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ISSN 1329-2676

The Effects of Individual and School Factors onUniversity Students Academic Performance

by

Rosemary Win and Paul W. Miller

Business SchoolThe University of Western Australia

CLMRDISCUSSION PAPERSERIES 04/4

the CentreforLabour Market Research, The University of Western Australia, Crawley WA 6009

Tel: (08) 6488 8672 Fax: (08) 6488 8671 email:pmadden@ecel.uwa.edu.au

http://www.clmr.ecel.uwa.edu.au

The Centre wishes to acknowledge the support of The Western Australian Department of Education and Training

We are grateful to Greg Marie (Institutional Research Unit, UWA) and Ross Kelly (Centre for Labour MarketResearch) for provision of data, and to Ken Clements, Anh Le, Greg Marie, David Treloar, two anonymous referees

and an editor for helpful comments. Miller acknowledges financial assistance from the Australian Research Council

and the Department of Employment, Science and Training. Opinions expressed in this paper are those of theauthors, and should not be attributed to the funding agencies or to the University of Western Australia.

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Abstract

This paper examines the factors that influence university students academic performance,

focusing on the role of student background and school factors. Using data on the first year

students at the University of Western Australia in 2001, two methodologies are employed.

The first is analogous to an input-output approach, and the second is a random coefficientsmodel. A key finding is that high schools have an impact on the academic performance of

students at university beyond students own background characteristics. Both immersion and

reinforcement effects are identified.

Introduction

What factors determine a students academic achievement during their first year at

university? University lecturers provide a variety of answers, including ability, motivation,

the school the student went to and the company they keep. Of these factors, university

administrators in Australia place most weight on ability, and currently ration places at theirinstitutions largely on the basis of academic achievement in the final year of high school,

namely Year 12. However, despite the importance to higher education decision making of

knowledge of the determinants of university students performance1, there have been

relatively few academic studies on this topic in Australia (exceptions that we draw upon later

are West and Slamowicz, 1976 and Everett and Robins, 1991). This contrasts with Year 12

outcomes, which have been analysed in detail (see Rowe, 1999; Rowe, Turner and Lane,

1999; Collins, Kenway and McLeod, 2000; Teese, 2000; Marks et al., 2001).

Academic performance at university can be viewed as a product of two sets of factors: one set

having its origin in the individualeach students unique combination of socioeconomic

elements and abilityand the other having its origin in the school attendedbeingassociated with the systems of education and patterns of imparting knowledge that are

organised within schools. This study seeks to ascertain the roles played by both these sets of

influences. In doing so, it uses information on first year performance in 2001 of the students

at the University of Western Australia (UWA).

The paper is structured as follows. Section 2 considers the way in which contemporary

educational research has developed and briefly discusses relevant empirical literature.

Specific issues relating to two generations of research that exist in the education literature are

highlighted in terms of how data are treated. Section 3 deals with data description and details

the methodological structure. Section 4 presents and discusses the empirical results. A

summary and conclusion are provided in Section 5.

The Model

Education is a service that attempts to develop the potential of students of different abilities.

In this context the effectiveness of the education sector is generally defined to mean its

impact on student performance. Most research to date has focused on the links between high

schools and outcomes such as student academic achievement, earnings of graduates, and

1Quantification of the influences on university students academic performance is relevant to policy making in a

number of areas, including student admittance, retention rates, on extra support provision for students whomight otherwise be disadvantaged, and at a more global level, evaluations of the success of the education system

as a whole.

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employment beyond schooling (Rumberger and Thomas, 1993; Ehrenberg and Brewer, 1994;

Akerhielm, 1995; Meyera, 1997; Jones and Zimmer, 2001). Very little attention has been

given in the academic literature to investigating the student and school determinants of

university students academic performance. However, university students academic

performance can be analysed with the same methods used to quantify students achievements

in high school.

These methods can be viewed in terms of the individual-oriented input-output production

function model outlined by Blau and Duncan (1967) that is representative of the early, or as

they are sometimes termed in the literature, first generation, studies on school effectiveness

(Hanushek, 1987; Kreft, 1993; Hill and Rowe, 1996). In this model, SAi is the scholastic

achievement of student i. For example, this might be an overall indicator of academic

performance such as a grade point average, or a mark obtained for a specific subject.

Variations in SA across students are accounted for via the production function F( ) as

follows:

SAi = F (BCi , Sj) , i = 1, . , n, j = 1,, m. (1)

where BCi denotes the background characteristics, including measures of early childhood

achievement, of the student and Sj denotes characteristics of the school attended by the ith

student. A wide range of background characteristics (BCi) could be considered, including

wealth and measures of early academic achievement. Included in Sj might be simple

descriptors such as private or public school, and more detailed descriptors such as the

resources available at the school attended (e.g. staff-student ratios, subject choice, extra-

curricular activities).

Contextual characteristics (such as effects of teachers and peers) in relation to the individual

rather than the school and exterior school characteristics (such as per-pupil expenditures,

turnover of teachers, salaries, and physical facilities) can be added to equation (1) (see

Murnane, 1975; Hauser, Swell and Alwin, 1976; Summers and Wolfe, 1977; Glasman and

Biniaminov, 1981; Hauser, Tsai and Sewell, 1983). In this instance, the production function

might be written as:

SAi = F (BCi , SCi , Sj ) (2)

where SCi are the individuals perceptions of the school attended, for example, whether the

teachers were effective. Indeed, the typical first generation study has employed estimating

equations that are linearised versions of (2), namely:

SAi = 0 + 1 BCi + 2 SCi + 3 Sj + i (3)

Hence, this traditional approach combines individual-level data with aggregated school-level

explanatory variables. It ignores the fact that these data are organised within a well-defined

hierarchy, where students are clustered within schools. Dealing with hierarchically structured

data on a one-level basis presents many problems, including aggregation bias, undetected

heterogeneity of regression among sub-units, misestimated parameter estimates and their

standard errors, and the failure to satisfy the assumptions of independence required by single-

level models (Hill and Rowe, 1996). Multicollinearity may be a problem, and can

substantially complicate the analytical work.

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More recent, so-called second generation, research has several defining characteristics,

though the main one of interest to the current research is the methodology employed. Termed

hierarchical linear modelling (HLM), the estimation procedures accommodate the specific

ways the data have been generated. These models take as their starting point the relationship

between individual-level variables only, namely:

SAi = 0j + 1j BCi + i (4)

The school-level variables are indexed by j whereas the individual-level variables are

indexed by i. In this model the intercept and slope parameters are treated as random

parameters. Variation in these can be modelled using school-level data, as follows:

0j = 0 + 0 Sj + j (5)

1j = 1 + 1 Sj + j (6)

where E (0j ) = 0 + 0 Sj and E (1j ) = 1 + 1 Sj

The HLM is a refined statistical method to estimate the effects of collective attributes on

individual behaviour (Kreft, 1993). The goal is to simultaneously analyse students within

schools without losing the distinction between the levels so that appropriate inferences can be

made to schools and to students. Therefore, all levels in the multilevel analysis are

recognised and analysed in relation to one another (Kreft, 1993).

Both first- and second-generation studies of the determinants of school performance have

reported a range of important findings. These can be divided into two broad categories: the

family background effects and the impact of different school types on scholastic

achievement.

The typical socio-demographic characteristics of families considered in empirical research on

scholastic achievement include parental education, income and family size (Hanushek, 1987).

It is generally reported that more educated and wealthier parents have children who perform

better on average (Murnane, Maynard and Ohls, 1981; Hanushek, 1986). In particular, the

skills of the mother, measured by the extent of her formal schooling, are found to be a critical

resource in determining childrens achievement (Murnane, Maynard and Ohls, 1981).

Murnane, Maynard and Ohls (1981) also found that goods inputs (which include things such

as nutritious food, comfortable housing and reading materials that stimulate intellectual

interests) in the home do not appear to have consistent effects on childrens learning.

The studies of school effects on student achievement show that schools do matter to the

performance of students in high school. In particular, the studies typically show that students

at private schools have better academic performance than their counterparts at public schools.

However, agreement has not been reached on the reasons for these differences in academic

performance. The reasons advanced include differences in resource levels, academic

organization and normative environments (Bryk et al., 1984; Lesko, 1988) and academic

experiences (Lee and Bryk, 1988). The latter refer to track placement and the number of

academic subjects taken. More specifically, Marks, McMillan and Hillman (2001, p.ix)

argue that a higher level of confidence among students in their own ability, a school

environment more conducive to learning, and higher parental aspirations for the students

education contribute to lifting student achievement, as measured by tertiary entrance

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performance. The research reported below aims to discover whether these school effects

extend into tertiary studies.

Data

Most of the data for this study are from details students provided at the time of entry toUWA. There are two broad types of students at commencement in UWA: (1) school-leavers

who took the Western Australian Tertiary Entrance Examinations (TEE) as either full TEE or

mature-age TEE candidates, and (2) those that are considered as non-school leavers. The

majority of students who entered UWA are in the school-leaver category, and it is the focus

of this study. It consists of the cohort of students who were in their first year of university in

2001 and in their final year of high school in 2000, but excludes non-school leavers (such as

students who transferred from other UWA courses or other universities and full fee paying

overseas students). School leavers represent 66.2 percent of the total first year student intake

(3,293 students) in 2001, and non-school leavers make up the remaining 33.8 percent (1,113

students) (Statistics Office, 2001).2

Data on 2,180 first year students who entered UWA as school leavers are available, and the

sample comprises students from all disciplines, with roughly equal numbers of males and

females. Cases with missing values of variables included in the study are omitted. This

leaves 1,803 students in the sample used in the statistical analyses, covering 54.75 percent of

all first year students at UWA in 2001.3 Discussion on sample attrition is presented later in

the paper.

First-year academic performance is measured in this analysis by the weighted average first-

year mark (wtav1). This is computed as the mark obtained in each unit enrolled in after the

dates specified for withdrawal from a unit without penalty, weighted by the relativecontribution of the unit towards completion of the students degree program.4

The main explanatory variable is the students prior academic achievement, as measured by

their Tertiary Entrance Rank (TER), or alternatively their Tertiary Entrance Score (TES).

The maximum possible TES is 510 (Admissions Centre, 2003). The TER is calculated from

the TES, and is a number between 0 and 99.95 that measures each years group of Year 12

students against each other. It is expected that there would be a strong positive relationship

between the first year university performance and the TER (or TES).

The TER has advantages for generalising the research reported below to include other states.

Marks et al. (2001) show that where comparisons are made across states in Australia, ameasure based on rank like the TER is more useful than the TES. Accordingly, results using

the TER are reported in the text. The simple correlation between the TER and TES is 0.94,

and so similar findings would be expected with the alternative regressor (and this is

confirmed in the experiments reported in Appendix B).

In 2001, UWA admitted new students with, in principle, a TER of 79.65 or more. However,

there exist some students within the data set with TER below this cut-off mark. There are a

number of reasons for this. One of them is the UWay scheme, whereby school-leaver

2 Of the non-school leavers, local non-school leavers make up 24.3 percent (801 students) and full fee paying

overseas students make up the remaining 9.5 percent (312 students) (Statistics Office, 2001).3 Appendix A lists all the variables used and identifies where the missing values are concentrated.4

The first year marks are for the first full-year of study.

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applicants and applicants doing mature-age TEE who believe their academic achievement in

Year 12 has been adversely affected by certain disadvantages are given special consideration

(Prospective Students Office, 2000). Some of the disadvantages include attending a school

where very few students aspire to tertiary education, education in rural and remote areas, a

lack of supportive study environment at home, and having to care for family members. Other

reasons include eligibility for the Universitys Programmes for Aboriginal people and TorresStrait Islanders who do not meet the usual admission requirements, and students admitted due

to the UWA Diversity and Merit Awards.5

Due to different levels of difficulties that are associated with different courses and limited

availability of places, variations in cut-off rank exist across courses. Table 1 lists cut-off

TER of all courses available at UWA in 2001.

A range of other influences on student outcomes can be considered with the institutional data

utilised in the study. As discussed in section 2, they include individual or level 1 variables,

such as gender and home location, and school or level 2 variables, such as school type, school

location, school size, and school sex (i.e., co-educational or single-sex school). The researchby West and Slamowicz (1976), Trinca (1988) and Everett and Robins (1991) suggest that

girls will outperform boys during the first year at UWA, and that students from Government

schools will do better than students from other schools. Figure 1 illustrates the general

patterns in the data in relation to this latter relationship. It shows Government school

students outperforming students from Catholic and Other Independent6 schools at UWA

when TER is held constant, which is the reverse of the lower levels of performance during

high school for Government school students (see Marks et al., 2001).7

Several other sources of data are also used in this research. The first of these comprises the

Index of Economic Resources (EconRes) and the Index of Education and Occupation

(EduOcc) produced by the Australian Bureau of Statistics for each census using a broad

range of social and economic characteristics of the population. These provide measures of

socio-economic (SES) characteristics of students. In this analysis the regions are defined

based on home postcodes, and the index scores have been standardised to have a mean of

1000 for the whole of Australia. The 1996 census data on SES are used since it was the most

recent published indicator at the time the research was undertaken.8

5 There is no achievement penalty or benefit associated with entering university through special channels such asUWay. These students benefit from special induction and other general programs. Students entering under theAboriginal and Torres Strait Islander program are eligible for supplementary instruction and support programs.6

From here on Other Independent schools will be simply referred to as Independent schools.7 The difference in intercepts (from that for Government schools) in Figure 1 is statistically significant for both

Catholic and Independent schools, though only Independent schools have a slope coefficient that is significantlydifferent from that for Government schools.8

Due to the nature of socio-economic characteristics, that are typically quite stable over the short- to medium-terms, using 1996 data to analyse the 2001 cohort of first year students should not have any significant

disadvantages.

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Figure 1 School Influence on First Year Performance, Holding TER Constant

The Index of Economic Resources highlights disposable income and focuses on the economic

resources of households in the region. Factors summarised by this index are the income,

expenditure, home ownership, dwelling size, and car ownership of families in the regions.

High index values indicate that there is a higher proportion of families on high income, a

lower proportion of low-income families, and more households purchasing or owning

dwellings and living in large houses (Stevenson etal., 2000).

The Index of Education and Occupation reflects the educational and occupational structure ofcommunities. High index values indicate that a region would have a high concentration of

persons with higher education or undergoing further education, and people being employed in

the higher skilled occupations (Stevenson et al., 2000).9

A second source of external data is the Curriculum Council of Western Australia, which

yearly provides various statistics on schools. Three of their indicators are chosen to reflect

the characteristics of schools, derived from collective characteristics of those attending the

school. They are the percentage of students who graduated from high school (a general

measure of school effectiveness), the percentage of students who took four or more TEE

subjects during that school year (a measure of the aspirations for tertiary study of the students

at a school), and the percentage of students who attained High TES upon completion of highschool (a proxy for the academic merit of the student body). All these statistics are based on

full-time students who were eligible to graduate in 2000, and only schools with 20 or more

full-time eligible students are considered (Curriculum Council, 2000). These variables are

termed Pergrad, PerTEE and HighTES, respectively, in the discussion that follows.10

Table 2 lists all variables in the analysis, together with the variable codes used in many

presentations.

9 With a simple correlation coefficient of 0.673 in the sample (compared to around 0.8 for the population), these

two indices are only weakly correlated, and both are considered simultaneously in the subsequent equations.10 While these variables are positively correlated, the binary relationships are not very strong. See Appendix Afor details on the correlation matrix.

0

20

40

60

80

100

65 70 75 80 85 90 95 100

Tertiary Entrance Rank

WeightedAverageFirstY

earMark Government Schools

Catholic Schools

Independent Schools

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Table 2 Variables and Codes Used in the Equations

The estimating equation used in the initial analysis of first-year university performance for

individual i is:

wtav1i = 0 + 1 TERi + 2 Femalei + 3 Rulhomei + 4 EconResi+ 5 EduOcc i + i (7)

In this equation, TER is the Tertiary Entrance Rank of UWA students who were in first

year in 2001. Female is a dummy variable, defined to be equal to 1 where the student is afemale, otherwise it is equal to 0. Similarly, Rulhome is a dummy variable for a rural

home location created from the home location data11 on the UWA student record system. It

is equal to 1 where the student comes from a rural home and 0 if the student comes from an

urban home. The variables EconRes and EduOcc are based on the indices for Economic

Resources at Home and Education and Occupation at home constructed by the Australian

Bureau of Statistics from Census data for 1996. i is a random error term.

To study school effects, the analysis recognises the multi-level nature of the data, where

students are nested within schools. Therefore, rather than considering all variables in one

linear equation (a first-generation approach), the individual-level variables are separated from

school-level variables. The following equation is taken as the starting point:

wtav1i = 0j + 1j TERi + 2 Femalei + 3 Rulhomei + 4 EconResi

+ 5 EduOcci + i (8)

Unlike the first-generation style approach, the constant term 0j and the TER coefficient 1jare now treated as random parameters and are allowed to vary according to the school-level

variables. This way of treating the data is usually called a random parameters model in the

economics literature. Variation in the constant term (0j) is specified as follows:

11This is based on permanent rather than term address.

Variable Variable Code

First Year Weighted Average Mark wtav1

Tertiary Entrance Rank TERFemale student FemaleRural home location RulhomeEconomic Resources at home EconRes

Education and Occupation at home EduOccCatholic school Cath

Independent school IndpRural school RulschlSmall school SmlschlMedium school Medschl

Boys school BoyschlGirls school Girlschl

Percent of students graduated from school Pergrad

Percent of students who took more than 3 TEE subjects PerTEEPercent of students who got high TES HighTES

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Table 3 Means and Standard Deviations of Variables for Sub-Sets of Data With and

Without Information on wtav1

Table 3 shows that students with missing information on the weighted average first-year

performance are more likely to be female, be from rural areas and to have attended rural

schools than other students. In terms of the remaining characteristics, however, there are no

notable differences between the two groups. In particular, there is a difference of less than

two points (or only one-third of a standard deviation) in the TERs of the two groups.

Before proceeding to review the estimates, a further qualification needs to be introduced.

The Cath and Indp variables are included in the model to reflect school type effects.

However, as choice of school type is linked to family background (see Le and Miller, 2003),these variables may also capture family background influences. As a favourable family

background is expected to be associated with superior academic outcomes (Marks, McMillan

and Hillman, 2001), and the variables for graduation from non-government schools attract

negative shift coefficients in the analyses report on below, this indirect family background

influence may serve to mute what would otherwise have been more pronounced school type

effects.

Empirical Results

The findings from the regression analyses of the factors that affect first year university marksare presented in table 4. In these analyses the dependent variable has been transformed to

reflect the nature of the weighted average score (wtav1), which is bounded by 0 and 100.0.

This has been achieved by using wtav1*, defined as:

wtav1* = Log [ wtav1 / (100.0 wtav1) ] (11)

This transformation ensures that both within-sample and out-of-sample predictions of first

year marks will not exceed 100 or fall below 0.

Partial effects may be calculated from the estimates obtained with this dependent variable

using:

Variable Code With wtav1 Without wtav1

TER 91.8 (5.90) 90.0 (6.17)

Female .521 (0.50) .602 (0.49)Rulhome .115 (0.32) 268 (0.44)EconRes 1067.4 (71.8) 1028.7 (168.7)EduOcc 1058.2 (93.6) 1022.7 (178.0)

Cath .229 (0.42) .187 (0.39)Indp .352 (0.48) .407 (0.49)

Rulschl .079 (0.27) .217 (0.41)Smlschl .083 (0.28) .136 (0.34)Medschl .314 (0.46) .383 (0.49)Boyschl .166 (0.37) .133 (0.34)

Girlschl .185 (0.39) .189 (0.39)Pergrad 94.3 (5.63) 93.2 (11.5)

PerTEE 72.1 (15.3) 69.4 (17.8)

HighTEE 40.8 (16.2) 39.0 (17.8)

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wtav1 / TER = TER[ (wtav1) (100.0 wtav1) / 100 ] (12)

These partial effects are usually evaluated at the mean value of the dependent variable, wtav1.

Following the practice of most second-generation studies, all individual-level variables are

entered in the model as deviations from the mean for that variable for the school attended.The variables Pergrad, PerTEE and HighTES are also entered in the model as

deviations from means, though in this instance the grand or overall mean is used.15 The slope

coefficients for TER, Female, Rulhome, EconRes, and EduOcc are to be

interpreted as impacts for students having a characteristic more than or less than the mean for

the school attended. That is, the reference point is the students school and so the estimated

impact deals with an intra-school effect. In comparison, the variables for heterogeneity in the

intercept and in the slope for TER assess the impact of attendance at a particular school in

comparison with the mean for all schools. In other words, they assess inter-school effects.

The first column of results in table 4 contains results from a specification that includes only

individual-level regressors, and is analogous to the first-generation style model. It follows

equation (8), and the estimates have been obtained using OLS. The second specification is

another first-generation model that combines both individual-level and school-level data.

This estimation ignores the different levels of aggregation (individuals and schools) in the

regression equation. The third specification is for a second-generation model that

incorporates high school effects into the estimating equation predicting first-year university

performance in a way that is fully cognisant of the different levels of aggregation

(individuals, schools) represented in the data. These estimates are obtained using simulation

estimation.16

Overall, the first-generation model does not have a strong performance, with the R

2

beingonly 0.25. The most striking feature of the results is that the first year weighted average mark

has a strong positive relationship with the TER. In the context of the current model, this

means that among students who attended the same high school, those with a TER score above

the schools average are likely to get a higher average mark in their first year of university

compared to those with a TER score lower than the average of the same high school. When

computed using equation (12), each extra point on the TER scale is associated with an

increase by about one in the mean first year academic performance.17

15 See Singer (1998).16

Simulation estimation is used when there is a need to maximize or minimize functions that involveexpectations. The LIMDEP econometrics package is used. Details can be found in the numerical analysis

literature, and a brief overview is provided in Greene (2002).17 Similar results are obtained when the Tertiary Entrance Score is used as an explanatory variable in place of

the TER. See Appendix B for details.

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Table 4 OLS & Random Parameter Estimates of Models of Determinants of 1st-Yr Uni. Performance

Variable OLS (i) OLS (ii) Random Parameters (iii)

Constant 0.5841*

(53.88)

0.6616*

(29.86)

0.6635

(43.65)*TER 0.0490*

(24.56)0.0490*(24.93)

0.0489*(18.67)

Female 0.1116*(3.81)

0.1116*(3.89)

0.1093*(6.33)

Rural Home Location -0.0117

(0.18)

-0.0117

(0.18)

-0.0141

(0.31)Economic Resources at home -0.0002

(0.88)-0.0002(0.90)

-0.0002(1.01)

Education and Occupation at home 0.0005**(1.88)

0.0005**(1.90)

0.0004*(2.77)

Intercept heterogeneityCatholic School (a) -0.1274*

(3.97)

-0.1135*

(5.48)Independent School (a) -0.0828*

(2.01)-0.0762*

(2.75)

Rural School (a) -0.1579*(3.44)

-0.1491*(5.33)

Small School (a) 0.0939

(1.54)

0.0941*

(2.39)Medium School (a) 0.0417

(1.13)0.0369(1.54)

Boys School (a) -0.0908*(2.30)

-0.0814*(3.44)

Girls School (a) -0.0671

(1.55)

-0.0848*

(3.27)% of students graduated from School (a) -0.0005

(0.19)-0.0004(0.24)

% of students taking over 3 TEE subjects (a) -0.0012

(0.83)

-0.0009

(0.97)% of students who got high TES (a) 0.0040*

(2.61)

0.0039*

(4.67)TER slope heterogeneity

Catholic School (a) (a) -0.0049(1.15)

Independent School (a) (a) 0.0032(0.69)

Rural School (a) (a) 0.0024(0.55)

Small School (a) (a) 0.0143*(2.05)

Medium School (a) (a) 0.0039

(0.82)Boys School (a) (a) 0.0035

(0.76)

Girls School (a) (a) -0.0109*(2.06)

% of students graduated from School (a) (a) 0.0009*(2.60)

% of students taking over 3 TEE subjects (a) (a) 0.0004*(1.99)

% of students who got high TEE (a) (a) 0.0001(0.44)

Adjusted R2 0.25251 0.27250F Statistic 122.75 46.00Max. Log Likelihood 1117.7

Sample Size 1803 1803 1803

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Notes: (a) = variable not entered; t statistics in parentheses; * = significant at the 5 percent level; ** =

significant at the 10 percent level; 2 for variables for heterogeneity in constant and in slope for TER is 44.8.

The results also confirm the expectations that, in general, female students outperform male

students during the first year of university. While this advantage is only about 2 points, it is

highly significant, and is one of the few individual- or school-level variables that has aconsistent effect on first year academic performance. This result is similar to the finding

reported in earlier studies of UWA students (Trinca, 1988; Everett and Robins, 1991).

In terms of students socioeconomic background, while there is no significant relationship

between the academic performance of students at first-year university and homes economic

resources, the education and occupation level at home has a positive impact on student

performance. In other words, not only do students from favourable family backgrounds have

a greater chance of attending university (see Marks et al., 2000), they also appear to do better

in their university studies, even after controlling for TER. This is consistent with existing

evidence presented by Marks, McMillan and Hillman (2001), in which parents occupational

status is shown to have a strong positive effect on students achievement. In addition, thechildcare provided by mothers with higher education levels has a positive impact on their

childrens cognitive skills, according to Murnane, Maynard and Ohls (1981). However, when

interpreting the relationship between academic performance of students and their

socioeconomic background, it is important to note that statistical errors could arise from

combining individual-level data with aggregated generalizations for the measurement of

socioeconomic factors. For example, a suburb might be assigned a low score on

socioeconomic indicators because there are less well-to-do households on average. Based on

this, a student from a wealthy family who lives in such as suburb could be assigned a low

score due to a low suburb score. The relationship between socioeconomic factors and first

year performance might then be distorted as a result.

Quadratic equations were also employed to examine whether there were any non-linearities in

the relationship between the weighted average score for first year UWA students and their

TER.18 The results show that, at the margin for entry into UWA, the relationship between

first-year performance and TER is quite flat, and perverse over some lower values of TER.

West and Slamowicz (1976), in a study of first year students enrolled at Monash University

in 1970, found that the relationship between the mean first year university mark and the mean

score of the final year of high school (Higher School Certificate or HSC) was negative at the

low levels of HSC. The important implication of these results is that a composite selection

index might be used instead of using HSC or TER as a sole university entrance criterion (see

West and Slamowicz, 1976; Everett and Robins, 1991). In many ways, this is what happens

in the Uway program at UWA, described earlier in section 3. For almost all the sample,

however, the linear TER variable is a useful descriptor of the relationship between first-year

university performance and prior achievement, and so this functional form is used in the

subsequent analyses based on the random parameters model.

Column (ii) of table 4 contains results based on equation (3). This specification, which is

typical of first-generation studies, combines individual-level data with aggregated school-

level explanatory variables. The results for the new variables show that students who

graduated from Catholic, Independent, rural or boys schools do less well during their first

year at university than other students. The results also show that students who attended

schools that had relatively high proportions of high performing (in the TEE) students do18

That is, over-and-above the non-linearities that result from the logistic form used for the dependent variable.

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relatively well during their first year at university. The inclusion of these additional variables

has little impact on the variables included in the specification listed in column (i). In

particular, the strong relationship between first-year academic performance and TER remains

as the dominant feature of the findings.

Further information on the links between TER and university students academicperformance could be achieved if those with low entrance scores (i.e., the UWay students)

could be analysed separately. However, the small number of UWay students (1.2 percent of

the initial sample) precludes this. An alternative would be to use data for universities with

lower cut-off marks, but attempts to obtain such data have not been successful at the time of

writing.

The sensitivity of the results to the underlying econometric specification was also examined.

One obvious candidate in this regard is to take account of the courses the students study.

There are differences in tertiary cut-off ranks for different courses at UWA19 and presumably

a range of other factors that might be related to both course choice and first year

performance, and so there is a likelihood that students subsequent performance at universitywould differ across courses. Table 5, which lists UWA courses with 20 or more students,

reveals that the mean of first year performance ranges widely, from 55 for an Economics

course to 72 for a Science-Engineering combined degree.

However, when course variables were added to the model, few were associated with

statistically significant effects, and among those that were, the estimated effects were quite

small. Importantly, the underlying results for the remaining variables had no significant

changes.20 Therefore, the results are robust to the specification changes that could be

considered.

The results of the second-generation random parameters model are listed in the third column

of table 4. They have a number of similarities and one difference from those reported earlier.

In terms of the similarities, TER has a strong positive linkage with student performance

during the first year of university, female students tend to achieve marks higher than the

average of the school compared to male students, and the rural home variable is statistically

insignificant, as is the economic resources at home variable.

The difference in results from those reported in column (i) concerns the students who come

from a home with higher family education and occupation level. These students are shown to

have slightly higher performance than the average of the school they attended. This result

under the random parameters model is in the same direction as, but is much more significantthan, the findings from the first-generational style individual-level analysis.

19

See table 1.20 This finding is important as it suggests that the large differences in first year grades across courses have their

origins in ability differences rather than grade inflation in some courses.

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Table 5 First Year Weighted Average Scores for UWA Courses with 20 or More

Students, 200121

Course Name Mean Std.Dev. No. of StudentsSignificant

Standardised Effect

Economics 54.8 14.3 30 No

Computer & Math Sciences 57.2 11.3 77 NoHealth Science 59.9 9.1 40 No

Commerce 60.0 10.7 176 Yes (-ve)

Science 60.6 12.1 365 Benchmark

Engineering 62.0 11.3 116 Yes (-ve)

Arts-Commerce 62.8 12.3 65 Yes (-ve)

Arts 63.7 11.3 190 Yes (+ve)

Architecture (Environmental Design) 64.4 5.0 38 No

Music/Musical Arts/Music-Education 65.0 11.4 40 No

Arts-Science 66.3 13.0 31 No

Science-Commerce/Economics 66.5 9.0 64 No

Dentistry 66.5 7.1 21 No

Commerce-Engineering 68.0 10.4 81 No

Medicine/Medicine-Arts 69.6 7.1 80 No

Law combined (5 yrs) BCom 70.1 7.5 54 No

Law combined (5 yrs) BA 70.3 7.9 55 No

Science-Engineering 71.8 10.3 96 Yes (+ve)

All Courses 63.6 11.5 1803

Refers to whether a dummy variable for the particular course is significant in a first generation model using the table 4,

column (ii) specification.

The inter-school effects on the first year university mark are modelled through the variables

that account for the heterogeneity in the intercept and in the slope for TER in the secondcolumn of table 4. As with the first-generation model in column (ii), both school type

variables have negative effects on the intercept term. That is, the mean university

achievement of students from Catholic schools or Independent schools is less than the mean

achievement for students who had attended Government schools. However, their impact on

the relationship between the first year university mark and the TER is small and insignificant,

meaning the outperformance of the Government school counterparts is consistent across

various levels of TER scores.

The relatively low first-year university marks for students from non-government schools may

be explained with reference to the conclusions of Marks, McMillan and Hillman (2001).

They show that students attending Independent and Catholic schools have higher mean TERthan students attending Government schools, with the standardised differences being 5.9 and

5.0 percentage points respectively. There are various reasons for why school type is

associated with student performance at high school, including the superior resources and

more attentive coaching of non-government schools. Hence, in many respects, the TER of

the students coming from Independent and Catholic schools may be viewed as being

artificially inflated compared to that of students from government schools. In this situation,

some reversion towards the mean would be expected, and this should show up in this

statistical analysis as a negative effect on first-year university performance among students

who attended non-government schools once TER is held constant.

21 Only those students enrolled in courses listed in this table are considered. Science is taken as a reference

point. The specification used is based on column (ii) of table 4.

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There are a number of other school variables that affect the level of academic performance at

university through the intercept term. Attending a rural school rather than an urban school

has a negative impact on university performance. Students who attended a small school are

shown to have superior university achievement compared to the benchmark category of

students who attended large schools22

. Co-educational schools have a positive effect onstudents achievement at university compared to all-boys schools and all-girls schools.23 It is

noted that the significant results associated with the rural schools and girls schools are a

feature of the findings from the random parameters model that is not found in the first-

generation model in column (ii).24

The results also indicate that students from schools with a larger percent of students who

performed well in the final year of high school (HighTES) have better average performance

at university compared to students from schools that have a smaller percentage for that

measure. This means that while a bright student who attended a school where there are many

other bright students does well at university for two reasons: their individual academic merit

and the schools academic merit, a more mediocre student who attended the same school alsobenefits via this school academic merit route. This might be termed an immersion effect, or a

positive externality.

In terms of effects on the slope, however, HighTES has a negligible impact while both

Pergrad and PerTEE have significant positive effects. Therefore, the increments in

marks during the first year at university with TER are greater among students who attended

schools with a high percentage of students graduating each year and with a high percentage

of students taking four or more TEE subjects. In this regard, these schools can be argued to

have a positive reinforcement on the subsequent performance of their students at university.

In addition, the impact of attending small schools is significant, and these schools are

associated with an increase in the slope for TER. Lastly, all-girls schools reduce the slope for

TER slightly.

Treating the data at different levels makes the model much more versatile for capturing

various effects on first year university performance. By separately studying effects at the

intra-school level and the inter-school level, it is now possible to differentiate the impacts on

achievement coming from individual background as opposed to schools influence. Policy

implications on these results are discussed in the conclusion section that follows.

Conclusion

This paper aimed to determine the factors that influence university students performance. In

doing so, two dimensions were considered (individual factors and school factors) within the

context of two methodologies (first generation model and second generation model). A key

22 The first year academic performance of students who attended medium-size schools is not statistically

different from that of students who attended large schools.23 Although there have not been studies identifying effects on subsequent academic performance of attending

either a single-sex or co-educational school, there are several past studies that have conclusively demonstratedthat students in general do better in single-sex schools (Lee and Marks, 1992; Sax, 2002; Spielhofer et al.,

2002).24 It is noted that tests show that this is not due to the more general specification for the slope coefficient on the

TER variable in column (iii) of table 4. Rather it is associated with the different approach to modeling.

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finding is that schools have an impact on the academic performance of students at university

beyond students own background characteristics. From the analyses, four main conclusions

could be drawn for policy purposes.

Firstly, under both first- and second-generation methodologies, there is a strong positive

relationship between the first year mark and the TER. This substantiates the credibility ofusing TER as a criterion in the student selection process for tertiary entrance.

Secondly, a non-linearity between the weighted average first year mark and the TER at the

region of the UWA cut-off score implies, that at the margin, it might be appropriate to use

other mechanisms in addition to the TER when selecting students into this institution. This

issue was previously explored by Everett and Robins (1991), who suggested that composite

scores might be formed from a range of predictor variables (including the school assessment

and external examination components of the TES, and individual subject scores). Similarly, a

broader range of criteria is currently used in the UWay scheme. However, it is important to

note that the broader range of criteria may have to be complemented by compensatory

programs to stimulate student performance. At present, Student Services at UWA provide aTransition Support Programme for students who entered via special channels such as the

UWay Scheme (Student Services, 2003). This support could be augmented to include

students who scored TER in the vicinity of the cut-off score. Extending this argument, there

is currently debate over lowering entrance scores at many universities should differential

HECS25 be introduced or should a greater FFPOS26 intake become a priority, and using

ability to pay as a selection mechanism. While the research in this paper does not offer a

strong basis for comment on this, it does seem that the move might need to be complemented

by addition to the standard curriculum for students who enter with less than the current cut-

off scores.

Thirdly, the underperformance of students from Catholic and Independent schools compared

to Government schools at university level is more likely to be a reflection of a correction that

has taken place in terms of relative TER achievement rather than due to the specific school

characteristics that were examined in this paper. In other words, it suggests that the TER of

students from non-Government schools may have been artificially inflated relative to the raw

abilities of these students27. According to Marks, McMillan and Hillman (2001), school

sector has a substantial impact on tertiary entrance performance (accounting for

approximately 22 percent of the variation in students tertiary entrance scores); on average,

students attending Independent schools have higher tertiary entrance scores than students

attending Catholic schools, who in turn have higher scores than students attending

government schools. Moreover, under the second-generation analysis, school characteristicvariables were significant alongside the school type variables, meaning the students first

year performance differences between school types are not primarily due to differences in the

school factors included in this paper.

Finally, the second-generation research has shown the effects arising from school

characteristics are important to an understanding of subsequent academic performance. The

inter-school effects in the second-generation model imply two broad phenomena persist in

schools: immersion effects and reinforcement effects. The immersion or positive externality

25 HECS = Higher Education Contribution Scheme.26

FFPOS = Full Fee Paying Overseas Students.27Another interpretation is that some of the value-added of Non-Government schools is short-lived.

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effect28 arises when a students subsequent performance is enhanced by learning amidst a

high achieving school environment, regardless of each students past academic performance.

The reinforcement effects29 are realised when a students rate of achievement in university is

higher because of the overall academic climate of the school they attended. Therefore, it is

beneficial to encourage all students to attain high standards of academic achievement in

schools.

In investigating schools, this research has relied mainly on the school descriptors. What was

lacking was more contextual-based information that might reveal school factors contributing

to lifting subsequent student performance. In terms of further studies on this matter, specific

processes that take place at schools (including qualitative information such as school culture,

composition of teachers and various school programmes), resources endowed and the ways in

which schools are organised should be examined in order to identify whether they ultimately

affect subsequent performance at tertiary level. Understanding the economics behind the

divergence among performance of students between school types would allow the

stakeholders, including the schools, the state and the federal governments and the

universities, to devise appropriate funding and selection policies to increase studentslearning outcomes.

The focus of this study has been on the 2001 first year students at UWA, and various

extensions could be made to allow comparative analysis across years and institutions. Within

UWA, further studies are needed to analyse whether the effects identified in this paper carry

forward to subsequent years of study or, as expected, if there are diminishing impacts from

schools over time due to the convergence of student learning styles within a university

environment. In addition, studies also need to be done for first year students from other years

to confirm that the relationships generally hold true across time. Apart from UWA, similar

studies could be carried out for other universities in WA and in other states of Australia30 so

that some general benchmarks could be established for factors that influence performance at

university.

Although some clear relationships have been established, the analyses indicate that the model

used in this report can explain only about 25 percent of the variation in student performance

at university. This means that a very large proportion of the variance is still unaccounted for

by this model. Various policies within UWA itself are likely to be highly influential in

addition to background factors. Included here might be target mean marks for some units.

Furthermore, students individual qualities and personal traits (such as their study habits,

motivation, ambition, extra-curricular interests, and other related factors) would also greatly

affect their academic performance.

In summary, this paper connects the many issues that are raised by an attempt to understand

better the relationship between individual and school factors and academic performance at

university. Without understanding such factors, we cannot hope to understand either the

nature of student performance or of the university education system. The results show that

this relationship can be modelled, but that further research is needed in order to develop a

fuller understanding of the processes at work.

28 This is shown by the positive intercept for schools with higher percent of students who attained High TES.29

This is shown by the positive slope terms for schools with higher percent of students who graduate each yearand schools with higher percent of students who took four or more TEE subjects.30

This is subject to the availability and access to similar data.

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APPENDIX A

Variables Used in the Analyses

The variables used in the study are described below. Reference groups for categorical

variables are listed in bold.

(a) Sourced from UWA institutional records; (b) Constructed linking the school students attended withCurriculum Council of Western Australia data; (c) Constructed linking students home postcode information

with Australian Bureau of Statistics data..

The school characteristics variables (Pergrad, PerTEE, HighTES) are only moderately

correlated, as shown below:

VariableVariable

TypeValidCodes

NumberMissing

Gender(a) categorical 1 = Male None

2 = Female

Home Location(a) categorical 1 = Metro None

2 = Rural

School Type(a) categorical 1 = Catholic None

2 = Government

3 = Independent

School Location(a) categorical 1 = Metro None

2 = Rural

School Size(a) categorical 1 = Large None

2 = Medium

3 = Small

School Sex(a) categorical 1 = All-Boys None

2 = All-Girls

3 = Co-Educational

UWA TER(a) continuous 70.00 - 99.95 4

UWA TES(a) continuous 269.3 - 507.3 4

First Year Weighted Average Score(a) continuous 2.00 - 99.99 332

% of Students Graduated from School (Pergrad)(b) continuous 0.00 - 100.00 25

% of who took more than 3 TEE subjects (PerTEE) (b) continuous 0.00 - 100.00 25

% of Students with High TES (HighTES)(b) continuous 0.00 - 100.00 25

Economic Resources at home(c) continuous 700-2000 43

Education and Occupation at home(c) continuous 700-2000 43

Pergrad PerTEE HighTES

Pergrad 1.000

PerTEE 0.412 1.000

HighTES 0.551 0.706 1.000

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APPENDIX BTable B.1 OLS and Random Parameter Estimates of Models of Determinants of First-Year University

Performance Based on TES

Variable OLS (i) OLS (ii) Random Parameters (iii)

Constant 0.5841*

(55.57)

0.6616*

(31.17)

0.6580

(44.08)*

TER 0.0074*(28.29)

0.0074*(28.78)

0.0073*(20.64)

Female 0.1354*(4.73)

0.1354*(4.83)

0.1354*(8.20)

Rural Home Location 0.0262(0.40)

0.0262(0.41)

0.0221(0.49)

Economic Resources at home -0.0003(1.16)

-0.0003(1.18)

-0.0002(1.41)

Education and Occupation at home 0.0005*(2.00)

0.0005*(2.02)

0.0004*(3.08)

Intercept heterogeneityCatholic School (a) -0.1274*

(4.04)

-0.1210*

(6.06)Independent School (a) -0.0828*

(2.07)

-0.0773*

(2.91)Rural School (a) -0.1579*

(3.44)-0.1526*

(5.69)Small School (a) 0.0939

(1.56)

0.0980*

(2.57)Medium School (a) 0.0417

(1.17)

0.0402**

(1.72)Boys School (a) -0.0908*

(2.38)-0.0863*

(3.72)Girls School (a) -0.0671

(1.58)

-0.0715*

(2.87)% of students graduated from School (a) -0.0005

(0.19)

-0.0002

(0.15)% of students taking over 3 TEE subjects (a) -0.0012

(0.85)-0.0010(1.10)

% of students who got high TES (a) 0.0040*(2.70)

0.0038*(4.61)

TER slope heterogeneity

Catholic School (a) (a) -0.0005(0.74)

Independent School (a) (a) 0.0001(0.14)

Rural School (a) (a) -0.0002(0.03)

Small School (a) (a) 0.0015(1.55)

Medium School (a) (a) 0.0008(1.20)

Boys School (a) (a) -0.0001(0.12)

Girls School (a) (a) -0.0010(1.38)

% of students graduated from School (a) (a) 0.0001*(2.18)

% of students taking over 3 TEE subjects (a) (a) 0.0001**(1.91)

% of students who got high TEE (a) (a) 0.0000

(0.92)Adjusted R2

0.29739 0.31763F Statistic 153.54 56.92

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Max. Log Likelihood 1064.3

Sample Size 1803 1803 1803

Notes: (a) = variable not entered; t statistics in parentheses; * = significant at the 5 percent level; ** =

significant at the 10 percent level; 2

for variables for heterogeneity in constant and in slope for TER is 34.6.

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