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A panel-data study of the effect of student attendance on university performance.



The literature indicates that absenteeism ab·sen·tee·ism  
n.
1. Habitual failure to appear, especially for work or other regular duty.

2. The rate of occurrence of habitual absence from work or duty.
 from university classes is a common phenomenon in Australia Australia (ôstrāl`yə), smallest continent, between the Indian and Pacific oceans. With the island state of Tasmania to the south, the continent makes up the Commonwealth of Australia, a federal parliamentary state (2005 est. pop.  and North America North America, third largest continent (1990 est. pop. 365,000,000), c.9,400,000 sq mi (24,346,000 sq km), the northern of the two continents of the Western Hemisphere. . Whether this constitutes a problem from society s point of view depends upon whether absenteeism has a detrimental det·ri·men·tal  
adj.
Causing damage or harm; injurious.



detri·men
 effect on student learning. Several authors in the economics discipline have argued the affirmative AFFIRMATIVE. Averring a fact to be true; that which is opposed to negative. (q.v.)
     2. It is a general rule of evidence that the affirmative of the issue must be proved. Bull. N. P. 298 ; Peake, Ev. 2.
     3.
 although none has established a causal causal /cau·sal/ (kaw´z'l) pertaining to, involving, or indicating a cause.

causal

relating to or emanating from cause.
 linkage linkage

In mechanical engineering, a system of solid, usually metallic, links (bars) connected to two or more other links by pin joints (hinges), sliding joints, or ball-and-socket joints to form a closed chain or a series of closed chains.
 using experimental data and appropriate statistical analysis. The study reported here used panel data on business and economics students in an introductory statistics class at an Australian Australian

pertaining to or originating in Australia.


Australian bat lyssavirus disease
see Australian bat lyssavirus disease.

Australian cattle dog
a medium-sized, compact working dog used for control of cattle.
 university to estimate the effect of attendance on performance. The methodology takes account of unobserved heterogeneity het·er·o·ge·ne·i·ty
n.
The quality or state of being heterogeneous.



heterogeneity

the state of being heterogeneous.
 among students and, in so doing, constitutes an improvement over cross-section cross section also cross-sec·tion
n.
1.
a. A section formed by a plane cutting through an object, usually at right angles to an axis.

b. A piece so cut or a graphic representation of such a piece.

2.
 regression regression, in psychology: see defense mechanism.
regression

In statistics, a process for determining a line or curve that best represents the general trend of a data set.
 results reported previously. Attendance is found to have a small, but statistically significant, effect on performance.

I Introduction

Absenteeism from university classes is not a new phenomenon. The historian, Barbara Barbara

maid exemplifying personal and domestic neatness. [Br. Lit.: Old Curiosity Shop]

See : Orderliness
 W. Tuchman Tuch·man   , Barbara Wertheim 1912-1989.

American historian who won a Pulitzer Prize for The Guns of August (1962) and for Stilwell and the American Experience in China (1971).

Noun 1.
 (1979, p.119) states that in the 14th century `dwindling dwin·dle  
v. dwin·dled, dwin·dling, dwin·dles

v.intr.
To become gradually less until little remains.

v.tr.
To cause to dwindle. See Synonyms at decrease.
 attendance at Oxford was deplored in sermons by the masters'. In 14th century England England, the largest and most populous portion of the United Kingdom of Great Britain and Northern Ireland (1991 pop. 46,382,050), 50,334 sq mi (130,365 sq km). It is bounded by Wales and the Irish Sea on the west and Scotland on the north. , low attendance might reasonably have been attributed to war and pestilence pestilence /pes·ti·lence/ (pes´ti-lins) a virulent contagious epidemic or infectious epidemic disease.pestilen´tial

pes·ti·lence
n.
1.
; today the reasons are less obvious. For whatever reason, both in North America and Australia, substantial numbers of university students regularly skip classes, Romer
This page is about the cartographic mechanism called a "Romer" or "Roamer"; for people named Romer see Romer (surname)


A Romer or Roamer is a simple device for accurately plotting a grid reference on a map.
 (1993, p. 167) described absenteeism in economics subjects at three `relatively elite' United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area.  universities as `rampant', having found that approximately one third of students were absent from class on a given day. Rodgers and Rodgers (2000, p. 17) report attendance rates in an Intermediate Microeconomic mi·cro·ec·o·nom·ics  
n. (used with a sing. verb)
The study of the operations of the components of a national economy, such as individual firms, households, and consumers.
 Theory class at an Australian university that range from 68.4 per cent in the first half of the semester se·mes·ter  
n.
One of two divisions of 15 to 18 weeks each of an academic year.



[German, from Latin (cursus) s
 to 54.5 per cent in the second half of the semester.

Several analyses of cross-section data have found a strong association between students' attendance and performance. Devadoss and Foltz (1996), Durden and Ellis ELLIS - EuLisp LInda System. An object-oriented Linda system written for EuLisp. "Using Object-Oriented Mechanisms to Describe Linda", P. Broadbery <pab@maths.bath.ac.uk> et al, in Linda-Like Systems and Their Implementation, G. Wilson ed, U Edinburgh TR 91-13, 1991.  (1995), Romer (1993), Park and Kerr Kerr   , Walter 1913-1996.

American playwright, writer, and drama critic for the New York Herald-Tribune (1951-1966) and the New York Times (1983-1996). In 1978 he won a Pulitzer Prize for criticism.
 (1990), and Schmidt (1983) report strong correlations in classes as diverse as agricultural economics Agricultural economics originally applied the principles of economics to the production of crops and livestock - a discipline known as agronomics. Agronomics was a branch of economics that specifically dealt with land usage.  and agribusiness agribusiness

Agriculture operated by business; specifically, that part of a modern national economy devoted to the production, processing, and distribution of food and fibre products and byproducts.
, microeconomic principles, macroeconomic mac·ro·ec·o·nom·ics  
n. (used with a sing. verb)
The study of the overall aspects and workings of a national economy, such as income, output, and the interrelationship among diverse economic sectors.
 principles, intermediate macroeconomics macroeconomics

Study of the entire economy in terms of the total amount of goods and services produced, total income earned, level of employment of productive resources, and general behaviour of prices.
, and money and banking. No study has established a causal relationship between attendance and performance, using experimental data and sound statistical methodology. A very recent paper by Marbuger (2001) tackled the problem of absenteeism by using a panel of observations on 60 students in an introductory microeconomics microeconomics

Study of the economic behaviour of individual consumers, firms, and industries and the distribution of total production and income among them. It considers individuals both as suppliers of land, labour, and capital and as the ultimate consumers of the final
 class at a medium-sized Me´di`um-sized`

a. 1. Having a medium size; as, a medium-sized man s>.

Adj. 1. medium-sized - intermediate in size
medium-size, moderate-size, moderate-sized
, state-funded, regional university in the United States. He estimated a probit model In statistics, a probit model is a popular specification of a generalized linear model, using the probit link function. Probit models were introduced by Chester Ittner Bliss in 1935.  in which the probability of a student responding incorrectly to each question in a set of multiple-choice mul·ti·ple-choice
adj.
1. Offering several answers from which the correct one is to be chosen: a multiple-choice question.

2.
 questions was related to the student's attendance at the lecture when the relevant material was covered. Marburger found that absenteeism increased the probability of an incorrect response by as much as 14 per cent.

This study is also based on observational data but, like Marburger's study, it employs panel data: observations were collected on each student's performance on several tests and his or her attendance at classes covering the material examined on those tests. (1) The availability of panel data allows the use of methodology that takes account of heterogeneity among students in unobservable variables that affect both attendance and performance, such as intelligence and motivation. Estimates of the effect Of attendance on performance so obtained are free of some of the bias that is present in estimates based on cross-section regression studies. (2)

The remainder of the paper is organised as follows. In Section 2 the model of the relationship between attendance and performance is presented. The data used to estimate the model are described in Section 3. In Section 4, the results of the estimation estimation

In mathematics, use of a function or formula to derive a solution or make a prediction. Unlike approximation, it has precise connotations. In statistics, for example, it connotes the careful selection and testing of a function called an estimator.
 are presented and interpreted. Finally Section 5 summarises the conclusions of the study.

2 Model

Academic performance is hypothesised to be a function of the student's class attendance and other variables, some of which are unobservable, such as the student's motivation and aptitude for the subject matter. These same variables are also likely to affect the student's propensity to attend class and lead to an upward bias in estimates of the effect of attendance on performance obtained from regression analyses of cross-section observations. If each student's attendance could be determined randomly, a regression of performance on attendance (and other relevant variables) would be able to detect a causal relationship, if one exists, and accurately estimate its magnitude. Experimental data of this type are difficult to obtain because of the requirement that students be treated equally. An alternative approach is to observe attendance rates that are self chosen and to model the unobserved heterogeneity among students using fixed-effects and random-effects regressions in which the dependent variable is performance by student i on assessment task t ([P.sub.it]) and the independent variable is attendance by student i at classes on which assessment task t is based ([A.sub.it]).

The models estimated in this paper include as independent variables dummy variables This article is not about "dummy variables" as that term is usually understood in mathematics. See free variables and bound variables.

In regression analysis, a dummy variable
 for all but one of T assessment tasks, [TEST.sub.1], [TEST.sub.2], ... [TEST.sub.T].

The fixed-effects model is:

(1) [P.sub.it] = [[alpha].sub.1] + [beta][A.sub.it] + [[gamma].sub.1][TEST.sub.1] +[[gamma].sub.2][TEST.sub.2] + ... + [[gamma].sub.T-1] [TEST.sub.T-1] + [[epsilon].sub.it]

where i=1,2, ... n; t=1,2 .... T. [[epsilon.sub.it] is an error term that is identically and independently distributed with E([[epsilon].sub.it]) = O, Var([[epsilon].sub.it]) = [sigma].sub.[epsion].sup.2] The coefficient coefficient /co·ef·fi·cient/ (ko?ah-fish´int)
1. an expression of the change or effect produced by variation in certain factors, or of the ratio between two different quantities.

2.
, [beta], reflects the impact of attendance on performance in any given assessment task. (3)

The random-effects model is:

(2) [P.sub.it] = [alpha] + [beta][A.sub.it] + [[gamma].sub.1][TEST.sub.1] + [[gamma].sub.2][TEST.sub.2] + ... + [[gamma].sub.T-1][TEST.sub.T-1] + [delta][X.sub.i] + [[epsilon.sub.it] + [u.sub.i] where i=1,2, .. n; t=1,2, .. T and [X.sub.i] is a vector of time-invariant observable ob·serv·a·ble  
adj.
1. Possible to observe: observable phenomena; an observable change in demeanor. See Synonyms at noticeable.

2.
 characteristics of student i. [[epsilon].sub.it] + ui is an error term with E([[epsilon].sub.it]) = E([u.sub.i]) = 0; Var([[epsilon].sub.it] + [u.sub.i]) = [[sigma].sub.[epsilon].sup.2] = [[sigma].sub.[upsilon up·si·lon or yp·si·lon
n.
Symbol The 20th letter of the Greek alphabet.
].sup.2]; + Cov([[epsilon].sub.it],[u.sub.j]) = 0 for all i, t and j; Cov ([epsilon.sub.it], [[[epsilon].sub.js]) = 0 for t [not equal to] for t [not equal to] j; and Cov([u.sub.i], [u.sub.j]) = 0 for i [not equal to] j. Cov([[epsilon].sub.it] + [u.sub.i], [[epsilon].sub.is] + [u.sub.i]) =[rho] = [[sigma].sub.[upsilon].sup.2] / [[sigma].sup.2] for t [not equal to] s, that is, for a given student the errors on different assessment tasks are correlated cor·re·late  
v. cor·re·lat·ed, cor·re·lat·ing, cor·re·lates

v.tr.
1. To put or bring into causal, complementary, parallel, or reciprocal relation.

2.
 because of their common component, u.

The time-invariant control variables included in the random-effects model are those suggested by other studies and those that seem intuitively plausible to experienced teachers of the subject matter. The first control variable is the student's average mark (out of 100) on other subjects taken during the same semester. It is a proxy for ability but it probably also reflects attendance in those other subjects. Assuming attendance is correlated across subjects, the inclusion of this variable is likely to result in an underestimate of the effect of attendance on performance in my class. (4) The second control is a dummy variable for students in their first year at university. Assuming the transition from high school to university requires some adjustment, it was hypothesised that first-year adj. 1. Being in the first year of an experience especially in a U. S. high school or college; - of a person.

Adj. 1. first-year - used of a person in the first year of an experience (especially in United States high school or college); "a
 students would perform at a lower level than later-year students. The less able students tend to drop out after the first year of university studies, so that those who remain tend to be better academic performers. The third control is a dummy variable for students who are part-time. Many part-time students are mature-age, full-time workers with heavy demands on their time. The opportunity cost of time spent in class and in private study is higher for part-time students than for full-time students Full-Time Student

A status that is important for determining dependency exemptions. An individual enrolled in a post-secondary institution may be eligible for certain tax breaks.

Notes:
The full-time status is based on what the individual's school considers full time.
. Part-time students are likely, therefore, both to attend fewer classes and to perform at lower levels, than full-time students. The fourth control is a dummy variable for students who pay full fees. Other studies have found that private students perform better than students who are on scholarships or are supported by their parents, possibly because they are more motivated mo·ti·vate  
tr.v. mo·ti·vat·ed, mo·ti·vat·ing, mo·ti·vates
To provide with an incentive; move to action; impel.



mo
 than students whose tuition For tuition fees in the United Kingdom, see .

Tuition means instruction, teaching or a fee charged for educational instruction especially at a formal institution of learning or by a private tutor usually in the form of one-to-one tuition.
 is subsidised Adj. 1. subsidised - having partial financial support from public funds; "lived in subsidized public housing"
subsidized

supported - sustained or maintained by aid (as distinct from physical support); "a club entirely supported by membership dues";
. The fifth control variable is a dummy variable for gender. Two dummy variables are included to reflect the type of degree undertaken by the student: a single degree, other than a Bachelor of Commerce The Bachelor of Commerce is a bachelor's degree in business management, accounting and economic fields. The degree is also known as the Bachelor of Commerce and Administration (BCA). , or a double degree. The omitted category is a Bachelor of Commerce degree. Finally the method of entry into the university is represented by six dummy variables, the omitted category being standard matriculation ma·tric·u·late  
tr. & intr.v. ma·tric·u·lat·ed, ma·tric·u·lat·ing, ma·tric·u·lates
To admit or be admitted into a group, especially a college or university.

n.
 from an Australian secondary school. The included categories are (a) entry via another Australian university, (b) entry via an overseas tertiary tertiary (tûr`shēârē), in the Roman Catholic Church, member of a third order. The third orders are chiefly supplements of the friars—Franciscans (the most numerous), Dominicans, and Carmelites.  educational institution, (c) articulation articulation

In phonetics, the shaping of the vocal tract (larynx, pharynx, and oral and nasal cavities) by positioning mobile organs (such as the tongue) relative to other parts that may be rigid (such as the hard palate) and thus modifying the airstream to produce speech
 from an Australian TAFE TAFE (in Australia) Technical and Further Education  (technical and further education) college, (d) special entry, such as mature age, (e) entry via a professional qualification or an institutional assessment or examination, and (f) entry according to according to
prep.
1. As stated or indicated by; on the authority of: according to historians.

2. In keeping with: according to instructions.

3.
 `other' criteria.

3 Data

The data used in this study were collected in a one-semester, introductory statistics subject taught to undergraduates at a medium-size Australian university. There were three 50-minute lectures per week for 13 weeks delivered to the class of approximately 200 students using Power Point presentations. Printed Power Point slides, with certain key words, calculations and diagrams omitted, were made available in the library and could be purchased at a modest price from the university bookshop. Each student was required to attend one 50-minute tutorial An instructional book or program that takes the user through a prescribed sequence of steps in order to learn a product. Contrast with documentation, which, although instructional, tends to group features and functions by category. See tutorials in this publication.  in each of Weeks 2 to 13. Tutorial groups consisted of 20 or fewer students. As tutorial preparation, students were instructed to attempt a problem set involving the application of material covered in lectures in the preceding week. Eight of the twelve tutorial meetings were held in a regular classroom where a tutor TUTOR - A Scripting language on PLATO systems from CDC.

["The TUTOR Language", Bruce Sherwood, Control Data, 1977].
 presented the answers to as many of the problems as time permitted and responded to students' questions. Students could mark their own work using an answer key, which was made available in the library at the beginning of the week following the tutorial in which the problem set was discussed. The remaining four tutorial meetings were held in a computer laboratory where students, with the help of their tutor, used a statistical package to generate output with which to solve statistical problems. Attendance was recorded at all tutorials.

There were three tests during the semester. The mid-semester test was based on the first six weeks of lectures and was held on Saturday at the end of Week 7. It was multiple-choice, and contributed 15 per cent of the total score. The tutorial test was worth 10 per cent and consisted of problems similar to those assigned as·sign  
tr.v. as·signed, as·sign·ing, as·signs
1. To set apart for a particular purpose; designate: assigned a day for the inspection.

2.
 as tutorial preparation. The computer test was worth 15 per cent and examined knowledge of the output generated by the statistical package used in the subject. The tutorial and computer tests were both held in Week 13. The final examination was worth 50 per cent and concentrated on material taught in Weeks 7 to 12. It consisted of both multiple-choice questions and problems. The remaining 10 per cent of the final score was contributed by unannounced short quizzes held at the end of 12 randomly chosen lectures, six in each half of the semester. The quizzes provided a mechanism for estimating attendance in the first and last six weeks of lectures.

Two weeks into the semester there were 229 students in the class (5), 31 (13.5 per cent) of whom later withdrew. (6) Nine of the remaining 198 students took none of the four assessment tasks. Another 20 students missed at least one of the progressive assessment tasks and had the weight attached to that task transferred to the final examination. Two students completed all progressive assessment but did not take the final examination. Therefore 167 students received scores for the four assessment tasks. These students contribute data to the balanced panel that are used in the econometric e·con·o·met·rics  
n. (used with a sing. verb)
Application of mathematical and statistical techniques to economics in the study of problems, the analysis of data, and the development and testing of theories and models.
 analysis reported in Section 4. Their characteristics appear in Column 1 of Table 1. The characteristics of the 22 students who completed some but not all assessment tasks appear in Column 2 of Table 1. These students, together with the 167 students who completed all assessment, provide data to the unbalanced panel of 189 students used in the econometric analysis. The characteristics of the nine students who missed all of the assessment tasks but did not withdraw from the course are given in Column 3 of Table 1. These nine students are not included in the econometric analysis.

Table 1 indicates that students who completed all assessment tasks attended 70.86 per cent of lectures in the first half of the semester, 64.27 per cent of lectures in the second half of the semester, 78.89 per cent of regular tutorials and 82.63 per cent of computer labs. These attendance rates are significantly higher than those of students who did not complete all assessment tasks. Lecture attendance fell in the second half of the semester (7) and performance on the final examination was lower than on the mid-semester test. (8)

Only three observable characteristics display significant differences between students who completed all assessment tasks and students who missed some or all assessment. The latter scored significantly lower on other subjects taken in the same semester as this introductory statistics subject. Students who missed some or all assessment were more likely to be part-time. (9) A larger percentage of those who missed all assessment were full-fee paying students. (10) The relative similarity Similarity is some degree of symmetry in either analogy and resemblance between two or more concepts or objects. The notion of similarity rests either on exact or approximate repetitions of patterns in the compared items.  of the three groups of students whose descriptive statistics descriptive statistics

see statistics.
 are reported in Table 1 suggests that the econometric analysis that is based on the panel is unlikely to be biased by the necessary omission omission n. 1) failure to perform an act agreed to, where there is a duty to an individual or the public to act (including omitting to take care) or is required by law. Such an omission may give rise to a lawsuit in the same way as a negligent or improper act.  of data on students who did not complete all assessment tasks.

4 Results

The fixed-effects model (FEM FEM Female
FEM Finite Element Method
FEM Feminine
FEM Finite Element Model
FEM Fédération Européenne des Métallurgistes (European Metalworkers' Federation)
FEM Faculdade de Engenharia Mecânica (Brasil) 
) and the random-effects model (REM (REMarks) A programming language statement used for documentation. Rem statements are not executed by the compiler. They are created for people to read. Rem is also used in DOS batch files for comments as well as for disabling instructions. ) described in Section 2 were estimated by using a panel of data to which each student contributed at least one and at most four observations. The four observations were: (a) score on the mid-semester test and attendance at lectures in Weeks 1 to 6; (b) score on the final examination and attendance at lectures in Weeks 7 to 12; (c) score on the tutorial test and attendance at regular tutorials; and (d) score on the computer lab test and attendance at computer labs. The fixed-effects model was estimated using LIMDEP's least squares dummy variable routine and the random-effects model was estimated using LIMDEP's generalised Adj. 1. generalised - not biologically differentiated or adapted to a specific function or environment; "the hedgehog is a primitive and generalized mammal"
generalized

biological science, biology - the science that studies living organisms
 least squares routine (Greene, 1998, pp.318-325). For comparison purposes, the OLS OLS Ordinary Least Squares
OLS Online Library System
OLS Ottawa Linux Symposium
OLS Operation Lifeline Sudan
OLS Operational Linescan System
OLS Online Service
OLS Organizational Leadership and Supervision
OLS On Line Support
OLS Online System
 estimates are also reported. The results of four models estimated with the balanced panel appear in Table 2.

The coefficient on attendance is statistically significant at the 5 per cent level in all models reported in Table 2. The FEM indicates that attending an extra one per cent of classes increases performance in introductory statistics by approximately 0.05 percentage points. According to the REM, the increase is 0.10 percentage points. The coefficient in the OLS model (0.20) indicates a larger effect of attendance on performance than the other two models. This was to be anticipated because the OLS estimate is positively biased, whereas the FEM and REM models control for unobservable characteristics of students that are likely to affect both performance and attendance. (11) The F-test and Breusch and Pagan's Lagrange multiplier multiplier

In economics, a numerical coefficient showing the effect of a change in one economic variable on another. One macroeconomic multiplier, the autonomous expenditures multiplier, relates the impact of a change in total national investment on the nation's total
 test indicate that the OLS model should be rejected in favour of the FEM and REM respectively. Hausman's test indicates that the FEM is preferred to the REM. Based upon the FEM, a student with the average attendance rate, which was approximately 74 per cent of all classes, is predicted to score 1.30 (26 times 0.05) points (out of 100) lower than a student who attended all classes. Based upon the REM, the loss would be 2.6 (26 times (0.10) points. Although statistically significant, the differential is quite small. (12)

Among the control variables included in the REM, only two are statistically significant at the 5 per cent level. First, the student's average score on other subjects taken in the same semester as introductory statistics has a positive effect on his or her score on introductory statistics. In fact, each additional one-point difference in this average score on other subjects between two otherwise identical students is associated with a difference of 0.99 points in introductory statistics. Second, students who gain `special entry' into the university are predicted to score approximately 15 points lower in introductory statistics than an otherwise identical student who matriculated into university from high school.

The results of the models estimated with the unbalanced panel appear in Table 3. The coefficient on attendance is statistically significant at close to the one per cent level in all models. The effect of attendance on performance estimated by using the unbalanced panel (Table 3) is slightly larger than in the corresponding model that was estimated by using the balanced panel (Table 2). For example, the coefficient on attendance in the FEM is 0.06 in Table 3, rather than 0.05 in Table 2, indicating that a student with average attendance of 74 per cent of all classes would score 1.56 (26 times 0.06) percentage points lower than a student who attended all classes.

Finally the sensitivity of the attendance coefficient to the exclusion from the data set of students with atypically a·typ·i·cal   also a·typ·ic
adj.
Not conforming to type; unusual or irregular.



atyp·i·cal
 low levels of attendance is investigated. The results in Table 4 apply to the majority of students, who are not chronically absent. Columns 1, 2 and 3 report the estimation of the OLS regression, the FEM, and the REM by using only those students who attended at least one of the eight regular tutorials, at least one of the four computer labs, and at least one of the six randomly chosen lectures in each half of the semester. Columns 4, 5, and 6 report the estimation of the OLS regression, the FEM and the REM by using only those students who attended at least one of the eight regular tutorials, at least one of the four computer labs, and at least two of the six randomly chosen lectures in each half of the semester.

All the results in Table 4 are as strong statistically as those obtained with the full panel. Again the FEM is the preferred model, but its coefficient is larger than in Tables 2 and 3. For example, the coefficient on attendance in the FEM is 0.13 (see Column 2 of Table 4), which indicates that a student with average attendance of 74 per cent of all classes would score 3.38 (26 times 0.13) percentage points lower than a student who attended all classes. This is large enough to make the difference of one letter grade for some students. (13)

5 Conclusions

This study has estimated the effect of absenteeism on performance in an introductory statistics class of about 200 business and economics students at a medium-size Australian university. Absenteeism from lectures and tutorials was common among these students. On average, students attended approximately 68 per cent of lectures during the semester, 71 per cent in the first half of the semester and 64 per cent in the second half of the semester. The average tutorial attendance rate was 80 per cent, 87 per cent in the first half of the semester and 74 per cent in the second half of the semester. Computer laboratories were better attended (83%) than regular tutorials (79%).

The results reported here are based on a panel of four observations per student, each observation pertaining per·tain  
intr.v. per·tained, per·tain·ing, per·tains
1. To have reference; relate: evidence that pertains to the accident.

2.
 to performance on a particular test and attendance at the set of classes covering material examined on that test. The methodology takes account of unobserved heterogeneity among students and in so doing constitutes an improvement over cross-section regression results reported previously. Both fixed-effects and random-effects regression models were estimated and the fixed-effects model was judged to be superior. It was able to `explain' more than 70 per cent of the variation in performance among students on four different tests. Attendance was found to have a small, but statistically significant, effect on performance. A one per cent increase in attendance was found to result in an increase of between 0.05 and 0.13 points out of 100. This means that a student with average attendance of 74 per cent of classes would score between 1.3 and 3.4 percentage points lower than an otherwise identical student with perfect attendance. Although modest in size, this forfeited for·feit  
n.
1. Something surrendered or subject to surrender as punishment for a crime, an offense, an error, or a breach of contract.

2. Games
a.
 score is large enough to make the difference of one letter grade for some students. One explanation for the small size of the effect of attendance on performance could be the fact that the students in the class reported had access to printed versions (with `gaps') of the Power-Point slides that were presented in lectures. This may have both encouraged absenteeism and contributed to the ease with which students could substitute private study for lecture attendance.

Finally the total effect of attendance on performance may be greater than its impact in one subject suggests. When a subject is a prerequisite pre·req·ui·site  
adj.
Required or necessary as a prior condition: Competence is prerequisite to promotion.

n.
 for others, the knowledge foregone fore·gone
v.
Past participle of forego1.

adj.
Having gone before; previous.

Usage Note: The word foregone has recently developed a new meaning as a truncation of the phrase
 through absenteeism in the first subject may have negative consequences for performance in subjects that build upon that knowledge.

Keywords

academic achievement economics education performance factors attendance learning university outcome assessment
Table 1 Descriptive statistics

                                  Completed    Completed    Completed
                                     all          some          no
                                  assessment   assessment   assessment
                                     (1)          (2)          (3)

Mean mid-semester test
  score (100)                       62.69      52.34 **     n.a.
Mean tutorial test score (100)      64.49      44.63 ***    n.a.
Mean lab test score (100)           64.15      51.13 *      n.a.
Mean final exam score (100)         53.65      25.88 ***    n.a.
Total weighted score (100)          57.67      n.a.         n.a.
Mean % lectures attended in
  Weeks 1-6                         70.86      49.62 ***    14.82 ***
Mean % lectures attended in
  Weeks 7-12                        64.27      31.95 ***     1.85 ***
Mean % regular tutorials
  attended                          78.89      53.98 ***    27.78 ***
Mean % labs attended                82.63      57.95 ***    16.67 ***
Average mark on other
  subjects (100)                    61.85      48.63 ***    29.77 ***
Percentage in 1st year              44.91      36.36        44.44
Percentage part-time students       34.13      50.00        66.67 *
Percentage paying full fees         12.57       9.09        44.44 **
Percentage male                     60.48      72.73        66.67
Percentage Bachelor of Commerce     83.83      86.36        66.67
Percentage other single degree       6.59       9.09        11.11
Percentage double degree             9.58       4.55        22.22
Percentage entry via final year
  of secondary school               27.54      27.27        11.11
Percentage entry via higher
  educ. instit. (Au)                16.77      13.64        22.22
Percentage entry via higher
  educ. instit. (o/s)                1.80       4.55        00.00
Percentage entry via TAFE           16.17       9.09        11.11
Percentage entry via special
  entry                              1.80       4.55        00.00
Percentage entry via prof. or
  instit. exam                      12.57      27.27        11.11
Percentage entry via other
  method                            23.35      13.64        44.44
Number of students                   167       22            9

*** Significantly different at the 1% level from the students who
completed all assessment tasks

** Significantly different at the 5% level from the students who
completed all assessment tasks

* Significantly different at the 10% level from the students who
completed all assessment tasks
Table 2 Effect of attendance on performance: Balanced panel of 167
students

                             OLS               FEM
                       Coeff (P-value)   Coeff (P-value)
Model                        (1)               (2)

Constant:              40.92 (0.0000)
Attendance              0.20 (0.0000)     0.05 (0.0474)
TEST 1                  7.74 (0.0002)     8.71 (0.0000)
TEST 2                  7.94 (0.0002)    10.10 (0.0000)
TEST 3                  6.86 (0.0013)     9.57 (0.0000)
Av. score on
other subjects
1st year student
Part-time student
Full-fee paying
Male student
Course: Other
  single degree
Course: Double
  degree
Entry: higher
  educ. (Australia)
Entry: higher
  educ. (overseas)
Entry: TAFE
Entry: special entry
Entry: instit or
  prof. exam.
Entry: other

R-sq                   0.1286            0.7251
R-sq (adj)             0.1234            0.6310
F                      24.47 (0.0000)    7.71 (0.0000)

                             REM         REM + Controls
                       Coeff (P-value)   Coeff (P-value)
Model                        (3)               (4)

Constant:              47.50 (0.0000)     -9.77 (0.1993)
Attendance              0.10 (0.0000)      0.06 (0.0033)
TEST 1                  8.41 (0.0000)      8.61 (0.0000)
TEST 2                  9.44 (0.0000)      9.89 (0.0000)
TEST 3                  8.74 (0.0000)      9.31 (0.0000)
Av. score on
other subjects                             0.99 (0.0000)
1st year student                          -0.29 (0.9112)
Part-time student                          3.67 (0.1470)
Full-fee paying                            5.78 (0.1092)
Male student                              -2.12 (0.3066)
Course: Other
  single degree                           -3.51 (0.3918)
Course: Double
  degree                                   1.58 (0.6526)
Entry: higher
  educ. (Australia)                       -0.27 (0.9317)
Entry: higher
  educ. (overseas)                         0.71 (0.9286)
Entry: TAFE                               -4.93 (0.1484)
Entry: special entry                     -15.41 (0.0431)
Entry: instit or
  prof. exam.                             -4.34 (0.2352)
Entry: other                              -3.03 (0.3324)

R-sq                   0.1286            0.3950
R-sq (adj)
F

Notes:

No. of observations = 668

F test of FEM (column 2) versus OLS (column 1):
6.495 (P-value=0.0000)

Lagrange Multiplier test of REM (column 3) versus
OLS (column 1): 303.81 (P-value=0.0000)

Hausman test of FEM (column 2) versus REM (column 3):
17.73 (P-value=0.0014)
Table 3 Effect of attendance on performance: Unbalanced panel of 189
students

                             OLS               FEM
                       Coeff (P-value)   Coeff (P-value)
Model                        (1)               (2)

Constant:              38.44 (0.0000)
Attendance              0.22 (0.0000)     0.06 (0.0121)
TEST1                   8.37 (0.0000)     9.89 (0.0000)
TEST2                   8.13 (0.0001)    10.33 (0.0000)
TEST3                   7.33 (0.0005)    10.06 (0.0000)
Av. score on
  other subjects
1st year student
Part-time student
Full-fee paying
Male student
Course: Other
  single degree
Course:
  Double degree
Entry: higher
  educ. (Australia)
Entry: higher
  educ. (overseas)
Entry: TAFE
Entry: special entry
Entry: instit. or
  prof. exam
Entry: other
R-sq                   0.1494            0.7276
R-sq (adj)             0.1446            0.6270
F                      31.09 0.0000)      7.23 (0.0000)

                             REM         REM + Controls
                       Coeff (P-value)   Coeff (P-value)
Model                        (3)               (4)

Constant:              43.90 (0.0000)      1.28 (0.8454)
Attendance              0.12 (0.0000)      0.07 (0.0007)
TEST1                   9.27 (0.0000)      9.82 (0.0000)
TEST2                   9.55 (0.0000)     10.21 (0.0000)
TEST3                   9.07 (0.0000)      9.93 (0.0000)
Av. score on
  other subjects                           0.81 (0.0000)
1st year student                          -0.50 (0.8431)
Part-time student                          2.88 (0.2344)
Full-fee paying                            4.42 (0.2192)
Male student                              -2.69 (0.1879)
Course: Other
  single degree                           -5.05 (0.2005)
Course:
  Double degree                            2.35 (0.5022)
Entry: higher
  educ. (Australia)                       -0.29 (0.9260)
Entry: higher
  educ. (overseas)                        -6.90 (0.3505)
Entry: TAFE                               -6.64 (0.0460)
Entry: special entry                     -11.34 (0.1060)
Entry: instit. or
  prof. exam                              -5.94 (0.0869)
Entry: other                              -3.88 (0.2082)
R-sq                   0.1494            0.3829
R-sq (adj)
F

Notes:

No. of observations = 713

F test of FEM (column 2) versus OLS (column 1): 5.87 (P-value=0.0000)

Lagrange Multiplier test of REM (column 3) versus OLS (column 1):
300.3 (P-value=0.0000)

Hausman test of FEM (column 2) versus REM (column 3): 23.49
(P-value=0.0001)
Table 4 Sensitivity of the effect of attendance on performance to
students included in the panel

                   OLS               FEM               REM
             Coeff (P-value)   Coeff (P-value)   Coeff (P-value)
                   (1)               (2)               (3)

Estimated using 136 students with more than 0% attendance in each
component

Model

Constant:    31.33 (0.0000)                      41.08 (0.0000)
Attendance    0.31 (0.0000)     0.13 (0.0004)     0.18 (0.0000)
TEST 1        7.47 (0.0011)     8.19 (0.0000)     7.99 (0.0000)
TEST 2        9.58 (0.0000)    10.32 (0.0000)    10.11 (0.0000)
TEST 3        8.04 (0.0005)     9.88 (0.0000)     9.35 (0.0000)

R-sq         0.1560            0.7390            0.1560
R-sq (adj)   0.1498            0.6492
F            24.91 (0.0000)     8.23 (0.0000)

Notes:

No. of observations = 544

F test of FEM (column 2) versus OLS (column 1): 6.684 (P-value=0.0000)

Lagrange Multiplier test of REM (column 3) versus OLS (column 1):
258.45 (P-value-0.0000))

Hausman test of FEM (column 2) versus REM (column 3): 12.38
(P-value=0.0147)

                   OLS               FEM               REM
             Coeff (P-value)   Coeff (P-value)   Coeff (P-value)
                   (4)               (5)               (6)

Estimated using 121 students with more than 25% attendance in each
component

Model

Constant:    26.61 (0.0000)                      42.01 (0.0000)
Attendance    0.36 (0.0000)     0.11 (0.0098)     0.17 (0.0000)
TEST 1        7.09 (0.0037)     7.43 (0.0000)     7.34 (0.0000)
TEST 2        9.90 (0.0001)     9.85 (0.0000)     9.86 (0.0000)
TEST 3        8.74 (0.0004)    10.16 (0.0000)     9.80 (0.0000)

R-sq         0.1385            0.7335            0.1385
R-sq (adj)   0.1313            0.6414
F            19.25 (0.0000)     7.97 (0.0000)

Notes:

No. of observations = 484

F test of FEM (column 2) versus OLS (column 1): 6.678 (P-value=0.0000)

Lagrange Multiplier test of REM (column 3) versus OLS (column 1):
220.33 (P-value=0.0000)

Hausman test of FEM (column 2) versus REM (column 3): 17.02
(P-value=0.0019)


Acknowledgements

I would like to thank two anonymous referees for their comments on an earlier draft of this paper.

NOTES

(1) To my knowledge, only one other study has used Australian panel data. It is reported in an unpublished working paper by Rodgers and Rodgers (2000).

(2) Although my study is of just one class in one faculty at one university during one semester, when considered in conjunction with results from other studies it contributes to an informed judgement as to the seriousness of absenteeism in universities.

(3) Interactions between attendance and the assessment tasks were also included in the models to allow the effect of attendance on performance to be different for the various assessment tasks.

(4) This point is made by Romer (1993, p.172) and by Park and Kerr (1990, pp.105-108).

(5) In the first two weeks of each semester, a considerable amount of subject sampling takes place as students finalise Verb 1. finalise - make final; put the last touches on; put into final form; "let's finalize the proposal"
finalize, nail down, settle

terminate, end - bring to an end or halt; "She ended their friendship when she found out that he had once been convicted of
 decisions about which subjects to take. Students can drop subjects and avoid fees until the middle of the fifth week of the semester; they can drop without having an F recorded on their academic transcript A generic term for any kind of copy, particularly an official or certified representation of the record of what took place in a court during a trial or other legal proceeding.

A transcript of record
 prior to the end of Week 8.

(6) Only four of these students completed any of the progressive assessment tasks before withdrawing.

(7) Attendance at tutorials (regular plus labs) was also lower in the second half of the semester (73.55 per cent) compared with the first half (86.53 per cent).

(8) Correlation coefficients Correlation Coefficient

A measure that determines the degree to which two variable's movements are associated.

The correlation coefficient is calculated as:
 between attendance rates in the various components of the course based on the 167 students in the balanced panel are:
Attendance correlations

                 Lect                    Lect
                 Wk1-6   Tuts   Labs   (Wk7-13)

Lect (Wk 1-6)     1.00
Tuts              0.37   1.00
Labs              0.29   0.46   1.00
Lect (Wk 7-13)    0.76   0.42   0.41     1.00


Correlation between performance in the various components of the course based on the 167 students in the balanced panel are:
Performence correlations

             Mid-s   Tut    Lab    Final
             test    test   test   exam

Mis-s test    1.00
Tut test      0.54   1.00
Lab test      0.49   0.61   1.00
Final exam    0.67   0.67   0.68    1.00


(9) In this paper, a part-time student is defined as a student taking less than the normal load of 24 credit points per semester.

(10) In the Australian context at this time most full-fee-paying students were international students.

(11) Multicollinearity does not appear to be a problem. The correlations among the independent variables in Columns 1, 2 and 3 of Table 2 are:
         Attend   Test 1   Test 2   Test 3

Attend     1.00
Test 1    -0.07     1.00
Test 2     0.10    -0.33     1.00
Test 3     0.17    -0.33    -0.33     1.00


The largest correlations among the control variables in Column 4 are: r(attendance, average score on other subjects) = 0.53 r(1st year student, part-time student) = -0.43 r(full-fee paying, entry by higher education higher education

Study beyond the level of secondary education. Institutions of higher education include not only colleges and universities but also professional schools in such fields as law, theology, medicine, business, music, and art.
 overseas) = 0.38 r(1st year student, entry by institute or professional exam) = -0.35 r(full-fee paying, entry by `other' method) = 0.32 r(part-time, entry via TAFE) = 0.30 All but six of the remaining correlations are less in absolute value than 0.20.

(12) The models were also estimated with interactions between attendance and the three dummy variables for the assessment tasks. None of the coefficients on the interactions was significant at the 5 per cent level.

(13) The models in this paper assume that performance in a later component (such as the final exam Noun 1. final exam - an examination administered at the end of an academic term
final examination, final

exam, examination, test - a set of questions or exercises evaluating skill or knowledge; "when the test was stolen the professor had to make a new set of
) depends only on attendance in classes when the subject matter of the later component was covered (Weeks 7-12), not on attendance in earlier classes (Weeks 1-6). To the extent that this assumption is untrue un·true  
adj. un·tru·er, un·tru·est
1. Contrary to fact; false.

2. Deviating from a standard; not straight, even, level, or exact.

3. Disloyal; unfaithful.
, the total effect of attendance on performance may be greater than results in this section suggest.

References

Devadoss, S. & Foltz, J. (1996). Evaluation of factors influencing student class attendance and performance. American Journal of Agricultural Economics, 78(3), 499-507.

Durden, G. C. & Ellis, L. V. (1995). The effects of attendance on student learning in principles of economics. American Economic Review, 85(2), 343-346.

Greene, William H. (1998). Limdep, Version 7.0, user's manual (Rev. ed rev.
abbr.
1. revenue

2. reverse

3. reversed

4. review

5. revision

6. revolution


rev.
1. revise(d)

2.
.). New York New York, state, United States
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of
: Econometric Software Econometric software is a statistical software that is specialised for econometric analysis. List of statistical packages used mainly for econometric analysis
This is an incomplete list of software that is designed mainly for the purpose of performing econometric analyses.
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Marburger, Daniel R. (2001). Absenteeism and undergraduate exam performance. Journal of Economic Education, 32(2), 99-109.

Park, Kang H. & Kerr, Peter M. (1990). Determinants of academic performance: A multinomial logit In statistics and economics, a multinomial logit model is a regression model which generalizes logistic regression to where can be more than two cases. Introduction  approach. Journal of Economic Education, 2(2), 101-111.

Rodgers, John Rodgers, John, 1812–82, American naval officer
Rodgers, John, 1812–82, American naval officer, b. Harford co., Md.; son of John Rodgers. He became (1828) a midshipman and saw varied service.
 L. & Rodgers, Joan R. (2000). An investigation into the academic effectiveness of class attendance in an intermediate microeconomic theory class (Working paper). Wollongong: University of Wollongong History
The University of Wollongong was founded in 1951 when a Division of the then New South Wales University of Technology (re-named the University of New South Wales in 1958) was established in Wollongong.
, Department of Economics. (http: www.uow.edu.au~jrrodger)

Romer, David. (1993). Do students go to class? Should they?Journal of Economic Perspectives, 7(2), 167-174.

Schmidt, Robert. (1983). Who maximizes what? A study in student time allocation The apportionment or designation of an item for a specific purpose or to a particular place.

In the law of trusts, the allocation of cash dividends earned by a stock that makes up the principal of a trust for a beneficiary usually means that the dividends will be treated as
. American Economic Review, 73(2), 23-28.

Tuchman, Barbara Tuchman, Barbara
 orig. Barbara Wertheim

(born Jan. 30, 1912, New York, N.Y., U.S.—died Feb. 6, 1989, Greenwich, Conn.) U.S. historian. She wrote for The Nation and other publications before beginning to write most of the books that made her a leading
 W. (1979). The distant mirror. London: Macmillan.

Dr Joan Rodgers is in the Department of Economics, University of Wollongong, Northfields Avenue, Wollongong, New South Wales Wollongong is the 3rd largest city in the state of New South Wales, Australia, after Sydney and Newcastle. It is also a Local Government Area administered by the Wollongong City Council.  2522.
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No portion of this article can be reproduced without the express written permission from the copyright holder.
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Author:Rodgers, J.R.
Publication:Australian Journal of Education
Article Type:Statistical Data Included
Geographic Code:8AUST
Date:Dec 1, 2001
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