Page 59 - Exploring the Potential of Self-Monitoring Kidney Function After Transplantation - Céline van Lint

Patient Acceptance of a Self-Management Support System 57
hierarchical multiple linear regression to understand how much each factor explains the observed variation between patients’ behavioural intention. To understand the possible underlying factors, correlations between patients’ characteristics, factors from RTPTA model, and behavioural intention were analysed, for which Pearson correlation, Kendall rank correlation, or point-biserial correlation were used depending on the data level. Bootstrapping procedure with 1000-sample was applied to the above analyses. This procedure is less biased by deviation from normality assumptions and by extreme values in a small sample [53, 54]. Furthermore, the analysis included Cronbach’s  and principal component analysis to examine the constructs’ reliability. As there are currently limited reports available that directly support the proposed model, the principal component analysis helped to explore how well questionnaire items of the same construct correlated with each other, and how they related with items from other constructs. Note that at a later stage when the model is more mature, the application of statistical techniques such as confirmative factor analysis would be desirable [55].To examine the position of the rating on a 1-7 Likert scale, scores were compared with 4, which was regarded as the middle point of the scale.
RESULTS
Reliability and Principal Component Analysis
Table 2 shows the results of the reliability analysis for each construct at T1. The table also shows Cronbach’s  after items deletion for those constructs with initially low reliability level. The construct performance expectancy was split into three dimensions: 1) insight, meaning gaining insight into one’s renal condition; 2) health improvement, meaning gaining a better health status; and 3) time, meaning spending less time on outpatient appointments. As the dimension health improvement had a low reliability level, these items were excluded in further analyses.
A principal component analysis (PCA) was conducted on the remaining 20 independent items with orthogonal rotation (varimax). The Kaiser–Meyer–Olkin (KMO) measure verified the sampling adequacy for the analysis, KMO = 0.64, respectably above the 0.5 criterion. Two individual items had a KMO value clearly below the acceptable limit of 0.5 [56], indicating that these items share limited variance with other items. Bartlett’s test of sphericity Χ2 (153) = 662.24, p < .001, indicated that correlations between items were sufficiently large for PCA. The analysis resulted in five components with an eigenvalue over Kaiser’s criterion of 1. Combined they explained 73.26% of the variance. The factor loading after rotation, sampling adequacy, eigenvalue, the percentage of variance, and communality scores can be found in Additional file 2.
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