Student Behaviours for Large Sample at Time 1 and Time

Determining how well the student behaviour subscales represented six various behaviours necessitated, in the first instance, calculations of reliability coefficients, means and standard deviations, as shown in Table 6.3, to fine tune the survey. Behavioural data was also expected by the members of the cross-campus working party to capture student behaviour norms and/or the need for future support initiatives. Unlike the reliability coefficient (Cronbach’s alpha), results for connectedness, the behaviour subscales for the 94 participants at Time 1 conveyed low and low-moderate results ranging from 0.642 - 0.775, with an overall subscale reliability coefficient of 0.699, which borders the recommended alpha level of 0.70. At Time 2 the overall subscale reliability, for 66 participants, decreased to 0.649 and conveyed a variety of low, moderate and high results ranging from 0. 526 – 0.840.

Of the six behaviours subscales, mood (0.775) and sleep (0.772) reliability coefficients were of an acceptable level at Time 1, both increasing to above 0.800 (high level) at Time 2; this

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indicated the subscale items were acceptable measures of mood and sleep behaviours, and should be retained for future collections. In comparison, reliability coefficients for the remaining four subscales of wellbeing (0.685), academic behaviour (0.677), risk taking (0.674) and substance use (0.642) presented reliability coefficients (alphas) below the recommended result of 0.70. Coefficient decreases continued, at Time 2, for three of the four subscales; the exception was well-being (0.719) which was of an acceptable level.

Table 6.3

Reliability Coefficients, Behaviour Means and Standard Deviations Overtime and Significance of Changes Represented as p Values

Student Behaviours (Items per subscale)

Time 1 (n=94) α x̅ s Time 2 (n=66) α x̅ s p values (0.05 level) Sleep (4) 0.742 10.89 2.29 0.840 10.21 2.62 0.286 Risk taking (4) 0.674 15.62 1.13 0.510 15.70 0.80 0.204 Emotional Well-being (7) 0.685 21.21 3.23 0.719 20.35 3.31 0.028* Academic behaviours (14) 0.677 47.92 3.80 0.486 47.03 2.96 0.578 Substance use (6) 0.642 21.74 2.28 0.526 22.26 1.69 0.868 Mood (9) 0.775 29.40 3.99 0.816 27.98 4.44 0.676 Overall 0.699 24.46 2.78 0.649 24.07 2.61

Note. * denotes significance change at the 0.05 risk level from Time1 to Time 2.

Convention indicates that an alpha value below the 0.70 level should be revised or deleted from the survey and excluded from the analysis, as it does not measure the construct. However, a suboptimal alpha for the other four behaviour subscales may also reflect something useful, and therefore provide valid and representative information (Tavakol & Dennick, 2011).

Poor reliability coefficients (α) for the other three subscales of substance use (0.526), risk taking (0.510) and academic behaviours (0.677) at Time 1 uniformly decreased over time, as did the overall scale at 0.649. In this instance, decreases in internal consistency, over time, indicated that three of the six scale items did not measure the behaviour constructs and/or that the participants, were a low risk group for the adverse behaviours of risk taking and

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substance use. This finding necessitated subscale modifications, or that replacements occur before future collections. However, the positive behaviour measures of well-being, which took time to increase to an acceptable levels, may also be true for and academic behaviours (0.677), both considered significant elements of positive development, warrant consideration that academic behaviour data may have also been collected too early to be reflective of the university journey. As such, the cross-campus working party decided to retain the four subscales, of mood, sleep and well-being and academic behaviours, for future collections.

Examination of the six behaviour sub-scales, as shown in Table 6.3, present the means and standard deviations for the various behaviour subscales at Time 1 and Time 2. The scoring system utilised four categories to reflect how often participants felt this way in the past two to four weeks; daily = 1, most days = 2, occasionally (1-2 times per week) = 3 and never = 4. A low score indicates the behaviour occurs regularly while a high score signifies the behaviour did not occur.

At Time 2 the overall behaviour scale mean decreased non-significantly to 24.07. This lower score represents an increase in behaviours but it was essential to know the details. An examination of the six behaviours subscales revealed a mean of 15.62, out of 16 for the risk taking behaviour subscale Time 1, rising non-significantly to 15.70 at Time 2 indicating minimal engagement in risk-taking activities of unprotected sex or driving under the influence of drugs or alcohol. The six substance use items generated a mean of 21.74 out of a possible score of 24; increasing non-significantly to 22.62 at Time 2, reflecting lower levels alcohol, tobacco and recreational drug use which also aligned with staff understandings of substance use later in first semester. The sleep scale mean, based on four items, was 10.89 out of a possible score of 16, decreasing non-significantly to 10.21 at Time 2 which signals that on average, the population slept well, woke up refreshed and maintained concentration in class most days of the week, during their first month at university. The emotional well-being scale, comprised of seven items, reported a mean of 21.21 out of maximum score of 28, at Time 1. It was the only subscale to decrease significantly (p <0.028) at Time 2, showing participants mostly ate well, felt relaxed, were happy, were satisfied and exercised in the past month. The mood subscale, of nine items, presented a mean of 29.40 out of a maximum score of 36 at Time 1, decreased non-significantly to 27.98 at Time 2 indicating participants experience positive mood 1-2 times weekly over the past fortnight. The academic behaviours subscale contained 14 items with a mix of favourable (4) interactions and non-favourable (10) activities. An academic behaviour mean of 47.92 out of a possible 56, at Time 1,

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fluctuating non-significantly at Time 2, represents little, if any, positive academic engagement during the first semester of university. Non-significant mean increases for the behaviour subscales of risk taking and substance use at Time 2 signify reduced participation in adverse anti-social behaviours. Non-significant, mean reductions for sleep, mood and academic behaviours, which represent increase in positive behaviours, may be worth monitoring to reveal if supports are required at later times to assist student improvement.

An examination of standard deviations for student behaviours at Time 1, ranged between 1.13- 3.99; with risk taking (1.13) and substance abuse (2.28) reflecting the assumption that the group was homogenously at low risk in these adverse behaviours. When grouped together, standard deviations for sleep behaviours (2.29), mood (3.99), well-being (3.23) and academic behaviours (3.80), which are considered positive indicators of adjustment to university, indicated a broader range of behaviour responses at Time 1. Standard deviations at Time 2 also add to the complexity of findings, as three of the six subscales (risk-taking, substance use and academic behaviours) show decreases and group the responses more closely at Time 2. By comparison, the standard deviation increases for the other three subscales, of sleep, mood and well-being, overtime, revealed group responses to be more variable at a later collection point. Knowledge of bimodal distribution of scores is important to understanding the complexity of local context. For example, mean and standard deviation for the academic behaviours also uniformly decreased over time; although the difference was not statistically significant (p= 0.578), the descending directionality of academic behaviours and a reduced standard deviation over a short period of time was of concern. In addition, the data also revealed that standard deviations for the behaviour subscales are half that of the standard deviations reported for connectedness.

Despite significance differences being restricted to wellbeing, changes for the other five behaviour subscales are also of interest to the working party for the following reasons: multiple collections suggest what data is needed and when to collect it; an understanding of behaviour norms reinforces the notion that change is a complex issue; and the importance of understanding local contexts is paramount to identity the nature of support priorities as well as the targeting and timing of support and resources. Overall, such data represents normative baseline data for students who are at a significant transition period which demands adjustments to a new setting and unfamiliar academic expectations. However, the possibility that not enough time at university had passed to represent established university behaviours, necessitated that student behaviours with sub-optimal alphas were not deleted at this time.

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Such information was useful to the working party to challenge, or confirm, existing understandings.