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Lai, Tanner, Stevens                                                                                                                                                      Advances in Business Research 

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The Importance of Mathematics Competency in Statistical Literacy 
 

Guolin Lai, University of Louisiana at Lafayette 

John Tanner, University of Louisiana at Lafayette 

David Stevens, University of Louisiana at Lafayette 
 

 

Competence in mathematics and statistics are related, but are not the same thing. To measure the impact 
of mathematical competence on statistics performance, ACT math scores together with remedial and 

required math grades were analyzed together with final grades in a two-course sequence of 
undergraduate statistics. Results indicate that math competence is correlated with success in the first 
statistics course, but generally not the second. In addition, success in the first statistics course does not 

imply success in the second. These findings support the claims that mathematics and statistics are two 
separate disciplines, each deserving of its own pedagogy. 
 

 

It is widely recognized that statistical literacy, statistical reasoning, and statistical thinking are key 

components of the skills needed by employees in various industries (Ben-Zvi and Garfield, 2004; Snee, 

1993). To provide the skills, college degree programs in the United States require a course in introductory 

statistics. However, American college students often regard their statistical learning experience very 

negatively (Hogg, 1991). As a result, many students postpone taking statistics course(s) until the end of 

their degree programs (Onwuegbuzie and Wilson, 2003; Zeidner, 1991).  

To understand the causes of such perceptions, various factors have been investigated including 

mathematical competence, mathematical anxiety and attitudes, statistics anxiety and attitudes, motivation, 

educational background, self-efficacy, instructional strategies, and technology. For example, according to 

Gal (2002), An adult’s statistically literate behavior is predicated on the joint activation of five 

interrelated knowledge elements including literacy skills, statistical knowledge, mathematical knowledge, 

context knowledge, and critical questions, together with a cluster of supporting dispositional elements 

including beliefs, attitudes, and critical stance. As envisioned by Gal (2002), mathematical competence 

plays an integral role in the achievement of statistical literacy.  

There has been a general consensus in the literature that students’ statistical performance is positively 

related to their mathematical competence (Adams and Holcomb, 1986; Feinberg and Halprin, 1978; 

Galagedera, 1998; Galagedera and Woodward, 2000; Johnson and Kuennen, 2006; Lalonde and Gardner, 

1993; Nasser, 1999; Wisenbaker et al. 2000). Moreover, there are intricate relationships among students’ 

mathematical competence, mathematics attitude and anxiety, statistics attitude and anxiety, and statistics 

performance. Students tend to experience mathematics anxiety (Bessant, 1995; McLeod, 1992; Stodolsky, 

1985), and mathematics anxiety is negatively related to statistical performance (Adams and Holcomb, 

1986; Onwuegbuzie and Seaman, 1995; Wisenbaker et al., 2000; Zeidner, 1991). Mathematical 

competence has a positive effect on attitudes toward statistics (Carmona, 2004; Lalonde and Gardner, 

1993; Schutz et al., 1999) and on anxiety toward statistics (Gal et al., 1997; Onwuegbuzie, 2003). 

Statistics anxiety is negatively related to statistics performance (Lalonde and Gardner, 1993; Zeidner, 

1991), whereas positive statistics attitude is associated with better statistics performance (Lalonde and 

Gardner, 1993; Roberts and Bilderback, 1980; Wise, 1985). Silvia et al., (2008) examined these 

relationships. They found that students with poor math competence showed more sustained negative 

attitudes toward statistics throughout the semester; whereas students who did not fail the introductory 

statistics course had improved statistical attitudes. Moreover, anxiety toward statistics was found among 

the students that eventually fail the course. Nasser (2004) also found that mathematical competence, 

mathematical anxiety, attitudes toward mathematics and statistics, and motivation, together accounted for 

36% of the variance in statistics performance. 

It has been reported that mathematics and statistics are two distinct methodological disciplines (Gal 

and Garfield, 1997; Groth, 2007; Moore, 1988, 1992), and statistics education is a new and emerging 



Lai, Tanner, Stevens                                                                                                                                                      Advances in Business Research 

2011, Vol. 2, No. 1, 115-124 

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discipline (Zieffler et al., 2008). Given the disciplinary differences between mathematics and statistics 

outlined by Gal and Garfield (1997), it seems rational that statistics education should shift its focus from 

mathematical computation and procedure to an emphasis on statistical literacy, statistical reasoning, and 

statistical thinking (Garfield, 2003; Jeffries, 2001; Moore, 1997; Tempelaar et al., 2007). To address 

whether statistics education is a new and emerging paradigm calling for vastly different pedagogies from 

mathematics education, the present study focuses on the relationship between mathematical competence 

and statistics performance. Specifically, this research investigates the relationship between undergraduate 

business students’ mathematical competence measured by American College Testing (“ACT”) math 

score, their grades in remedial and required mathematics courses and their performance (as measured by 

final grade) in required business statistics courses.   

Since the authors work in a department offering the undergraduate degree in Management Information 

Systems (“MIS”), the primary interest was in determining the effects of mathematics preparation on 

statistics performance for students majoring in MIS. Therefore, this study investigates the following 

research questions: 
 

1. Does an undergraduate MIS student’s ACT math score have any effect on their final grades in 

business statistics courses?  

2. Does an undergraduate MIS student’s success in a remedial mathematics course have any effect on 

their final grades in business statistics courses? 

3. Does an undergraduate MIS student’s success in other required mathematics courses have any effect 

on their final grades in business statistics courses? 
 

LITERATURE REVIEW 

 

Lalonde and Gardner (1993) examined various factors in relation to statistics performance of 

psychology students in an introductory statistics course. Factors in three classes (aptitude, situational 

anxiety, and attitudinal-motivational characteristics) were used. Mathematical aptitude factors include 

mathematics background level ranging from low to high, and a 10-question test designed to measure basic 

mathematical ability. Their correlation analysis revealed that mathematical background was positively 

correlated (r = +.37) with statistics performance.  Similarly, score on a basic mathematics test was also 

positively correlated (r = +.29) with statistics performance.  

Galagedera (1998) investigated the influences of a remedial mathematics course on success in an 

elementary statistics course, where the remedial mathematics course was required by students who failed 

to satisfy the entry requirements for mathematics. Regression analysis suggested that the performance in 

the remedial mathematics course tended to be positively correlated with the statistic score. Musch and 

Broder (1999) conducted a study to determine the relative contribution of test anxiety, study habits, and 

math skills to the performance of 66 students on a statistics exam. Regression analysis revealed that all 

three variables together explained about 25% of the variance in the final statistics exam score, while math 

skills explained 17% of the variance and contributed significantly to exam performance. 

Johnson and Kuennen (2006) conducted an ordered probit regression to identify factors that 

contributed to undergraduate students’ success in an introductory business statistics course, as measured 

by final grade in the course. Independent variables included (1) whether the student had taken calculus or 

business calculus, (2) whether the student had taken compulsory remedial mathematics, (3) score on a 

basic math skills test, (4) ACT math score, and (5) ACT science/reasoning score. They identified that the 

most important determinants of student statistics performance are GPA, the ACT science score, the basic 

math quiz score, gender, and professor.  

Silvia et al., (2008) investigated the factors linked to the difficulties encountered by 442 psychology 

students in introductory statistics courses, using a between-subject design: those who never failed the 

final exam and those who failed at least once before passing it. Factors investigated included math 

background, math competence, and attitude and anxiety toward statistics. From t tests, they found that 

students’ math background and competence had a statistically significant effect on their final statistics 

performance. Moreover, students who enrolled in the course without an adequate level of mathematical 



Lai, Tanner, Stevens                                                                                                                                                      Advances in Business Research 

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competence showed more negative attitudes and high anxiety toward statistics learning, and such negative 

attitudes failed to change throughout the semester.   

Tanner et al., (2009) examined the relationship between undergraduate business students’ math skills 

and their performance in a business statistics course. The basic math/computational skills test with 40 

basic questions was administered to a convenience sample of 174 students in statistics classes. Results 

suggested that math skills have some influence on final statistics grade. For example, students who earned 

a grade of A in statistics classes had significantly higher scores on the math skills than those with grades 

of C, D, or F.  

The present study differs from previous studies in the following three aspects. First, unlike most of the 

studies (e.g., Lalonde and Gardner, 1993; Silvia et al., 2008) which used a convenience sample (e.g. the 

students taking the statistics course(s) taught by the researchers), the present study uses all students 

enrolled in MIS at the authors’ university at the time the data were collected. Second, to investigate the 

effect of mathematical competence on statistics performance, some researchers measured mathematical 

competence by administering a basic math/computation skills test (e.g., Musch and Broder, 1999; Tanner 

et al., 2009).  In this study, a student’s mathematical competence is measured by a combination of ACT 

math score, grades in remedial math course(s), and grades in required math courses. And lastly, the 

present study measures performance differences in statistics based on four separate initial mathematics 

placement paths. For instance, one path is to take remedial math, followed by a basic math, then 

introductory statistics. A second path is to take basic math immediately followed by introductory 

statistics.  These paths are explained further in the next section. 
 

METHODOLOGY 
 

Participants 
  

 The purpose of this paper is to study the relationships, if any, that exist between the performances (in 

terms of final course grade) in undergraduate mathematics classes, and the performances (also in terms of 

final course grade) in business statistics courses, for students majoring in MIS at a regional state 

university in the southern United States. The curriculum specifies that, in order to graduate, MIS majors 

need to pass various sets of mathematics courses depending upon their ACT math scores. In addition, 

these students must pass two statistics (called “quantitative methods” or “QMET”) courses in sequence. 

See Table 1 and Table 2 for further demographic information on these MIS majors. Table 1 shows the 

distribution of students by gender and classification, as well as their ACT math scores.  
 

Table 1: Student Demographics by Gender, Academic Classification, and ACT Math Score 
 

Gender %  ( n = 159) Academic Classification %  ( n = 159) ACT Math %  ( n = 135) 

Female 15.7 Freshman 12.5 < 17 3.7 

Male 84.3 Sophomore 17.6 17 or 18 13.3 

  Junior 25 19 or 20 18.5 

  Senior 44.9 21 – 24 38.6 

     !"# 25.9 

TOTALS 100.0  100.0  100.0 

 

Table 2: Percentages of Grades in Mathematics and Business Statistics Courses 
 

 % by Math Course % by Statistics Course 

Grade MATH 092 MATH 100 MATH 105 MATH 201 MATHh 250 QMET 251 QMET 252 

A 22.7 9.1 14.7 23.4 17.5 24.5 12.3 

B 31.8 34.1 51.6 36.9 25.8 43.6 49.2 

C 31.8 52.2 31.6 28.9 45.4 25.5 32.3 

D 9.1 2.3 0 9 8.2 4.3 4.6 

F 4.6 2.3 2.1 1.8 3.1 2.1 1.6 

 

 Table 2 shows the students’ final grades on mathematics courses, some of which are remedial, and 

some of which are required of all business majors at the authors’ university. Table 2 also shows final 

grades in the two business statistics courses, both of which are required of all MIS majors. Students most 



Lai, Tanner, Stevens                                                                                                                                                      Advances in Business Research 

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frequently earned a grade of B or C for all mathematics and business statistics courses. Descriptions of 

these courses appear below. 
 

Initial Mathematics Placement 
 

Students are placed into a mathematics course based on their ACT math score as follows. With an 

ACT math score less than 17, a student must take remedial mathematics course(s) at a community 

college, or pass a freshman placement exam. With a score of 17 or 18, a student will be placed in 

remedial MATH 092 (Elementary and Intermediate Algebra). With a score of 19 or 20, a student is placed 

in MATH 100 (College Algebra Fundamentals). All the afore-mentioned courses are remedial 

mathematics by nature. With a score between 21 and 24, a student is placed in MATH 105 (College 

Algebra). MATH 100 and MATH 105 are interchangeable for degree purposes. With a score of 25 or 

higher, a student receives credit for MATH 105 and proceeds directly to MATH 250 (Survey of 

Calculus).  

Course prerequisites are shown in Figure 1. For example, MATH 100 or MATH 105 is the 

prerequisite for both MATH 201 (Decision Mathematics) and MATH 250 (Survey of Calculus). Only 

MATH 201 is required before taking QMET 251 (Fundamentals of Business Statistics). QMET 251 is a 

prerequisite for QMET 252 (Advanced Business Statistics). Other than MATH 105 which is a five-credit-

hour course, all other courses mentioned above are worth three credit hours. Figure 1 depicts the 

recommended progression, or “path”, for mathematics and statistics courses at the college. 
 

Figure 1: Mathematics and Statistics Courses Progression Paths 
  

ACT math 17, 18

Math 092 (Elementary & 

Intermediate Algebra)

ACT math 19, 20

Math 100 (College 

Algebra Fundamentals)

ACT math 21-24

Math 105 (College 

Algebra)

Math 250 (Survey of 

Calculus)

Math 201 (Decision 

Mathematics)

QMET 251 (Fundamentals of 

Business Statistics)

QMET 252 (Advanced 

Business Statistics)

ACT math  !25

 
 

Data Collection 
 

Each author of this research advises MIS majors for their course registration. The university allows 

faculty advisors online access to students’ academic records. The student’s campus identification number, 

provided by the MIS department secretary, was used to obtain grades, gender, academic classification, 

and ACT math score. Data was collected from all MIS majors at the end of the Fall 2010 semester, 

resulting in a total of 159 students. The grades were not identifiable to individual students. Data sources 

for the quantitative analyses included students’ gender, classification (freshmen, sophomore, junior or 



Lai, Tanner, Stevens                                                                                                                                                      Advances in Business Research 

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senior), ACT math score, the final grade received for remedial math (MATH 092), final grades from 

required math courses (MATH 100 or 105, MATH 201, and MATH 250), and final grades from statistics 

courses (QMET 251 and QMET 252). If a student failed course(s) multiple times, only the final passing 

grade was recorded.    
 

Data Analysis 
 

A scale ranging from 0 to 4 was used to code the grades achieved in all mathematics and statistics 

courses (F = 0; D =1; C = 2; B = 3: and A = 4). SPSS version 17 was used to conduct all statistical 

analyses. First, correlation analyses were conducted to investigate the relationships between the students’ 

performance in mathematics courses and their grades in both business statistics courses. Second, as 

described earlier in the subsection of Initial Mathematics Placement, MIS majors were placed into 

different initial math classes based on their ACT math scores. One-way ANOVA tests were conducted to 

test for differences in students’ business statistics grades, treating the four different initial math placement 

paths (see Figure 1) as independent variables. If the group difference was significant, the post-hoc 

multiple comparisons tests using Gabriel’s procedure were conducted to compare all different 

combinations among the four ACT math paths. Third, a paired t-test was conducted to test the difference 

in  mean  grades  between  the  two business statistics courses. The alpha level for all analyses was set at 

 !"!#$%#  
 

RESULTS 
 

Correlation Analyses 
 

Table 3 shows 12 correlations, only four of which were positive and statistically significant between: 
  

a) ACT math scores and fundamentals of business statistics (QMET 251) grades, r = .231, p = .04. 

b) College algebra (Math 105) grades and QMET 251, r = .263, p = .048. 

c) Math 105 and advanced business statistics (QMET 252), r = .405, p = .012. 

d) Decision mathematics (Math 201) and QMET 251, r = .266, p = .015. 
 

Other than MATH105, none of the other math-related scores or grades were statistically correlated to 

performance of QMET 252, the advanced business statistics course. 
 

Table 3: Correlations between Mathematics Performance and Statistics Performance 
 

Correlations Between R (Coefficient of Correlations) p-value** 

ACT Math & QMET 251 .231 .040** 

ACT Math & QMET 252 .031 .819 

Math 092 & QMET 251 .221 .540 

Math 092 & QMET 252 .423 .498 

Math 100 & QMET 251 .120 .545 

Math 100 & QMET 252 .258 .286 

Math 105 & QMET 251 .263 .048** 

Math 105 & QMET 252 .405 .012** 

Math 201 & QMET 251 .266 .015** 

Math 201 & QMET 252 .028 .833 

Math 250 & QMET 251 .041 .737 

Math 250 & QMET 252 .075 .607 

  &&'()*(+(,-*.!,/0012-.(/*!-.! !"!#$%!321412!/+!5()*(+(,-*,16   
 

ANOVA on QMET Grades for Different Math Placement Paths 
 

Analysis of variance (ANOVA) showed that initial mathematics placement and path of progression, as 

shown in Figure 1, had no statistically significant effect on performance in QMET 251, with F (3, 75) = 

1.272, p = .290, and effect size r = .22. Similarly, initial mathematics placement and path of progression 



Lai, Tanner, Stevens                                                                                                                                                      Advances in Business Research 

2011, Vol. 2, No. 1, 115-124 

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had no significant statistical effect on performance in QMET 252, with F (3, 52) = .513, p = .0675, and 

effect size r = .17.   
 

Paired t Test 

 

A paired t-test was performed on the mean difference of grades between QMET 251 and QMET 252 

(each pair of values consisted of a single student’s grade in each course). The results indicated a 

statistically significant difference with t = 3.609 and p = .001 in final grades between the two courses. 
 

Discussion 

 

The purposes of the study were to investigate the effects of ACT math scores, performance in remedial 

mathematics courses, and required mathematics courses on MIS majors’ performance in two business 

statistics courses. Correlation analyses revealed statistically significant correlations only between ACT 

math scores and QMET 251, between MATH 105 and both business statistics courses; and between 

MATH 201 and QMET 251. These would seem to indicate that students’ remedial mathematics grades 

(MATH 092), fundamental college algebra (MATH 100), and survey of calculus (MATH 250) have little 

or no effect on business statistics grades in either statistics course.  

These results indicate that math performance is generally a significant indicator of performance in the 

first statistics course, but not the second one. In addition, success in the first statistics course is 

significantly different from success in the second course as indicated by the significant t-test result for 

mean difference in grades. Basically, QMET 251 is an introduction to statistics featuring one variable 

methods and QMET 252 is primarily concerned with multiple variable methods. Thus the content of the 

second statistics course is more statistical than mathematical. In fact, there is very little mathematics 

required in either of the two statistics courses. A very basic knowledge of algebra is all that is required.  

Calculus is not required in either statistics course, and probabilities for distributions are tabulated in 

statistical tables. This fact seems to support the previous research which indicates other factors such as 

anxiety and attitude are very important in both mathematics and statistics performance. Students who 

succeeded in math have overcome the math anxiety, and therefore succeeded in the first statistics course.   

ANOVA analyses revealed that different mathematics preparation (i.e. whether remedial mathematics 

was required) had no effect on MIS majors’ performance in both business statistics courses, similar to the 

research findings of Gnaldi (2006) and Johnson and Kuennen (2006). The present findings contradict the 

general consensus in the literature that students’ statistical performance is positively related to their 

mathematical competence. Instead, these findings corroborate the claim that mathematics and statistics 

are two distinct methodological disciplines (Carmichael et al., 2009; Chance and Garfield, 2002; Groth, 

2007; Johnson and Kuennen, 2006; Moore, 1992; Zieffler et al., 2008). According to Gal and Garfield 

(1997), statistics differs from mathematics in the following perspectives:  
 

a) In statistics, data are numbers within a context. The context motivates procedures and is the source 

of meaning and basis for interpretation of results of such activities.  

b) Context-bounded statistical problems usually do not have a single mathematical solution, but 

mathematics is characterized by precision and finiteness.  

c) Mathematical concepts and procedures function only as a part of the attempt to solve statistical 

problems. Moreover, the computation or execution of mathematical procedures is being replaced by 

the use of sophisticated computer software programs.   

d) A primary goal of statistics education is to enable students to render reasoned descriptions, 

judgments, inferences, opinions and interpretation of data with the help of mathematical tools when 

needed.  
 

Statistics education is a new and emerging discipline (Zieffler et al., 2008). Given the disciplinary 

differences between mathematics and statistics outlined by Gal and Garfield (1997), it seems rational that 

statistics education should shift its focus from mathematical computation and procedure to an emphasis 



Lai, Tanner, Stevens                                                                                                                                                      Advances in Business Research 

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on statistical literacy, statistical reasoning, and statistical thinking (Garfield, 2003; Jeffries, 2001; Moore, 

1997; Tempelaar et al., 2007). In the statistical literacy model that Gal (2002) proposed, mathematical 

knowledge is only one of five cognitive elements. According to Gal, statistical literacy involves 

knowledge elements including literacy skills, statistical knowledge, mathematical knowledge, context 

knowledge, and critical knowledge, and dispositional elements including beliefs, attitudes and critical 

stance. Furthermore, such disciplinary differences drive the Board of Directors of the American Statistical 

Association to endorse a set of six guidelines for teaching an introductory college statistics course 

(Franklin and Garfield, 2006). The guidelines specify that instruction should: 
 

a) Emphasize statistical literacy and develop statistical thinking. 

b) Use real data. 

c) Stress conceptual understanding rather than mere knowledge of procedures. 

d) Foster active classroom learning.  

e) Use technology for developing conceptual understanding and analyzing data. 

f) Use assessments to improve and evaluate student learning. 
 

Note that none of these guidelines specify “mathematical” skills or competencies. These guidelines, 

together with the results of the present study, are in agreement with Gal’s (2002) model which describes 

mathematical knowledge as only one of the seven elements in statistical literacy. 
 

Future Research 

 

Findings were based solely on a population of MIS majors, which were predominantly male. 

Consequently, generalizations of these results may be limited. Future studies should include a larger 

proportion of females.  Other studies should also focus on majors outside MIS. 

As reviewed by Zimmer and Fuller (1996), there are numerous factors that affect undergraduate 

students’ statistics performance: statistics factors (statistics anxiety and statistics attitude), mathematical 

factors (math anxiety and math attitude), technological factors (computer anxiety, computer attitude, and 

ability to use calculators), and personal factors (GPA, test anxiety, gender, spatial ability, age, and 

personality). This study primarily examined the effect of mathematical performance on performance in 

business statistics courses. More research incorporating the other factors is warranted.   

As indicated in the literature and confirmed by the present study, mathematics and statistics are two 

separate disciplines with separate pedagogy.  Researchers should investigate instructional methods for 

success in statistics separately from those adopted in mathematics education. 
 

Conclusion 

 

In recent years a paradigm shift has occurred from traditional views of teaching statistics as a 

mathematical topic (which emphasizes computations, formulas and procedures) to the current view that 

statistics is a distinct methodological discipline from mathematics. Such disciplinary differences call for 

statistics education to emphasize statistical literacy, statistical reasoning, and statistical thinking. This 

study finds that undergraduate students’ mathematical preparation and competence had very little effect 

on their performance in business statistics courses. This study supports the claim that statistics education 

is different from mathematical education. Consequently, instructors of statistics should adhere to the new 

guidelines of statistical teaching and learning as prescribed by the Board of Directors of the American 

Statistical Association. 
 

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Guolin Lai is an instructor in the department of business systems, analysis, and technology at University 

of Louisiana at Lafayette. His research interests include leveraging the affordances of emerging 

technologies in the design and development of computer-based performance support mechanisms, and in 

innovative teaching methods in quantitative methods. He has published in Educational Technology 

Research and Development, Journal of Research on Technology in Education, Journal of Technology and 

Teacher Education, International Journal of Technology in Teaching and Learning, and others. 
 

John Tanner is a professor in the department of business systems, analysis, and technology at University 

of Louisiana at Lafayette. He has published in Omega, Journal of Management Information Systems, 

Information and Management, Journal of Computer Information Systems, Journal of Informatics 

Education Research, and Journal of Education for Business, Journal of Business and Economic 

perspectives, Public personnel Management, International Journal of Innovation and learning, and others. 
 

David Stevens is an associate professor in the department of business systems, analysis, and technology 

at University of Louisiana at Lafayette. His research interests include mathematical optimization, 

innovative teaching methods for quantitative methods, and in development of business information 

systems. He has published in Decision Sciences, Journal of Computer Information Systems, Quality 

Engineering, Mortgage Banking, Journal of Applied Radiology, and others. 

 


