Data from the parent measure was analysed to determine if the proposed parenting scale was appropriate and sound. The scale included 80-items and was completed by 57 participants. The 80-items, made up of the proposed Helicopter and Uninvolved parenting scales, were subjected to Exploratory Factor Analysis, using an oblique rotation, to test if the two proposed parenting styles were evident in the data. Oblique rotation (promax) was employed, as there is no theoretical reason that factors should not be correlated. Phenomena in psychology are assumed to be generally interconnected, indicating that oblique rotations often better represent psychological research (Matsunaga, 2010), which was true in this case.
Suitability of Data for Analysis. As noted above, the number of items exceeded the number of participants in the current data set. This brings to the fore the issue of sample size in factor analysis. There is much debate over how large a sample size should be, nevertheless, the typical recommendation is that a larger sample size is better (Pallant, 2010). Small sample sizes may indicate less reliable correlations between variables, more tendency to vary
between samples and limited ability to generalise to other populations (Tabachnick & Fidell, 2007). Furthermore, it is suggested that the ratio of items to participants is an important consideration. There are suggestions of 10 participants per one item and also that the number of items should never to exceed the sample size (Aleamoni, 1976; Nunnally, 1978). However,
contrary to this belief, research indicates there is no absolute threshold for minimum sample size, with exploratory factor analysis demonstrating the ability to obtain reliable solutions with small sample sizes under 50 participants (De Winter et al., 2009). Increasing the number of items on a questionnaire has been shown to improve factor structure, particularly with low factor loading patterns, with no existing objection to having more items than participants. Furthermore, with regard to participant-item ratio, studies have indicated that having a greater number of items compared to participants may not impede factor analysis (De Winter et al., 2009; Marsh & Hau, 1999). The current analysis was conducted to identify the general trend between items, rather than confirm a factor structure. Therefore, given the exploratory nature of the current analysis and the review of the literature, exploratory factor analysis was
conducted despite the low participant-item ratio.
Data Consideration. Items were considered to load onto a factor if the absolute value was greater than .30 (Tabachnick & Fidell, 2007). Items above this criterion were retained to ensure that each item contributed a significant proportion in the measurement of the factor. Examination of items indicated that 17 items loaded below the recommended value. Each of these items was removed individually; item 2.77, 2.120, 2.109, 2.121, 2.71, 2.89, 2.119, 2.135, 2.81, 2.103, 2.131, 2.138, 2.70, 2.111, 2.107, 2.72, and 2.99. See Appendix K (Table 11) for a full list of removed items. Appendix L (Table 12) outlines the final 63 items that were retained and their significant factor loadings. Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were unable to be computed as the data set was too large, given the participant–item ratio. As noted above, the current factor analysis was exploratory in nature, to identify general trends; however, results should be interpreted
with caution given the lack of sampling adequacy and suitability of responses for the current analysis, as shown by KMO and Bartlett’s test of sphericity.
Determining Number of Factors to Retain.20 factors had eigenvalues greater than 1.00. However, examination of the scree plot suggested a two factor model. It is
recommended that when eigenvalues are plotted according to size, factors above the point of the elbow or inflection should be retained (Cattell, 1966). Appendix L (Table 12) presents the results from the factor analysis discussed below.
Following rotation and examination of the pattern matrix, a two factor solution was produced. This solution indicated that 27% of the variance across the 63 items could be explained by a two factor solution. Both factors one and two consisted of primarily one type of question, with over half of the questions within the factor being from one particular scale. Factor one, accounted for 16% of the total variance and consisted predominantly of items relating to the Helicopter parenting scale and was labelled Helicopter. Factor two accounted for 11% of the variance and consisted predominantly of items relating to the Uninvolved parenting scale and was labelled Uninvolved. From this, it can be established that the
proposed parenting categories are reasonably represented by the proposed question set, with no evidence to suggest other reasonable combinations. Therefore, the proposed parenting categories can be used for the analysis of this sample.
Dominant Parenting Styles. Table 2 reports the mean, standard deviations and internal consistency for the parenting measure. Results show that 100% of parents within the sample reported Authoritative parenting as their highest average score. This is perhaps not an unexpected result as Authoritative parenting is highly socially desirable. The questions on this subscale demonstrate socially desirable parenting behaviours, which may limit the
accuracy of results. The original study on the Parenting Practices Questionnaire, reported average scores that are comparative to the current sample (Robinson et al., 1995). Robinson et al. (1995) reported scores on the Authoritative scale almost double that of the Authoritarian and Permissive parenting scales.
Aim 3: To Describe the Type and Frequency of Cyberbullying and Risk Taking