PSYC 2203-Introduction to Quantitative Methods in Psychology Analysis – Medical Science Assignment Help

Assignment Task

Task 

How mental toughness influences perceived workload during a challenging task

Background

Completing any task requires us to expend mental effort to perform well. For example, even what would be described as physical tasks, such as playing a competitive sport, might call upon us to make ongoing judgements about where a ball is located, what strategy my opponents might be employing, and to coordinate actions with my team members.  The objective demands a task places upon a participant can be referred to as task load.  However, it is important to distinguish this from the impact task load has on individuals which is referred to as “workload”.

Critically, a number of studies have shown that excessive workload that exceeds the capacity of an individual to perform a task can lead to poorer performance outcomes in a variety of settings (Young et al., 2015).  Moreover, the subjective impression of workload is related not just to changes in task load (e.g., Lee, Jeon & Choi, 2012), but also to individual differences in qualities such as experience (Patten et al., 2006), and levels of anxiety (Guastello et al. 2015). This suggests that the same task could result in a variety of different experiences of workload across individuals.  It also suggests that a better understanding of factors that influence workload could be helpful in reducing the chances that workload exceeds an individual’s capacity, thereby triggering reductions in task performance.

To this end, the present study examined the impact of individual differences in self-rated mental toughness on perceptions of workload during a simulated air-traffic control scenario.  Mental toughness refers to the ability to consistently achieve performance goals in the face of challenge, stress and adversity (Gucciardi et al., 2015), and is related (but distinct) from concepts such as resilience, grit and hardiness. Higher levels of mental toughness have been linked to a variety of positive performance metrics in areas such as professional sport and the military (Gucciardi et al., 2015). 

In the experiment, UWA students and community members were asked to complete a challenging thirty-minute air-traffic control simulation in which they had to acknowledge aircraft entering and leaving their control sector as well as prevent collisions between aircraft flying in their sector.  Prior to the session, participants completed a measure of mental toughness (Gucciardi et al., 2015), while after the session they completed the NASA-TLX (Hart & Staveland, 1988; Hart, 2006) which is a widely-used measure of subjective workload that assessed their workload during the simulation. 

Materials

Air Traffic Control (ATC) Task (Fothergill et al., 2009). Participants viewed an enroute sector display (Figure 1). Individual aircraft were represented by a circle attached to a data block, which provided the call sign, speed, aircraft type and altitude. A projection line indicated aircraft heading. Trials commenced with 7 or 8 aircraft presented at various locations on flight paths (indicated by the black lines) which followed designated flight paths before departing the sector. Aircraft positions updated every second and new aircraft entered the sector throughout the trial (average 1.5 per min). All aircraft travelled at a constant cruising altitude unless the participant intervened to resolve a conflict (see below).

Participants had 20 seconds to ‘accept’ aircraft approaching (press ‘A’ key, then click on aircraft) and ‘hand-off’ aircraft departing the sector (press ‘H’ key, then click on aircraft) the sector. Aircraft requiring ‘‘acceptance’ or “hand-off’ flashed and changed colour (blue for acceptances, orange for hand-offs) for 20s prior to entering or exiting the sector. Additionally, participants had to detect and resolve conflicts between aircraft pairs that would violate five nautical mile lateral and 1000ft vertical separation in the future (click aircraft altitude triggering a prompt box requesting them to select the second conflict aircraft to initiate an altitude change for one of the aircraft). Aircraft pairs which were not intervened with and conflicted turned yellow when minimum separation was violated, then returned green once separation was re-established.

Figure 1: Air traffic control sector display. Aircraft flashed blue when they approached the participants’ sector (light grey polygon) indicating they required acceptance (i.e. EK18), and flashed orange indicating they required hand-off (i.e. EK11). Actions performed by the participant were logged in the ‘Events’ box on the right of screen.

Mental Toughness Scale (Gucciardi et al., 2015). Participants completed a computerized version of the eight-item Mental Toughness Index prior to completing the ATC task.  The questionnaire took approximately 5 minutes to complete.

NASA-TLX (Hart & Staveland, 1988; Hart, 2006). Participants rated how much each of six attributes (mental demand, physical demand, temporal demand, performance, effort, frustration) were required during the ATC task on a 21-point scale from 0 (low) to 100 (high). Participants then chose between all possible pairs of these attributes to indicate which they considered the ‘more important contributor’ to the workload they experienced during the ATC task (e.g., ‘Performance’ OR ‘Frustration’). Each attributes’ score out of 100 was then weighted by the number of times it was selected as the most important contributor (zero to five) to produce a weighted subscale score for each attribute. A global workload score ranging from 0 (low) to 100 (high) was calculated as the mean of the weighted subscale scores.  The measure took approximately 10 minutes to complete.

Procedure
The experiment had ethical approval from the relevant body.  Participants gave signed consent to participate.  Each task was preceded by written instructions. After the experiment, participants were debriefed. 

Data Analysis
Three groups varying in mental toughness were created by dividing the participants into equal thirds based on their Mental Toughness Index scores.  You can use these groups to investigate the relationship between mental toughness and workload. 
If you examine how workload scores differ across these groups, there do appear to be numerical differences. But are these differences statistically significant? Put differently, are the differences bigger than one you’d expect due to chance alone (can you reject the null hypothesis)?

We could estimate the probability of obtaining the observed differences between the different AQ groups by means of several t-tests. However, taking this approach increases the likelihood that we will make a Type-I error.  This is bad!  To protect ourselves against making a Type-I error, we should first conduct a one-way analysis of variance (ANOVA).  If this ANOVA is significant, we can then safely conduct follow-up tests to see which groups are significantly different from each other (criteria for choosing an appropriate follow-up test will be discussed during lectures and/or labs).  Otherwise, if this ANOVA is non-significant, you should not conduct any follow-up tests.

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