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Does Lecture Attendance Matter for Grades?

Evidence from Longitudinal Tracking of Irish Students*

W o r k i n g D r a f t : 1 0 / 1 2 / 1 0 : V e r s i o n : 3 . 0

M a rt i n R ya n , University College Dublin

Liam D el aney, University College DublinColm Harmon, University College Dublin, IZA

Abstract

This paper examines the relationship between lecture attendance and grades, presenting evidencefrom longitudinal tracking of Irish students. This is only the second study, which the authors are awareof, to examine the highereducation production function across multiple subject areas. Previous researchby the authors suggests that empirical models of highereducation production functions may be biasedif they do not include measures of non-cognitive ability and other individual differences. In addition, aclassical criticism about causality from within labour economics can be applied to emphasise thatwhile more motivated, dedicated and future-orientated students may be more likely to attend theirlectures, students with those same characteristics are also more likely to achieve higher grades.Therefore, an important contribution is the measurement and inclusion of the following constructs:willingness to take risks, consideration of future consequences and non-cognitive ability traits. Theauthors also control for the effects of additional study-hours, prior academic achievement and universityfixed effects. The data were collected through a web-survey that the authors designed. Preliminaryresults suggest that lecture attendance matters for grades, both before and after the inclusion of controlsfor potentially confounding factors.

JEL: I21, J2, D90

Keywords: higher education, education inputs, lecture attendance, hours of study, future-orientation, attitude to risk, non-cognitive ability, conscientiousness

*Acknowledgements: Thanks to seminar participants at the UCD School of Economics and the Geary Institute forproviding comments; and to participants at the annual conference of the Irish Society of New Economists (Trinity CollegeDublin; September 2010).

Corresponding author. Em ai l: [email protected]. Postal correspondence: Martin Ryan, Desk 7.1, 2nd Floor, Geary

Institute, University College Dublin, Be lf ie ld , Dublin 4, Ireland. The corresponding author acknowledges financial supportfrom the Irish Research Council for the Humanities and Social Sciences (IRCHSS).


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Does Lecture Attendance Matter for Grades? Evidence from Longitudinal Tracking of Irish Students

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I . I n t r o d u c t i o n

There is evidence that lecture attendance is an important determinant of academic achievement, for

example: Schmidt (1983); Romer (1993); Durden and Ellis (1995); Dolton, Marcenaro and Navarro

(2003); Martins and Walker (2006) and Cohn and Johnson (2006). In fact, a large literature has grown

around the question of whether (and to what extent) lecture attendance affects student achievement. A

perennial motivation for research in this area is the issue of absenteeism amongst university students.

Past estimates place the rate of student absenteeism as high as 40% (Romer, 1993), and even as high

70% (Moore et al., 2003). In 1993, Romer published an article which ignited a lively debate about

mandatory attendance policy. Of course, non-attendance at lectures bears a heavy economic cost. The

National Centre for Educational Statistics in the United States estimates that American colleges anduniversities spend $32 billion yearly on student instruction. This is approximately $12,000 per full time

student (Dobkin, Gil and Marion, 2007). Despite this, a substantial fraction of students fail to attend

lectures, which are traditionally the primary means by which educational material is presented.1

Crucially, it is empirically established that grades affect future earnings; see Wise (1975), Filer (1983),

Jones and Jackson (1990), Loury and Garman (1995), McIntosh (2006) and Naylor, Smith and McKnight

(2007). In addition, there are strong indications that there is a positive correlation between lecture

attendance and exam performance (see section 3 of this paper), notwithstanding concerns about

causation. Therefore, students who fail to attend their lectures are potentially disadvantaged in relation

to their academic achievement and their subsequent earnings in the labour market.

Unlike the United States, where annual tuition costs average $13,424 at four-year public institutions

($30,393 at four-year private institutions), there have been no tuition fees for higher education in

Ireland since 1996.2 In addition, the tuition fees that were charged prior to 1996 were never

comparable to the full economic cost of providing higher education in Irelands (mainly public)

institutions. According to the OECD (2004), the annual tuition fees paid by Irish students before 1996

covered approximately 30% of the operating costs of higher education institutions (HEIs) in Ireland.

These fees averaged approximately 2,500 per annum and accounted for about one-third of the total

1 However, 4.6 million students in the United States (1 out of every 4) took a college-level online course at the start of the2008/09 academic year. This was a 17% increase from the previous academic year, according to the seventh annual SloanSurvey of Online Learning (Allen and Seaman, 2009). A large majority of students about three million weresimultaneously enrolled in face-to-face courses.2 Ireland refers to the Republic of Irelandthroughout this paper. Figures for tuition costs in the United States are for theacademic year 2007/08 (National Centre for Educational Statistics).


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cost of attending higher education, the remainder being mainly housing and maintenance costs (OECD,

2004). Despite students currently having to pay an annual registration fee of 1,500 to attend Irish

institutions, tertiary education is predominantly subsidised in Ireland.3 1,346 million was allocated

to current activities in Irelands HEIs in 2009 (Comptroller and Auditor General, 2010). 185,542

students attended these institutions (both universities and institutes of technology) in the academic

year 2008/09. This paper examines only the students attending Irelands seven universities; there were

97,001 of these students in 2009, and the institutions they attended received 742 million in funding

for current activities (Comptroller and Auditor General, 2010). A notable comparison with the United

States is that there is a considerable subsidy for the provision of higher education in Ireland. Therefore,

besides the potential for individual costs related to lower academic achievement and lower levels of

earnings, the cost of poor student performance also falls to a large extent on society. Approximately

8,244 per student (not including student contributions) is spent on current expenditure in Irishuniversities each year.

Of course, establishing causality between lecture attendance and grades is difficult; however, this

research paper benefits from the use of longitudinal data and the inclusion of the following constructs:

willingness to take risks, consideration of future consequences and non-cognitive ability traits. Ryan,

Delaney and Harmon (2010) suggest that empirical models of higher education production functions

may be biased if they do not include measures of the aforementioned individual differences.4 In

addition, in the literature on the micro-level returns to education, there is a concern that higher-ability

individuals are more likely to attain higher levels of education as well as higher levels of earnings. In the

area of research examined in this paper, there is aparallelconcern that more motivated, dedicated and

future-orientated students are more likely to attend their lectures as well as achieve higher grades.

There is evidence that unobserved heterogeneity amongst students explains more about student

achievement than observable inputs such as lecture attendance (Martins and Walker, 2006). Also, it is

3 Irish students that qualify for a higher education maintenance allowance, or the grant, as it is colloquially known, do nothave to pay the registration fee. To qualify for the full grant, the (pre-tax) family income of the student must be no more than41,110 (if the family has four or fewer children.) There are slightly higher thresholds for larger numbers of children. Inaddition, reduced grant payments are available up to a family income threshold of 51,380. The maintenance allowance isnever more than 3,342 and is often closer to 1,370, depending on how far the student lives away from college. The most

recent figure for the average industrial wage in Ireland is from 2006: 29,910.4 The findings from Ryan, Delaney and Harmon (2010) suggest that non-cognitive abilities may be more important thanfinancial constraints in the determination of higher education inputs such as lecture attendance and additional study-hours. Infact, the impact of non-cognitive ability on the extent of highereducation inputs is often more significant than other variablessuch as course or institutional choice, or parental background. However, while non-cognitive attributes have exhibitedpredictive grade-related validities (e.g., Lievens, Coetsier, De Fruyt, & De Maeseneer, 2002; Robbins et al., 2004), they are not asgood predictors of college grades as the actual academic behaviours that they are thought to influence (Cred, Roch, andKieszczynka; 2010).


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generally accepted that more able (and motivated and hard-working) students are more likely both to

attend and to score highly in their courses (Arulampalam, Naylor and Smith; 2009).

In the analysis, the authors also control for the effects of additional study-hours, prior academic

achievement and university fixed effects. There is evidence that additional study-hours are positively

related to grades, for example: Martins and Walker (2006); Arulampalam, Naylor and Smith (2007) and

Stinebrickner and Stinebrickner (2007). Bratti and Staffolani (2002) find that the effect of lecture

attendance on performance is not robust to the inclusion of the number of hours of study. Other

research suggests that ability (proxied by prior academic achievement) has a significant independent

effect on grade, and in some studies it exceeds the effect of attendance (Park and Kerr, 1990). Finally,

this is only the second study, which the authors are aware of, to examine a higher education production

function across multiple subject areas.

The remainder of the paper is organised as follows. The next section describes the theoretical

framework: a higher education production function. The third section reviews the existing literature on

the relationship between lecture attendance and academic performance. The fourth section presents

the survey data; these were collected through a web-survey that the authors designed. The fifth section

presents the method and results. Preliminary results suggest that lecture attendance matters for grades,

both before and after the inclusion of controls for potentially confounding factors. The sixth section

concludes with a discussion.

I I. A H ighe rEducation Production Function

A simple production model lies behind much of the analysis in the economics of education. Some

common inputs are school resources, teacher quality, and family attributes, and the outcome is student

achievement (Hanushek, 2007). Much of the work using this model of production has concentrated on the

educational attainment of pupils in compulsory schooling, with less attention paid to higher education

(Arulampalam, Naylor and Smith; 2009). However, there is a precedent for the theoretical consideration

of highereducation production functions (Freire and Silva, 1975; Johnson, 1978; Hopkins, 1990; Douglas

and Sulock, 1995). There is also a much wider empirical literature on higher education production

functions, in which researchers give attention to student inputs, in particular: lecture attendance and


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additional hours of study.

After enrolling in a course of higher education, students allocate their time between educational

inputs (primarily lecture attendance and additional study-hours), and other activities, such as leisure

and part-time work. Ryan, Delaney and Harmon (2010) ma p th e e du ca ti on a l i np u ts o f lecture

attendance and additional study-hours into the Juster and Stafford (1991) model of inter-temporal

time use. The theoretical framework used in this paper draws on the literature on highereducation

production functions. Production functions in economics are meant to represent the process by

which an institutionin this case a college or universitytransforms inputs into outputs (Hopkins,

1990). In this case, the authors are particularly concerned with the role of student inputs in the

production function. A linear higher education production function is presented in equation (1):

(1)

where Yis a measure of educational achievement for student i in universityjin year t, R is a vector

for the lecture attendance of student i in universityjin year t,Xis a vector of observable student and

family characteristics for student i in university j in year t, C is a vector of typically unobservable

individual differences for student i attending university j in year t, and and are a set of fixed

effects for university jand year t, respectively. Finally, is a stochastic error term for student i in

university jin year t. The effect of R on Yis the focus of this study; direct measurement of C(over

time) is also a major part of this papers contribution. represents a set of estimates for the effect of

attendance on the educational outcome in question. In a difference formulation, any time invariant

variables contained in Cdrop out, resulting in the estimating equation (2):

(2)

Equation 2 includes an individual fixed effect , for student i which allows one to analyse the

effect on educational outcomes for an individual student moving from one level of lecture attendance

to another as compared to the difference for students who do not change their lecture attendance

during the time they are observed in the dataset. Finally, Cred, Roch, and Kieszczynka (2010)

outline the possible structures which might explain the relationship between students individual


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differences, their lecture attendance and their academic achievement, as shown in Figure 1. We

proceed with a unique effects model, as we want to estimate the effect of lecture attendance on grades

while controlling for individual differences.

Fig.1: Possible Structures between Individual Differences, Attendance and Grades


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I I I . E x i s t i n g L i t e r a t u r e

In the international literature, many researchers have attempted to measure the impact of

absenteeism on student performance (Anikeeff, 1954; Schmidt, 1983; Jones, 1984; Buckalew, et al.,

1986; Brocato, 1989; Park and Kerr, 1990; Van Blerkom, 1992; Romer, 1993; Gunn, 1993; Durden and

Ellis, 1995; Devadoss and Foltz, 1996; Marburger, 2001; Bratti and Staffolani, 2002; Dolton, et al.,

2003; Kirby and McElroy, 2003; Rodgers, 2001; Rocca, 2003; Stanca, 2006; Lin and Chen, 2006;

amongst others). In each study, the authors find a positive correlation between exam performance

and attendance. While there is an older literature established by educational psychologists, the rest of

this section is mainly focused on evidence produced by economists on whether lecture attendance

matters for grades.

In a paper examining student time allocation in a University of Wisconsin-Madison

Macroeconomics Principles course (n = 216), Schmidt (1983) reports that hours spent attending

lectures and class-discussions positively affects course grades, even after controlling for hours of

study. Park and Kerr (1990) use a multinomial logit model in order to identify the determinants of

academic performance in a Money and Banking course (n = 97). They find that higher attendance is

associated with better performance, although students GPA and college entrance exam scores are

more important factors overall. Romer (1993) surveys attendance at all undergraduate economics

classes during one week at a large public institution, a medium-sized private university, and a small

liberal arts college. He runs regressions of student performance on fraction of lectures attended, both

excluding and including some proxies for motivation. The effect of class attendance is always positiveand significant; however, its magnitude is greatly reduced by the inclusion of proxies for motivation.

In the light of this evidence Romer (1993) suggests that a policy of mandatory attendance might

enhance student academic performance.

Durden and Ellis (1995) use students self-reported number of absences in order to explore the

relationship between absenteeism and academic achievement (n = 346) in a Principles of Economics

course. Controlling for student differences in background, ability and motivation, they find a nonlinear

effect of attendance on learning: while a few absences do not lead to worse grades, excessive

absenteeism does. Marburger (2001) examines the effect of absenteeism on exam performance in aPrinciples of Microeconomics course (n = 60). Students absences records over the semester are

matched with records of the class meetings when the material corresponding to each question of

three multiple-choice exams was covered. Results show that missing class on a specific day


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significantly increase the likelihood to respond incorrectly to a multiple-choice question based on the

material covered that day compared to students who were present. This finding suggests a negative

relationship between absenteeism and academic performance.

Rodgers (2001) finds a small but statistically significant impact of attendance on academic

performance in a sample of students enrolled in her Introductory Statistics course (n = 167). Cohenand Johnson (2006) examine the relationship between class attendance and academic performance in

a sample of 347 economics students. Their findings indicate a strong positive correlation between

attendance and performance. Using data on a sample of approximately 400Agricultural Economics

students at four large U.S. universities, Devadoss and Foltz (1996) find that, after taking into account

motivational and aptitude differences across students, the difference in exam performance between a

student with perfect attendance and a student attending only half of the classes is, on average, a full

grade. Dolton, Marcenaro and Navarro (2001) use data from the University of Malaga drawn from a

survey conducted in April 1999 on first and final year students. Their sample includes 3722

observations taken from students from forty different subject areas. Dolton, Marcenaro and Navarro

(2001) is the only study (that we are aware of) to examine a higher education production function

across multiple subject areas. They find that lectures are four times more productive than self-study.

Using a sample of (n = 371) first-year Economics students from Italy, Bratti and Staffolani (2002) find

that, after controlling for the number of study hours, the positive and significant effect of class

attendance on performance is not robust to the inclusion of self study.

Kirby and McElroy (2003) base their analysis on a sample of first year Economics students in

Ireland (n = 368). They find that class attendance is significantly affected by hours worked and travel

time to university. On the other hand, tutorial attendance appears to enhance exam performance

more than class attendance. Maloney and Lally (1998) find that both lecture attendance and previous

results are positively and significantly related to examination results for second and third year

economics students at the National University of Ireland, Galway. In the Maloney and Lally (1998)

study, lecture attendance and previous results are more important in explaining the examination

results of second year students than of third year students. Stanca (2006) uses a large panel data set

collected from an Introductory Microeconomics course in an Italian university (n = 766). The data

combine administrative and survey sources. However, a notable feature of the data is that attendance

to classes and tutorials is self-reported by students. Applying three different econometric approaches(OLS-proxy regression, instrumental variables and panel estimators) to address the endogeneity of

the attendance rate variable, Stanca (2006) concludes that attendance has an important independent

effect on academic performance.


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Marburger (2006) examines the introduction of a mandatory attendance policy; he focuses on two

groups of students studying an introductory module in Microeconomics in two consecutive years at

the same university. Students in one group (no-policy) were told that the university-wide attendance

policy would not be applied to them; and students in the other groups (policy) were subject to the

university-wide attendance policy. Marburger (2006) expresses concern that absenteeism may be

endogenous to the day and timing of a particular class so he uses students from the same teaching slot

in 2003 as a control group. Members of the no-policy group (n = 38) attended the module in 2002,

while members of the policy group (n = 39) attended the same module in 2003. Marburgers (2006)

findings concur with the previous result that lecture attendance matters for grades. In addition, (in

the absence of any mandatory policy on attendance) absenteeism in the no-policy group increased

throughout the semester.

Chan et al. (1997) examines the relationship between class attendance and academic performance

in a Principles of Finance course (n = 71). After correcting for selectivity bias (due to student

withdrawals) by using Tobit and Heckman two-stage models, they find a positive effect of attendance

on performance. They also find that a mandatory attendance policy would not significantly enhance

course grades. Dobkin and Marion (2010) estimate the effect of class attendance on exam

performance by implementing a policy in three large economics classes that required students scoring

below the median on the midterm exam to attend class. This policy generated a large discontinuity in

the rate of post-midterm attendance at the median of the midterm score. Dobkin and Marion (2010)

estimate that near the policy threshold, the post-midterm attendance rate was 36 percentage points

higher for those students facing compulsory attendance. They also estimate that a 10 percentage

point increase in a student's overall attendance rate results in a 0.17 standard deviation increase in

the final exam score.

Research by Thomas and Webber (2001) emphasises the effects of peer groups on student choice,

while Webber and Walton (2006) illustrate that peer groups can be gender-specific. Attendance at

university seminars may be the result of ones friends attending either that seminar or another class

on the same day, and not an independent decision made by the student. While the presence and

significance of friendships are difficult to model, many freshmen place a high value on the chance to

socialise while at university, such that attendance in class is often a by-product of this socialising with

friends (Allen and Webber, 2010).

Some studies have been undertaken that demonstrate no relationship between attendance and

academic performance. Browne et al. (1991) finds that students who did not attend lectures did

equally well in the Test of Understanding College Economics (TUCE), a standard American multiple-


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choice based test, than those who did attend lectures regularly. However, Browne et al. (1991) also

find that students that attended lectures regularly performed better in an essay assessment. Rodgers

(2002) implements an incentive scheme in an undergraduate introductory statistics module in an

Australian university. The scheme was designed so that each students overall mark was reduced by 1

per cent for every tutorial missed in excess of two. Students attendance and performance were

compared with the performance of students who had undertaken the same module in the previous

academic year, prior to the introduction of the incentive scheme. The results from Rodgers (2002)

indicate that while attendance did improve, improved attendance did not translate into improved

academic performance, even when the penalty points that had been deducted for non-attendance

were added back on to the students overall marks.

I V . D a t a

The data that we use in this paper were collected through the longitudinal component of a web-

survey that the authors designed: rounds 2 and 3 of the Irish University Study. Round 2 was conducted

during spring 2009; Round 3 was conducted during spring 2010. Analysis is restricted to observations

where students are enrolled in full-time courses; this is because part-time students are a

characteristically different group. In addition, the sample is restricted to full-time undergraduates

because post-graduates are also a characteristically different group. All of the students in the analytical

sample are studying for honours bachelor degrees (n = 782). The Honours Bachelor Degree in Irish

universities is normally awarded following completion of a programme of three or four years

duration (180-240 ECTS credits), although there are examples of longer programmes in areas such as

architecture, dentistry and medicine. Entry to a programme leading to an honours bachelor degree is

determined by students performance in the Leaving Certificate (Leaving Cert.), which is the senior

state examination at the end of secondary school in Ireland. Seven subjects are typically examined in

the Leaving Cert., with students required to take compulsory courses in Maths, English and the Irish

Language. They are free to choose additional courses from a range of language, science, business and

humanities subjects, depending on availability within the secondary school. Exams can be sat at

Ordinarylevel or Higherlevel; this distinction is important for the number of points awarded. Entry

into Irish higher education is based on the points system" in which the more advanced papers get

higher points.


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Fig.2: Distribution of Leaving Cert. Points-Scores (Irish Universities Study)

0

.01

.02

.03

.0

4

.05

Percentageofstudentsineach

bin

200 300 400 500 600

Leaving Cert. scores in bins of 10-points

Points are awarded for the six examinations in which a student performs best.5 The distribution of

Leaving Cert. points-scores for the students from the Irish Universities Study is shown in Fig. 2.

Robustness checks between the data from the Irish Universities Study and data available for the

population of Irish university students are presented in Appendix A (across gender, institution and area

of study). Overall, on several observables, the sample is representative of its underlying population.

Fig. 4 shows histograms of percentage average (university) grade-scores from the university

sample. This is the dependent variable in the production function estimated in this paper. The

variable comes from asking students the following question: On a scale of 0-100, what is your

average grade-score at university? As this is a self-reported outcome, we make three data-

comparisons which all show that the dependent variable is representative of population grade-scores.

The first is a comparison to an alternative measure of grade-score: 1h1, 2h1, 2h2, Pass or A+, A, A-, B+,

B, B-, C+, C, C-, D+, D, D- (whichever grading-scheme the student is actually marked on). This

alternative measure of grade-score comes from a question at a different position in the survey-

questionnaire. Table 1 (for Round 2 of the study) and Table 2 (for Round 3 of the study) show that

students are answering questions about their grade-scores in a consistent manner.

5 Entry is through a centralised application system - the Central Applications Offce (CAO). This office was established in 1976to streamline and co-ordinate student applications for university places. A total of ten higher education courses may bechosen in order of preference. Each applicant is given a place in the highest of his course preferences in which his meritrating will allow (Coolahan, 1991).


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Fig.4: Histograms of Per cent age Average (Univer sity) Gr ade

0

.02

.04

.06

Density

0 20 40 60 80 100percent last year

kernel = epanechnikov, bandwidth = 1.86

Round 2

0

.02

.04

.06

Density

40 60 80 100percent this year


Round 3

Source: Irish University Study

The second data-comparison is a test/re-test analysis on students percentage average grade-score

from Round 2 and their reporting of their percentage average grade-score from last year, taken in

Round 3. This comparison also shows that students are answering questions about their grade-scores

in a consistent manner. Finally, the third data-comparison outlined in Table 3, shows students grade-

scores (from Round 3) against grade-scores for the university population. Here, the measure 1h1,

2h1, 2h2, Pass must be used; students who are marked by A+, A, A-, B+, B, B-, C+, C, C-, D+, D, D-

have their grades re-coded to be comparable. Comparisons are available by gender and field of study.

The self-reported grade-scores are representative across gender breakdowns, but are upward-biased

across fields of study. Therefore, re-weighting grade-scores by field of study would be a useful

robustness check.

Another issue is salient when discussing the self-reported nature of the grade-data examined in

this paper. It is possible that students may be over-stating their grades due to the Lake Wobegon

effect. The Lake Wobegon effect, also known as illusory superiority or the above average effect, is a

cognitive bias that causes individuals to overestimate their positive qualities and abilities and to

underestimate their negative qualities, relative to others. This is evident in a variety of areas including

intelligence, performance on tasks or tests and the possession of desirable characteristics or


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personality traits (Colvin et al., 1995; Kruger and Dunning, 1999).6 This concern can be easily

circumvented if official records data are available; however, many researchers can only access

student-reported data. In 1994, Maxwell and Lopus published an article about the Lake Wobegon

Effect in student self-reported data; demonstrating biases stem from two sources. First, below-

average students tend to inflate their academic achievements, and second, they often fail to report

their inferior accomplishments.

In a more recent paper, Haley, Johnson and McGee (2010) also examine whether using student-

survey data in place of official records data biases regression estimates. They motivate their

contribution by noting a useful statistical feature of over-reporting on bounded variables such as

grade point average. "Specifically, the misreports will be negatively correlated with the true grade

point average, yielding a form of non-classical measurement error that actually counteracts the bias."

Haley et al. (2010) connect this observation to reliability ratios used in labour economics. In two

applications, they find that it is unnecessary to correct for the bias from the Lake Wobegon effect

because it is so small.

Lecture attendance (the independent variable of particular interest) is measured as the self-reported

percentage of university lectures attended by each student. The distribution of percentage lectures

attended for Rounds 2 and 3 of the study is shown in Fig. 5. Approximately 12% of students claim to

attend all of their lectures. 32% of students claim to attend 90% or more of their lectures. 47% of

students claim to attend 80% or more of their lectures. 57% of students claim to attend 70% or more of

their lectures. 67% of students claim to attend 50% or more of their lectures. Overall, the mean-level of

percentage lectures attendedis 83% in Round 2 of the study, and 84% in Round 3 of the study. This is

a self-reported behaviour, and one that is subject to much comparison of anecdote amongst academic

instructors. Furthermore, there is reasonable ground to suspect self-reported lecture attendance to be

over-stated due to the phenomenon of social desirability bias. Social desirability bias is a term used to

describe the tendency of respondents to reply in a manner that will be viewed favourably by others;

see Bound et al. (2001) on Measurement Error in Surveys for a discussion.

While benchmarking against official data is difficult in the case of lecture attendance, there is some

reassurance from mean-levels ofpercentage lecture attendance being similar across both Round 2 and

Round 3 of the study. In addition, a data-comparison can be made with a comprehensive attendance

survey (measured by head-count) that was conducted at one Irish university during the academic

year 2008/09. Under the guidance of Gabrielle Kelly (2010), students in an undergraduate Survey

Sampling class carried out a survey to estimate the attendance rate at lectures in science modules in

6 The phraseology of the Lake Wobegon effect comes from Garrison Keillor's fictional town, Lake Wobegon, where "all the


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UCD. Only first-years in the UCD College of Engineering, Mathematical and Physical Sciences and the

UCD College of Life Science were included (this was due to student drop-out in first-year being

prevalent in these colleges). 7 The overall attendance rate was 47.3%. However, there is also a

(statistically significant) decrease in attendance rate as class size increases. Figure 6 (taken from the

report on the UCD Attendance Survey; 2009) shows a plot of attendance rate vs. enrolment. The

Table 1: Percentage Average Grade vs. Ordered Grade-Score (Round 2)

Grade Option 1 % Average Grade N

1st 73 7 792:1 64 7 1632:2 59 7 81

Pass 51 9 18Fail 50 0 1


A+ 81 11 5A 78 8 18A- 75 7 19B+ 70 10 41B 68 8 48B- 65 9 35C+ 60 6 30

C 59 8 16

C- 56 10 7

D+ 56 7 10

D 52 3 3

Table 2: Percentage Average Grade vs. Ordered Grade-Score (Round 3)


1st 74 5 1592:1 66 6 2512:2 60 7 52

Pass 54 5 4


A+ 80 9 5

children are above average".7 Engineering modules were excluded. This was done because according to the authors of the UCD Attendance Survey, thesemodules are very practical-work orientated and so would have a higher attendance rate.


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A 80 8 29A- 77 7 31B+ 73 6 60B 69 6 39B- 66 4 13

C+ 63 5 18C 58 5 12

C- 50 9 4D+ 52 4 2

D 44 5 4

Table 3: Population Grades (from HEA Data) vs. Survey Grades

Gender/Field of Study 1h1 2h1 2h2 Other Pass

Male: Population 44.0 35.5 38.5 44.0 40.0Female: Population 56.0 64.5 61.5 56.0 60.0

Male: Survey 44.0 32.0 46.0 ////// 50.0Female: Survey 56.0 68.0 54.0 ////// 50.0

Education: Population 9.2 54.0 28.5 5.8 2.5

Education: Survey 44.0 56.0 ////// ////// //////

Humanities and Arts: Population 9.3 43.0 26.4 19.9 1.4Humanities and Arts: Survey 28.7 61.74 9.7 ////// //////

Social Science/Business: Population 13.6 46.4 19.6 19.3 1.2Social Science/Business: Survey 29.5 57.0 13.5 ////// //////

Science: Population 21.4 42.0 22.4 11.7 2.5Science: Survey 45.0 45.0 8.0 2.0 //////

Engineering/Construction: Population 26.1 35.2 28.2 8.1 2.3

Engineering/Construction: Survey 36.0 44.0 20.0 ////// //////

Health and Welfare: Population 13.5 37.1 21.6 13.2 14.6

Health and Welfare: Survey 29.0 52.0 14.0 5.0 //////


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Fig. 5: Histograms of Per cent age Lectu res Att ended

0

.01

.02

.03

.04

Density

0 20 40 60 80 100PERCENT LECTURES ATTEND


Round 2

0

.01

.02

.03

.04

Density

0 20 40 60 80 100Lectures


Round 3


higher attendance rates between 70% and 80% occurred for smaller class sizes of between 30 and

160, whereas the lower rates of 15% to 35% occurred for larger class sizes of between 185 and 485.

The Irish Universities Study includes students from all courses but the UCD Attendance Survey

only includes students enrolled in STEM (science, technology, engineering and maths) courses. These

STEM courses have a greater amount of lectures to attend, which could possibly result in lower levels

of attendance (compared to non-STEM courses). The above consideration would lead one to expect a

higher level of percentage lectures attended in the Irish University Study, compared to the UCD

Attendance Survey. It should also be noted that there is heterogeneity in students level ofpercentage

lectures attendedacross the seven Irish universities; the range spans from 78% in one university to

87% in another. In addition, students may be attending more of their lectures in the recession than

they used to beforehand. University students in the UK study for two hours and 12 minutes more (per

week) now than they did two years ago (in 2007), according to the Higher Education Policy Institute

(2009).8

Finally, there remains the possibility that the web-survey designed by the authors may have

received an unrepresentative level of response from more conscientious (or diligent) students; and

that this conscientiousness is correlated with higher levels of lecture attendance. As mentioned earlier

8 Given lower levels of labour demand in the part-time jobs market, there is certainly less opportunity for students toallocate their time to work (that is, there is diminished opportunity-cost of study-time). In addition, the evolving crisis in thegraduate labour market may motivate students to be more patient; and achieve higher academic standards (1 in 3 menunder the age of 25 are currently unemployed in Ireland).


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in this section, robustness checks between the data from the Irish Universities Studyand data available

for thepopulation of Irish university students are presented in Appendix A (across gender, institution

and area of study). Overall, on several observables, the sample is representative of its underlying

population. Of course, conscientiousness is a typically unobserved variable and therefore it is

unavailable in the official data on the population of Irish university students. However,

conscientiousness is measured in the Irish University Study, and Figure 7 shows that the sample

examined in this paper score highly on that variable.

The independent variables are grouped into four themes: (i) student characteristics (ii) family

demographics (iii) individual differences, and (iv) fixed effects (based on controlling for university).

Student characteristics include students age, students gender (whether the student is male), the year

of the course that the student is studying in, additional study-hours and any financial supports that are

received. Additional study-hours are measured using the following question: How many hours per week

do you spend on average on personal study time? Students give their answer in a grid comprised of

hours (per week), categorised as follows: 0, 1-5, 6-10, 11-15, 16-20, 21-30, 31-40, 41-50, 51-60, 60+.

Fig. 6: Attendance Rate vs. Enrolment (UCD Attendance Survey; 2009)


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Fig. 7: Kernel Histogram of Students Conscientiousness

0

.05

.1

.15

Density

0 5 10 15conscie

Financial supports include finance received from students parents, and finance received from the

state. Finance received from students parents is the sum of direct transfers and indirect payments on

the behalf of students. The family demographics are as follows: whether the students father has some

higher education, whether the students father has a professional or lower professional occupation,

and the family income of the student. Individual differences are measured by willingness to take risks

(on a scale of 0-10), consideration of future consequences (on a scale of 4-25) and the personality traits:

openness, conscientiousness, extraversion, agreeableness and neuroticism. These personality traits are

proxies for non-cognitive ability traits. The measurement of these variables relating to individualdifferences was discussed in detail in Ryan, Delaney and Harmon (2010).

Table 4 (for Round 2 of the study) and Table 5 (for Round 3 of the study) show summary

statistics for all of the data discussed in this section. The average percentage grade-score is 65-58, the

average percentage of lectures attended is 83-84, the average year in university is 2-2.5, and the

average students age is 21-22. About 36% of students are male, approximately 50% of students

fathers have some higher education, and average family-income is in the range of 60,000-80,000.

Students average points-score in the Leaving Certificate is 475; their average time spent studying per

week is 15 hours. Table 6 shows the test/re-test reliability of the data discussed in this section. The

top half of the table shows variables where variation is expected over time; the bottom half of the

table shows variables where variation is not expected over time. Leaving Cert. points, students age

and students gender are virtually identical, which gives the data string credibility. It is possible that

the variable indicating whether fathers have some higher education could be changing over time; the


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correlation for this variable is 0.9. A correlation coefficient of 0.7 is viewed by most researchers as the

threshold for stability. Theory suggests that students willingness to take risks and consideration of

future consequences should change very slowly over time; the correlation for these variables are 0.59

and 0.61, respectively. Figure 8 illustrates the willingness to take risks variable; Figure 9 illustrates

the consideration of future consequences variable.

Table 4: Summary Statistics: Round 2

Variable Mean S.D. N

Average grade 65.0 9.87 604Lecture attendance 83.4 15.7 741

Year in university 2.00 0.97 801Age of student 21.2 5.07 803Student is male 0.35 0.47 803

Students father has HE 0.50 0.50 752Family-income bracket 4.21 2.51 773Future-orientation 13.9 3.54 751

Willing to take risks 6.33 1.66 755

Leaving Cert. points 474 76.0 719

Study-time interval 2.99 1.30 733

Table 5: Summary Statistics: Round 3

Variable Mean S.D. N

Average grade 68.5 8.34 681Lecture attendance 84.3 18.2 683

Year in university 2.64 0.99 799Age of student 21.9 5.08 803Student is male 0.35 0.48 803Students father has HE 0.50 0.50 756Family-income bracket 4.09 2.35 755Future-orientation 14.0 3.51 732

Willing to take risks 5.96 1.88 745

Leaving Cert. points 476 73.9 706

Study-time interval 3.44 1.52 769


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Table 6: Test/Re-Test Reliability of the Data

Variable R3-Mean R2-Mean Corr.

Year in university 2.64 2.01 0.44Average grade 68.5 65.1 0.50

Study-time interval 3.44 2.99 0.56

Lecture attendance 84.4 83.4 0.65Family-income bracket 4.09 4.21 0.81

Willing to take risks* 5.96 6.33 0.59Future-orientation* 14.1 13.9 0.61Students father has HE* 0.50 0.50 0.90

Leaving Cert. points* 476 474 0.99

Age of student* 22.0 21.2 0.99

Student is male* 0.35 0.35 0.99

*Do not expect variation across time

V I . M e th o d a n d R e su l ts

The determinants of students academic achievement (university grades) are estimated using the

following econometric model:

Fig. 8: Attitude to Risk over Time

0

.1

.2

.3

Density

0 2 4 6 8 10RISKS

kernel = epanechnikov, bandwidth = .35

Round 2

Kernel density estimate

0

.05

.1

.15

.2

.25

Density

0 2 4 6 8 10Willing to take risks


Round 3




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Fig. 9: Future Orientation over Time

0

.05

.1

.15

Density

5 10 15 20patience_2


Round 2


0

.05

.1

.15

Density

5 10 15 20patience


Round 3



where Y is grade-score for student i in university j in year t, R is a vector for the lecture attendance of

student i in university j in year t, X is a vector of observable student and family characteristics for

student i in university j in year t, C is a vector of typically unobservable individual differences for

student i attending university j in year t, and and are a set of fixed effects for university j and year

t, respectively. Finally, is a stochastic error term for student i in university j in year t. The effect of R

on Y is the focus of this study; direct measurement of C (over time) is also a major part of the analysis.

represents a set of estimates for the effect of attendance on the educational outcome in question.

Academic achievement is modelled using ordinary least squares (OLS) regression on the same

individuals over two periods in time: spring 2009 and spring 2010. The results are shown in Table 7.The first column is the baseline specification. The second column (Lagged) estimates the same

model but adds lags of university grade-score, lecture attendance and hours of study. The third

column (Replaced) substitutes Round 3 measures of future-orientation and attitude to risk with

measures from Round 2. The fourth column (Big 5) adds the Big 5 personality traits. The fifth

column (STEM) adds a binary indicator to control for whether a student is enrolled in a STEM course.

The sixth column (FE) adds institutional fixed effects based on university attended. Where they apply,

control variables for missing value adjustment, STEM-enrolment (statistically insignificant) and

institutional fixed effects (all statistically insignificant) are not shown in the results. Outliers and

missing values are adjusted only for independent variables. Making adjustments for outliers and

missing values (in particular on the lecture attendance variable) does not affect the results.

There is a clearly positive and very statistically significant relationship between lecture

attendance and grades, throughout every specification in Table 7. A one percentage increase in


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lecture attendance predicts on average (across the six specifications) an extra 6% of onepercentage

grade score. This means that a student could improve their grade-score by one per cent if they attend

approximately 15% more of their lectures. The relationship between lecture attendance and grades is

visualised by a local polynomial smooth plot in Figure 10 (grade-score is on the vertical axis; lecture

attendance is on the horizontal axis). Round 2 is shown on the left; Round 3 on the right.

Fig. 10: The Relationship between Lecture Attendance and Grades

Table 7: Students Average Grade at University: Robust OLS Regression

(1) (2) (3) (4) (5) (6)

Baseline Lagged Replaced Big 5 STEM FE

Ave. % of lectures attended 0.075*** 0.040* 0.089*** 0.073*** 0.075*** 0.088***

(0.022) (0.023) (0.021) (0.021) (0.022) (0.022)

Students year of course -0.284 0.004 -0.209 -0.286 -0.241 -0.055

(0.323) (0.335) (0.325) (0.321) (0.335) (0.327)

Age of the student 0.003 -0.074 0.001 -0.000 0.005 -0.040

(0.149) (0.139) (0.151) (0.151) (0.149) (0.145)

Whether the student is male 0.549 0.460 0.180 0.839 0.530 0.247

(0.617) (0.560) (0.627) (0.636) (0.618) (0.617)

Whether the father has HE -0.343 -0.716 -0.181 -0.231 -0.331 -0.032

(0.722) (0.653) (0.688) (0.720) (0.721) (0.696)Family income in brackets 0.219 0.224* 0.219 0.249* 0.214 0.199

(0.136) (0.130) (0.136) (0.137) (0.136) (0.133)

Students future-orientation 0.620*** 0.486*** 0.572*** 0.624*** 0.600***

(0.094) (0.089) (0.095) (0.095) (0.096)

Students att. to take risks -0.055 0.099 -0.069 -0.054 -0.024

(0.186) (0.172) (0.197) (0.186) (0.177)


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Students LC points-score 0.002 -0.008* -0.000 0.002 0.003 0.002

(0.005) (0.005) (0.002) (0.005) (0.005) (0.005)

Hours of study in brackets 0.275 0.051 0.285 0.266 0.272 0.337

(0.208) (0.223) (0.214) (0.211) (0.208) (0.206)

Students openness 0.291*

(0.164)

Students conscientiousness 0.184

(0.146)

Students extraversion -0.157

(0.112)

Students agreeableness 0.115

(0.149)

Students neuroticism 0.094

(0.126)

Lag of university grade 0.360***

(0.054)

Lag of lecture attendance 0.020

(0.027)

Lag of study-hours 0.014

(0.303)

Round 2 future-orientation 0.542***

(0.104)

Round 2 att. to take risks 0.258(0.199)

Constant 51.581*** 36.761*** 50.580*** 46.883*** 51.325*** 51.824***

(4.580) (4.899) (3.855) (5.573) (4.603) (5.262)

Observations 655 581 655 655 655 655

R-squared 0.139 0.322 0.123 0.149 0.140 0.197*** p


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to some previous findings in the literature.

V I . C on cl us io n

The key results from this paper show that lecture attendance is more important for academic

achievement than scholars additional hours of study, what subject area they enroll in9, what university

they study at, many of their personality traits and all of their family background, including parental

education and family-income. Indeed, the only predictors of better academic performance across the

six specifications in Table 7 are lecture attendance and future-orientation. Furthermore, these

relationships are observed over a longitudinal setting. Strikingly, the relationship between lecture

attendance and academic achievement is virtually the same after controlling for personality traits

related to non-cognitive ability. This addresses the concern that more motivated, dedicated and future-

orientated students are more likely to attend their lectures as well as achieve higher grades.Of course, the finding related to students lecture attendance is most central to institutional

policy setting in higher education. Attendance is voluntary in many college classes, primarily because

of the difficulty in taking attendance on a regular basis, but also because of the view that students should

have some autonomy in determining the manner in which they engage with academic material

(Stephenson, 1994).10 Mandatory attendance policy becomes more of an issue, however, where there is a

professional element to a programme. In nursing, for example, there is a high minimum attendance

stipulated by the Irish Nursing Board (2005): students must attend 80% of a minimum of 1,533 hours.

Another argument in favour of requiring attendance is that it is needed to develop a strong sense of

community in a classroom. Indeed, there may be outcomes other than academic achievement which have

an important relationship with students lecture attendance. A recent report from the institutional

research office at one Irish university has recommended that mandatory attendance policy could be

one mechanism to reduce student drop-out (Blaney and Mulkeen, 2009).11 Attendance monitoring

has been used as a trigger for student interventions at Newcastle University (Bevitt et al. 2009). The

authors are currently working on a research note that will report on the relatively unexplored topic of

students lecture attendance and their well-being.

Future research should examine the impact of experimental designs related to enforcing

and/or monitoring lecture attendance. There is much debate on what incentives or penalties are

9 This is only the second study, which the authors are aware of, to examine the higher education production function acrossmultiple subject areas10 Analogies can be drawn here with libertarian paternalism debates in public behavioural economics.11 The report is entitled Student Retention in a Modular World - A Study of Retention of UCD Entrants: 1999-2007.


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appropriate in this regard as penalising students for not showing up can be seen as double jeopardy:

they would be punished by lower test scores in addition to a lower attendance score. While some

instructors may dislike mandatory attendance policies because they can be a lot of work to enforce,

there are recent technological advances such as dibbers (used at the Lancaster University

geography department) or clickers (Hoekstra, 2008) which substantially ease the burden of

collecting attendance data. Smart-card technology is available explicitly for the use of measuring

student attendance;12 there are even new electronic systems which are being used to detect the ID

cards students are carrying as they enter classrooms at Arizona University. Future research should

also make comparisons between headcount data and self-reported lecture attendance. One of the

authors is currently working on a small project which will compare self-reported grade-scores and

administrative grade data for one Economics class at an Irish university.

Finally, new innovations such as online learning also bear consideration. 4.6 million students

in the United States (1 out of every 4) took a college-level online course at the start of the 2008/09

academic year.13 Given the importance of face-to-face lectures for students academic achievement (as

demonstrated in this study), it should be an immediate priority to establish how online lectures

compare. Indeed, some amount of online learning may be inevitable in the future due to recent

pressures on resources in higher education: both reduced levels of funding and higher levels of

student enrolment. Another possibility is that the availability of online materials may discourage

attendance. According to the results of a MIT survey, the penalty to not going to a lecture is reduced

by the presence of online learning materials (Clay and Breslow, 2006).

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Appendix A: IUS (Survey) Data versus HEA (Population) Data

Gender IUS Data Set 2 (09/10) HEA 2009 IUS Data Set 1 (08/09) HEA 2008

Male 37% 43% 36% 42%

Female 63% 57% 64% 58%

University

DCU 6% 9% 6% 9%

NUIG 12% 16% 16% 16%

NUIM 12% 8% 10% 7%

TCD 19% 15% 18% 15%

UCC 20% 18% 17% 18%

UCD 24% 23% 23% 23%

UL 7% 12% 10% 11%

SubjectEducation 2% 4% 3% 5%

Humanities & Arts 23% 25% 24% 25%

Social Science 11% 7% 10% 6%

Business 11% 13% 12% 13%

Law 4% 6% 5% 7%

Science 16% 12% 15% 11%

Maths 3% 1% 2% 1%

Computing 3% 3% 3% 3%

Engineering 7% 8% 8% 8%

Agriculture 2% 2% 2% 2%

Health 15% 18% 12% 18%

Sport 0% 0% 1% 0%

Other 3% 2% 4% 2%

Documents

Effects of Orientation Program to Academic Programs