Dependent Variable Independent Variable B SE Β t p
Reflective and Complex Openness .24 .09 .21 2.78 <.01
Agreeableness .20 .09 .17 2.18 <.05
Note: Adjusted R2= .09, F (7,177) = 3.47, p< .01
Intense & Rebellious Openness .45 .15 .24 3.05 <.01
Note: Adjusted R2= .03, F (7,177) = 1.82, p>.05 (ns)
Upbeat & Conventional Agreeableness .39 .11 .27 3.51 <.01
Openness -.33 .11 -.23 -3.09 <.01
Note: Adjusted R2=.10, F (7,177) =4.05 p< .001.
Energetic & Rhythmic Extraversion .49 .12 .32 4.00 <.001
3.17 Identity dimensions to predict music preferences
The generally low levels of significance of both Commitment and Exploration to predict music preferences were disappointing. Both the studies carried out by Rentfrow and Gosling (2003) and Zweigenhaft (2008) found that a preference for Upbeat and Conventional music is associated with conservative self-views, eschewing liberal ideology or rejecting consideration of alternative viewpoints. Therefore, it was hypothesized that negative scores on Exploration in conjunction with positive scores on Commitment would be significant, but the hypothesis was not supported. Regression analysis revealed that for preferences for Upbeat and Conventional music, Exploration was a significant positive not a negative predictor as expected which is consistent with the earlier correlation analysis. In addition, Commitment was not a significant predictor which was unexpected. These results clearly differ from both the Rentfrow and Gosling (2003) and the Zweigenhaft (2008) studies, where significant negative correlations were found between Upbeat and Conventional music and Openness.
Openness and Exploration as positive predictors for Reflective and Complex music respectively were expected given the strong positive correlations identified earlier between this identity dimension and Openness. This supports previous research which has suggested that individuals who give high ratings to complex music are more likely to be stimulated but are open-minded to a breadth of ideas and different cultures, and who enjoy engaging with difficult art forms and complex ideas (Bourdieu; Chamorro- Premuzic & Furnham, 2007, Chamorro-Premuzic et al, 2009, 2012; North & Hargreaves, 2007a, 2007b, 2007c; Peterson & Kern, 1992; Peterson & Simkus, 1992; Rentfrow & Gosling 2003, Zweigenhaft, 2008).
The Energetic and Rhythmic music dimension is perhaps less easy to analyse. Extraversion was a very strong predictor followed by Agreeableness, but both Commitment and Conscientiousness were significant negative predictors. These results appear to suggest that individuals who give high ratings to
this music are gregarious, sociable pleasant and unreserved who are less worried about their
responsibilities and ‘doing the right thing’. In a negative light, these same individuals may also be seen as self-centred, frivolous, irresponsible and over-bearing. Yet the reasons behind why individuals who share these individual differences prefer this type of music, clearly requires further examination. Age does not appear to be a factor as it is not a significant predictor for the whole population and neither is it a factor even when the age groups were bifurcated into younger and older age groups. The answer may lie in the genesis of the music genres. Arguably, four of the five genres that make up this dimension (funk, rap/hip-hop, reggae and Soul/R&B) are largely made up of music that may be considered as music of black origin, the exception being dance/electronica. However further investigation and exploration of the data in relation to racial category and personality typology is beyond the scope of this thesis primarily because of the design of the demographic data form. There were only three different types of racial categories available on the demographic data form (white, other and black). These three categories were solely used for the estimation of IQ assessment tool. It is accepted that the term ‘other’ is rather ambiguous and to reduce the broad array of ethnicity into just three categories is self-limiting.
Consequently, this is why no further analysis has been made in relation to the relationship between race and music preference, but further research may explore this relationship in greater detail and in fact, go beyond self-descriptions of ethnicity to examine the influence of cultural experiences on the trajectory of the music taste palate.
3.18 Age Differences
Analysis also reveals that levels of significance in relation to the identity dimensions are not always consistent across the age groups. When I compared the Commitment and Exploration scores for each of the age groups were compared using independent-samples t-tests, older participants demonstrated significantly higher scores for both identity dimensions than the younger participants. For the
Commitment dimension, MeanComm>=25years= 61.39, SD10.41, t = 2.21 (df= 761), p< .01, 95% CI .20 to 3.41, though the magnitude of the effect was small (r= .08). The scores for the Exploration dimension
show similar results with only a small magnitude of effect, MeanExpl>=25years= 62.36, SD 10.15, t = 2.86 (df= 267), p < .01, 95% CI .73 to 3.98 (r=.17). These results appear to support the notion that for some adults, identity choices are not a fixed phenomenon. If there had been significantly higher Exploration scores for the younger age group and higher Commitment scores for the older age group, then this would suggest that in later years, tastes and choices pertaining to one’s identity become fixed after a period of searching and repositioning of one’s identity. However these results do not support that notion. Although the higher levels of Commitment in the older age group would suggest that there is a degree of consolidation in response to maturation, this is not matched by lower levels of Exploration. In fact, as is evident quite the opposite has been found. The higher levels of Exploration in the older age group also appear to suggest that for some older individuals, choices are not static but under constant scrutiny and re-examination. These results support Erikson’s (1968) notion that through a continual interplay of the two identity dimensions, identity development remains active throughout the lifespan as individuals (re-) examine and (re-)consolidate their choices in response to the psycho-social challenges of maturation (Erikson, 1950, 1968; Fadjukoff, 2010; Stephen et al, 1992).
Analysis also reveals that levels of significance are not always consistent across the age groups. For example, Openness was a significant predictor across both age groups for all music dimensions except for the Energetic and Rhythmic music. In addition, for younger participants who expressed preferences for Upbeat and Conventional music, Openness was not a predictor. Agreeableness was also a significant predictor across the four music dimensions except for Intense and Rebellious music and older participants who expressed preferences for Energetic and Rhythmic music. To compare the mean ratings of the two separate age groups on the music dimensions, independent-samples t-tests were carried out. Results demonstrate that significance was achieved only in the Reflective and Complex dimension with older participants, those aged 25 years or more, giving higher ratings than the younger participants: Mean>=25years = 4.79, SD.081, t=7.61, (df=761) p<.001, 95% CI .41 to .70. The
magnitude of the effect was moderate (r=.07). This suggests that significant age differences were found in this dimension alone, but this does not exclude the possibility that there may be different age related patterns on each of the 23 music genres. Therefore rather than run separate factor analyses of the dimensions for the two age groups to determine if there were significant differences in the clustering of music genres, I ran a one-way ANOVA on the four dimensions and age categories, followed up by ANOVA tests on each of the 23 music genres separately. Through these methods, I was able to identify significant age related patterns in a much simpler way rather than indulge in the complexities of factorial analysis.
3.19 Independent-samples t-tests of the 23 genres
A one-way between-groups analysis (ANOVA) was performed to explore the variance of mean ratings of the four dimensions using age category as the independent variable. Inspection of the mean ratings as demonstrated below in figure 3, reveal interesting patterns of music taste across the lifespan, some which were against expectations.
Figure 3. ANOVA of mean ratings of music dimensions