Why use multiple-choice questions on accounting - Bryant

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36 Simkin, Keuchler, Savage, and StiverTABLE 2Sources of Sample DataClass Class Title Number of StudentsClass 1 Introductory Financial Accounting 46Class 2 Introductory Financial Accounting 46Class 3 Introductory Financial Accounting 45Class 4 Introductory Financial Accounting 45Class 5 Introductory Financial Accounting 46Class 6 Accounting Information Systems 54Class 7 Accounting Information Systems 39Class 8 Accounting Information Systems 37Class 9 Intermediate Financial Accounting 45Class 10 Intermediate Financial Accounting 34of students enrolled in each of them. To ensure independent sample observations, we excludedsecondary test scores from the few students who were simultaneously or consecutively enrolled intwo of these classes.Dependent and Independent VariablesThe dependent variable for the study was the (percentage) score on the CR portion of eachclass examination. From the standpoint of this investigation, the most important independentvariable was the student’s score on the MC portion of the examination. As illustrated in Figure 2,the CR <strong>questions</strong> required students to create journal entries or perform similar tasks. To ensureconsistency in grading CR answers, the same instructor manually graded all the CR <strong>questions</strong> on allexaminations for a given class, indicating the correct answer(s) to each CR question as well as a listof numerical penalties to assess for common errors.Another important independent variable <strong>use</strong>d in this study was “gender.” As noted above,a number of prior studies have detected a relationship between “gender” and “computer-relatedoutcomes” (Hamilton, 1999; Gutek and Bikson, 1985). Accordingly, “gender” was included in thelinear regression model as a dummy variable, using “1” for females and “0” for males. Finally, theexaminations in each class had different, and differently-worded, <strong>questions</strong>, and the tests were takenby different students. Accordingly, we added dummy (0-1) variables to the regression equation foreach class to account for these differences.In summary, the regression equation tested in this study <strong>use</strong>d student performance on the CRportion of a final or midterm examination as the dependent variable, and student performance onthe MC portion of the exam, gender, and semester dummy variables as independent variables. Table3 lists these independent variables and provides brief descriptions of them.ResultsTable 4 displays the results from the regression analysis described above. Coefficient values,t-statistics, and probabilities are only shown for nine of the ten classes beca<strong>use</strong> we <strong>use</strong>d 0-1 dummy
<strong>Why</strong> Use Multiple Choice Questions 37TABLE 3Independent Variables for the Regression ModelIndependent VariableMCGenderClass (1, 2, etc.)DescriptionThe (percentage) score on the <strong>multiple</strong>-<strong>choice</strong> portion of anexaminationA dummy variable: 0 = male, 1 = femaleA dummy variable (0-1) to account for the differentexamination <strong>questions</strong> given to each classTABLE 4Linear Regression ResultsCoefficient (â) t-statistic ProbabilityIntercept 47.11 13.89
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