academic effort and college grades

Academic Effort and College GradesAuthor(s): James W. Michaels and Terance D. MietheSource: Social Forces, Vol. 68, No. 1 (Sep., 1989), pp. 309-319Published by: Oxford University PressStable URL: http://www.jstor.org/stable/2579230 .

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Academic Effort and College Grades*

J A M E S W. M I C H A E L S, Virginia Polytechnic Institute and State University

T E R A N C E D. M I E T H E, Virginia Polytechnic Institute and State University

Abstract

This study examines the possibility that specification errors contribute to the Schuman et al. (1985) findings of a weak relationship between study time and college grades. Our analyses investigate both main and interactive effects, measures of quantity and quality of study, and various context-specific models of college grades. In contrast to previous findings, we observe significant main and interactive effects of academic effort on college grades.

The work ethic is firmly entrenched in American culture. We are led to believe that effort and perseverance will reap rewards and that hard work can overcome personal shortcomings. It is within this cultural context that Schuman et al.'s (1985) study of effort and reward in academia is of sociological interest. In a series of studies spanning a decade, these authors uncover only a marginally significant relationship between amount of study and grade-point average. While they note small differences across studies, their general findings appear to be quite robust, holding across different contexts, measurement and analytic procedures, and time.

The present study challenges this counterintuitive conclusion by examining types of specification error which may alter the observed relationship between study time and grades. Measures of the social and employment value of good grades, pressures to maintain good grades, and quality of study are included to further investigate this relationship. Context-specific models are tested to assess whether the relationship between study time and grades holds across groups of students identified by their study habits, high school performance, year in college and academic field. The results are then interpreted in light of their implications for future research on effort and reward.

*We are grateful for the helpful comments and suggestions of anonymous reviewers. Direct correspondence to James W. Michaels, Department of Sociology, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061. ? The University of North Carolina Press Social Forces, Sept. 1989, 68(1):309-19



310 / Social Forces Volume 68:1, September 1989

Sources of Specification Error

One explanation for the small return of study time on college grades is that the relationship is suppressed by one or more forms of model misspecification. Two common types of specification error involve the functional form, and the exclusion of relevant variables (Hanushek and Jackson 1977; Miethe and Moore 1986). In terms of functional form, Schuman et al. (1985, p. 949) acknowledge that there is a slight departure from linearity, with very high levels of study time returning better grades than low or moderate levels of study time. The authors also report (p. 952) that no sizable interaction was observed between study time and four other academic variables (SAT scores, class attendance, year in college, or major field of study). These findings suggest that the impact of study time on college grades is similar across different groups of students.

In spite of the Schuman et al. findings, however, there are several reasons to expect interactive or context-specific effects. First, quantity of study may have different effects on grades depending on the quality of that effort. For example, the return for studying should be greater for students who have better study habits (e.g., study throughout the week rather than cramming before exams, study in the library rather than dorms, have a study routine, study without noise distractions). Second, students should learn over their college careers what to concentrate on (e.g., how to "second guess" instruc- tors) and how to study more efficiently. Given a relatively new environment and a certain mystique about the demands of college, freshmen and sophomores may be more conscientious in their study habits. More advanced students may learn short-cuts that enable them to actually reduce study time and class attendance without adversely affecting grades. Third, particular academic fields (e.g., natural sciences, business, engineering) may have higher entrance requirements and demand more extra-classroom activities. If higher grading standards are also used to select and sort students in these fields, the return for increased effort may vary by academic major. Contrary to an additive effects model, each of these considerations suggests that quantity of study may have a varying impact on grades depending on the context and other characteristics of students.

Another type of specification error is exclusion of relevant variables. While this type of error is common in all survey research, it is especially problematic when excluded variables are highly correlated with included variables. Schuman et al.'s (1985) analysis focused primarily on the impact of SAT scores, class attendance, and study time, which in combination accounted for 15% of the variation in grades (p. 952). However, several other factors included in the present study should exhibit independent effects on grades, and also may condition the impact of study time. For example, in addition to measures of the quality of study habits, both external pressures to achieve good grades (e.g., to continue participation in activities, to get into graduate



Effort & Grades / 311

or professional school) and the perceived value or utility of high grades (including social and economic value) may influence both study time and grades. As is true of functional misspecification, failure to consider these factors may also attenuate the impact of effort on academic rewards.

RESEARCH QUESTIONS

Drawing on Schuman et al.'s insights, the current study extends their research by examining the relationship between study time and college grades across different contexts and after controlling for other variables that may alter these relationships. A comparison of results from additive and interactive models will be undertaken to assess how specification errors may alter the conclusion about the relationship between effort and reward in an academic setting.

Methods

A questionnaire on study habits was administered to 676 undergraduate students at a large mid-Atlantic university. The questionnaire was distributed during regular class hours in undergraduate sociology and political science classes. Students were informed that participation was voluntary and that their responses would remain anonymous. Because these courses can be used to fulfill social science requirements at the university, the respondents should be fairly representative of the entire student body. Although a slightly dis- proportionate number of females and social science majors are included, the sample approximates the university population in terms of grade-point average and year in college.

MEASURES OF VARIABLES

College grade-point average was measured on a 9-point scale ranging from 0 (less than 1.5) to 8 (3.5 to 4.0). The mean for this scale was 4.04, which corresponds to a grade-point average between 2.50 and 2.75 on a 4.0 grading scale.

The independent variables are grouped into two categories: (1) study time and study habits and (2) background or control variables. The first category includes measures of the frequency of class attendance (0 = less than 60% to 3 = 95-100%) and study time per week (0 = less than 4 hours to 6 = 40 hours or more). Study habits were measured by a series of binary variables reflecting whether the student typically: (1) rewrites lecture notes after attending a class (rewrite), (2) studies without listening to radio or television (no noise), (3) studies throughout the term rather than just cramming before exams (non-cram), (4) designates particular hours each day for studying (routine), and (5) studies in the library or other quiet setting (library). Affirm-




Table 1. CORRELATION MATRIX AND DESCRIPTIVE STATISTICS Y1 Xi X2 X3 X4 X5 X6

Y1 1.000 Xi .180 1.000 X2 .110 .337 1.000 X3 -.073 .033 .072 1.000 X4 .136 .116 .103 -.056 1.000 X5 .073 .402 .295 .119 .081 1.000 X6 -.043 .288 .189 .007 .148 .247 1.000 X7 .034 .089 .015 .018 .152 .052 .096 X8 .131 .105 .027 -.028 .119 .023 .051 X9 .159 .119 .100 .023 .010 .091 .077 X1o .141 .129 .108 -.041 .036 .006 .013 Xi .153 .166 .134 -.015 .106 .097 .110 X12 .163 .117 .106 .074 -.067 .082 .000 X13 .031 .106 .108 .084 .102 .089 .093 X14 .114 -.145 -.065 -.009 .027 -.091 -.047 X15 -.056 -.221 -.093 -.023 .068 -.041 -.025 MEAN 4.038 2.720 2.288 .089 .636 .493 .405 STDDEV 2.008 1.547 .923 .285 .481 .500 .491 KEY: Y1 = Grade average; Xl = Study time; X2 = Class attendance; X3 = Rewrite notes; X4 = No noise distraction; XS = Non-cram; X6 = Have study routine, X7 = Study in library, X8 = Need grades to participate in activities; X9 = Aspire to go on to graduate or professional school; X10 = Employment value of good grades; X1i = Social

ative answers to the questions are assumed to represent higher quality of study habits.

The average study time in this sample was 2.72, corresponding to about 17 hours per week (see Table 1). The average rate of class attendance was 2.29 (between 90 and 95% of classes attended). Nearly 80% of the students reported attending over 90% of their classes. Concerning study habits, only about 9% rewrote lecture notes; 64% did not typically listen to a radio or television while studying; 49% studied throughout the week rather than cramming; 40% reported having a studying routine; and only 8% usually studied in a library or other quiet environment.

The background or control variables involve demographic characteristics (year in college, gender, field of study, high school rank),' external pressures on academic performance, and composite measures of the perceived value of good grades. The need to obtain a minimum grade average to participate in university activities (e.g., sports, clubs) and aspirations to attend graduate or professional school were treated as separate indicators of external pressures for good grades. A two-item scale measured the extent to which students agreed that good grades are valuable for employment after gradua- tion (employ value). The specific items were: "employers consider grades to be important in hiring" and "the higher one's grades, the better one's chances of getting a good job." A four-item index of the social value of grades was also constructed (social value). The items involved the level of agreement with the




Table 1. (continued) X7 X8 X9 X1o X1i X12 X13 X14 X15

1.000 .016 1.000 .036 .120 1.000 .006 .008 .053 1.000 .009 .110 .109 .304 1.000 -.101 -.002 .018 .043 .043 1.000 .038 .052 .002 -.098 .167 .116 1.000 .062 .084 .027 -.093 -.104 -.064 -.040 1.000 .022 .042 .066 -.168 -.107 -.147 .146 .198 1.000 .084 .343 .364 4.590 8.908 2.774 .499 1.396 .368 .278 .475 .481 1.203 1.873 .954 .500 1.072 .483

value of good grades; X12 High school rank; X13 = Female; X14 = Year in colege; X15 = Social science major.

Correlations greater than .089 are significant at p < .05

statements "getting good grades is something to be proud of," "grades are good predictors of success in later life," "other people admire students who get good grades" and "doing well in school increases my self-confidence." While there was some variation within and across items, the vast majority of students agreed with each item in these scales. In fact, the modal response for many of these items was "strongly agree," suggesting that students perceive grades to have high levels of both instrumental and social value.

ANALYSIS PLAN

A series of regression models was estimated to assess the predictive power of the independent variables on college grades. An additive model was first estimated to examine the net effects of measures of the amount of academic effort (study time, class attendance) on grades once controls are introduced for quality of study and background variables. Then, separate analyses were performed on subgroups of students to assess the level of interaction or context-specific effects of study time and class attendance on grades. Specifi- cally, separate regression models were estimated for students with different study habits (i.e., crammers versus non-crammers, level of background noise while studying, having a study routine), by year in college (freshmen/sophomores versus juniors/seniors), high school rank (top 10% versus others), and academic field (natural science, business, engineering, social




science). Separate analyses also were performed for groups of students identified by advanced educational aspirations (no versus yes), and for low versus high levels of social and employment value of good grades. Tests of differences in slopes were used to assess the statistical significance of these interactive effects.

Results

ADDITIVE MODELS OF COLLEGE GRADES

Our initial analysis examines the main effects of all independent variables on college grades. The zero-order correlations and regression coefficients for models of grade-point average are presented in Table 2.

Most of the bivariate correlations are in a direction consistent with expectations, but they are relatively small. For example, both study time and class attendance are significantly and positively related to college grades. Concerning study habits, only studying without background noise is significantly correlated with grades. With the exception of gender and major, the background measures are significantly correlated with grades and similar in magnitude.

The regression model of grades reveals several trends. Contrary to Schuman et al.'s (1985) findings, study time has a significant net impact on grades, whereas class attendance does not.2 The effect of studying in a noise- less environment remains even after control variables are introduced, but, unexpectedly, rewriting lecture notes and having a study routine has significant negative effects on grades.3 Of the background variables, advanced educational aspirations, the employment and social value of grades, high school rank, and year in college have significant net effects on grade-point average. The overall model of grades, however, fits rather poorly. Only 15% of the variation in grades is accounted for by this additive model.

CONTEXCr-SPECIFIC MODELS OF COLLEGE GRADES

To examine whether the impact of study time and class attendance on grades is conditioned by other factors, separate regression models were estimated for specific categories of students. The results of these analyses are summarized in Table 3.

As indicated in Table 3, the return of study time and class attendance on grades varies by type of study habits. Among students who study throughout the week (non-crammers), both study time and class attendance have significant positive effects on grades. For crammers, the amount of study and class attendance have no appreciable affect on college grades. In other words, students who study throughout the week benefit from increased study




Table 2. CORRELATION COEFFICIENTS AND STANDARDIZED PARTIAL REGRESSION COEFFICIENTS FOR MODEL OF COLLEGE GRADESa

Independent Variables r Beta

Amount of study: Study time .180** .135** Class attendance .110** .033

Study habits: Rewrite -.073 -.083* No noise .136** .114** Non-cram .073 .018 Routine -.043 -.128** Library .034 .017

Background/Controls: Need grades for activity .131** .069 Graduate educ. aspirations .159** .121* Employment value of grades .141** .081* Social value of grades .153** .088* High school rank .163** .152** Female .031 .002 Year in college .114** .152** Social science major -.056 -.039

N 676 676 R2 .154**

aSee text for description of the variables. *p < .05; **p < .01

time and class attendance, whereas students who concentrate their studying before exams do not reap rewards from increases in effort. The beneficial net effect of increased study time is also more pronounced for students who do not have a study routine, but those who do have a study routine benefit more from increased class attendance. There is no significant interaction involving the level of background noise and study time or class attendance.

The effects of study time and class attendance are similar for students who report being either below or above the top 10% of their high school graduating class. However, study time has a differential impact on grades by year in college. Specifically, study time influences grades among freshmen and sophomores, but has no significant net impact for juniors and seniors.




Table 3. ZERO-ORDER CORRELATIONS AND UNSTANDARDIZED PARTIAL REGRESSION COEFFICIENTS FOR CONTEXT-SPECIFIC MODELS OF COLLEGE GRADESa

Study Time Attendance Comparison Groups N r b r b

1. Study throughout week a. Crammers 343 .097 .097 .007 -.073 b. Non-crammers 333 .230** .279**t .206** .381*tt

2. Study with noise distractions? a. Yes 246 .145* .156 .097 .057 b. No 430 .180** .179** .098 .067

3. Study routine? a. No 402 .230** .232** .094 -.018 b. Yes 274 .157** .034t .173** .298t

4. High school rank a. Below top 10% 252 .138* .129 .128* .120 b. Upper 10% 424 .184** .190** .075 .033

5. Year in college a. Freshmen/Sophomores 361 .244** .348** .100 .040 b. Juniors/Seniors 315 .127* -.041tt .129* .155

6. Academic majorb a. Natural science 103 .114 .028 .082 -.022 b. Business 140 .151 .214 .019 -.086 c. Engineering 100 .034 .092 .074 .397 d. Social science 249 .164** .150 .167** .206

7. Graduate educ. aspirations a. No 430 .198** .228** .147** .113 b. Yes 246 .111 .095 006 -.083

8. Social value of grades a. Low 393 .152** .146 .174** .214* b. High 283 .175** .142 -.024 -.190 tt

9. Employment Value of Grades a. Low 307 .157** .184* .111 .144 b. High 369 .180** .156* .096 .007

a"r" is the bivariate correlation between either study time or class attendance and grade-point average, whereas "b" refers to the unstandardized partial regression coefficient. These regression coefficients are based on a model that includes all independent variables except the grouping or comparison variable. bTests of significance for differences in slopes use social science majors as the comparison group. *Significant main effect at p < .05; **p < .01. tSignificant interaction between attribute and quantity of study measure at p < .05; ttp < .01.




Class attendance has no appreciable net impact on grades for either group. Concerning academic major, study time and grades are least correlated for engineers, and class attendance is most highly correlated with grades for social science majors. A comparison of the unstandardized partial regression coefficients, however, reveals no significant net differences between social science and other majors on the relative importance of the amount of study on college grades.

Finally, differences in the effects of effort on grades were examined separately for groups defined by their levels of advanced educational aspirations and the perceived value of good grades. Regardless of the perceived employment value of grades, increased study time, ceteris paribus, was associated with higher grades. Although this difference is not statistically significant, increased study time is slightly more beneficial to the grades of students with no advanced educational aspirations than for those who planned to continue their education. The return of class attendance on grades also varies across groups. Higher class attendance is associated with better grades for students who perceive grades to have lower social value, whereas class attendance has a slightly detrimental effect on academic performance for persons who view grades as having higher social value. Although most of these differences are not statistically significant, there is a tendency for class attendance to have a more positive net impact on grades among students who are low rather than high in terms of academic aspirations and beliefs about the social and economic utility of college grades.

Discussion and Conclusions

When amount of study is considered, our results suggest that higher academic effort is rewarded by higher grades. Although class attendance and grades are related only at the bivariate level, amount of study time influences grades even after controlling for other factors that may mediate this relationship. Concern- ing the quality of study habits, only studying without noise distractions significantly influences grades in the expected direction. Grades are also significantly influenced by students' self-reported high school rank, year in college, and their perceptions of the social and instrumental value of good grades. However, we also found that the relationship between effort and reward is conditioned by several factors. Specifically, both class attendance and amount of study are associated with higher grades for those who study throughout the week, but not for "crammers." Similarly, study time significantly improves the grades of freshmen and sophomores, but has no significant net effect for more advanced students.

Our findings hold several implications for future research. First, our results are inconsistent with those of Schuman et al. (1985) in several respects. These authors note very few context-specific effects, and find that class atten-




dance (but not study time) significantly influences college grades. Several differences in study design may account for the inconsistent findings. For example, we used slightly different measures of college effort (e.g., study time per week rather than per day), included more and different control variables (rather than only SAT scores, class attendance, year in college and major field of study), and sampled from a different geographical area. If students in the Schuman et al. (1985) studies were disproportionately "crammers" or more advanced students, these differences in sample characteristics also may account for some of the variation in findings across studies.

Second, our results highlight the difficulty in measuring "quality of effort" as it relates to academic performance. It seemed reasonable to us that good study habits should include having a study environment with minimal noise distractions, rewriting lecture notes, having a study routine, and studying throughout the week. However, the fact that studying without noise distractions was the only study habit significantly associated with higher grades suggests that quality of effort is a more complex attribute than quantity of effort. Although this does not predude investigators from attempting to measure quality of study, it does suggest that the concept requires better specification of its dimensions.

Third, our results are similar to those of Schuman et al. in that our models account for very little variation (15%) in college grades. While our model fitted some categories of students (e.g., non-crammers, freshmen/sophomores) better than others, most of the variation in grades remained unaccounted for even for these groups.4 Considering that our model includes measures of ability, quantity and quality of effort, and background and motivational variables, its modest predictive power is puzzling. While it is possible that more or different measures of such variables would enhance predictive power, further refinements of the basic model did not substantially improve its explanatory ability.5

Our preferred explanation for the modest relation between effort and outcome was suggested by Schuman et al. and, in somewhat different terms, by an anonymous reviewer who likened the effort-outcome relationship to the economists' notion of production function. Specifically, if the "effort" (study time and class attendance) is not itself efficient or productive, the link between effort and outcome will be attentiated. Therefore, an imnportant explanatory variable missing from both studies may be the study skills students apply when putting forth effort. These skills might include attention or concentra- tion, association, organization or encoding, and reflection. As Schuman et al. note, such skills do not fall strictly within the category of "effort," yet they are not measured directly by aptitude tests either. Nevertheless, differences in these skills may explain more of the variation in grades than study time, study habits (as measured in the present study), or proportion of classes attended.

Finally, the interactive effects observed in this study suggest that effort and reward are associated more strongly within specific contexts. Amount of




study is associated with better grades among freshmen and sophomores, but has little net impact on the grades of more advanced students. Students who have survived various stages of sorting and selecting in the educational process may develop more efficient study skills or learn short-cuts to obtain the same level of academic reward. Extended to the business world and other aspects of social life, this finding suggests that personal effort and hard work may be more expected or required among those new to an occupational role, whereas incumbents may become more efficient or develop alternative strategies for obtaining organizational rewards. As revealed by the context- specific effects observed here, further research also may show that rewards are distributed according to both the quality and quantity of effort. Consistent with conventional wisdom, how much is done and how well it is done may both contribute to the magnitude of return for effort in many aspects of life.

Notes 1. Field of study was measured by both major and college in which the degree would be awarded. Initial comparisons were made between students in Social Science, Arts and Humanities and all other fields. Within this "other" category, Engineers and Business majors had the highest grades. Of all majors, Engineers also had the highest average study time, whereas Social Science majors had the lowest study time, poorest class attendance, and lowest grade-point averages. Four categories were used to measure self-reported high school rank, ranging from the bottom 50% to the top 5% of the graduating class. 2. In an earlier study based on 671 students, we found that both study time and class attendance had significant net effects on college grades. As in Schuman et al.'s study, however, few control variables were introduced. The impact of class attendance in the present study becomes non-significant once controls are introduced for study habits (especially "cramming") and study time. The bivariate correlation between attendance and non-cramming was .29, and it was .34 between study time and class attendance. As we will show, there is also significant interaction between class attendance and other factors. 3. The negative relationship between these two study habits and grades may be partially due to the tendency for counselors and faculty to advise students who are performing poorly to rewrite lecture notes and develop a study routine as ways to improve performance. 4. The R2 levels for these context-specific models ranged from .116 for students who reported lower employment value of good grades to .383 for engineering majors. 5. For example, we also estimated non-linear models of study time and grades, used alternative measures of class attendance (e.g., percent of classes attended last week versus "in general") and examined normed grade-point averages (standardized by the grades given to all students in the students' major area). In each of these cases, the results were comparable to those reported in the text.

References Hanushek, Eric A. and John E. Jackson. 1977. Statistical Methods for Social Scientists. Academic

Press. Miethe, Terance D. and Charles A. Moore. 1986. "Racial Differences in Criminal Processing: The

Consequences of Model Selection on Conclusions About Differential Treatment." Sociological Quarterly 27:217-37.

Schuman, Howard, Edward Walsh, Camille Olson, and Barbara Etheridge. 1985. "Effort and Reward: The Assumption that College Grades Are Affected by Quantity of Study. Social Forces 63:945-66.



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