Statistics Anxiety Update: Refining the Construct and Recommendations for a New Research Agenda

Abstract

Appreciation of the importance of statistics literacy for citizens of a democracy has resulted in an increasing number of degree programs making statistics courses mandatory for university students. Unfortunately, empirical evidence suggests that students in nonmathematical disciplines (e.g., social sciences) regard statistics courses as the most anxiety-inducing course in their degree programs. Although a literature review exists for statistics anxiety, it was done more than a decade ago, and newer studies have since added findings for consideration. In this article, we provide a current review of the statistics anxiety literature. Specifically, related variables, definitions, and measures of statistics anxiety are reviewed with the goal of refining the statistics anxiety construct. Antecedents, effects, and interventions of statistics anxiety are also reviewed to provide recommendations for statistics instructors and for a new research agenda.

Keywords

statistics anxiety literature review measures antecedents effects interventions

The importance of statistics to society cannot be denied. In newspapers, reporters often use statistics to portray trends such as “crime rate, population growth, spread of diseases, industrial production, educational achievement, or employment trends” (Gal, 2002, p. 3). Government policy decisions are often based on statistics. Hence, knowledge of statistics is a prerequisite for individuals to play their part as well-informed citizens of a democracy. Unfortunately, most citizens lack the necessary skills to read and to evaluate statistics (Utts, 2003). The American Statistical Association has long recognized this problem. For instance, in her presidential address to the American Statistical Association, Wallman (1993) emphasized the importance of statistical literacy and defined it as follows:

the ability to understand and critically evaluate statistical results that permeate our daily lives—coupled with the ability to appreciate the contributions that statistical thinking can make in public and private, professional and personal decisions. (p. 1)

Consequently, with statistical literacy as a goal, an increasing number of degree programs are making statistics courses mandatory for university students (Gould, 2010). Unfortunately, taking a statistics course is often a negative experience for students in nonmathematical disciplines (e.g., social sciences). Students enrolled in undergraduate psychology programs expect to study psychology-related topics, often without realizing the relevance of statistics to the science of psychology. Indeed, only 46.7% of such students were aware of the statistics element in a psychology program (Ruggeri, Dempster, Hanna, & Cleary, 2008). The lack of awareness is further compounded by the importance assigned to statistics courses in psychology.

Out of 374 universities surveyed in North America, 98% of them require students to complete at least one statistics course—with most of those requiring students to complete two—as a requirement for their psychology degree program (Stoloff et al., 2009). Furthermore, statistics courses are often used to determine entry into an Honors program, which, in turn, is often essential for entry into postgraduate studies. Given the mandatory and high-stakes nature of statistics courses, it is not surprising that students regard them as the most anxiety-inducing course in their degree programs.

Although a literature review exists for statistics anxiety (Onwuegbuzie & Wilson, 2003), it was done more than a decade ago, and newer studies have since added findings for consideration. Therefore, the purpose of this article is to provide a current review of the statistics anxiety literature to offer directions for future research and ideas for statistics education. (For information on our literature search and results, see the Appendix and the Supplemental Material available online.)

Refining the Statistics Anxiety Construct

Currently, research on statistics anxiety has been hampered by the lack of distinction between statistics anxiety and related variables, such as mathematics anxiety and attitudes toward statistics. Therefore, for research on statistics anxiety to flourish, researchers need to (a) distinguish statistics anxiety from related variables, (b) redefine statistics anxiety, and (c) select appropriate measures of statistics anxiety.

Distinguishing statistics anxiety from related variables

Mathematics anxiety

Mathematics anxiety rose to prominence in the early 1970s with Richardson and Suinn’s (1972) classic article on the Mathematics Anxiety Rating Scale (MARS). According to Richardson and Suinn, “mathematics anxiety involves feelings of tension and anxiety that interfere with the manipulation of numbers and the solving of mathematical problems in a wide variety of ordinary life and academic situations” (p. 551). Initially, mathematics anxiety was conceptualized as a unidimensional construct (Richardson & Suinn, 1972); however, subsequent studies suggested it was a multidimensional construct (e.g., Kazelskis, 1998). The availability of the MARS has resulted in constant, if not growing, research attention to the field of mathematics anxiety, and many books have been written to help instructors and students overcome it (e.g., Arem, 2009; Burns, 1998; Kogelman & Warren, 1978; Tobias, 1978).

When statistics anxiety was first identified, researchers conceived the construct to be the same as mathematics anxiety. For example, the MARS was used to evaluate the use of humor as an intervention for statistics anxiety (Schact & Stewart, 1990). The lack of distinction could be due to several reasons. First, researchers cannot agree on the definition of statistics. Although there is a lack of more recent reviews, in an article written more than 70 years ago, Willcox (1936) documented more than a hundred definitions of statistics. In one of these definitions, it is asserted that “statistics is [simply] higher mathematics” (Wilson, 1927, p. 586). Researchers who defined statistics in this manner might be inclined to view statistics anxiety to be the same as mathematics anxiety. Second, although the emphasis on mathematics can differ from one introductory statistics course to another (Papousek et al., 2012), the importance of mathematics is made prominent to researchers by the numerous mathematical formulas found in introductory statistics textbooks (e.g., Gravetter & Wallnau, 2007). Lastly, mathematics anxiety has been extensively studied and is better understood than statistics anxiety. Thus, the similarities between them (Baloğlu, 1999) may have prompted researchers to assimilate the latter under the former.

Cruise, Cash, and Bolton (1985) were the first to advocate for a distinction between statistics anxiety and mathematics anxiety. The authors argued that existing measures of mathematics anxiety did not adequately assess all aspects of statistics anxiety, and they developed the Statistical Anxiety Rating Scale (STARS) to address this need. Furthermore, statistics learning has often been conceptualized as second language learning (Lalonde & Gardner, 1993; Onwuegbuzie, 2003) rather than mathematics learning. This notion was supported by findings that linguistic intelligence, in addition to mathematical intelligence, is related to lower statistics anxiety (Daley & Onwuegbuzie, 1997).

Subsequently, similarities and differences between statistics anxiety and mathematics anxiety in terms of definitions, nature, antecedents, effects, and interventions were documented (Baloğlu, 1999, 2004). More important, although many researchers found a significant positive relationship between statistics anxiety and mathematics anxiety, the relationship was moderate, and mathematics anxiety, at a maximum, explained less than 50% of the variance in statistics anxiety (Baloğlu, 2004). Taken together, these reports suggested that statistics anxiety is related to, but distinct from, mathematics anxiety.

Attitude toward statistics

Similar to the lack of distinction between statistics anxiety and mathematics anxiety, “the literature makes little if any distinction between the concepts of attitudes and anxiety and the terms are often used interchangeably” (Nasser, 2004, p. 3). A literature review reveals two possible reasons for this confusion.

First, although statistics anxiety has been clearly defined as an affective construct (Cruise et al., 1985; Onwuegbuzie, Da Ros, & Ryan, 1997; Zeidner, 1991), there is a lack of consensus among researchers regarding the definition of attitudes (Gal & Ginsburg, 1994; Schau, 2003). Most researchers defined attitudes as a purely affective construct (Evans, 2007; Gal & Ginsburg, 1994; Mills, 2004; Rhoads & Hubele, 2000; Roberts & Bilderback, 1980), whereas others defined it as consisting of affective, cognitive, and behavioral components (Chiesi & Primi, 2009; Olson & Zanna, 1993). The former definition assumes both anxiety and attitude to be noncognitive (i.e., affective) constructs, whereas the latter assumes attitude to be the overarching construct, with anxiety subsumed under it as an affective component (e.g., Schau, 2003).

Second, the widespread use of the STARS (Cruise et al., 1985) may have exacerbated the situation. A recent study suggests that the measure assesses both statistics anxiety and attitudes toward statistics rather than only statistics anxiety (Papousek et al., 2012). Specifically, it has been suggested that the first three subscales of the STARS (Interpretation Anxiety, Test and Class Anxiety, and Fear of Asking for Help subscales) assess statistics anxiety, whereas the last three subscales (Worth of Statistics, Computation Self-Concept, and Fear of Statistics Teachers subscales) assess attitudes toward statistics. Earlier researchers tended to use all six subscales as a measure of statistics anxiety. This procedure likely resulted in high negative correlations between the STARS and measures of attitudes toward statistics. Consequently, researchers might conclude that they are measuring the same construct (e.g., see Perepiczka, Chandler, & Becerra, 2011; Watson, Lang, et al., 2003) and remove one of the variables from their study (Nasser, 2004).¹

In Table 1, we summarized the data from Nasser (2004) on the basis of the factor structure of the STARS, as suggested by Papousek et al. (2012). The correlations between the statistics anxiety subscales and the Survey of Attitudes Toward Statistics Scale (Schau, Stevens, Dauphinee, & Vecchio, 1995) were mostly small, with some moderate correlations (ranging from −.19 to −.49). In contrast, the correlations between the attitudes toward statistics subscales and the Survey of Attitudes Toward Statistics Scale were mostly moderate, with some large correlations (ranging from −.26 to −.76). Hence, to prevent multi-collinearity, researchers should make a distinction between statistics anxiety and attitudes toward statistics and should only use the first three subscales of the STARS to measure statistics anxiety (see Chiesi & Primi, 2010).

Table 1.

Correlations Between the Subscales of the STARS and the SATS

	SATS
STARS	Cognitive	Value	Difficulty	Affect
Statistics anxiety subscales
Interpretation Anxiety	−.19*	−.21*	−.06	−.06
Test and Class Anxiety	−.28*	−.35*	−.22*	−.49*
Fear of Asking for Help	−.22*	−.24*	.01	−.33*
Attitudes toward statistics subscales
Worth of Statistics	−.34*	−.76*	−.26*	−.48*
Computation Self-Concept	−.63*	−.33*	−.34*	−.50*
Fear of Statistics Teachers	−.36*	−.33*	−.33*	−.35*

Note: Data summarized from Nasser (2004) on the basis of the factor structure of the Statistical Anxiety Rating Scale (STARS), as suggested by Papousek et al. (2012). SATS = Survey of Attitudes Toward Statistics Scale.

p < .05.

Among researchers who have distinguished between statistics anxiety and attitudes toward statistics, the general consensus has been that negative attitudes toward statistics result in statistics anxiety (Chiesi & Primi, 2010; Mji & Onwuegbuzie, 2004; Onwuegbuzie, 2000; Watson, Kromrey, & Hess, 2003; Watson, Lang, & Kromrey, 2002; Zanakis & Valenzi, 1997). This distinction affords researchers more insights into their data. For example, when the STARS was administered 4 months before an oral examination, the statistics anxiety subscales, but not the attitudes toward statistics subscales, significantly predicted subjectively rated stress and anxiety. On the other hand, attitudes toward statistics, but not statistics anxiety, significantly predicted physiological responses (i.e., diastolic blood pressure) to the task (Papousek et al., 2012). These results underscored the importance and potential advantage of distinguishing between statistics anxiety and attitudes toward statistics.

Currently, the critical question is whether the construct of statistics anxiety offers additional advantages for researchers and instructors compared with mathematics anxiety and attitudes toward statistics. Literature suggests that a reliable and valid measure of statistics anxiety allows researchers to identify students who are high in statistics anxiety, to predict scores on a statistics examination, and to evaluate the relative effectiveness of interventions designed to reduce statistics anxiety. Therefore, future researchers should examine the utility and predictive ability of statistics anxiety, mathematics anxiety, and attitudes toward statistics concurrently. For example, a point can be made for the distinctiveness of statistics anxiety if it predicts scores on statistics examinations better than the other two variables. However, before such research can be conducted, statistics anxiety needs to be redefined.

Redefining statistics anxiety

Statistics anxiety may be narrowly defined “as the feelings of anxiety encountered when taking a statistics course or doing statistical analyses” (Cruise et al., 1985, p. 92). Offering a broader perspective, statistics anxiety is anxiety that occurs as a result of encountering statistics in any form and at any level (Onwuegbuzie et al., 1997). Thus, it may also be defined as follows:

a performance characterized by extensive worry, intrusive thoughts, mental disorganization, tension, and physiological arousal . . . when exposed to statistics content, problems, instructional situations, or evaluative contexts, and is commonly claimed to debilitate performance in a wide variety of academic situations by interfering with the manipulation of statistics data and solution of statistics problems. (Zeidner, 1991, p. 319)

However, none of the definitions address its relationship with mathematics anxiety and attitudes toward statistics. Additionally, although there is some evidence for the positive effects of statistics anxiety on statistics achievement (Keeley, Zayac, & Correia, 2008), the majority of the literature focuses on the negative effects of statistics anxiety. Therefore, one recommendation is to extend the definition of statistics anxiety (Onwuegbuzie et al., 1997) and to redefine statistics anxiety as follows:

a negative state of emotional arousal experienced by individuals as a result of encountering statistics in any form and at any level; this emotional state is preceded by negative attitudes toward statistics and is related to but distinct from mathematics anxiety.

This definition should distinguish statistics anxiety from mathematics anxiety and attitudes toward statistics and serve as a guide in the selection of measures.

Selecting appropriate measures of statistics anxiety

A literature review revealed six measures purported to assess statistics anxiety. They were the STARS (Cruise et al., 1985), the Statistics Anxiety Inventory (Zeidner, 1991), the Statistics Anxiety Scale (Pretorius & Norman, 1992), an unnamed instrument (Zanakis & Valenzi, 1997), the Statistics Anxiety Measure (Earp, 2007), and the Statistical Anxiety Scale (Vigil-Colet, Lorenzo-Seva, & Condon, 2008). These measures and their subscales are summarized in Table 2.

Table 2.

Measures and Subscales of Statistics Anxiety (By Date of Publication)

Measure	Subscale
51-item STARS (Cruise, Cash, & Bolton, 1985)	Interpretation Anxiety
	Test and Class Anxiety
	Fear of Asking for Help
	Worth of Statistics
	Computation Self-Concept
	Fear of Statistics Teachers
40-item Statistics Anxiety Inventory (Zeidner, 1991)	Statistics Test Anxiety
	Statistics Content Anxiety
10-item Statistics Anxiety Scale (Pretorius & Norman, 1992)	Unidimensional
36-item unnamed instrument (Zanakis & Valenzi, 1997)	Student Interest in and Perceived Worth of Statistics
	Anxiety When Seeking Help for Interpretation
	Computer Usefulness and Experience
	Math Anxiety
	Understanding
	Test Anxiety
44-item Statistics Anxiety Measure (Earp, 2007)	Anxiety
	Attitude Towards Class
	Fearful Behaviour
	Attitude Towards Math
	Performance
24-item Statistical Anxiety Scale (Vigil-Colet, Lorenzo-Seva, & Condon, 2008)	Examination AnxietyAsking for Help AnxietyInterpretation Anxiety

Note: STARS = Statistical Anxiety Rating Scale.

Two of these measures assume statistics anxiety to be similar to mathematics anxiety. Both the Statistics Anxiety Inventory (Zeidner, 1991) and the Statistics Anxiety Scale (Pretorius & Norman, 1992) were developed by replacing words related to mathematics with words related to statistics in the 40-item version of the MARS (Richardson & Woolfolk, 1980) and the 10-item version of the Mathematics Anxiety Scale (Betz, 1978), respectively. Another two measures make no distinction between statistics anxiety and attitudes toward statistics. The unnamed instrument (Zanakis & Valenzi, 1997) and the Statistics Anxiety Measure (Earp, 2007) assess both statistics anxiety and attitude toward statistics. The use of any of these four measures might result in high correlations among statistics anxiety, mathematics anxiety, and attitudes toward statistics. Consequently, researchers might assume the constructs to be similar or even identical.

Therefore, researchers who wish to measure statistics anxiety are recommended to use either the STARS (Cruise et al., 1985) or the Statistical Anxiety Scale (Vigil-Colet et al., 2008). Currently, the STARS has been extensively utilized by researchers because of the superiority of its reliability and validity data compared with that of other measures (Baloğlu, 2002; Hanna, Shevlin, & Dempster, 2008; Liu, Onwuegbuzie, & Meng, 2011; Mji & Onwuegbuzie, 2004; Papousek et al., 2012). However, as mentioned earlier, researchers should use only the first three subscales of the STARS as a measure of statistics anxiety. A second option is to use the Statistical Anxiety Scale, a promising instrument that affords researchers a specific measure of statistics anxiety. Nevertheless, the measure seems to be in its infancy, with only one validity study conducted (Chiesi, Primi, & Carmona, 2011). Thus, future research is needed to confirm its factor structure with diverse samples.

With the lack of distinction between statistics anxiety and related variables addressed, in the next section of this article, we review and evaluate the antecedents, effects, and interventions of statistics anxiety to provide recommendations for statistics instructors and for a new research agenda.

Antecedents of Statistics Anxiety

The antecedents of statistics anxiety are classified as situational, dispositional, and environmental (Onwuegbuzie & Wilson, 2003). Situational antecedents refer to factors that surround the stimulus object or event, whereas dispositional antecedents refer to the personality characteristics of an individual, and environmental antecedents refer to events that occurred in the past.

Situational antecedents of statistics anxiety

Given the relationship between mathematics and statistics, a number of mathematics-related variables have been implicated in statistics anxiety. For example, statistics anxiety was found to be positively related to mathematics anxiety, number anxiety, mathematics course anxiety, and mathematics exam anxiety (Baloğlu, 2004).

Some characteristics of statistics courses have been implicated in statistics anxiety. In general, students taking accelerated courses experienced higher levels of statistics anxiety than students taking regular courses (Bell, 2005). In addition, students taking an online statistics course had higher levels of statistics anxiety than their counterparts taking a statistics course on campus (DeVaney, 2010). However, students were not randomly assigned, and a major limitation of the study was the different characteristics of the groups. For example, students in the on-campus group (n = 27) were predominantly Black (66.7%), whereas students in the online group (n = 93) were predominantly White (74.2%).

The probabilistic nature of statistics has also been implicated in statistics anxiety. In statistics courses, students often have to deal with ambiguous scenarios in their learning. For example, when the null hypothesis is rejected, students have to accept that there is a 5% chance of making a Type I error (i.e., rejecting the null hypothesis when it is true). Students who are uncomfortable with such ambiguity might experience statistics anxiety. Indeed, intolerance of uncertainty and students’ tendency to worry (a dispositional antecedent) were found to be positively correlated with one another and with statistics anxiety (Williams, 2013).

Dispositional antecedents of statistics anxiety

Procrastination has been found to be related to statistics anxiety. Students who procrastinated because of fear of failure and task aversiveness tended to experience higher levels of statistics anxiety. However, procrastination and statistics anxiety might affect each other in a bidirectional manner. Students who procrastinate might experience higher statistics anxiety because of the increasing difficulty and workload of the course. Conversely, students with high levels of statistics anxiety might procrastinate because of task aversiveness (Onwuegbuzie, 2004).

Reading ability and learning strategies have also been implicated in statistics anxiety. Students with poor reading ability tend to experience higher levels of statistics anxiety (Collins & Onwuegbuzie, 2007). The results provided support for the notion that a well-written statistics textbook might help meet the needs of students and alleviate statistics anxiety (Schact, 1990). With regard to learning strategies, students who used rehearsal, elaboration, organization, critical thinking, and effort regulation strategies experienced lower levels of statistics anxiety (Kesici, Baloğlu, & Deniz, 2011).

Environmental antecedents of statistics anxiety

The research on the effects of age and gender differences on statistics anxiety has yielded mixed results. Although some studies reported that older students (i.e., 25 years of age and older) had higher statistics anxiety than younger students (Baloğlu, 2003; Bell, 2003), in a more recent study, Bui and Alfaro (2011) found no age differences. With regard to gender differences, although some researchers reported that women experience higher statistics anxiety than men (Baloğlu, Deniz, & Kesici, 2011; Rodarte-Luna & Sherry, 2008), other researchers found no gender differences (Baloğlu, 2003; Bui & Alfaro, 2011; Hsiao & Chiang, 2011). The mixed results could be due to various sources of inconsistencies, such as type of analysis (e.g., t tests, discriminant function analysis, or multivariate analysis of variance), country (e.g., United States, Turkey, or Taiwan), and the inclusion of other variables in the analysis (e.g., controlling for grade point average or previous mathematics experience). Nevertheless, among studies that reported age or gender differences, the effect sizes were mostly small to moderate (e.g., Rodarte-Luna & Sherry, 2008). This suggests that the practical significance of the differences might be negligible. For example, although women reported higher statistics anxiety than men, there were no differences in statistics achievement (Bradley & Wygant, 1998). In addition, gender was not related to statistics examination grades (Furnham & Chamorro-Premuzic, 2004). Thus, future researchers should assess statistics achievement in conjunction with statistics anxiety. Specifically, researchers should examine whether age and gender differences in statistics anxiety affect statistics achievement.

Cross-cultural and ethnic differences have also been implicated in statistics anxiety. International students in the United States reported higher statistics anxiety than domestic students (Bell, 2008). In addition, American college students in the United States reported higher statistics anxiety than Turkish college students in Turkey (Baloğlu et al., 2011). With regard to ethnicity, although no significant differences in statistics anxiety were found between Latino/Hispanics and Caucasians (Bui & Alfaro, 2011), African Americans were found to have higher levels of statistics anxiety than their Caucasian American counterparts (Onwuegbuzie, 1999).

Most of the antecedent research has assessed statistics anxiety and another variable (e.g., reading ability) concurrently in a semester. Subsequently, because of the multidimensional nature of the variables, canonical correlation analysis or multivariate analysis of variance was used to analyze the data. It should be acknowledged that the nonexperimental design of the studies did not afford assessments of causality. Nevertheless, most of the antecedents cannot be manipulated because of their nature (e.g., gender, age, ethnicity) or because of ethical concerns (e.g., procrastination). Hence, researchers should recognize these limitations and use antecedent research as a source of ideas for the development of interventions (e.g., a program designed to improve reading ability). Subsequently, the effectiveness of the interventions should be evaluated in an experimentally designed study.

Effects of Statistics Anxiety

A consistent negative relationship has been found between statistics anxiety and statistics achievement in a variety of studies (Bell, 2001; Hanna & Dempster, 2009; Onwuegbuzie, 1995, 2003; Onwuegbuzie & Seaman, 1995; Tremblay, Gardner, & Heipel, 2000; Zanakis & Valenzi, 1997). In other words, students who experience higher levels of statistics anxiety tend to have lower performance on a statistics examination.

In one study, Galli, Ciancaleoni, Chiesi, and Primi (2008) assessed the relationship between statistics anxiety and the difficulty of passing a statistics course by measuring statistics anxiety in a sample of psychology students enrolled in an introductory statistics course in the first semester of 2005. Their course failures (if any) were recorded from June 2005 to February 2007. Out of 442 students, 99 (22%) students failed once, 42 (9.5%) failed twice, and 21 (5.1%) failed three times, leading to a total of 162 (37%) students who failed the course at least once. These 162 students had higher levels of statistics anxiety than the 280 students who passed the course at the first attempt. Because statistics anxiety was assessed in the first semester, students could not have been reporting higher statistics anxiety because of failing the statistics course previously.

Despite the numerous researchers who found a negative relationship between statistics anxiety and statistics achievement, it has been suggested that statistics anxiety may have a facilitative component (Onwuegbuzie & Wilson, 2003). Indeed, high and low levels of statistics anxiety were related to lower performance, whereas midlevel anxiety corresponded to higher performance (Keeley et al., 2008). This has important implications for the implementation of statistics anxiety interventions. It should be cautioned that “anxiety is not a fire that needs to be stamped out for students to be successful . . . some anxiety is acceptable” (Keeley et al., 2008, p. 13).

Current research on the effects of statistics anxiety is limited because of the lack of cutoff scores for anxiety levels. For example, although moderate statistics anxiety facilitates performance (Keeley et al., 2008), it is unclear at which point statistics anxiety may change from being debilitative to facilitative and, finally, to debilitative again (Onwuegbuzie & Wilson, 2003). Most clinical instruments have a set of cutoff scores to identify individuals for interventions. For example, on the Beck Anxiety Inventory (Beck & Steer, 1993), a total score of 0 to 7 indicates minimal anxiety, 8 to 15 indicates mild anxiety, 16 to 25 indicates moderate anxiety, and 26 to 63 indicates severe anxiety. From a practical point of view, statistics instructors are better served by knowing the various percentages of students experiencing low, medium, and high statistics anxiety rather than knowing the mean anxiety levels of the class. Therefore, future researchers should determine a set of cutoff scores that could differentiate students in need for intervention from those who do not.

Interventions for Statistics Anxiety

Given the negative effects of statistics anxiety, researchers have explored how innovative instructional methods might reduce that anxiety. One method involves presenting participants with nine short “sleuthing” stories and asking them to use statistical analyses to “solve” the puzzle (D’Andrea & Waters, 2002). A pretest–posttest design showed a significant decrease in statistics anxiety scores in the posttest. Another method requires statistics instructors to employ application-oriented teaching methods (applying statistics to real-world problems, critiquing of journal articles, etc.) while being attentive to students’ anxiety (humorous teaching style, providing coping strategies, etc.) in class (Pan & Tang, 2004). Similarly, a pretest–posttest design showed a significant decrease in statistics anxiety scores in the posttest.

The effectiveness of a gender-sensitive and culture-sensitive statistics course in alleviating statistics anxiety has been examined (Davis, 2003) because some research showed that women and minorities had higher statistics anxiety (e.g., Baloğlu et al., 2011). Davis (2003) designed a statistics course around the six factors of statistics anxiety (Cruise et al., 1985). For example, the Fear of Asking for Help factor was addressed by discussing statistics anxiety with students. More important, participants had weekly discussions on the role of women and minorities in research. A pretest–posttest design revealed significant reductions in statistics anxiety at posttest.

Lastly, the role of instructor immediacy in reducing students’ levels of statistics anxiety was examined (Williams, 2010). Immediacy refers to a set of behaviors (e.g., addressing students by name) communicated by the instructors to influence the perception of psychological and physical distance. A pretest–posttest control group design revealed a significant decrease in statistics anxiety scores for the treatment group.

Although since the last literature review (i.e., Onwuegbuzie & Wilson, 2003), there has been an increase in the use of experimental designs to evaluate interventions, the design of the studies can be further improved. Only one included a control group design (Williams, 2010); the others used a one group pretest–posttest design (D’Andrea & Waters, 2002; Davis, 2003; Pan & Tang, 2004). A common argument for this design is the ethical issue of withholding a potential beneficial intervention from the control group (Pan & Tang, 2004). Nevertheless, the lack of a control group is problematic because it does not take into account several alternative competing explanations for improvement, such as history, maturation, testing, and statistical regression (Campbell & Stanley, 1963). For example, there is some evidence that statistics anxiety decreases over time in the absence of interventions (Chew & Dillon, 2012; Keeley et al., 2008). Hence, the effectiveness of the interventions in these studies is in question.

In education research, it is often impractical or impossible to randomly assign students to groups. Consequently, most researchers use preexisting groups, such as students from two comparable classes. Therefore, future researchers should use the nonequivalent control group design, a commonly used quasi-experimental design, to evaluate interventions for statistics anxiety. The nonequivalent control group design is essentially a pretest–posttest control group design without random assignment. Although the design has its limitations, it is vastly superior to the one group pretest–posttest design in terms of interpretation (Campbell & Stanley, 1963). Subsequently, researchers are recommended to run both analysis of variance (on the change scores—posttest minus pretest) and analysis of covariance (with pretest as covariate and posttest as outcome) to increase their confidence in the conclusions (Van Breukelen, 2006).

Recommendations for Statistics Instructors

We make five recommendations for statistics instructors on the basis of statistics anxiety literature. First, the emphasis on mathematics in a statistics course should be reduced. Although formulas and calculations might help students understand statistics (however, see Rumsey, 2002), they might aggravate the situation because students have to deal with mathematics anxiety in addition to statistics anxiety. Furthermore, with the plethora of commercial and free statistical software, the need for manual calculations should be diminished. Thus, instructors should devote most of their time to helping students understand the assumptions and the appropriate use of statistical tests.

Second, given the relationship between academic procrastination and statistics anxiety (Onwuegbuzie, 2004), instructors should structure the statistics course to discourage procrastination. Similar to how students procrastinate on enrolling in the statistics course until their last semester (Roberts & Bilderback, 1980), anecdotal evidence suggests that students procrastinate on studying for statistics until the last week or two before their examinations. Therefore, instructors can introduce weekly quizzes to encourage students to keep up with their required readings. Furthermore, incorrect answers on these quizzes help instructors to identify the problematic areas for the students. In addition, instructors should award marks to students for participation rather than for correct answers. The idea is to encourage students to be consistent in studying for statistics instead of experiencing statistics anxiety or test anxiety because of the potentially evaluative nature of the quizzes.

Third, a system should be in place to allow for anonymous questions because some students experience anxieties related to Fear of Asking for Help and Fear of Statistics Teachers (Cruise et al., 1985). For example, the BlackBoard Learning System allows instructors to set up forums for students to post questions anonymously. Subsequently, instructors can either answer the questions on the forums or collate the questions and address them in class.

Fourth, humor should be integrated into statistics courses through the inclusion of cartoons on lecture slides (Schact & Stewart, 1990) or by adopting a humorous teaching style (Pan & Tang, 2004). The Consortium for the Advancement of Undergraduate Statistics Education (CAUSE, 2013) Web site contains a wide array of materials ranging from cartoons to videos to make the learning of statistics fun and engaging. More recommendations and resources on the use of humor in statistics teaching can be found in Lesser and Pearl (2008). However, instructors should only include materials related to the topic being taught instead of random humor. For example, a cartoon that illustrates the importance of labeling the axes of a graph would be appropriate in contrast to one that makes fun of a politician. In addition, care should be taken to ensure that the materials are gender and culture sensitive (Davis, 2003).

Lastly, instructors could try to exhibit certain anxiety-reducing behaviors in class. In a recent study, Beilock, Gunderson, Ramirez, and Levine (2010) found that female teachers’ mathematics anxiety negatively affects elementary school girls’ mathematics achievement. Although no similar studies have been done on statistics anxiety, instructors should manage their own anxieties (if any) to appear confident and composed to students. In addition, instructors should exhibit immediacy behaviors to increase psychological and physical closeness and to reduce statistics anxiety (Williams, 2010). A list of such behaviors are found in the Verbal Immediacy Scale (Gorham, 1988) and the Revised Nonverbal Immediacy Scale (McCroskey, Richmond, Sallinen, Fayer, & Barraclough, 1995). For example, instructors can address students by name (verbal) and move around the classroom while teaching (nonverbal).

Recommendations for a New Research Agenda

Current research on statistics anxiety is limited in several ways. First, antecedent research is not being used to inform interventions. For example, despite procrastination being an antecedent of statistics anxiety (Onwuegbuzie, 2004), no researchers have evaluated the effect of reducing procrastination as an intervention for statistics anxiety. In this instance, antecedent research has served no purpose other than informing researchers about the correlates of statistics anxiety. Second, although research on the effects of statistics anxiety clearly emphasizes the need for instructors to be aware of this anxiety and for researchers to develop interventions for it, the research does not explain how statistics anxiety negatively affects statistics achievement. Lastly, most of the intervention research has been instructor centered. In these studies, researchers assume, perhaps incorrectly in certain instances, that instructors have the autonomy, ability, and time to implement these interventions in class.

In view of these limitations, there is a need for the field of statistics anxiety to move toward a new research agenda. Specifically, we suggest that researchers adopt an information processing perspective on statistics anxiety. According to cognitive theories (Beck, 1976; Bower, 1981), individuals with anxiety have an attentional bias to process information congruent with their anxiety. Furthermore, in these theories it is asserted that this bias plays an important role in the etiology and maintenance of anxiety in individuals. Evidence of this bias has been documented among individuals using experimental paradigms, such as the emotional Stroop task² and the dot probe task (MacLeod, Mathews, & Tata, 1986).³ More recently, researchers have modified the dot probe task and have applied it successfully as an intervention (commonly known as the Attentional Bias Modification Program) for nonclinical populations (such as high trait anxious students) and for clinical populations (such as patients with generalized anxiety disorder, social phobia, or alcohol dependence; Bar-Haim, 2010; Browning, Holmes, & Harmer, 2010; Schoenmakers et al., 2010). The intervention is remarkable considering the absence of a therapist in its implementation.

An information processing perspective is appropriate because of the similarity between statistics anxiety and specific phobias. In fact, statistics anxiety can be considered a form of specific phobia because the symptoms only emerge when students are learning or applying statistics (Onwuegbuzie, 1999). An information processing perspective suggests that an attentional bias toward threatening stimuli might characterize students who are high in statistics anxiety. For example, these students should be faster in responding to a dot that replaces a statistics-related threatening word (e.g., “mode”) than a neutral word (e.g., “deck”) on the dot probe task (MacLeod et al., 1986). Hence, the role of attentional bias as an antecedent and effect of statistics anxiety should be explored. The presence of an attentional bias provides hints as to the mechanisms by which statistics anxiety operates. For example, students high in statistics anxiety might be allocating a disproportionate amount of cognitive resources (attention) in processing threatening words. This leads to poor concentration and impaired learning (Beck & Clark, 1997), which eventually results in poor statistics achievement.

More important, the presence of an attentional bias informs intervention. For example, future researchers can explore the effectiveness of the Attentional Bias Modification Program as a student-centered intervention for statistics anxiety. The program can be completed online (MacLeod, Soong, Rutherford, & Campbell, 2007), with minimal participation from instructors, allowing students to assume at least part of the responsibility for their anxiety.

Summary

The purpose of this article is to provide a current review of the statistics anxiety literature, with the goal of refining the statistics anxiety construct and providing recommendations for statistics instructors and for a new research agenda. Statistics anxiety can be refined by redefining it, which will inform the selection of appropriate measures. Recommendations for statistics instructors include the following: (a) reducing the emphasis on mathematics, (b) using humor, (c) discouraging student procrastination, (d) allowing anonymous questions, and (e) using immediacy behaviors. Lastly, the adoption of an information processing perspective to motivate a new research agenda addresses several limitations in the statistics anxiety literature and suggests a potentially effective, student-centered intervention for statistics anxiety. In particular, with statistics courses being a compulsory component of psychology degree programs (Stoloff et al., 2009) and a well-documented prevalence of anxiety about such courses, there is a pressing need for well-evaluated interventions to be documented and shared with researchers, instructors, and students. The goal of promoting statistics literacy for citizens of a democracy might eventually come to fruition when the negative effects of statistics anxiety are attenuated from the process of statistics learning.

Supplemental Material

Chew_Supplemental_Material – Supplemental material for Statistics Anxiety Update: Refining the Construct and Recommendations for a New Research Agenda

Supplemental material, Chew_Supplemental_Material for Statistics Anxiety Update: Refining the Construct and Recommendations for a New Research Agenda by Peter K. H. Chew and Denise B. Dillon in Perspectives on Psychological Science

Footnotes

Appendix

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Supplemental Material

Additional supporting information may be found at

Notes

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