State test anxiety (STA) is the anxiety one experiences in evaluative situations (Zeidner, 1998). Many researchers believe that STA is involved in several psychosocial phenomena. For example, the stereotype threat theory postulates that test anxiety may mediate the deleterious effect of stereotype threat on performance (Smith, 2004). Another example is self-efficacy theory, which considers that the impact of self-efficacy on performance is at least partially mediated by state anxiety (Bandura, 1989). Testing such hypotheses requires a tool for measuring STA. Currently available questionnaires have a number of limitations, one of the most important being the fact that they are often based on outdated theories.

• Test Anxiety Inventory Spielberger Pdf Files 2017

Recent test anxiety research has shown that test anxiety consists of two components: worry (concerns about one’s performance) and emotionality (perception of autonomic arousal and unpleasant feeling states, see Zeidner, 1998). However, several measures of STA, such as the widely used State-Trait Anxiety Inventory (STAI; Spielberger, 1983) or the Comparative Anxiety subscale of the Test Attitude Survey (TAS; Arvey, Strickland, Drauden, & Martin, 1990), do not make a distinction between these two components. In another scale, the Anxiety subscale of the Test Emotions Questionnaire (TEQ; Pekrun, Goetz, Perry, Kramer, Hochstadt, & Molfenter, 2004), the emotional and cognitive components of STA are conceptually distinguished but the subscale only provides a single global STA score. This point is of importance since worry and emotionality are not involved in the same phenomena. For example, worry and emotionality are known to have different, even opposite, effects on test results in that worry can impair performance (e.g., Blair, O’Neil, & Price, 1999), whereas emotionality can improve it (e.g., Hong, 1999).

Thus, a global measure of test anxiety may be inadequate for predicting performance and may lead to falsely concluding a null effect. Some recent questionnaires have been designed to measure worry and emotionality in STA; however, they have practical limitations that often conflict with organizational considerations in experiments or field studies. First, the time available for measuring STA before or after a performance, for example an academic exam, is often limited. Second, the scale used must be appropriate for the population studied (children, students, adults, etc.) and suited to the test-situation (academic exam, psychological testing, recruitment, etc.). To the best of our knowledge, none of the existing scales meets these two criteria.

The Revised Worry-Emotionality Scale (RWES; Morris, Davis, & Hutchings, 1981) and the Hong and Karstensson (2002) short state version of the Test Anxiety Inventory (TAI; Spielberger, 1980) are specific to academic test anxiety; therefore, they cannot be used in other situations (e.g., memory testing of the elderly). Although it can be used in all test settings, the Cognitive Interference Questionnaire (CIQ; Sarason, Sarason, Keefe, Hayes, & Shearin, 1986) is a post-performance report, which can be problematic as post-performance anxiety assessments can be biased by perceived performance or self-handicapping (Smith, Snyder, & Handelsman, 1982).

The Endler Multidimensional Anxiety Scale-State (EMAS-S; Endler, Edwards, & Vitelli, 1991) and the Self- versus Other-Referenced Anxiety Questionnaire (SOAQ; Proost, Derous, Schreurs, Hagtvet, & De Witte, 2008), which are completed just before taking a test, contain a large number of items (20 and 24, respectively), thereby increasing the risk of disrupting manipulation effects. Eighteen items were created or selected from existing scales or from test anxiety definitions. Ten items described the presence or absence of the emotional manifestations of STA, for example, the presence of autonomic arousal (expressions such as “my hands are moist”) and the absence of an unpleasant feeling state (expressions such as “I feel relaxed”). Eight items described the presence or absence of cognitive manifestations of STA: negative expectations, concerns about oneself, the situation at hand, and potential consequences ( e.g., “I can’t stop myself thinking the test will go wrong”, “My mind is untroubled”). These items were randomly arranged in a questionnaire and presented with the instruction to evaluate how well each of the statements describes the participant’s present condition using a 6-point scale ranging from 1 (“not at all”) to 6 (“very well”). Item Selection Methods. Once the applications had been registered, the interviewer invited the candidates to complete a short and anonymous questionnaire.

Before the test outset, the candidates who were willing to participate in the study completed a questionnaire containing socio-demographic questions (age, gender and activity status) and the 18 initial items of the EAEE. In addition, five items describing performance expectancies ( e.g., “I expect to perform well on this test”) were included with the anxiety items. This was done for two reasons. First, it enabled us to test the reliability of a performance expectancy scale that we planned to use in later validity studies of the EAEE. Second, although performance expectancy and test worry are two distinct theoretical constructs, they are related and items measuring them may partially overlap ( e.g., “I’m afraid of doing poorly on this test”).

Thus, the inclusion of the expectancy items in the EAEE factor analysis helped us to identify worry items that measure worry more than they measure performance expectancy. We excluded four anxiety items from the factor analysis because of the floor effect.

We then submitted the 19 remaining items to an exploratory factor analysis, testing for a three factors solution. The unweighted least squares parameters estimation method was chosen because several items were not perfectly normally distributed. Sample adequacy for factorization was good according to the Kaiser-Meyer-Olkin index (KMO =.90) and Bartlett’s sphericity test (? 2 (171) = 1531.62, p. Several variables associated with STA were measured to test the construct validity of the EAEE. Depending on the study (see the Methods section), the variables collected were participants’performance expectancy for the test, their performance on the test, and their gender.

Several studies have shown that in test settings women report more state worry and emotionality than men do (Bors, Vigneau, & Kronlund, 2006; Hong & Karstensson, 2002; Malpass et al., 1999; O’Neil & Abedi, 1992); therefore we hypothesized that women would have higher EAEE-C and EAEE-E scores than men. The participants completed a questionnaire just before starting an oral examination (reading a paper). The questionnaire included the EAEE, the STAI-State (? =.96) and a performance expectancy scale (inter-item r =.77, p.

Fifty-one of the participants completed a questionnaire at the beginning of a lecture. The questionnaire included the EAEE, which had to be completed with reference to an end-of-year written examination that would occur tree months later, and the STAI-Trait (? Three months later, just before the start of the examination, the 114 participants completed a questionnaire including the EAEE, the performance expectancy scale used in Study 1 (inter-item r =.72, p. Confirmatory factor analyses (CFA) were conducted on the participants’responses to the six EAEE items for all but one of the studies (Study 4), which was omitted because of insufficient sample size ( N = 36). CFAs were conducted using either a maximum likelihood or a robust parameters estimation method, depending on data multinormality (according to Mardia’s coefficient).

Test Anxiety Inventory Spielberger Pdf Files 2017

Participants with missing data were deleted (6% at most). For the CFAs conducted using the EQS maximum likelihood parameters estimation method, we displayed the maximum likelihood Chi-Square statistic (? 2) as an exact-fit index and five approximate-fit indices.

These indices included two incremental fit indices (the comparative fit index CFI and the non-normed fit index NNFI), one absolute fit index (the adjusted goodness of fit index AGFI), and two absolute misfit indices (the root mean square error of approximation RMSEA and the standardized root mean squared residual SRMR). CFAs conducted using the EQS robust parameters estimation method provided robust fit indices: the Satorra-Bentler Chi-Square statistic (S-B? 2), the robust CFI (CFI.), NNFI (NNFI.) and RMSEA (RMSEA.). We followed Kline’s (1998) recommendations for interpreting the robust goodness of fit indices; that is to say, AGFI, CFI and NNFI values greater than.90 were interpreted as indicating a good fit, as were SRMR values of less than.10. RMSEA values of less than.08 were taken to indicate a satisfactory fit and values below.05 were taken to indicate a good fit.

For each study, the expected two-factor model was tested first. Given the factor correlations observed (.41. ItemFactorStudy 1aStudy 2Study 3aStudy 5Study 6aaStudy 6ba?SE?SE?SE?SE?SE?SEI’m thinking of things EAEE-C.50.18.50.19.70.13.40.13.47.20.54.10 I’m preoccupied withI can’t prevent myself from EAEE-C.92.11.82.21.51.16.71.12.76.10.83.07 thinking the test will go wrongI’m afraid of losing EAEE-C.85.13.64.20.68.16.67.14.72.10.79.07 my head during the testI feel relaxedEAEE-E.87.11.66.17.72.14.83.09.77.11.78.07I feel my heart beating fastEAEE-E.70.14.55.17.70.12.68.11.47.10.50.08I feel calmEAEE-E.95.10.81.19.79.12.79.10.72.11.83.07Note.

Study 6a and Study 6b correspond to the figure and the story recall task situations, respectively.aRobust estimation method used. p. Table 3 shows that the two-factor model adequately fits the data in all but two of the studies (Study 5 and Study 6), with each item significantly contributing to its factor (Table 4). 2-difference showed its relative superiority over a single-factor model in all but one of the studies (Study 3), in which the factor correlation was unusually high ( r =.97) (Table 3). We further examined the reasons why the model did not show an acceptable fit in Study 5 and Study 6.

In Study 5, despite the significance of the? 2, the approximate fit indices appeared satisfactory.

The case of Study 6 appears more problematic as the two test situations (figure and story recall tasks) in this study gave poor fits for both the two-factor and single-factor models. We carried out Lagrange Multiplier and Wald tests to determine whether parameters should be added or dropped in order to improve their overall fit (Byrne, 2006).

For both test situations the results showed that allowing the EAEE-E’s “I feel my heart beating fast” item to load on the Worry factor would significantly increase the overall fit (? 2 (1) = 77.123, p.05; RMSEA. =.02; SRMR =.03; AGFI =.97; NNFI. =.99; CFI. = 1). It gave a better fit than the single-factor model (S-B? 2 (5) = 56.70, p.46, ps.

The convergent validity of the EAEE appears good, as each of the two EAEE scores strongly correlated with the other three anxiety scales. Indeed, the EAEE-C scores were significantly correlated with the STAI-State scores in Study 1 ( r =.82, p. We assessed the criterion-related validity of the EAEE in Study 3 by comparing the EAEE scores obtained on two occasions, namely several months before an end-of-year examination and just before the start of this examination.

Criterion-related validity is satisfactory, as the results showed that EAEE-C and EAEE-E scores were significantly higher in the test setting ( M SD = 4.64 1.23 and M SD = 4.58 1.23, respectively) than outside the test setting ( M SD = 3.07 1.12, t 50 = 8, p. We assessed the construct validity of the EAEE by the means of three indices. First, EAEE-C and EAEE-E scores were regressed on the participants’gender (male and female, coded 1 and 2, respectively) in order to test for the effect of gender on state test worry and emotionality (with women reporting higher anxiety levels than men). These relationships were not tested in Study 1, Study 3 or Study 4 because of an insufficient number of cases in each cell. Second, the expected correlation between performance expectancies and worry was tested using zero-order correlation analyses involving EAEE-C and performance expectancy scores. Third, the participants’actual performances on the tests were regressed on their EAEE-C scores to test the relationship between worry and performance.

In addition, the relationships between EAEE-E scores and performance expectancy scores, and between EAEE-E scores and actual test performance were examined from an exploratory perspective. Each statistical analysis was carried out after eliminating outliers (3% at most), which were diagnosed using distance, leverage and influence statistics (Howell, 1998). Relationship between EAEE scores and performance expectancy. The results showed negative correlations between performance expectancy and EAEE-C scores for all the studies ( rs.05). Relationship between EAEE scores and performance. The relationships between each of the two EAEE scores and the participants’actual test performance were examined by using linear regression analyses to predict performance scores from EAEE-C scores and from EAEE-E scores. EAEE-C scores and EAEE-E scores were entered in the same regression model in order to avoid possible confounded or suppressor effects.

In Study 6, because there was a problem with one emotionality item in the factor analysis, the EAEE-E scores were replaced with an emotionality measure consisting of the means of the scores for the two reliable EAEE-E items. In addition, perceived preparedness was included as a predictor in the Study 3 and Study 5 analyses in order to statistically check its influence on performance. This was done to ensure that the correlation expected between anxiety and performance was not explained by this common determinant. The results showed that the EAEE-C scores negatively predicted performance in all of the studies except the study conducted on the candidates for the written part of the driving test (Table 5).

For the emotionality component of test anxiety, the results of all but one of the studies revealed a positive correlation between the EAEE-E scores and actual test performances. Study (N)SubscaleBSE?tStudy 2 (93)EAEE-C-0.5690.292.203-1.945 t EAEE-E1.1220.311.3773.605.Study 3 (105)EAEE-C-0.8490.420.219-2.004.

EAEE-E 1.7220.430.437 4.001.Study 4 (35)EAEE-C-2.0310.726.590-2.799. EAEE-E 2.1650.780.585 2.776.Study 5 (220)EAEE-C 0.1780.175.086 1.017 EAEE-E-0.1240.170.062-0.731Study 6a (212)EAEE-C-0.3110.128.183-2.435. Emotionality 0.0520.147.026 0.350Study 6b (212)EAEE-C-5.4161.347.322-4.020. Emotionality 1.8951.453.104 1.304Note. Perceived preparedness was included as a predictor in the Study 3 and Study 5 regression analyses.t p. In general, the results support the construct validity of the EAEE-C subscales as the expected correlations (reported in the literature) with gender, test performance expectancy and actual test performance appeared in almost all the studies.

Exceptions were found in Study 6, where the results did not show the expected gender effect, and in Study 5, where we did not find the correlation between state test worry and performance that is commonly reported in the literature. Furthermore, the construct validity of the EAEE-E is supported by the gender effect we found in almost all the studies.

Although the inconsistencies reported in the literature indicate that these relationships cannot be used as evidence of construct validity, we nevertheless found that the EAEE-Es negatively correlated with performance expectancy scores and positively correlated with actual test performance in all but one of the studies. The objective of these studies was to draw up, and then evaluate the validity of a short state test anxiety scale that distinguishes between the worry and emotionality components of STA and that can be applied in various test settings.

For this purpose, we created an initial item pool which we then reduced and submitted to a first confirmatory factor analysis. These construction steps led to the EAEE, a reliable and factorially valid 6-item scale comprising both a worry and an emotionality subscale. The EAEE was administered to several samples in various test settings, along with other measures that would allow us to confirm its factorial validity, reliability and sensitivity and to test its convergent, criterion and construct validity. The results of the validation studies generally supported the psychometric properties of the EAEE.

First, its bifactorial structure was confirmed in various test settings, as was the reliability and sensitivity of its two subscales. Second, the EAEE-E and EAEE-C subscales appeared valid as they converged with valid state, trait and test anxiety measures, they were sensitive to the presence or absence of a test and, with few exceptions, they correlated with variables generally linked to STA, such as gender, performance expectancy and test performance. Nevertheless, several points need to be discussed. First, the factorial structure of the EAEE showed a satisfactory fit in all but one of the studies, namely the one involving elderly participants. It should be noted that the instructions for the EAEE have to be read very carefully as the test does not have a simple response scale. It includes items with different directions (absence or presence of anxiety signs) and asks participants to evaluate to what extent each statement describes their condition.

The examiners who collected the data for this study frequently reported problems with the administration of the scale. For example, it was noted that participants often reacted negatively to the scale, with many stating that they did not care about the result; therefore, they completed the scale as quickly as possible in order to start the memory tasks.

Thus, it is possible that the older participants did not complete the EAEE carefully enough. As the bifactorial structure was confirmed in the other four studies, it is unlikely that these results call into question the factorial validity of the EAEE.

Nevertheless, the results of this study have to be taken with caution and further studies are needed in order to assess the factorial validity of the EAEE in such test situations. Second, the results revealed the expected negative relation between EAEE-C scores and actual test performance in all of the studies except the one involving candidates for the written part of the driving test. In this case, the absence of a relationship may be due to the nature of this test. Most test anxiety researchers postulate that test anxiety has a negative effect on performance because of the interference it produces during testing (Zeidner, 1998).

It is argued that test anxiety affects performance by increasing the amount of attention given to worrying thoughts, and this distracts examinees from the task (Eysenck & Calvo, 1992; Keogh, Bond, French, Richards, & Davis, 2004; Kurosawa & Harackiewicz, 1995). Download pokemon fire red hack rom. It has also been shown that state anxiety is linked to a general distractibility, with attention directed towards worry or various other internal and external cues (Braunstein-Bercovitz, 2003; Hopko, Ashcraft, Gute, Ruggiero, & Lewis, 1998; Keogh & French, 2001). Recently, Eysenck, Derakshan, Santos and Calvo (2007) put forward their attentional control theory, which postulates that state anxiety is likely to decrease attentional control, thereby decreasing the influence of the controlled, goal-driven attentional system, which focuses attention on the task at hand, and increasing the stimulus-driven attentional system, which automatically diverts attention toward salient and conspicuous stimuli, such as internal ( e.g., worry) and external distractors.

During the written part of the French driving test, candidates are presented with several driving situations in conjunction with multiple-choice questions about a driver’s behavior or decisions. These situations are presented by means of slides, which are shown sequentially and for a limited time. The sequential presentation of the slides controls the speed of the test and focuses the candidate’s attention.

By doing this, the written part of the driving test is distinct from the tasks commonly used in test anxiety studies (in which it is usually up to the examinees to focus their attention on the task). Hence, the fact that the driving test focuses the participants’attention on the task to be completed may have precluded the participants’attention being diverted from the task, thereby leading to performances that are unaffected by STA. This explanation could be tested in an experimental design that varies the mode of presentation of test material.

For example, undergraduate students could undertake a multiple choice questionnaire either presented on an usual exam paper or with individual items sequentially presented by the mean of slides. In conclusion, the EAEE appears to provide a valid measure of state worry and emotionality test anxiety. In addition to its good psychometric properties, the EAEE has two practical advantages over existing scales: it is brief and its items do not contain any reference to a particular test situation ( e.g., academic examinations), such that it can be used across a range of test settings. The EAEE therefore appears to provide a useful tool for researchers who have to work within time constraints and who are interested in assessing state test anxiety in non-academic test situations. However, further validation studies are needed in order to confirm the psychometric validity of the EAEE in non-academic test settings (e.g., recruitment, neuropsychological assessment, driving test).

Citer cet article ISO 690 Beaudoin Marine, Desrichard Olivier, « Validation of a Short French State Test Worry and Emotionality Scale », Revue internationale de psychologie sociale, 2009/1 (Tome 22), p. URL: MLA Beaudoin, Marine, et Olivier Desrichard. « Validation of a Short French State Test Worry and Emotionality Scale », Revue internationale de psychologie sociale, vol. APA Beaudoin, M. & Desrichard, O. Validation of a Short French State Test Worry and Emotionality Scale. Revue internationale de psychologie sociale, tome 22,(1), 79-105.

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