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Absenteeism and UndergraduateExam Performance

Daniel R. Marburger

Abtract: The authior investigates the relationship between stutdets' absenteeismndurng a principles of microeconomics course and their subsequent performalnceoni exams. Records were maintained regarding the specific class periods that eachstudent missed during the semester. Records were also kept of tLe class meetingwhen the material corresponding to each multiple-chioice test question was cov-ered. A qualitative choice model reveals that students who missed class on agiven date were significantly more likely to respond incorrectLy to questionsrelating to material covered that day than stidents who were present.

Key words: absenteeism. attendance, exam, undergraduateJEL codes: AO, A2

Lectures and classroom discussion represent the ri`mary metins of teachingeconomics to undergraduates.' Ilowever, Ronmer (1993) found a significant levelof absenteeism during class meetings. Surveying three colleges/universities, hefound that one-third of the class was absent during the typical ciass period. Thehypothesis that class attendance is likely to be correlated with student learninghas been investigated emipirically in economic education literature. Not surpris-ingly, most studies have found an inverse relationship between absenteeism andcourse performance (Romer 1993. Park and Kerr 1990; i)urden and Ellis 1995;Schmidt 1983).

In each of these stidies, researchers employed a niacro approach to examinethe attendance/perfori.nance relationshir. Specifically, each study regressed a

Daniel K. Marburger is a proIelssor ojeconomzcs at Arkansas State lIniversity (e-mad: Marburgt@.hemkee.astatc.eda) Ihe author thanks Peter Kennedy, W.iliam B.ker and tvo anonymous rel:jeers lb r their suggestionls onl an ca- iier drafi of this article.

Spring 2001 99

mcasure of the students' overill performarice for the course against their atten-

dance, either over the entire semester or during a sample period. 'ile fundamen-

tal limitation in eacn of these studies was the uncertain cause-and-effect between

absenteeism and performance. Do students who miss a lot of class perform poor-ly on exams because they were not presenit when the material was covered'? Or,

alternatively, are students with highi levels of absenteeism less co nmitted to aca-

demics in general? Each study noted this problem and attempted to control for it

by micluding tie students' grade-point-averages (GPAs), scores on college

entrance exams, or even the students' self-reported ratings of their interest in the

course. Noiethieless, the cause and effect remains inferred rather tihani directlyobserved and measured.

In this study, I eliminated the problem by enploying a micro approach. I main-

tained records of die specific class periods each student missed over the entire

semester. In addition, I kept records of the class meetings when the material cor-

responding to each test question was covered. This allowed me to estimate a

qualitative choice mlodel in which the likelihood of responding incorectly to a

multiple-choice question was related to whether the student was absent during

the corresponding class period. The evidence points to absenteeism during the

relevant class meeting as a significant determinant of incolTect responses on amultiple-choice exam.

PRIOR LITERATURE

Romer (1993) examined attendance in undergraduate econiomics classes. He

surveyed attendance at all undergraduate economics classes at three academic

institutions during a sample week of class. One institution was a medium-,-sizedprivate university, onie was a large public institution, and the third was a small lib-

eral arts college. All three were classified as highly competitive by Bar-on's Pro-

files ofArnerican Colleges. The study revealed that roughly one-third of the class-es was absent during a typical class meeting. Absenteeism tended to be highest

at the large public university ani( iowest at the small liberal arts college. Absen-teeism was lower for courses with a significant mathermatical component. Atten-

dance also tended to be lower for core courses. Finally, the study revealed thatattendance was higher when the perceived quality of instruction was greater.

Ronier also investigated the relationship between student attendanice and over-

all coLurse peribmriance. in one of his intermediate macroeconomics courses, hetook attendance during six class meetings. Overall performance for the coursewas then regressed against a set of independent variables that included the stu-

dents' record of attendance during the sample veriod. 'To control for the cause-

and-effect problem of attendance versus motivation, lie restricted his sample toonly those students who had completed all of the problenm sets assigned duriig

the semester. He also included thie students' GPAs as an explanatory variable.The evidence showed a positive correlationi between attendance and course per-forTmance.

Park and Kerr (I1990) measured the determinants of student performance in a

single instructor's money and banking course over a four-year period. They esti-

JOtlRNAI OF ECONOMIC EDUCATIONIt00

mated numerous logit equations to measure the probability of receiving a specif-ic letter grade for the course as a function of several independent variables. Thestudents' overall attendance during the semester was included among theexplanatory variables. The evidence showed an inverse relationship between thestidents' course grades and their attendance. To control for the motivationalaspects of performance, they controlled for the students' self-reported hours ofstudy time devoted to the class and also their perceived value of the course.

Durden and Ellis (1995) also investigated the link between attendance and(' stu-dent learning. Their study included several principles of economics classes overthree semesters. The dependent variable was the students' overall course gradeaverages. Each student's attendance record for the semester was self-reportedthrough a questioniaire administered at the end of each sernester. 'I'he resultsshtowed that low levels of absenteeism had no appreciable effect on student leam-ing but that excessive absenteeism had a deleterious impact on course perfor-manice.

Finally, Schmidt ([983) perforned a study on student tine allocationi. Herelied on data gathered by Allen tC. Kelley during his evaluation of his TeachingInformation Processing System ('I'IPS). Thc TIPS study processed weekly mul-tiple-choice responses that were optional and not a component of the students'grades. A production function was estimated in which the dependenit variable wasthe students' percentage scores on final ex22m questions that related to the TIT'Sexperiment. The independent variables included the students' self-reported studyhours during the experiment. 'I'he evidence revealed a positive correlationbetween time spent in lectures and d.scussion sessions and exam performance.

THE MICRO APPROACH

Many of these studies acknowledged the problem of disentangliing the impactof attendance on course performance fiom the motivational factors associatedwith high absentccisrm. Although attempts were made to control for student moti-vation, the cause of the inverse relationship between class absences and courseperformance remained inferred rather than observed.

lo measure more directly the impact of class absences on performance, I useda micro approach in a study conducted in a single section of principles of micro-econonics during the 1999 fall semester?2 'llhe class met from 10:00 AM-10:50AM on lMondays, NWednesdays, and Fridays. 'ihrece noncumulative 28-questionmuiltiple-choice exams were administered covering the entire course. Althoughthe student.s were not given any sample test questions during class, they had accessto a prior semester's exams on my web site. None of these questions appeared olany of their tests. Furthermore, although students received their corrected examsafter each test, we did not go over any of the exam questions in class. All of thequestions could have been answered correctly by a student who had never attend-ed class but who had relied exclusively onl the text to prepare f.or the exams.

Records were maintained of each student's attendance or absence at each classperiod. The attendance logs were sufficiently detailed that the specific class meet-ings that each student missed were recorded. In addition, for each multiple-choice

Sprinig 200t tO1

question, records We e maintaiied as to the class mneting when the matefial per-taining to that question was covered. Consequently, if a student responded incor-rectly to a specific exam question, tde records revealed whether the student hadbeen absent during the class period in which the relevant. material was covered.

EMPIRICAL RESULTS

Trends in Absentecism

The characleristics of the class are reported ir. Table 1. in contrast to Romer'sfindings, absenteeismi for the class averaged only 18.5 percent for tht semester.3

Absenteeism for specific class mieetinigs ranged from a low of 8.5 percent to ahigh of 44.1 percent. Thirty-eight nonexain class meetifngs miade up the semes-ter The average student who completed the course missed 5.59, or 14.7 percent,of the class meetings. 4 The range for individual absences was tfom 0 to 29 classmeetings.

Figure I shows absenteeism as a time series. fhe figure clearly indicates ahigher level of absenteeism on Fridays, as well as a possible upward trend in

TABLE ICharacteristics of Class Sample

Absenteeism characterislics

Class characteristic Mean % of class absentNumber of classes missed

hy individual students

Number of studentsenrolled in the co ursethrough extam I

Number of studentscompleting the course

Mean grade pointaverage

Meni number of hoursworked eaich week

MeaiL nunider of credithours that semester

Percentage of businessmelajors

lPercentage ofsophomores

Percentage of juoiorsPercentage of seniors

OverallIliglh

60 LowExam I

56 1iighLo(w

3.06 bxans 11High

17.4 LowExam Ill

15.5 HigihLow

80

11.155.633.3

18.544.1

8.514.725.4

8.519.1

44.111.920.633.914.3

Overall'Total numither of class

meetitgs 38Mean 5.59High 29low 0

ExamlNumber of class

meetingsMeaun 1.86High iOLow C

Exam ilNumber of class

meetingsMean 2.54High 1CLow 0

Exam tIINumber of class

teetilgsMean 2.49High 1 3Low 0

13

12

1 3

lIMe overall absenuteisun figures include only students who compleied the course, The indvidual absentceism fig-ures per exam include ali studenls in tim cou se at thme time toe ex.a wa. giveim.

JOURNAL OF ECONOMIC EDMTATIONIf 2

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Spring 2001

103

Spl)iiig 20)01 103

absenteeism over the scmester. To test the signiticaice of those factors, I esti-mated the following equatimn:

PCTABSENT. = 0.112 - 0.001 WEDNESDAY. + 0.090FRIDAY. ()(4.62) (-.066) (4.06)

0.024HOMEWORK,. + 0.0026TIMEJ,(--i 17) ~~(3.26)

where PCTABSLSN'i' is the percentage of class absent during class ineeting j;WEDNESDAY. is a dumminy variable equal to I if class meeting j was on aWednesday; FRIDAY is a dumimiy variable equal to i if class tmeeting j was on aFriday; HOME WORK. is a dummy varable equal to I if a homnework assignmentwas due on class meeting j; and, TIME;. is a time trend variable to indicate thesequence of class meetings. The t statistics are shown in parentheses below thecorresponding coefficients.

'I'he data show that the percentage of the class that was absent did not signifi-cantly differ between Mondays and Wednesdays but increased by an average of9 percent on Fridays. In addition, absenteeism gradually increased as the semes-ter progressed. Absenteeism did not appear to be affected by scheduling a homrie-work due date.5

Absenteeism and Exam Performanice

'I'ihe mricro approach allows a test of the hypothesis that an incorrect responsewas related to the stident's absence during the relevant class mecting. I estimat-ed a probit equation in which the dependenit variable, RESPONVSE, was codedwith a I for an incorrect response to the question and a 0 for a correct responise.'iTo examine the irnpact of class absences, I coded thc variable ABSETN as a I ifthe student had not attended class during the period the material relevant to thatquestion was covered and a 0 if the student had attended class that day.

Previous empirical studies used continuous variables associated with individ-ual student.s (such as GPA) to control for oilier factors that might influcice examperformance. Because my intent in this study was to identify the "pure" atten-dance cffect rather than to measure the impact of various other factors, each stl-dent was assigned a dumnmy variable. By assigning student-specific durmriy vari-ables, all other external factors associated with each student's exam performance(i.e., ability/imotivation/study habits) could be controlled. Similarly, to control forthe relative difficulty and/or ambiguity of individual test questions, I assigneddummy variables to each question. Consequently, the mnodel took the fonni:

RESPONSE', = p + P,ABSENT, + X2TS'UDENThj = J3Q(UESTIONVji + Eo

J=i k-I

where RFSPONSE. is an inobseivable latent variable that is assumed to be nor-mally distributed; AB.SE.VT is a dummy variable equal to I if the i-h student wasabsent during the corresponding class period; STUDJENT.. is a dumimy variable

JOtJRNAL Ot: ECONOMIC EDUCATIONItl4

equal to ! if the ith student takes the identity of j; QUESTION.. is a dLiiiimy vaii-able equal to I if the ith student was answering question k, and F is the normal-ly distributed error form for the ith student.

Separate counations were estimated for each of the thlree exams. 'I'he results ofthe estitialions are shown in Table 2.6 As expected, ABSENT is positive and sig-nificant for every exam. A positive and significant coefficient means that student.swho were absent during a given class period were significantly more likely tomiss test questions pertaining to dte material covcred tlhat day than students whowere present.' Overall. absenteeism increased the probability that the averagestudent would respond incorrectly to the average exan question by 14.6 percenton the first test, 14.4 percent on the seconid exam, and 7.5 percent on the thirdexam.

To obtain a better grasp of the effect of absernteeism on exam performance, Iestimated the mean perfonnance on each test if 10K0 percent attendance had pre-vailed throughout the semester. If the mcani examn-specific values for the inde-pendent variables are plugged into the equation, the fitted value is a z score thatcan be transfoimed to indicate the mean percentage of incorrect responses forthat test. The transformned z score witl, of course, be equal to the actual percent-age of incorrect responses on the exam. if all students were oresent for each classmeeting, then the mean numnber of absences would be zero. Consequently bysubtracting the ABSFNT coefficient times the mean number of absences duringthe exam period fromri the fitted z score, the transiormed vector can be manipu-lated to determine the percentage of incorTect responses if every student hadattended each class session.8

The mean percentage of correct responses on the first examu was 65.7 percent.had cach student attended every class period, the mean would have risen to 68.1percent. ilad 100 percent attendance prevailed for the second exam, the mcan

TABLE 2Absenteeism and Exam Performanec.

Exam I F.xam 11 Exam III

Number of students taking exam 51 52 53Mean % of incorrect responses 34.3 30.6 274MUan % of class absent 15.2 183 17. 1

Variable Coefficient

CONSTANT -.023 -1.508 -2.77(-.07 7) ((al))* ( 5.22)"

ABSENT .423 .471 .275(3.71l) (4.18)" (2.25)5*

N 1,596 1,456 1,484Log likelihood -772.3 622.6 61X.58McFadden R' .300 .306 .311

I statistic s are in, pan: ntlicese"Significant at .01 lp, I ceror level; ""Significtaat .05 Type I error level.IC lass absence aa,y dffer ao l hgures reported in Table I because .st alt studants enrolled took the exm.

Spring 20()1 14)5

score wolid have risei fron 69.4 percent to 72.2 percent. Witlout any absen-teeism, the third exam score would have risen from a mean of 72.6 percent to74.1 percent. Overall, absenteeism. reduced the mnean score by 2.3 percent.

The results of this study are relevant to issues raised by Becker (1982). Ifabsenteeism is tolerated or accommtiodated in the final grade distribution, then thecost of skipping class is lowered. If tolerance of absenteeism hinlders the col-lege's ability to screen accurately its students, then the result could be less learn-ing overall and particularly among the more able students.

Student Responses Toward Absenteeism

At the end of' the semester, the students were asked to Ell out an anonymoussurvey. Thc purpose of the survey was to elicit their attitudes toward absenteeismand also to understanid how the students' study habits chanige in response to theirown absentecism. A copy of the survey aloig with the overall responses appearsin the appendix.

In addition to the overall responses reported in the appendix, the sample wasbroken down into two groups: those with low absenteeismi (six or fewerabsences) and those withi high absenteeism (more than six absences). Eighty-onepercent of tiose who had missed at least one class meeting indicated that theyhad gottcn another student's notes either all or most of the time. The percentagewas 89 percent for stidents with low absenteeism, whereas only 66 percent ofthose with high absenteeism regularly availed themselves of another student'snotes when they mlissed class. This difference is significant atthe .10Type I errorlevel. Students did not appear to use thie text when they missed class. Less than30 percent of students with low absenteeism relied on the book when they missedclass (i.e., indicated they read the text all or most of the time), and only 27 per-cent of those with high absentecismn reported using the book in response to classabsentces.

'T'he relance on class notes rather thani on the textbook apparently reflects astrategic attempt by students to identify the "most importait'' concepts coveredin the text. All of the material pertaining to the test questions was covered bothmn the text and during lectures. Consequently, by obtaining another stident'snotes, they could identify the content most likely to appear on an exam. Of'course, the limitation of relying on anothier student's notes ratber than on the textis that their comprehension oi' the missed material is only as good as thie qualityof the notes.

The students were also asked what the maximum acceptable number of classabsences during a semester ought to be. Significant differences in attitudes exist-ed between studenits with high and low absenteeism. Seventy-nine percent of therespondents with low absenteeism defined the maximum acceptable number ofabsences as six or iewer. Only 26 percent of those with high absenteeism indi-caled that six or fewer absences were an acceptable maximum. Peediaps mosttelling is a comparison between the student's self-rcported absenteeism and thenumber of absences he or she deemed to be acceptable. One hundred percent ofstudents with low absenteeism indicated that their own record of absentecism

JOURNAL OF ECONOMIC EDUCATION10t6

was equal to or lower than the mraximum acceptable number. For those with highabsenteeism. 73 percent of the respondenits indicated that their own absenteeismwas at or below the maximnum acceptable level. Overall, 92 percent of the respon-dents defined their own propensity to miss class to be acceptable.

CONCLiUSI.ON

Thle evidence f:rom this study provides an assessment of the pure relationshipbetween absenteeism and student learning. The findings suggest that the meanexam score wa!s signifcandy affected by absenteeism. Of course, the precisem-easurement of the impact of absenteeism on student learning is institution spe-cific. This study took place in a medium-sized state-supported regional universi-ty in a relatively small classroom setting. Other differentials n;ay be observed inalternative teaching environmlents. Mofeovet; the differentials reported bi thisstidy a;re also likely to be insiruictor specific. In general, we would expect thatthe miore effective the instructor, th;e greater the value added from attending class.f'his may be inferred frorn Romner's findings that attendance was higher in class-es in which the perceived quiality of instruction (i.e., the perceived value addled)was higih. Consequently, less effective instructors mnay experience high absen-teeism, yet the differentaln may be small.

One should also note that the differentials reported here are likely to be biasedon the low side. 'Ihe model does not mcasure any spillover effects firom absen-teeism. Many iectures build uponI material covered in the previous class mieet-ings). Although a student may be present during a given class period, the valueadded myay be low relative to other students if the individual had been absent dur-ing the previous class meeting.

APPENDIXAbsenteeism Survey Results

I Place an "X" to indicate the approximate number of class meetingss you missed in this class.(xon't incltde exam dales you missed.)

Numn,ber oftbsen,es Tj.tal resp mscs6

1 2 16

7-9 910-13 614-18 219 25 1Over 25 0

2. Whenever I miss this class, I make sure I get someone else's notes.

Tntal rcspcnsesAlways 21Most of the time 13Sonic of dtc time 4Never 4

Spring 20)01 10O7

3. Whenever I miss this class, I make sure to read the chapter.

Always 5Most of the tine 7Some of Ihe time 15Never 15

4. What should the maximum acceptable numbei of absences in a semester be?

Nunuil,r of absences Totalnrevonises0 01 2 83-6 227-9 11

10-13 214-18 019 25 tOver 25 4

NOTES

* Siegfried, Saunders, Stinar, and Zhang (1996) surveyed 180 introductory economics classes andfound that thIe courses were taught primarily as lecmure courses, with the largest component of stu-dents' grades determined by multiple-choice exatims.

2. An earlier version of thte study was conducted during a summer session with a smaller enrollment.The results were similar to those reported in this study.

3. Attendance was taken by circulating a class roster for the students to sign. They were told the pur-pose of the sign-up sheet was to further this study and that the attendance sheets would tnot beincorporated into their grades.

4. TIhc university has a seldom-enforced class attendance policy for freshmen and sophomore class-es. The policy defines the iuaxirnun, acceptable number of class absences as no more than twicethe number of lectures that woLld normally be scheduled during a week. Students who exceed thismaximum may receive a failing grade by the instucto. Thlus, students with more than six classabsences could receive a failing geade for excessive absenteeism according to university policy.O) the 56 students who completed this course, 1 8, or 32 percent, exhibited excessive absentecism.

5. Students werc required to tumn homework in by thc due date to receive any credit. However, anotli-cr student could tum in their assignment for then,. Alternatively, students were encouraged to faxor e-mail their homework to me if they could not attend class on the dtie date.

6. 'Ithe dumLmy variable coefficients for individuial students and exam qestions are not reported, butare available from the author om request.

7. To see if any differences existed between stutlents with high and low levels of absenteeism,1 I cre-ated a dmrinmy variable in which students with excessive absenteeism by university standards(seven absences or more) were assigned a value of 1. The dummy variable was interacted withABSENT. The dummy coefficient was not significant in any of the estimations.

8. Of course, this calculation is valid only if the students are identical. Nonetheless, it provides anapproximation of thc overall inipuct of absenteeism on exam perfonnances.

REFERENCES

Biecker, W. 1982. The educational process and student achievement given unceitainty in measurement. American Economic Review, 72 (March): 229 36.

Durden, i. C., anl L. V. Ellis. 1995. The effects of attendance on student learning in principles ofeconomics. American Fconomic Review Papers antd P?uceedings 85 (May): 343 46.

I'ark, K. H., and P. M. Kerr. 1990. Determinants of academic performance: A muiltinomial logitapproach. Jounzal of Econotnic Education 21 (Spring): 101-1 1.

Romer, D. 1993. Do students go to class? Should they? Journal of Economic Perspectives 7 (Sum-mer): 167-74.

JOURNAL OF ECONOMIC EDUCATION1(8

Schmidt, R. M. 1983. Who maxmiizes what?A study iD student timie allocation. America/l EceownIc Review Papers and Pmvceedings 73 (May): 2-3 28.

Siegfried, I. J., P. Sauiders, E. Stinar, and 11. Zbang. 1996. Teaching tools: How is introductory eco-nomics taught in America? Econonmc Inquiry 34 (January): 182-92.

Sprin,g 2001

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