People detection results

Statistical anxiety is one of the predictors of SELS beliefs. It defines as the individual‟s feeling experienced when doing statistical analysis. This involves the gathering, processing and interpreting of data in any type and at any time. The Statistical Anxiety Rating Scale (STARS) is a tool to measure statistical anxiety. Finney and Schraw (2003) settled a questionnaire tool to measure self-efficacy. They found a negative relationship between SELS and statistics anxiety. The students with high level of SELS beliefs tend to have less level of statistics anxiety when achieving statistics tasks.

Similarly, those with less level of SELS beliefs are likely to have high level of statistics anxiety. Hsu, Wang & Chiu (2009) report that many students completed high STARS in the social sciences. Females are more pronounced with STARS compared to males. In this study, statistics anxiety is categorised into two sub-groups, which include predisposition and enabler factors.

Predisposition factors consider previous knowledge, beliefs factors and socio-demographic factors. Enabler factors are interpersonal factors that are related to post-graduate programmes, type of study, student status and academic institutions. These enabler factors directly influence the performance, or ability of graduate students in

their current programmes. Statistics anxiety contributes to the change in students‟

learning in an academic programme. Many studies examine statistics anxiety using undergraduate students (Bell, 2003; Rodarte-Luna & Sherry, 2008; Mji, 2009; Stalder

& Olson, 2011; Beurze et al., 2013; Teman, 2013; Chiou, Wang & Lee, 2014), other studies focus only on graduate students (DeVaney, 2010; Teman, 2013; Williams, 2014), and the last group places emphasis on both undergraduate and postgraduate students (Teman, 2013). Regarding the context, the majority of studies in this review are conducted in the USA (Bell, 2003; Rodarte-Luna & Sherry, 2008; Hsu et al., 2009;

DeVaney, 2010; Stalder & Olson, 2011; Teman, 2013; Williams, 2014); in Europe (Beurze et al., 2013), in Asia (Chiou et al., 2014), and in South Africa (Mji, 2009).

Concerning the research design, numerous studies apply a quantitative method (Bell, 2003; Rodarte-Luna & Sherry, 2008; Mji, 2009; Stalder & Olson, 2011; Beurze et al., 2013; Teman, 2013; Chiou et al., 2014; Williams, 2014), while only one research uses mixed methods (DeVaney, 2010). The Statistical Anxiety Rating Scale (STARS) is employed as an tool to measure statistical emotion. The first category completes the full set of factors of STARS (Bell, 2003; Rodarte-Luna & Sherry, 2008; Beurze et al., 2013;

Teman, 2013; Chiou et al., 2014; Williams, 2014), while DeVaney (2010) uses only three factors of STARS, including, “interpretation anxiety; “asking for help”; “test and class anxiety”. However, Mji (2009) assesses STARS in a different manner by dividing the total scores for each item into low and high anxiety groups. However, Stalder and Olson (2011) assess statistics anxiety by using eleven mnemonics with four questions about each.

Bell (2003) explores statistics anxiety among undergraduate business students, and divides them into traditional and nontraditional students at a USA University. He reveals that the nontraditional students scored significantly higher anxiety on one of the factors “test and class anxiety” and not significantly higher on four of the remaining five factors. The only factor where the traditional students scored higher (indicating more anxiety), was “worth of statistics”. According to the final grades, the traditional group scored significantly higher, while the nontraditional group achieved significantly lower grades. Bell (2003) acknowledges that statistics anxiety is not the only reason of the lower grades. Other reasons may include, being absent from the mathematical environment for a period of time, and family responsibilities. In the same vein,

traditional postgraduate students may also experience a low level of anxiety, compared to nontraditional postgraduate students, simply because they were using the same learning strategies, or method of study. However, the level of anxiety between undergraduate and postgraduate traditional students can be different, due to differences in age, experiences and family responsibilities. In addition, this will be applicable to postgraduate and undergraduate nontraditional students, as well. Regarding STARS, there were negative correlations between the final grades and many components of STARS. Bell (1998) found that international students significantly experienced higher level anxiety than their domestic counterparts in statistics.

In the study of Rodarte-Luna and Sherry (2008), students from a Southwestern University in America completed online surveys, using the STARS and procrastination.

Descriptive discriminant analysis (DDA) and canonical correlation analysis (CCA) examine how STARS is connected respectively to the male and for female learning strategies. “Interpretation of statistics”, “test and class anxiety”, “computational self-concept”, and “fear of asking for help”, present differences in groups, with females being meaningfully more worried compared to males. Men consider that statistics has slight value, while women believe that statistics has great value. Women have higher scores on business, meta-cognitive self-regulation, and, to a certain degree, on practice, whereas men achieve greater scores on critical thinking. Procrastination, rehearsal, and organisation together, positively predict “interpretation of statistics”, as well as “test and class anxiety” (Rodarte-Luna & Sherry, 2008). Rodarte-Luna and Sherry (2008) reveal that men with statistical anxiety can be assisted with tactics that will address their procrastination. This may be helpful if it can be incorporated with their study habits, which will enable them to improve their learning strategies as well as be more willing to “ask for help”.

Women apply more learning strategies than men do, while the latter are more focused, compared to their counterparts. By contrast, men procrastinate more in their study programme than do women do. Women‟s reluctance to “ask for help” in statistics has enhanced the differences between male and female scores. However, the results of the study may be different, if more institutions are considered, if more learning strategies for statistics are explored, and if both quantitative and qualitative research methods are

applied. In addition, Descriptive Discriminant Analysis makes it problematic to take a broad view of the findings.

In another study, Mji (2009) examines gender differences on anxiety and attitudes toward statistics using undergraduate students from taxation, marketing, or accounting at the South African University of Technology in the Eastern Cape. He administers the STARS, as well as the Attitudes towards Statistics (ATS) instrument, including demographic information. The assessment of statistics anxiety is divided into low and high anxiety groups, by using a median split of total scores on each of the STARS subscales. The values of internal consistency for scores on the Statistical Anxiety Rating Scale are .88 (95% CI = .86 to .90). The results reveal that among all STARS subscales, more than half of the participants in each of the three study programmes obtained high anxiety, with about two-thirds of Cost and Management Accounting on the “Fear of asking for help” subscale. The Bonferroni-adjusted subscale comparisons among means for anxiety subscales reveal that differences are mainly between students taking Taxation and Cost Management Accounting, with students of Taxation scoring higher in all anxiety scales, compared to those in the latter area. Students scored higher anxiety on items of the subscales “Test and class anxiety” and the “Fear of asking for help”. Mji (2009) suggests that, although the students scored high levels of statistical anxiety, if more students are considered across various fields of study, the level of anxiety may be different. Based on the fact that Mji (2009) uses a median to share participant emotions into two categories, the data of which are subsequently exposed to multivariate analysis, there is a need for more attentiveness in the analysis, in order to achieve accurate results.

A third study conducted at a USA University compares the level of STARS and attitudes towards statistics among students. Also, DeVaney (2010) uses mixed methods to check the differences or similarities based on the enrolment status in statistics courses. The respondents complete only three components of the (STARS) and the SATS-28 instrument, at the beginning and at the end of a course. The decrease in the completion of the second survey could not be properly addressed because the survey is anonymous. DeVaney (2010) discovers that the likely modification from pretest to posttest differs among the groups. The students with high pretest scores decrease on posttest while those students with low pretet scores increase their scores on post test.

These findings may be different if more students register on campus, which will enable them to have access to more resources, and to ask for the help from peers and lecturers

“face to face”. The anonymity of the participants in the completion of both surveys, and the decrease in the second survey, may explain the inconsistency observed in the DeVaney (2010) statement. It highlights that students with high pre-test scores, achieve lower in the test, while students with low pre-test scores, score higher on the post-test, while individuals who score around the average in the pre-post-test, score around the average on the post-test. Normally, if a student has a low level of anxiety in pre-test, the level of anxiety should remain low, or even lower in post-test (DeVaney, 2010; Chiou et al., 2014). However, this is contrary to the result of the DeVaney (2010) study, which implies that for such an increase in the post-test should be a research topic for future investigation.

Another study at a Midwestern university in USA focuses on the importance of aides-memoires to make statistics materials more accessible for undergraduate psychology students (Stalder & Olson, 2011). The participants review 11 mnemonics throughout the semester, using four questions as follows: (a) “To what degree was this mnemonic helpful in learning this information?” (b) “Did this mnemonic make learning this information easier or faster?” (c) “Did this mnemonic make the information easier to recall during homework or tests?” and (d) “Do you recall this mnemonic from the semester?” One-sample t-test compares mean student scores against scale midpoints.

The findings reveal that students significantly report as helpful, 8 of the 11 mnemonics, by using the three-item helpfulness measure. Half the sample of respondents report the 3 remaining aides-mémoires‟ perceived helpfulness ratings to significantly exceed the scale midpoint. For the overall use of statistical mnemonics, other measures reveal a relatively high rating. Stalder and Olson (2011) suggest that mnemonics improve learning and motivation. They confirm that moderate belief of mnemonics decreases statistics anxiety. Mnemonics reduce statistical anxiety prevalence; however, the mechanisms cannot be explained. Therefore, the method of assessment is not an experimental design to clarify the change observed, and even among lecturers, misconceptions about mnemonics exist. Additionally, Stalder and Olson (2011) report a significant difference, when relating the mean rating and scale midpoint of statistics anxiety, reduction ratings, with the mean rating exceeding the scale midpoint.

Beurze et al. (2013) investigates statistics anxiety among medical students at the Radboud University Nijmegen in the Netherlands. First and second year medical students complete the questionnaire on Statistical Anxiety Rating Scale (STARS).

There is no association between the STARS scores and the achievement in the medical professional training courses, for both the first and second year medical students. Only the second year students, who score higher on the statistics and epidemiology aspects, show lower on the STARS scores. Epidemiology courses contain many statistical aspects. Experience in statistics does not disturb the STARS scores, while poor mathematics scores during high school does connect meaningfully with high worry.

In a study conducted by Teman (2013), students at a midsized university, in the western part of the United States, complete the Rating Scale (STARS) with demographic information. The participants from various academic disciplines register in statistics and research methods courses. Teman (2013) explores statistics anxiety, using a confirmatory factor analysis (CFA) with the WLSMV estimator. Considering the component fit, all six factors are statistically significant for both sexes, indicating that the model appears to be appropriate for both men and women. A factorial invariance analysis assesses the configural invariance. However, latent mean differences concerning males and females for both the “Test and class anxiety” and the

“Interpretation of statistics” are statistically significant, revealing that women experience higher anxiety in those areas, while the contrary occurs in the four remaining factors for the men and women. Regarding the invariance analysis of the students‟ education level, undergraduate students fit adequately; however, the test of latent means was slightly lower than required. Therefore, graduate students fit well.

Estimates of all parameters are statistically significant for both groups. Teman (2013) advises that further inferences of the invariance of the thresholds, include the validity of between-group comparisons made for sex. If the researcher compares the observed or latent means of the two groups, an independent-sample t-test would be meaningful and readily interpretable, as a true mean difference.

In 2008, a study reveals that deprived communications among mentors and learners is the key motive for statistics worry. It encourages learners to communicate their greatest significant queries to instructors, using a one-minute paper strategy (OMPS) (Ruggeri et al., 2008). To further the research, Chiou et al. (2014) evaluates the efficiency of a

OMPS to reduce the students‟ anxiety. The study applies the quasi-experimental design with one pre-test and two post-tests. Learners in both categories receive identical information and worry, earlier to the time of directing the OMPS. Practical outcomes show that the OMPS expressively improves learning success, and expressively reduces worry of learners at together the post-tests. In addition, the average variations in knowledge outcomes reveal that the experimental group has improved learning achievement over time. Chiou et al. (2014) established that the OMPS meaningfully diminishes learners‟ statistics worry and improves learners‟ statistics knowledge accomplishment. Besides, most students believe that the OMPS is a dominant rereading instrument to rearrange main ideas and make used for examinations. The improvement occurs at two stages including students‟ attentiveness in probing queries and the eminence of instruction through consistent student-teacher connections. Chiou et al.

(2014) assume that OMPS reduces students‟ anxiety by reviewing lessons, which enable them to record their problems for consultation in the next class and reorganise main points for examination preparation. They also agree that empirical results may be influenced by the differences in student mathematical ability.

In a later study, conducted by Williams (2014), graduate students with different majors at a South Western University in USA completed the Statistics Anxiety Rating Scale (STARS), the Self-Description Questionnaire III (SDQIII), the Preference for Numerical Information Scale (PNI) and their demographic background. The findings revealed that four componentss of STARS (“worth statistics”, “computation self-concept”, “interpretation of statistics”, and “test and class anxiety”) were strongly related to PNI with a strong effect size. The PNI indicated a strong association with mathematics concept; or greater PNI was related with greater mathematics self-concept, which confirmed the validity of the PNI. The results revealed that a higher PNI was related to lower statistics anxiety among graduate students. All of the instruments of Williams‟ (2014) study were self-reporting and, therefore, subject to subjective bias.

Evidently, statistics anxiety has been explored for years; however, there are very few studies in which the authors first assessed whether the validity of the scores from STARS are equivalent across different sub-populations. Therefore, valid scores comparisons across different sub-groups are confusing. Mean differences in statistics anxiety, across different groups, could be seen as a measurement of items, rather than

real differences in perception of statistics anxiety, without measurement equivalent (Hutchinson, Raymond & Black, 2008). If there is no measurement equivalence across comparison groups, it is possible that prior research results are inaccurate, because the assumption of equivalent groups is incorrect. This issue could be a priority for a new investigation, to evaluate the cross-cultural comparability of STARS, because absence of measurement equivalence implies that sub-group responses are not meaningfully comparable.

In addition, it turns out that in the social sciences, all graduate students are expected to consider statistics as a portion of their educational preparation; however, this is not always the case. Prevalence of statistics anxiety is not only due to poor, or to inadequate expertise, but also because of some external factors, or previous negative experiences.