Validity of the German Test Anxiety Inventory (TAI-G) in an Australian sample

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1 bs_bs_banner Australian Journal of Psychology 2015; 67: doi: /ajpy Validity of the German Test Anxiety Inventory (TAI-G) in an Australian sample Tony Mowbray, 1 Kate Jacobs, 1 and Christopher Boyle 2 1 Faculty of Education, Monash University, Melbourne, Victoria, and 2 School of Education, University of New England, Armidale, New South Wales, Australia Abstract Test anxiety (TA) is a prevalent issue among students that can result in deleterious consequences, such as underachievement. However, a contemporary measure that has been validated for use with Australian students seems to be lacking. This study, therefore, investigated the suitability of the German Test Anxiety Inventory (TAI-G) for use with Australian university students. While the original TAI-G contains 30 items and was designed to measure four factors (worry, emotionality, interference, and lack of confidence), differing factorial models have been supported in the literature using either the original or a shortened 17-item version of the measure. These differing TAI-G models were tested and compared in the current study via confirmatory factor analysis using 224 Australian university students. As expected, results supported the superior fit of the 17-item four-factor model. Additionally, the convergent validity of the measure was supported since measures of self-esteem, self-efficacy, and general anxiety were all found to correlate significantly with the TAI-G in the hypothesised directions. Finally, the finding that all of the TAI-G subscales had acceptably high reliabilities led to the conclusion that the 17-item TAI-G is a valid and reliable measure of TA in an Australian university population. Key words: German Test Anxiety Inventory, TAI-G, test anxiety, test anxiety Australia, test anxiety measurement Test anxiety (TA) comprises emotional, physiological, cognitive, and behavioural responses in examination-type situations (Zeidner, 1998). Examinations usually determine students professional future, making such high-stakes situations anxiety-provoking. When students experience high levels of TA, this can impact upon memory (Mowbray, 2012), which can subsequently reduce performance and well-being (Zeidner, 1998). Furthermore, TA is a worldwide phenomenon (Bodas & Ollendick, 2005) and fairly prevalent, with Knappe et al. (2011) finding that over 28% of 14- to 24-year-olds in a sample of 3,021 had fears regarding testing situations. Moreover, onset of isolated fears regarding test taking were found to rise steadily as students aged, plateauing at around 21 years, thereby highlighting the importance of exploring TA in university student populations. Consequently, the need for instruments that accurately assess TA is paramount. Bodas and Ollendick (2005) attest to the need to take into account the prevailing Correspondence: Tony Mowbray, Faculty of Education, Monash University, Building 6, Wellington Road, Clayton Vic. 3800, Australia. Tony.mowbray@monash.edu Received 26 September Accepted for publication 24 March psychosociocultural conditions when investigating this construct. This means that TA measures need to demonstrate validity within the intended cultural context. Validity can be described as the degree to which all the accumulated evidence supports the intended interpretation of the test scores for the proposed purpose (American Educational Research Association, American Psychological Association, and National Council on Measurement in Education, 1999, p. 11). The factorial structure of a commonly used TA measure (the German Test Anxiety Inventory; TAI-G) is subject to ongoing debate. Therefore, this study sought to assess the validity of the TAI-G in an Australian sample, which included testing and comparing multiple factorial models. The TAI-G The TAI-G is a multidimensional measure consisting of four subscales: worry, emotionality, lack of confidence, and interference. Worry refers to the cognitive manifestation of anxiety over performance, while emotionality refers to anxious emotional and autonomic reactions in relation to an examination. Development of the TAI-G included the addition of a lack of confidence subscale, defined by Hodapp (1996) as the test taker s belief in his/her inability to perform well in an upcoming exam. The interference subscale was also added, which relates to the presence of thoughts that

2 122 T. Mowbray et al. interfere with on-task performance and are not a component of worry per se (e.g., being preoccupied with thoughts in general that cause distraction). Last, the TAI-G contains items referring only to an individual s experience during the examination situation. Cultural variants and the TAI-G The TAI-G has been validated across a range of cultures that include Germany (Keith, Hodapp, Schermelleh-Engel, & Moosbrugger, 2003; Rohrmann, Bechtoldt, Schnell, & Hodapp, 2010), Spain (Sese, Palmer, & Perez-Pareja, 2010), Canada (Harpell & Andrews, 2012), South Africa (Ringeisen, Buchwald, & Hodapp, 2010), and America (Hodapp & Benson, 1997). However, findings from different cultural samples used in these studies may not be generalisable to Australian students given that the reported occurrence and characteristics of anxiety seem to differ as a function of culture. Cultural differences in anxiety have been observed in cognitive, affective, and behavioural components (Zeidner, 1998). Sharma and Sud (1990), for example, conducted a comparative study of TA through using a sample of 7,679 high school students from four Asian and five Euro- American countries. While TA was found to be universal, differences in the intensity and pattern of TA were found both between and within the different cultural groups. When comparing Euro-American cultures, for example, American students reported higher TA when compared with their Italian and German counterparts (p <.001), and reported greater worry, but not emotion, when compared with Turkish (p <.01) and Hungarian (p <.001) students. The authors concluded that observed differences reflected sociocultural and socioeconomic variants. These cultural differences may also be observed in studies using the TAI-G itself. Sese et al. (2010) reported deleting a poor fitting item on the 30-item TAI-G in order to obtain adequate structural validity in a Spanish sample, while in the Argentinian version, a total of two items were removed in order to obtain adequate fit (Heredia, Piemontesi, Burlan, & Hodapp, 2008). Furthermore, out of these studies, only two have examined the 17-item TAI-G (Harpell & Andrews, 2012; Hodapp & Benson, 1997), making support for the 17-item version limited. Moreover, the participants used in one study were Canadian students from Grades 7 to 12 (Harpell & Andrews, 2012), making generalisation to university populations questionable. In contrast, participants utilised by Hodapp and Benson (1997) comprised undergraduate university students from American and German samples. However, the American sample contained a disproportionate number of graduate students, which may have restricted the range of scores observed, with authors calling for the need for replication in other national or binational samples (Hodapp & Benson, 1997, p. 240). Given the impact of cultural variants on validity, limited number of validation studies for the 17-item TAI-G, and limitations of previous research, the question as to whether the 17-item four-factor TAI-G also demonstrates a superior fit in an Australian university population is still to be assessed. Convergent and discriminant validity indicators of TA Consistent negative relationships have been found between TA and measures of self-efficacy and self-esteem. Moreover, the extant literature theorises and demonstrates the TAI-G as a measure of trait TA as opposed to state TA (Keith et al., 2003). Therefore, a significantly greater relationship with trait anxiety as opposed to state anxiety may provide discriminant evidence that the TAI-G primarily measures trait TA. Replication of the direction and strength of these relationships in an Australian sample would, therefore, provide evidence of both convergent and discriminant validity of the TAI-G in Australia. While other variables exist that have been found to significantly predict TA, such as neuroticism (Chamorro-Premuzic, Ahmetoglu, & Furnham, 2008), such measures were not included in the current study due to inadequate replication of these results in comparison to constructs of self-esteem, self-efficacy, and general anxiety (Hembree, 1988). Additionally, measures of self-esteem, and particularly self-efficacy, have been used in validation studies of the TAI-G among multicultural samples (Hodapp & Benson, 1997; Keith et al., 2003; Ringeisen et al., 2010; Rohrmann et al., 2010), thereby enabling a more direct comparison between the results of the current study and previous research utilising the TAI-G. Aims and hypotheses The aim of this study was to establish the reliability and structural validity of the TAI-G in an Australian university student sample by comparing competing structural models of the TAI-G reported in the extant research via confirmatory factor analysis (CFA). Further, the external validity of the measure was investigated via inspection of bivariate correlations with well-known correlates of TA. It is hypothesised that when compared with a two-factor conception of TA (Liebert & Morris, 1967), the four-factor TAI-G will be a more valid and reliable measure of TA as confirmed in other multiethnic groups. Moreover, the 17-item, four-factor model of the TAI-G discovered by Hodapp and Benson (1997), and confirmed by Harpell and Andrews (2012), is predicted to fit the data best compared with the 30-item TAI-G, as was found in their research. Moreover, it is predicted that the best fitting models will specify four first-order factors of worry, emotionality, interference, and lack of confidence, and one second-order factor (TA) that accounts for the covariation between the firstorder factors (Hodapp & Benson, 1997; Keith et al., 2003). It is also predicted that self-efficacy and self-esteem will have significant negative relationships with scores on the

3 Validity of the TAI-G in an Australian sample 123 TAI-G. Finally, it is hypothesised that while the TAI-G will have significant positive relationship with both measures of state and trait anxiety, the strength of the former association will be significantly less than the strength of the latter since the TAI-G was developed as a measure of trait TA (Keith et al., 2003). METHOD Participants Participants were recruited via opportunistic sampling from various Melbourne universities in Australia. Participants were 224 university students, comprising 184 female (82%) and 40 male (18%) respondents, aged years (M = 21.3, standard deviation (SD) = 4.6). The majority of respondents were born in Australia (79%), and described themselves as Australian, or a mixture of both Australian and another ethnicity, while the majority of the remaining sample reported a range of Asian ethnic identities (12%). By way of participant self-report, the sample, on average, had attended university for just under 3 years (M = 2.8, SD = 1.7), with the majority having completed either a high school diploma (60%) or undergraduate (31%) study as their highest level of education obtained. The most frequently reported courses in this sample were arts (22.3%), medicine, nursing and health sciences (20.5%), and science (18.3%). Measures TAI-G (Hodapp, 1996) The TAI-G is a 30-item self-report measure of TA consisting of four subscales: worry (ten items; e.g., I worry about my results ), emotionality (eight items; e.g., I tremble with fear ), interference (six items; e.g., I easily lose my train of thoughts ), and lack of confidence (six items; e.g., I think that I will succeed ). Each item is rated on a 4-point Likert scale, ranging from 1 (almost never) to 4(almost always). In contrast to the other subscales, the lack of confidence subscale contains positively oriented items and is subsequently reverse-scored. A total score ranging from 30 to 120 is created by summing all four subscales, with higher scores indicative of greater TA. Reliability of the TAI-G subscales has consistently been found to be adequate, with Cronbach alphas exceeding.70 in multiple samples (.73.92; Harpell & Andrews, 2012; Keith et al., 2003; Ringeisen et al., 2010; Sese et al., 2010). General Self-Efficacy Scale (GSE; Schwarzer & Jerusalem, 1995) The GSE Scale is a self-report, 10-item questionnaire that relates to an individual s belief in his/her ability to overcome a task or cope with adversity (e.g., I can always manage to solve difficult problems if I try hard enough ). Each item is rated on a 4-point Likert scale, ranging from 1 (not at all true) to 4(exactly true). High scores indicate higher levels of selfefficacy. To capture self-efficacy in relation to examinations, a brief instruction was given, adapted from Keith et al. (2003), which stated: In relation to how you feel toward your studies, please complete the following. The structure of the GSE has been validated across 25 countries, including Japan, Peru, Spain, America, and Great Britain (N = 19, 120; Scholz, Gutiérrez-Doña, Sud, & Schwarzer, 2002). The GSE has demonstrated Cronbach alphas in the range of (Luszczynska, Gutiérrez-Doña, & Schwarzer, 2005). Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965) The RSES is a self-report, 10-item questionnaire that is designed to measure both positive and negative feelings about the self (e.g., On the whole, I am satisfied with myself ). Each item is rated on a 4-point Likert scale, ranging from 1 (strongly agree) to 4(strongly disagree). Low scores are indicative of low self-esteem. The structure of the RSES has been analysed across 53 nations, including Australia, and has been found to be largely invariant, supporting the crosscultural validity of this measure (N = 16,998; Schmitt & Allik, 2005). The RSES has demonstrated reliability, with a Cronbach s alpha of.89 in an Australian sample (Schmitt & Allik, 2005). State Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983) The STAI consists of both state and trait subscales containing 20 items each. The STAI state subscale measures how anxious the respondent feels in the present moment (e.g., I feel tense ), while the STAI trait subscale is designed to measure how anxious the respondent generally feels (e.g., I am a steady person ). Items on both subscales are measured using a 4-point Likert scale, with the STAI trait subscale ranging from 1 (almost never) to 4(almost always), and the STAI state subscale ranging from 1 (not at all) to4(very much so). Higher scores indicate greater levels of anxiety. Validity of the STAI has been demonstrated through significant correlations with other anxiety measures (Spielberger et al., 1983). Average reliability coefficients calculated over 75 studies are acceptably high for both the STAI state and STAI trait subscales (.91 and.89, respectively; Barnes, Harp, & Jung, 2002). Procedure Ethics was first obtained from Monash University Human Research Ethics Committee. Recruitment methods included advertisement via posters, social networking sites, and at the

4 124 T. Mowbray et al. start of lectures. Participants were required to click on a web site link or enter the link into their Internet browser taking them to the Qualtrics survey web site where the questionnaires were located. The 30-item TAI-G was administered online, along with the GSE, RSES, and STAI, with participation taking approximately 10 min. Completion of the questionnaires implied consent. Participants were required to record demographic information that included gender, ethnicity, and a question asking if students were within 2 weeks of an upcoming exam. The majority of students reported having no major exams within the next 2 weeks (76%), helping ensure the majority of students did not have elevated scores on the TAI-G due to the proximity of an impending examination. The order of presentation for the measures was randomised, with the exception of the TAI-G, which was always presented first. Participants were given the option to enter a prize draw as an incentive for participation. RESULTS Sample size for the current study was within recommendations of 5 10 participants per scale item (Streiner, 1994), particularly since the TAI-G items have demonstrated strong factor loadings in previous studies (above.60; Guadagnoli & Velicer, 1988). The internal consistency of each subscale and the overall TAI-G for both 17-item and 30-item models was acceptably high, with all values above.70 (Cronbach, 1952). Table 1 displays the descriptive statistics for each of the TAI-G subscales and total score. CFA CFA analysis on the data was conducted using the maximum likelihood estimation procedure with AMOS version 21 (IBM Corp., Armonk, NY, USA). Akaike s information criterion (AIC) was inspected to allow for comparison of non-nested models for goodness of fit. Difference in AIC values, whereby one model has a lower AIC value than another non-nested model, indicates a superior fitting model (Kline, 2010). AIC was used to compare the first-order models with the secondorder models to determine which model is preferred. Table 2 presents the results of the CFA on the different TAI-G models using the criteria outlined above. Different models of the TAI-G were tested to identify how retaining specific factors and variables impacted model fit. Initially, two-factor (emotionality and worry) models were tested in order to examine the earlier conceptualisations of TA (Liebert & Morris, 1967; Spielberger et al., 1983). One of the two-factor models incorporated the 18 items from the emotionality and worry subscales of the original TAI-G (Hodapp, 1996), and the other used the nine items retained from Hodapp and Benson s (1997) shortened version. The fourfactor models were then specified for the original 30-item Table 1 Descriptive statistics for the TAI-G No. of items M SD Range Reliability (α) Skew Kurtosis TAI-G (30-item) Worry Emotionality Interference Lack of confidence Total TAI-G (17-item) Worry Emotionality Interference Lack of confidence Total Table 2 Overall model fit indices Model χ 2 df p CFI SRMR RMSEA AIC ΔAIC Two factors: worry and emotionality (18 items) Two factors: worry and emotionality (9 items) First-order (30 items) a 1, , Second-order (30 items) b 1, , First-order (17 item) a Second-order (17 item) b Note. SRMR = standardized root mean square residual. a Model with four first-order factors: worry, emotionality, lack of confidence, and interference. b Model with second-order factor accounting for covariation between four first-order factors: worry, emotionality, lack of confidence, and interference.

5 Validity of the TAI-G in an Australian sample 125 TAI-G and the shortened 17-item TAI-G. Last, a secondorder structure was imposed on both four-factor models explaining the covariance between first-order primary factors, with the second-order factor labelled Test Anxiety. Chi-square was significant for all models tested, indicating poor fit. However, chi-square tests for perfect model fit, making this statistic highly stringent; therefore, alternative fit indices were examined (Kline, 2010; Tabachnick & Fidell, 2013). The two-factor 18-item model did not meet the criteria for good fit, and while the comparative fit index (CFI) value for the two-factor eight-item model indicated adequate fit the root mean square error of approximation (RMSEA) did not. The first-order 30-item model also failed to meet the required specification for CFI, but indicated better fit over the two-factor models as shown by the RMSEA. Upon closer inspection, lack of fit could have been due to some items loading onto more than one factor and high standardised residual covariances between some of the items, particularly items 2 ( I think about how important the examination is for me ; z = to 3.587), 6 ( I worry about whether I can cope with being examined ; z = to 3.752), and 30 ( I have the feeling everything is really difficult for me ; z = to 4.576). Item 6 was also seen to load onto the interference subscale as opposed to worry, and item 30 was observed to load more strongly onto the emotionality subscale than the interference subscale. In contrast, the first-order 17-item model showed acceptable model fit over all indices, with the exception of chi-square. The covariance between factors for the 17-item first-order model ranged from.18 to.48 (p <.001), with the lack of confidence factor demonstrating the weakest relationship with the other factors of the TAI-G. However, items 17 and 18 had ambiguous factor loadings and high standardised residual covariance, potentially reducing observed fit statistics. Given the moderate covariation between subscales (with the exception of the lack of confidence subscale), it was expected that a higher order factor accounting for this covariation would produce adequate model fit (Keith et al., 2003). As Table 2 shows, adding a second-order factor improved model fit as seen by the decrease in AIC for the 30-item TAI-G (Fig. 1; ΔAIC = 3.29). However, according to Burnham and Anderson (2004), this value just borders on being evidently less supported than the second-order model. This means the second-order model does not offer strong support for improved fit over the first-order model. Improved fit was also seen for the second-order 17-item TAI-G (ΔAIC = 3.57). Again, the observed small AIC value offers marginal support for the second-order model over the first-order model (Burnham & Anderson, 2004). Overall, the 17-item second-order TAI-G model provided the best fit, with parameter estimates for this model presented in Fig. 2. All items demonstrated significant and strong loadings (p <.001; Tabachnick & Fidell, 2013), with the existence of a higher order construct relating to the four secondary factors supported. Correlational data Subscale correlations ranged from.46 to.71 (p <.01) for the 30-item TAI-G and.33 to.63 for the 17-item TAI-G. The lack of confidence subscale demonstrated the weakest correlations with the remaining subscales for both versions of the TAI-G, with the strongest relationships seen among the worry and emotionality subscales. In particular, the worry and emotionality factors of the 30-item TAI-G demonstrated a strong relationship. Table 3 displays the intercorrelations of the subscales for both long and short versions of the TAI-G. The relationship between the 17-item TAI-G and the selected correlates of TA were all significant (p <.01) and in the expected direction. Lack of confidence demonstrated the strongest relationships with self-efficacy and self-esteem, while emotionality had the strongest correlations with all measures of general anxiety. The difference between state and trait anxiety when correlated with the overall TAI-G score was significant. A t-statistic was used to test for significant difference in correlation between the TAI-G and either subscale of the STAI (Chen & Popovich, 2002). A value of t = 3.04 indicated a significantly lower correlation between the TAI-G and the STAI state subscale in relation to the TAI-G and the STAI trait subscale (p <.005). This provides some support for the contention that the TAI-G is a stronger measure of trait anxiety factors as opposed to transient state anxiety. Table 4 reports the correlations of each chosen correlate of TA with the 17-item TAI-G and TAI-G subscales. To guide researchers and clinicians when attempting to quantify scores, Table 5 shows percentile intervals for each scale and their given score. Table 3 Correlations of TAI-G subscales by model Worry Emotionality Interference Confidence Worry Emotionality Interference Confidence Note. All correlations significant at the p <.01 level (one-tailed). Above the diagonal line are values from the 30-item TAI-G; below the diagonal are from the 17-item TAI-G. Table 4 Intercorrelations of the 17-item TAI-G subscales and TAI-G total with selected TA correlates GSE RSES STAIT STAIS Emotionality Worry Interference Confidence TAI-G total Note. All correlations significant at the p <.01 level (one-tailed).

6 126 T. Mowbray et al. Figure 1 Standardised solution of the 30-item TAI-G confirmatory model consisting of the four primary factors (emotionality, worry, interference, and confidence) and a second-order factor (test anxiety). Variances are given in brackets, factor loading located on the arrows, and squared factor loadings located at the top right of the variables and inside the factor ovals. DISCUSSION The current study investigated the validity of the TAI-G in an Australian university student population by examining indicators of both internal and external validity. Specifically, four-factor versions of the TAI-G were analysed, the original 30-item TAI-G (Hodapp, 1996), and the shortened, 17-item TAI-G (Hodapp & Benson, 1997). Another two versions of the TAI-G were explored, which attempted to replicate the two-factor structure of emotionality and worry as the components of TA (Liebert & Morris, 1967; Spielberger et al., 1983). Convergent and discriminant validity was also examined through correlation of the TAI-G with selected correlates of TA. As expected, the 17-item TAI-G showed superior fit above the models tested, including the 30-item TAI-G, a result consistent with previous research (Harpell & Andrews, 2012). Further, the addition of a second-order factor to both

7 Validity of the TAI-G in an Australian sample 127 Figure 2 Standardised solution of the 17-item TAI-G confirmatory model consisting of the four primary factors (emotionality, worry, interference, and confidence) and a second-order factor (test anxiety). Variances are given in brackets, factor loading located on the arrows, and squared factor loadings located at the top right of the variables and inside the factor ovals. Table 5 Percentile scores for the 17-item TAI-G subscales and TAI-G total 10th 25th 50th 75th 90th Worry Emotionality Interference Confidence TAI-G total four-factor models of the TAI-G resulted in improved model fit, indicating that the subscales of the TAI-G are representative of the higher construct TA (Hodapp & Benson, 1997; Keith et al., 2003). However, statistics observed after the addition of a second-order factor only weakly supported the presence of a second-order factor for both models. Moreover, the 30-item TAI-G did not adequately fit the data as predicted. This is in contrast to previous research, which has found the 30-item TAI-G to provide at least an adequate fit (Harpell & Andrews, 2012; Hodapp & Benson, 1997; Keith et al., 2003; Ringeisen et al., 2010; Rohrmann et al., 2010). Ambiguous factor loadings, that is to say, items observed to load onto more than one factor, and high standardised residual covariances for items 2, 6, and 30 appeared particularly problematic. Specifically, item 30 of the interference subscale was also found to have an ambiguous factor loading in previous studies (Hodapp, Glanzmann, & Laux, 1995; Keith et al., 2003; Sese et al., 2010). Thus, item 30 may represent a problematic item to be removed from the TAI-G. Despite the 17-item TAI-G providing adequate fit of the data, the fit statistics may be considered just adequate by some authors, such as Hu and Bentler (1999), who consider a CFI of.95 or greater and an RMSEA of.06 or less as indicative of a close fitting model. While the 17-item TAI-G achieved statistics close to these criteria, the values fell short. This seemed to be partly due to the association between some items on the emotionality and worry subscales, particularly items 17 and 18 due to ambiguous factor loadings and high standardised residual covariance. Moreover, these cut-off criteria are rules of thumb, with strict adherence potentially resulting in higher probability of type I error, as variables such as sample size and model complexity need to

8 128 T. Mowbray et al. be taken into account (Worthington & Whittaker, 2006). Marsh, Hau, and Wen (2004) also caution against rigidly applying these criteria and point out that the misspecified models used to establish the cut-off criteria by Hu and Bentler (1999) misspecified by a small degree and were not representative of real data. In addition, the finding that smaller sample size led to increased rejection of these slightly misspecified models indicates that the 17-item TAI-G provides a good fit for the data. Similar to previous research (Ringeisen et al., 2010), the interference and lack of confidence factors were found to have the weakest association with the remaining subscales for both versions of the TAI-G. Moreover, both factors had the lowest loadings on the secondary TA factor. With regard to interference, Hodapp and Benson (1997) reported lower factor loadings (.42.52) than what was found in the current study, but interference did not load as strongly on TA when compared with other confirmatory studies that analysed the 30-item TAI-G (.74.84; Keith et al., 2003; Ringeisen et al., 2010). All subscales of the 17-item TAI-G demonstrated high internal consistency, which is consistent with previous studies (Keith et al., 2003; Ringeisen et al., 2010). Unlike previous studies, the interference subscale showed higher item means and variances (refer to Table 1; Keith et al., 2003), and relatively normal score distribution, which reflect endorsement of the items in this subscale. This may be responsible for the interference subscale demonstrating good psychometric properties in this sample for both versions of the TAI-G, whereas previous studies have found interference to be psychometrically the weakest (Hodapp & Benson, 1997; Keith et al., 2003). With regard to the lack of confidence subscale, factor loadings did not show any improvement from the 17-item version to the 30-item version (refer to Figs 1 and 2). Moreover, the factor loading for this subscale onto the secondary TA factor was consistent with previous findings (Keith et al., 2003; Ringeisen et al., 2010). However, earlier research has found lack of confidence to be better conceptualised as separate to TA altogether (Hodapp & Benson, 1997; Keith et al., 2003). CFA models have shown improved fit when lack of confidence is placed separate to TA, as a correlate of self-efficacy under a higher order factor labelled self-esteem (Hodapp & Benson, 1997; Keith et al., 2003). The data reflect this trend, with lack of confidence demonstrating the strongest associations with self-efficacy and self-esteem in relation to the remaining subscales, in addition to the smallest inter-scale correlation for both versions of the TAI-G. This pattern of results, however, may be due to the coding of the items in the lack of confidence subscale, which are coded positively while the remaining subscales are coded negatively. The self-efficacy scale used in this study also contains positively coded items; thus, response tendencies may be partly responsible for the lack of confidence subscale having relatively low interscale correlation and the strongest association with self-efficacy. As expected, relationships with the TAI-G and measures related to TA were significant and in the expected direction. The TAI-G demonstrated significant negative relationships with measures of self-esteem and self-efficacy. Moreover, significant positive associations were found between the TAI-G and measures of trait and state anxiety. As predicted, the TAI-G correlated significantly higher with the trait subscale on the STAI than the state subscale. This is in line with theory (Hodapp, 1996; Hodapp et al., 1995) and the findings of Keith et al. (2003), who found the TAI-G measured stable interindividual differences (trait anxiety) to a greater extent than situational specific anxiety (state anxiety). This provides convergent and discriminant validity evidence for the assertion that the TAI-G measures trait test anxiety and is less influenced by situational factors. Limitations of this study include sampling issues, as it utilised students primarily from Monash University and a significant majority of those participants were female, with males being underrepresented in the sample. A greater number of females are enrolled at Monash University (Monash University Office of Planning and Quality, 2013), but even when taking this larger ratio into account, the sample was still unrepresentative. Furthermore, data on the nature of enrolment (i.e., internal vs external enrolment) were not taken, so while it is assumed the majority of participants were enrolled internally, the actual number cannot be quantified. Moreover, while sample size could be considered adequate, larger sample sizes of 300 or more have been recommended when conducting CFA (Tabachnick & Fidell, 2013), and therefore caution should be taken when generalising these outcomes. Future studies may expand on the current design by attempting to incorporate a more diverse university sample with a larger number of male participants. Further, constructing and examining a lack of confidence subscale that is negatively coded, thereby being consistent with the remaining subscales, will help clarify the impact item wording has on the weaker relationship observed between the lack of confidence subscale and the remaining subscales. In conclusion, the findings of the current study are consistent with previous research supporting the four-factor conceptualisation of TA, as well as the use of the 17-item over the 30-item TAI-G. Furthermore, considering sample limitations, results partially support the 17-item TAI-G as a valid and reliable scale for use in ascertaining TA in Australian university students.

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