Statistics anxiety and science attitudes: age, gender, and ethnicity factors.
Article Type:
College students (Surveys)
Sciences education (Public opinion)
Bui, Ngoc H.
Alfaro, Michelle A.
Pub Date:
Name: College Student Journal Publisher: Project Innovation (Alabama) Audience: Academic Format: Magazine/Journal Subject: Education Copyright: COPYRIGHT 2011 Project Innovation (Alabama) ISSN: 0146-3934
Date: Sept, 2011 Source Volume: 45 Source Issue: 3
Event Code: 290 Public affairs
Product Code: E197500 Students, College
Geographic Scope: United States Geographic Code: 1USA United States

Accession Number:
Full Text:
We examined student characteristics, statistics anxiety and attitudes toward science among 104 (76 females, 23 males, 5 gender not disclosed) undergraduates. Younger students were more negative regarding the implications of science and the enjoyment they perceived in learning science. No significant gender differences were found and Latinos/Hispanics, Caucasian, and other ethnic groups did not differ on statistics anxiety and attitudes toward science. However, anxiety for interpretation of statistics and taking a test and class in statistics were moderately high for these groups. These findings support the notion of the ubiquity of statistics anxiety across groups, regardless of previous experience. We also found that statistics anxiety was inversely related to attitudes toward science, suggesting that future research examine how to improve attitudes toward science to lessen statistics anxiety among students. Limitations of the study are also discussed.

Keywords: statistics anxiety, science attitudes, age, gender, ethnicity


Different student characteristics have been shown to be related to academic success, such as low levels of procrastination (Rothblum, Solomon, & Murakami, 1986), high levels of work drive (Ridgell & Lounsbury, 2004), good emotional stability (Ridgell & Lounsbury), and low amounts of negative life stress (Petrie & Russell, 1995). However, of particular concern for many professors of statistics is anxiety associated with taking a course in statistics. Studies that have examined statistics anxiety have shown that a student's competence and success in a statistics course is influenced by their attitudes toward the course (Kottke, 2000). Baloglu (2004) pointed out that statistics anxiety is a relatively new construct and is related to but different from math anxiety. Although both types of anxiety have to do with the stress that students feel when dealing with mathematical reasoning, Baloglu states that statistics anxiety is distinct in that students also have apprehension involving the verbal reasoning and manipulation of the mathematical symbols that are required in understanding statistics. If this is true of statistics anxiety, do attitudes about science, which is a field that also involves a degree of verbal reasoning, relate to statistics anxiety? To better understand statistics anxiety, we explored student characteristics and attitudes toward related fields in the present study.

Student Characteristics and Statistics Anxiety

A variety of student characteristics, such as age and gender of the student, have been examined in relation to statistics anxiety. For example, nontraditional students (age 25 or older) were found to have greater statistics anxiety related to taking tests and being in the class (Bell, 2003). Additionally, traditional students had higher final statistics course grades compared to their non-traditional counterparts. Bell suggests that the lower grades of the non-traditional students can be partially explained by statistics anxiety, but they could also be due to non-traditional students' longer absence from math courses prior to enrolling in their current statistics course. Although Bell's comparisons between traditional and non-traditional students provide important findings for instructors who teach a variety of students, the study did not examine the relationships between statistics anxiety and other student characteristics and background, such as prior math experience (e.g., level of last math course and the number of years since the student's last math course). To explore these relationships, in the present study we compared non-traditional and traditional college students' statistics anxiety and the influence of previous math experience.

Mji (2009) examined whether the student characteristics of gender and college major were related to statistics anxiety. Using the Statistical Anxiety Ratings Scale (STARS; Cruise & Wilkins, 1980), Mji found that statistics anxiety was high among all 226 South African technical college students sampled. However, Mji found no gender or college major differences. A limitation of Mji's study was that it did not include a diverse ethnic sample nor was information collected on past mathematical experience or time between taking their last math course and their current statistics course. Furthermore, Rodarte-Luna and Shelley (2008) studied 323 undergraduates and found some small and weak gender differences in statistics anxiety, However, these differences were more defined when they examined the cognitive strategies that students used to learn, such as procrastination and seeking help from peers. Males were more likely to use procrastination as a strategy, and were also were more like to have statistics anxiety related to test and class anxiety, interpretation anxiety, and asking for statistics help. Females used many other strategies to learn and these other strategies were related to lower statistics anxiety. But for females who used procrastination, statistics anxiety was also higher overall. Some limitations to Rodarte-Luna and Shelley's (2008) study were that they administered their measures online and only used the criteria of whether the student was or ever had taken a statistics course. It is possible that students inaccurately recalled or reported their experiences of statistics anxiety because it may have been a while since they had taken the class. The contradictory findings from the studies discussed above demonstrate that the relationship between gender and statistics anxiety needs to be explored further.

In terms of studies that show ethnic differences in statistics anxiety, the research in this area is limited. However, one study that did examine ethnic related differences compared international to non-international students. Bell (1998) studied 61 college students (10 international who hailed mainly from Malaysia and 51 non-international) in a productions and operations management program, using the STARS (Cruise & Wilkins, 1980). According to Bell, international students are often better prepared in the subject of mathematics, which could be due to their greater experiences with higher level mathematic courses in high school (e.g., calculus). However, Bell's findings showed that overall, international students demonstrated higher levels of anxiety on several factors compared to their non-international counterpart, despite some international students having higher final grades in statistics and despite possibly having had more math classes at higher levels in high school. Furthermore, Bell states that this anxiety may be due to a language barrier, which supports Baloglu's (2004) notion that verbal reasoning is an important distinction between statistics anxiety and math anxiety. A limitation of Bell's study included the small sample and the uneven distribution of international to non-international students.

Science Attitudes and Statistics Anxiety

If statistics anxiety is distinct from math anxiety due to its emphasis on verbal reasoning (Baloglu, 2004), then how do attitudes about science, a field that also utilizes verbal reasoning to solve problems, relate to statistics anxiety? To date, there are no studies that have explored the relationship between attitudes toward science and statistics anxiety. However, there have been several studies that have investigated science attitudes. Caffrey and Lile (1976) studied 160 participants selected from a Southern university, randomly chosen using a random number table based on their student identification numbers and sorted by major. Of the 160 participants, 40 were English majors, 40 were sociology majors, 20 were physics majors, 20 were chemistry majors, and 40 were psychology majors. Participants were asked to complete a scientific attitude questionnaire. The mailed questionnaires had a 57% response rate (N = 94). Results indicated differences among the majors, with humanities having the lowest scores, or most negative attitudes, toward science. In contrast, psychology, physics, and chemistry majors had similar mean scores. Psychology and physics students both agreed that science is not the only path to truth, but physics students agreed more with the overall questionnaire statements regarding the value of science. Limitations to this study include the fact that it was conducted 30 years ago and attitudes may have changed, the small sample size representing each major, and attitudes toward science were not studied in relation to attitudes toward other areas that are usually perceived negatively, such as statistics.

To address gender differences in attitudes toward science, Desy, Peterson, and Brockman (2009) administered a 50-item questionnaire on attitudes toward science to 376 participants (199 males and 169 females) attending introductory science courses at Southwest Minnesota State University. Results showed that men tended to be more positive about science. Men with positive attitudes also tended to receive higher marks in the course and higher academic self-ratings. Overall, women demonstrated that their view towards science was less relevant to their overall academic success. The researchers hypothesized that women may have negative attitudes toward science because of their lack of interest in the subject. Overall, the researchers stressed the importance of acknowledging gender influences on attitudes toward science, and encouraged the development of techniques to help young children build positive attitudes toward all disciplines. A limitation of Desy et al.'s study is that attitudes toward science were not examined in relation to other academic areas that have also been perceived negatively, such as statistics. Additionally, the study was limited in the sample's ethnic diversity, which restricts generalization of the results.

Main Purpose and Hypotheses Based on the findings from previous studies regarding the relationships between student factors and statistics anxiety, and based on the limited research regarding attitudes toward science in relation to statistics anxiety, we proposed the following hypotheses:

1. Non-traditional students (age 25 or older) will have greater statistics anxiety (Bell, 2003) and will have more negative attitudes toward science compared to traditional aged students.

2. Females will have more statistics anxiety and more negative attitudes toward science than males (Desy et al., 2009).

3. Ethnic groups will differ in statistics anxiety score and attitudes toward science score (Bell, 1998).

4. Statistics anxiety score will be related to prior level of math and time at which last math class was taken (Bell, 1998).

5. Science attitudes will be negatively related to statistics anxiety.



The 104 participants were recruited from a university in Southern California from 7 different introductory statistics courses. This sample consisted of 76 females and 23 males, with 5 participants refusing to give gender information. The mean age of the sample was 23.42 years (SD = 6.76). The sample was comprised mainly of Latino/Hispanic students (46.5%, n = 46), with the second highest ethnic/racial group comprising of Caucasians (26.3%; n = 26). Because the other ethnic groups (e.g., African American, Asian, Native American, Bi-Racial) were individually small in number, the rest of the sample was grouped in the category of "Other" (30.8%, n = 32). The average grade point average (GPA) reported by participants was 3.11 out of 4.0 scale (SD = .48), and the majority of the students completed an Algebra or College Algebra course prior to taking their current statistics course (75.1%).


The measures used in this study included a demographic questionnaire, the Statistical Anxiety Rating Scale (STARS; Cruise & Wilkins, 1980) and the Test of Science Related Attitudes (TOSRA; Fraser, 1981).

Demographic questionnaire. A ten-item questionnaire asked participants to report the last 5-digits of their 8-digit student identification number, their birth date, gender, ethnicity, class standing (freshmen, sophomore, junior, or senior), student type (traditional or non-traditional), most recent math course completed, the year this most recent math class was completed, highest level of math completed and current grade point average (GPA).

Statistical Anxiety Rating Scale (STARS). A 51-item scale developed by Cruise and Wilkins (1980) was used to assess statistics anxiety. The STARS has six main factors that include: 1) worth of statistics (i.e., the value of statistics; items 24, 26, 27, 28, 29, 33, 35, 36, 37, 40, 41, 42, 45); 2) interpretation anxiety (e.g., finding it difficult to determine whether to retain or reject the null; items 2, 5, 6, 7, 9, 11, 12, 14, 17, 18, 20); 3) test and class anxiety (i.e., anxiety from taking the course and doing the course work; items 1, 4, 8, 10, 13, 15, 21, 22); 4) computation self-concept (e.g., feeling inadequate in understanding statistics; items 25, 31, 34, 35, 39, 48, 51); 5) fear of asking for help (items 3, 16, 19, 23) ; and, 6) fear of statistics teachers (items 30, 32, 43, 44, 46). Reponses for the first 23 items regarding anxiety-provoking situations were based on a Likert scale of 1 ("None") to 5 ("High). Items 24 to 51 regarding attitudes toward statistics were based on a Likert scale of 1 ("Strongly Disagree") to 5 ("Strongly Agree"). Higher scores indicate greater statistics anxiety in each of the factors.

Internal consistency and the ability to predict statistics anxiety have been demonstrated for the STARS (Hanna, Shevlin, & Dempster, 2008; Mji & Onwuegbuzie, 2004). Concurrent validity between the test and class anxiety factor on the STARS and the Mathematics Test Anxiety factor was 0.76 (Fennema-Sherman's Mathematics Attitudes Scales, as cited by Cruise, Cash & Bolton, 1985). Test-retest reliability for all 6 factors ranged from alpha coefficients of 0.67 to 0.83 (Cruise, Cash, & Bolton), and alpha coefficients for each factor ranged from 0.65 (fear of statistics teachers) to 0.96 (worth of statistics) (Cruise, Cash, & Bolton). For the present study, internal consistency for the 6 factors were found acceptable, ranging from 0.73 to 0.93, and the Cronbach's [alpha] for the overall scale was 0.75. See Table 1 for alpha coefficients for each of the 6 factors.

Test of Science Related Attitudes (TOSRA). Respondents answered Fraser's (1981) TOSRA's 70-item questionnaire regarding their attitudes toward science using a Likert scale ranging from 1 ("Strongly Disagree") to 5 ("Strongly Agree"). The TOSRA is comprised of 7 subscales: 1) social implications of science (items 1, 8, 15, 22, 29, 36, 43, 50, 57, 64); 2) normality of scientists (items 2, 9, 16, 23, 30, 37, 44, 51, 58, 65); 3) attitude to scientific inquiry (items 3, 10, 17, 24, 31, 38, 45, 52, 59, 66); 4) adoption of scientific attitudes (items 4, 11, 18, 25, 32, 39, 46, 53, 60, 67); 5) enjoyment of science lessons (items 5, 12, 19, 26, 33, 40, 47, 54, 61, 68); 6) leisure interest in science (items 6, 13, 20, 27, 34, 41, 48, 55, 62, 69); and, 7) career interest in science (items 7, 14, 21, 28, 35, 42, 49, 56, 63, 70). Higher scores indicate greater interest and more positive attitudes toward science. Alpha coefficients for the scale's reliability ranged from 0.66 to 0.93 among samples of students in grades 7 to 10 (Fraser, 1981).

Although the TOSRA has been typically used for high school populations, for the present study of college students, scale reliability was found to be quite good ([alpha] = 0.86). See Table 1 for alpha coefficients for each of the 7 subscales.


With permission of the statistics instructors, in the first week of the semester we asked students from introductory statistics courses in the psychology department at a small private Western university to participate in the research study. Participants read and signed a consent form detailing their rights as participants and the researchers told them the information collected would be kept confidential. Participants completed a 10-item demographic questionnaire (developed by the researchers), a 5 l-item Statistics Anxiety Rating Scale (STARS; Cruise & Wilkins, 1980), and a 70-item Test of Science-Related Attitudes measure (TOSRA; Fraser, 1981). An additional measure of procrastination (Lay, 1986) was also included and contained 20 items. This measure was not analyzed in the present study. Completion of the packets took approximately 35 minutes.


Independent samples t-tests were conducted to examine differences among non-traditional students (age 25 or older) (M age = 32.03, SD = 7.12) and traditional aged students (M age = 19.97, SD = 1.52) on the STARS (statistics anxiety measure). Traditional students comprised the majority of the sample (67.3%, n = 70). Results showed that these two groups did not differ in their statistics anxiety scores for all subscales of the STARS (p > .05). See Table 2 for means and standard deviations.

However, independent samples t-tests showed that the age groups differed on two subscales of the TOSRA (measure of attitudes toward science). Traditional students had more negative attitudes regarding the social implications of science compared to non-traditional students, t (91) = - 2.12, p < .05, [r.sup.2] = .047. Also, traditional students had more negative attitudes about their enjoyment of science compared to nontraditional students, t (93) = - 2.11, p < .05, [r.sup.2] = .046. See Table 2 for means and standard deviations.

Independent samples t-tests were conducted to test whether females would have more statistics anxiety than males. Contrary to the hypothesis, results showed no significant differences between females and males on the STARS subscales (p > .05). See Table 3 for means and standard deviations. Furthermore, there were no differences between females and males on the TOSRA (measure of attitudes toward science) subscales (p > .05), but both groups viewed science somewhat positively, with all subscales ranging from 10-50 points (medians ranged from 30-39).

One-way ANOVAs were conducted to test whether there were ethnic group differences in statistics anxiety score and attitudes toward science score among Latino, Caucasian, and other ethnic groups. Contrary to what was hypothesized, results showed that the groups did not differ in the STARS and TOSRA scores (p > .05). See Tables 4 and 5 for means and standard deviations.

One-way ANOVAs were used to compare STARS and TOSRA scores for participants based on their highest level of math experience. Results showed no significant differences among those having previously taken algebra, pre-calculus/trigonometry, or calculus on their STARS and TOSRA scores (p > .05). Also, Pearson's correlations also showed that statistics anxiety score was not related to time at which last math class was taken (p > .05).

Finally, Pearson's correlations showed that subscales on the TOSRA were significantly related to subscales on the STARS (p < .05). Every subscale on the TOSRA was significantly and negatively related to at least one STARS subscale, except for the TOSRA subscale of "normality of scientists", which had no significant relationship to any STARS subscales (p > .05). The STARS subscale of "fear of statistics teachers" had the least number of significant correlations, with only one TOSRA subscale ("social implications of science") significantly and negatively related to this STARS subscale (p < .05). Findings indicate that the more positive the science attitude the lower the statistics anxiety, which supports our hypothesis. See Table 6 for correlations.


Most participants in our study had moderate statistics anxiety, but also most felt positively about the field of science. The findings only partially supported our proposed hypotheses. There were only some differences found among traditional and non-traditional students in terms of attitudes toward science. However, contrary to what was hypothesized, it appears traditional students have more negative attitudes than non-traditional students about the social implications of science and how much enjoyment they perceive from learning science. This finding is also contrary to previous research that indicates that older age is related to more negative attitudes related to statistics (Bell, 2003; Onwuegbuzie & Wilson 2003), and what we also assumed would be attitudes related to science as well. It is curious why younger students do not feel as positively about these two areas (social implications and enjoyment of science) as older students. However, a recent report by McMurrer (2008) from the Center on Education Policy examined 10 elementary school districts and noted a marked decrease in amount of time spent teaching science among the schools. An average of 75 minutes among more than a third of the school districts was lost due to these schools spending more time in English language arts and mathematics courses. English language arts and mathematics saw an increase of an average of 141 and 89 minutes, respectively, on instruction. According to McMurrer, this was not the intent of the "No Child Left Behind" Act (NCLB), which took effect in 2002. However, based on the report it is apparent that the law has negatively impacted students' exposure to science and other subjects. Perhaps the traditional undergraduates in our sample, many of whom were in elementary school when the NCLB Act began, did not spend as much time studying science as they did English language arts and math and thus have more negative attitudes regarding science's implications or their perceived enjoyment in learning science.

There were no differences in statistics anxiety and attitudes toward science between males and females and among the three ethnic groups examined. Interestingly, for both gender and ethnic comparisons, groups scored moderately high on statistics anxiety related to interpretation of statistics and taking tests and a class in statistics. Perhaps statistics anxiety is more ubiquitous than we and other researchers have assumed.

Further, level of highest math and time between last math class and current statistics course were not related to either statistics anxiety or attitudes toward science. Regardless of preparation (highest level of math taken) or how long ago tile student had taken a math class, statistics anxiety was still moderately high for interpretation of statistics and taking tests and a class in statistics. This again gives credence to our assertion that statistics anxiety is quite pervasive among college students, in this case, independent of their math history.

There was support for the fifth hypothesis that predicted a relationship between statistics anxiety and attitudes toward science. All of the STARS subscales related negatively to most of the TOSRA subscales, except for attitudes regarding the "normality of scientists", which was not significantly related to any STARS subscales. This indicates those with more positive science attitudes also tend to have lower statistics anxiety. If science attitudes are related to statistics anxiety, then Desy et al.'s (2009) suggestion for developing techniques to help young children build positive attitudes toward all disciplines is especially important. Students are not typically exposed to statistics in primary or secondary school, but developing a more positive science attitude early in school may be related to lower statistics anxiety when they take the course later on in college.


Despite partial support for the hypotheses proposed, this study had several limitations. For instance, only student characteristics were explored in relation to these attitudes. However, Onwuegbuzie (2000) found that students who scored low on perceived creativity, perceived intellectual ability, and perceived scholastic competence scored high on all 6 dimensions of statistics anxiety measured on the STARS. This may indicate that it is not ability per se but perceived lack of ability that may influence statistics anxiety. Also, Tremblay, Gardner, and Heipel (2000) found that lack of interest in both math and in the statistics course were related to higher statistics anxiety, Further they found that students' attitudes toward the professor influenced the student's motivational intensity, which in turn impacted their statistics anxiety. In the present study we did not examine interest in math, motivational intensity, or perceptions of ability.

Finally, although we assert that based on our findings and contrary to our hypotheses, statistics anxiety may be experienced by all kinds of students, regardless of age, gender, or ethnicity, this study was limited due to the imbalanced groups of traditional versus non-traditional students and females compared to males. Also, the study could have benefited from including a larger sample of ethnic groups for comparison.

Future Directions

Understanding factors that influence statistics anxiety is a good beginning, but given the moderately high levels of statistics anxiety found in the sample, further research is needed to explore what instructors can do to help alleviate this anxiety in their students. For example, Pan and Tang (2005) found that students believed that fear of math, the lack of connection between statistics and daily life, the pace of instruction, and the instructor's attitude about teaching all contributed to their statistics anxiety. Pan and Tang suggest that instructors can alleviate anxiety by utilizing a multidimensional teaching approach. In a different study, Pan and Tang (2004) found that among 21 graduate students, statistics anxiety was decreased when the course focused on applying the statistics information and when the instructors were attentive to students' anxiety. Additional research into strategies for reducing statistics anxiety need to be conducted to see if these suggestions can be applied to undergraduates and among ethnically varied and age-diverse samples.

Our findings also showed that traditional age undergraduates have negative attitudes toward the social implications of science and they did not believe that learning science was enjoyable. This has been supported in the literature to an extent. For example, Wilson and Milson (1993) found that women have more negative attitudes toward science because they perceive the field of science to be an environment that is geared for men, they believe that only men are expected to perform at higher levels, they do not believe that females receive encouragement for their work, and they do not think that females are not given equal opportunities to succeed. Future considerations for research on attitudes toward science and statistics may have to answer the difficult question of how to change these environments to make the fields of science and math more encouraging and welcoming not just for women, but for everyone who feels that they are not given the same opportunities or encouragement for pursuing careers in these fields.


Baloglu, M. (2004). Statistics anxiety and mathematics anxiety: Some interesting differences. Educational Research Quarterly, 27(3), 38-48.

Bell, J.A. (1998). International students have statistics anxiety too! Education, 118(4), 634-636.

Bell, J.A. (2003). Statistics anxiety: The nontraditional student. Education, 124(1), 157-162.

Bruvold, W.H. (1974). Attitudes toward science and accompanying beliefs. The Journal of Social Psychology, 94, 269-274.

Caffrey, B., & Lile, S. (1976). Similarity of attitudes toward science on the part of psychology and physics students. Teaching of Psychology, 3(1), 24-26.

Cruise, R. J., Cash, R. W., & Bolton, D. L. (1985). Development and validation of an instrument to measure statistical anxiety. Paper presented at the annual meeting of the Statistical Education section of the American Statistical Association, Chicago, IL.

Cruise, R.J. & Wilkins, F.M. (1980). Statistical Anxiety Rating Scale. Unpublished manuscript. Andrews University, Berrien Springs, MI.

Desy, E., Peterson, S., & Brockman, V. (2009). Attitudes and interests among university students in introductory nonmajor science courses: Does gender matter? Journal of College Science Teaching, 39(2), 16-23.

Fraser, B.J. (1981). TOSRA: Test of science-related attitudes handbook. Hawthorn, Victoria: The Australian Council for Educational Research Limited.

Hanna, D., Shevlin, M., & Dempster, M. (2008). The structure of the statistics anxiety rating scale: A confirmatory factor analysis using UK psychology students. Personality and Individual Differences, 45, 68-74.

Kottke, J.L. (2000). Mathematical proficiency, statistics knowledge, attitudes toward statistics, and measurement course performance. College Student Journal, 34(3), 334-347.

McMurrer, J. (2008). Instructional time in elementary schools: A closer look at changes for specific subjects--A report in the series From the Capital to the Classroom: Year of the No Child Left Behind Act. Retrieved from Center on Education Policy website: DocumentByID&nodeID=1&DocumentID=2 34

Mji, A. (2009). Differences in university students' attitudes and anxiety about statistics. Psychological Reports, 104, 737-744.

Mji, A. & Onwuegbuzie, A.J. (2004). Evidence of score reliability and validity of the Statistics Anxiety Rating Scale among Technikon students in South Africa. Measurement and Evaluation in Counseling and Development, 36(4), 238-251.

Onwuegbuzie, A.J. (2000). Statistics anxiety and the role of self-perceptions, The Journal of Educational Research, 93(5), 323-330.

Onwuegbuzie, A.J., & Wilson, V.A. (2003). Statistics anxiety: nature, etiology, antecedents, effects, and treatments--a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195-209.

Pan, W., & Tang, M. (2004). Examining the effectiveness of innovative instructional methods on reducing statistics anxiety for graduate students in the social sciences. Journal of Instructional Psychology, 31(2), 149-159.

Pan, W., & Tang, M. (2005). Students' perceptions on factors of statistics anxiety and instructional strategies. Journal of Instructional Psychology, 32(3), 205-214.

Petrie, T.A., & Russell, R.K. (1995). Academic and psychosocial antecedents of academic performance for minority and nonminority college football players. Journal of Counseling and Development, 73, 615-620.

Ridgell, S.D., & Lounsbury, J.W. (2004). Predicting academic success: General intelligence, "Big Five" personality traits, and work drive. College Student Journal, 38(4), 607-618.

Rothblum, E.D., Solomon, L.J., & Murakami, J. (1986). Affective, cognitive, and behavioral differences between high and low procrastinators. Journal of Counseling Psychology, 33(4), 387-394.

Tremblay, P.F., Gardner, R.C., & Heipel, G. (2000). A model of the relationships among measures of affect, aptitude and performance in introductory statistics. Canadian Journal of Behavioural Science, 32(1), 40-43.

Wilson, J.S., & Milson, J.L. (1993). Factors which contribute to shaping females' attitudes toward the study of science and strategies which may attract females to the study of science. Journal of Instructional Psychology, 20(1), 78-86.


Department of Psychology

University of La Verne


University of La Verne
Table 1
Cronbach Alpha Reliability Coefficients for STAR and TOSRA Scales

                                                  # of      Alpha
Measure   Subscale                                Items   ([alpha])

STARS     Factor 1: Worth of Statistics            16        .927
STARS     Factor 2: Interpretation Anxiety         11        .884
STARS     Factor 3: Test and Class Anxiety          8        .871
STARS     Factor 4: Computation Self-Concept        7        .862
STARS     Factor 5: Fear of Asking for Help         4        .800
STARS     Factor 6: Fear of Statistics Teachers     5        .732
STARS     All Factors                              51        .748
TOSRA     Social Implications of Science           10        .831
TOSRA     Normality of Scientists                  10        .684
TOSRA     Attitude to Scientific Inquiry           10        .901
TOSRA     Adoption of Scientific Attitudes         10        .691
TOSRA     Enjoyment of Science Lessons             10        .912
TOSRA     Leisure Interest in Science              10        .877
TOSRA     Career Interest in Science               10        .884
TOSRA     All Subscales                            70        .865

Table 2
Means and Standard Deviations for STARS and TOSRA by Age Group

                                           Traditional     Traditional
                                          (Ages 18-24)      (Ages 25+)

                                             N = 70          N = 28

                                          M       SD      M       SD

STARS Factor 1                            38.76   11.98   38.25   14.81
STARS Factor 2                            31.23   8.15    28.23   8.15
STARS Factor 3                            27.03   6.46    27.07   8.20
STARS Factor 4                            17.30   6.31    17.50   6.83
STARS Factor 5                            9.29    3.16    9.07    3.69
STARS Factor 6                            10.12   3.70    9.39    3.30
* TOSRA-Social Implications               36.79   5.13    39.26   5.00
TOSRA-Normality of Scientists             35.40   4.36    33.89   4.49
TOSRA-Attitude to Scientific Inquiry      34.94   7.67    35.33   6.31
TOSRA-Adoption of Scientific Attitudes    38.61   4.73    39.96   4.20
* TOSRA-Enjoyment of Science Lessons      33.73   7.55    37.31   6.73
TOSRA-Leisure Interest in Science         29.01   8.16    32.00   6.14
TOSRA-Career Interest in Science          30.32   8.22    31.43   6.17

Note. * mean differences between groups are significant at p < .05

Table 3
Means and Standard Deviations for STARS and TOSRA by Gender

                                 Males         Females
                               (N = 23)        (N = 74)

               Score Range   M       SD      M       SD
  Factor 1     13-65         40.13   13.63   38.09   12.50
  Factor 2     11-55         28.08   8.86    31.11   7.88
  Factor 3     8-40          25.52   6.60    27.53   6.98
  Factor 4     7-35          16.86   6.81    17.58   6.33
  Factor 5     4-20          9.39    3.94    9.16    3.08
  Factor 6     5-25          10.56   3.96    9.71    3.44

  Subscale 1   10-50         37.82   5.23    37.42   5.17
  Subscale 2   10-50         34.39   4.10    35.13   4.51
  Subscale 3   10-50         37.04   7.06    34.50   7.26
  Subscale 4   10-50         40.59   4.71    38.55   4.47
  Subscale 5   10-50         36.00   6.09    34.37   7.82
  Subscale 6   10-50         31.09   6.69    29.59   8.05
  Subscale 7   10-50         31.74   7.50    30.37   7.72

Table 4
Means and Standard Deviations for STARS by Ethnic Group

                                 Caucasian        Latino

Subscale         Score Range   M       SD      M       SD

STARS Factor 1   13-65         39.61   12.12   37.33   12.84
STARS Factor 2   11-55         30.62   8.34    31.09   8.26
STARS Factor 3   8-40          27.08   6.60    26.89   6.13
STARS Factor 4   7-35          16.81   6.75    17.24   6.05
STARS Factor 5   4-20          9.56    4.02    9.15    3.07
STARS Factor 6   5-25          10.50   3.79    9.65    3.75


Subscale         Score Range   M       SD

STARS Factor 1   13-65         39.80   13.43
STARS Factor 2   11-55         29.00   8.04
STARS Factor 3   8-40          27.33   8.53
STARS Factor 4   7-35          18.30   6.84
STARS Factor 5   4-20          9.00    2.96
STARS Factor 6   5-25          9.77    3.05

Note. Caucasian N = 26, Latino/Hispanic N = 46, "Other" N = 27

Table 5
Means and Standard Deviations for TOSRA by Ethnic Group

                           Caucasian       Latino         Other

Subscale   Score Range   M       SD     M       SD     M       SD

1          10-50         38.83   5.52   36.77   4.82   37.54   5.34
2          10-50         34.80   4.72   34.26   4.42   36.30   3.93
3          10-50         33.40   8.09   35.48   7.27   36.08   6.35
4          10-50         39.28   4.04   39.06   5.05   38.68   4.33
5          10-50         33.32   9.57   34.95   6.53   35.81   6.66
6          10-50         28.24   8.59   30.70   6.88   30.30   8.26
7          10-50         28.27   8.95   31.80   6.48   31.11   7.92

Note. Caucasian N = 26, Latino/Hispanic N = 46, "Other" N = 27

Table 6
Pearson Correlations for STARS and TOSRA Subscales

STARS      SI         NS      ASI        ASA        ESL        LI

Factor 1   -.267 **   -.036   -.393 **   -.311 **   -.399 **   -.438 **
Factor 2   -.136       .004   -.186      -.301 **   -.249 *    -.294 **
Factor 3   -.158       .093   -.267 **   -.255 *    -.272 **   -.391 **
Factor 4   -.254 *     .095   -.248 *    -.183      -.315 **   -.321 **
Factor 5   -.180      -.047   -.164      -.298 **   -.281 **   -.222 *
Factor 6   -.252 *    -.155   -.186      -.170      -.193      -.193


Factor 1   -.338 **
Factor 2   -.239 *
Factor 3   -.277 **
Factor 4   -.211 *
Factor 5   -.303 **
Factor 6   -.090

Note. * p < .05; ** p < .01. STARS Subscales: Worth of Statistics
(Factor 1), Interpretation Anxiety (Factor 2), Test and Class
Anxiety (Factor 3), Computation Self-Concept (Factor 4), Fear of
Asking for Help (Factor 5), and Fear of Statistics Teachers
(Factor 6). TOSRA Subscales: social implications (SI). normality
of scientists (NS). attitude of scientific inquiry (ASI),
adoption of scientific attitudes (ASA), enjoyment of science
lessons (ESL. leisure interest in science (LI), and career
interest in science (CI).
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