sizes (size of the standardized statistical relationship between injuries and emotional exhaustion) was fairly large (.11 to .30).

Too spent to go through the rigmarole of reporting injuries? Quite possibly.

References

Zadow, A. J., Dollard, M. F., McLinton, S. S., Lawrence, P., & Tuckey, M. R. (2017). Psychosocial safety climate, emotional exhaustion, and work injuries in healthcare workplaces. Stress Health. doi:10.1002/smi.2740