The exploratory factor analysis (EFA) procedure was carried out on data in the present study using SPSS software in order to investigate the validity of a four variable model for goal orientations. The EFA procedure measures for the presence of an underlying structure of correlations in the data, and is helpful to identify how the data ‘clumps’ together. The EFA is also used to compare with similar analyses conducted in other studies, so that the fit of the model to the data can be compared in different contexts.
Table 6-13 Comparison of Exploratory Factor Analyses of three studies of Achievement Goal Orientation
Elliott & McGregor (2001) study
Present study Lau & Lee (2008)
variable PAP MAV MAP PAV PAP MAV MAP PAV PAP M PAV
factor 1 2 3 4 3 2 1 4 3 1 2 PAP Q1 0.93 0.84 0.61 PAP Q5 0.89 0.78 0.30 0.79 PAP Q9 0.89 0.74 0.64 MAV Q2 0.9 0.84 MAV Q6 0.86 0.80 MAV Q10 0.84 0.83 MAP Q3 0.91 0.74 0.79 MAP Q7 0.9 0.82 0.67 MAP Q11 0.8 0.72 0.67 PAV Q4 0.87 0.76 0.59 PAV Q8 0.85 0.86 0.59 PAV Q12 0.74 0.50 0.75
Table 6-14 Variance accounted for by each factor according to Exploratory Factor Analyses of three studies of Achievement Goal Orientation
Elliott & McGregor (2001) study
Present study Lau & Lee (2008)
1 PAP 36.40% 1 MAP 33.34% 1 MAP 18.03%
2 MAV 21.30% 2 MAV 14.73% 2 PAV 15.00%
3 MAP 14.90% 3 PAP 10.80% 3 PAP 14.60%
4 PAV 8.80% 4 PAV 8.00%
TOTAL 81.50% 66.88% 47.63%
Principal factor loadings for achievement goals, and data concerning the percentage of variance accounted for by each factor, are shown in Table 6-13 and Table 6-14. The
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data from the present study is shown against comparable data from the Elliot &
McGregor (2001) study, and also the Lau & Lee (2008) study, conducted in Hong Kong. While the Elliott & McGregor and the present study share the same structure of items with the four variable model, the Lau & Lee (2008) study is based on the earlier three variable model, in which Mastery goals are not differentiated into Approach and Avoidance forms. It includes 9 of the 12 items appearing in the present study, so it was decided that a comparison with a study using a Chinese population sample, albeit in Hong Kong, rather than Taiwan, would still be of interest in this analysis. The authors stated that their Chinese version for the questionnaire was validated through exploratory factor analysis (EFA).
On comparison of the data in the tables, it is interesting to note that although the question items generally group together in the expected way, with factors clearly aligning with the different goal orientations, the factor loadings are somewhat less than those found in the Elliott & McGreggor study. The Chinese study does not include Mastery-Avoidance items, so the analysis just identifies 3 factors, yet together, these 3 factors account for just 47.63% of the variance. The Elliott & McGreggor paper on the other hand, using 12 items in a 2x2 matrix of achievement goal orientations, reported a 4-factor solution, which accounted for 81.5% of variance. Although the studies are not quite the same, they share 9 items in common (3 each for Mastery-Approach,
Performance-Approach and Performance-Avoidance), and a question arises as to why there should be such a reduced account of variance reported.
In the present study, which replicated the same items as the Elliott & McGreggor study, an EFA conducted in a similar way yielded a solution with 4 factors accounting for 67.01% of reported variance. The factor loadings were also relatively low compared to the American study, and this finding suggests that there are important questions to be asked about the application of the achievement goal theory to a Chinese population. It seems clear that the theoretical model for achievement goal motivation, while it can be supported by the data presented here, does not represent the whole picture.
On further investigation, in the present study, one question item in particular (PAV Q12) stands out, in that its principal factor loading aligns, albeit relatively weakly, with the
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Mastery-Approach questions, although it is intended as a Performance-Approach question item. An investigation of correlations between individual items revealed that item PAV-12 seemed to be working as a MAP item, having a higher correlation with other MAP items than with the other PAV items. This is also reflected in the
Confirmatory Factor Analysis, and so the relevant item was further inspected to see if there might be a likely explanation for this inconsistency. The wording of the question was checked, and it is interesting to speculate how either the wording or the translation may have contributed to this shift. The actual English wording is “My fear of
performing poorly in English classes is often what motivates me”, and the Chinese translation has been judged to be consistent. The back-translation of the Chinese
wording is: “Because I am worried about performing badly in English classes, this often arouses my motivation to learn”, and the verb form in Chinese for ‘perform’ is
consistent with the other two questions in the Performance-Avoidance category. However, the word ‘motivate’ is unique to this item, and it could well be this aspect which is viewed differently in this item.
In another study, Murayama, Zhou, & Nesbit (2009) conducted a comparison of Japanese and Canadian samples using the same AGQ instrument, and also found that the item PAV Q12 had an unexpected correlation with another factor, although in their case it was ‘mis-aligned’ with the mastery-avoidance goal factor, rather than the
mastery-approach one, and the effect occurred within the Canadian sample. The authors conjectured that affective components such as ‘fear’ or ‘anxiety’ were responsible for the deviation, and suggested that such components should be removed from the survey items in future research. This approach is also echoed by Elliot and Murayama (2008) who have also since recommended a full revision of the AGQ items in their AGQ-R in order to reduce this and other problems with the original AGQ. However, there still remains some question as to why this item would align with a different factor, and also why it appeared in the Canadian sample, but not in the Japanese. Donnellan (2008), in a psychometric evaluation of the AGQ with a sample of 780 in the United States, also reported the PAV Q12 item to be problematic due to its relatively low loading on its primary factor in the Confirmatory Factor Analysis.
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Apart from the aforementioned question item, all other items in the present study were broadly in agreement with the analysis of the Elliot & McGregor (2001) paper, although there remains the question of why the factor loadings are consistently lower in general, and that the 4 factors account for only 67% of variation, as opposed to 81% for the American study. The initial conclusion of this analysis therefore suggests that, although there is a measure of agreement between the studies in Chinese and American contexts, there is also significant variation, and this therefore brings into question whether the theoretical constructs underlying achievement goal theory need to be re-evaluated in a Chinese context.
The Chen & Zhang (2011) study mentioned above also reported some questions concerning the observed factor loadings. Although they were judged to be acceptable for supporting the four-goal model, their study also reported fairly low factor loadings for a number of items, particularly in the Performance-Avoidance category, suggesting possible issues concerning the validity of the theoretical construct for this sample. This study employed a revised form of the AGQ developed by Elliot (Elliot and Murayama 2008).
To summarize, while the EFA generally supports the applicability of the AGQ using the four variable 2x2 model of achievement goal orientation in the Chinese context, there is a significant drop in its ability to account for variance, and this suggests that more work needs to be done to identify both the causes of this uncertainty, and also how to improve the instrument in order to achieve higher explanatory power. Since the time of the administration of the survey in the present study, the revised AGQ has been developed by Elliot & Murayama (2008), and it would be interesting to see whether applying these revisions will increase the explanatory power of future surveys.