Data analysis

Data were analysed using Predictive Analytics Software (PASW) version 18. No inferential analyses were carried out until data collection was complete, to prevent bias and preserve the false positive error rate. For all statistical analyses, an alpha of 0.05 was set to control for the Type 1 error rate (Warner 2008).

4.11.1 Descriptive Statistics

Descriptive statistics were generated to describe the demographic and clinical characteristics of the entire sample and to examine the equality of the randomised groups. Between-group characteristics were analysed and compared using chi-square tests for categorical data, and are presented as raw numbers and percentages. Independent samples t-tests were used to analyse and compare continuous data, the results of which are presented as means and standard deviations. For all t-tests, Levene’s test for equality of variances was tested to ensure that the variability of scores for each group was similar. When the assumption of homogeneity of variance was violated (indicated by a significant result) this suggested that the variances of scores between groups were not equal. In this situation the result reported was that of equal variances not assumed.

The assumption of normality of distribution was assessed by examining histograms and p-p plots for each continuous variable. For non-normally distributed variables, a Mann Whitney U test was used to compare medians.

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According to Field (2011) it is not uncommon for scales measuring psychological attributes to be abnormally distributed. Thus, for knowledge, attitude and belief scores at baseline, the mean, median and interquartile range scores were reported. Raw numbers and percentages were used to present the results of individual questions from the ACS Response Index at baseline and study end (12 months). Knowledge scores were presented sequentially, from the most well-known to the least well-known symptoms. At all three time-points the unadjusted mean scores, standard deviation and confidence intervals were calculated. These are presented in the appropriate appendices.

4.11.2 Analysis of variance (ANOVA)

Repeated measures analysis of variance (ANOVA) was used to test whether there was a difference in ACS patients’ knowledge, attitudes and beliefs about ACS between those randomly assigned to the control and intervention groups at 3 and 12 months after the intervention. This procedure was chosen over multiple t-tests, as the mean scores for the same people were being tested on more than two occasions (Pallant 2007, Polit & Beck 2010) and the use of multiple t-tests increases the probability of making one or more Type I errors (Polit & Beck 2010, Field 2011). This is a false positive error, whereby one can conclude that there is a difference between the groups, when there is none (Pallant 2007, Polit & Beck 2010).

Assumptions of ANOVA

The initial step involved testing that the general assumptions of ANOVA were met. This included ensuring that the dependent variable was measured using a continuous level variable and that random assignment to the control or intervention group occurred (Pallant 2007, Field 2011). Any differences between randomised groups at baseline were adjusted for at the analysis stage. A further assumption of ANOVA refers to independence of observation and measurement. In repeated measures designs, scores in the intervention group are expected to be non-independent for a given participant, but measurement between participants should be independent (Pallant 2007, Polit & Beck 2010), as was the case in this study.

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The assumption of normality of distribution was assessed by examining histograms for each dependent variable and they appeared to be approximately normally distributed. The Central Limit Theorem asserts that with sufficiently large sample sizes, sampling distributions of means are normally distributed, regardless of the distributions of variables (Tabachnick & Fidell, 2007). Furthermore, distribution is considered normal when the mean, trimmed mean and the median are nearly equal (Tabachnick & Fidell, 2007), as was the case for each of the knowledge, attitude and belief variables used in this study’s analyses.

Another assumption of ANOVA is the assumption of sphericity and the homogeneity of variances. This assumption tests that the variance in the groups being compared, is equal in the population (Polit & Beck 2010, Field 2011). For every case of ANOVA, this was tested using Mauchly’s test. When this assumption is violated, the suggestion is that the variances of scores between the groups are not equal. In this situation, SPSS produces a choice of three corrections, which can be applied to produce a valid F-ratio (Field 2011). The first two options include the application of either the Greenhouse-Geisser correction or Huynh-Feldt correction. Another option is to use multivariate test statistics, which are not dependent on the assumption of sphericity. Stevens (2002) advocates the use of a univariate approach when there is a small violation of sphericity (>0.7). As the violation of sphericity was minimal in the analyses in this study, a decision was made to report the Greenhouse-Geisser corrected result. However, it is worth noting that the effect of the intervention in this study, remained statistically significant at p<0.001, in all three options. Furthermore, it is noteworthy that analysis of variance is reasonably robust to violations of this assumption; provided the sample size is reasonably large and groups sizes are somewhat similar (Pallant 2007, Polit & Beck 2010). This was the case in this RCT.

118 Repeated measures ANOVA

Three separate repeated measures ANOVAs were used to analyse each dependent variable in turn. Dependent variables were: knowledge scores at baseline, 3 and 12 months for the first repeated measures ANOVA; attitude scores at baseline, 3 and 12 months for the second repeated measures ANOVA; and belief scores at baseline, 3 and 12 months for the third and final repeated measures ANOVA.

Comparison of mean scores from the ACS Response Index were analysed with group assignment (with two levels, control and intervention) as the independent variable for each ANOVA. Covariates that were significantly different between the groups at baseline (age, education level, employment status, health insurance status and the presence of diabetes) were adjusted for within the model.

ANOVA results are presented as estimated marginal means and confidence intervals, for each dependent variable separately, after adjusting for covariates. Due to the number of primary hypotheses being tested (three), a Bonferroni adjustment was made and the significance level was reset for these analyses at p ≤0.0167. Effect size was calculated and reported using partial eta squared (η2

). This statistic indicates the proportion of variance of the dependent variable that is explained by the independent variable (Pallant 2007). A result of η2=.01 was considered a small effect size, η2_{=.06 a moderate and η}2

=.14 a large effect size, as guided by Cohen (1988) (Pallant 2007, Polit & Beck 2010). The results of these analyses are presented in Chapter 5.