ABSENTEEISM ATTITUDES OF UNIVERSITY …. Doç. Dr. Hatice... · Absenteeism Attitudes Of Universıty Students: Logistic Prediction Between Variables 173 predictor variables, a structured

The Journal of Academic Social Science Studies

International Journal of Social Science

Doi number:http://dx.doi.org/10.9761/JASSS3633

Number: 50 , p. 169-182, Autumn II 2016

Yayın Süreci

Yayın Geliş Tarihi / Article Arrival Date - Yayınlanma Tarihi / The Published Date

29.07.2016 31.10.2016

ABSENTEEISM ATTITUDES OF UNIVERSITY STUDENTS:

LOGISTIC PREDICTION BETWEEN VARIABLES ÜNİVERSİTE ÖĞRENCİLERİNİN DEVAMSIZLIK TUTUMLARI:

DEĞİŞKENLER ARASI LOJİSTİK YORDAYICILIK Asst. Prof. Dr. Hatice Gonca USTA

Cumhuriyet University Faculty of Education Department of Educational Sciences

Assoc. Dr. Celal Teyyar UĞURLU


Res. Assist. Ahmet Salih ŞİMŞEK


Abstract

The aim of the education is to create a permanent behavioral changes in stu-

dents. Inducing lasting behavioral changes in students is largely dependent on experi-

ence-based and constructivist environments. Especially in formal education, the stu-

dents’ levels of attendance has a fairly important effect on their level of learning. ). Edu-

cational institutions should pay close attention to student absenteeism, and consider the

factors that ensure attendance. Absenteism can change according to school success,

school climate, socioeconomic conditions, teacher support, academic knowledge, com-

munication problems and anxiety. In this study, data on absenteeism attitudes obtained

from students studying at Cumhuriyet University during the 2013-2014 academic year

were analyzed statistically. “Absenteism Attitue Scale” (AAS) was used in the study as a

scale. Also datas prapered for the analysis analysed missing value and extreme value.

The absenteeism attitudes of the students were separated into three groups, which were

the low, medium and high level groups. Two-stage clustering analysis was employed

when performing this distinction. Since the dependent variable had an ordinal structure

with three categories, ordinal logistic regression analysis was used. Study results re-

vealed that the students’ weekly course hours, weekly hours of absence, and satisfaction

with the department variables have a significant effect on their attitude towards absen-

teeism.

Keywords: Absenteeism Attitude, Logistic Regression, Hours of Absence,

Course Hours, Satisfaction with the Department

Öz

Eğitimin amacı bireyde kalıcı izli istendik davranış değiştirmektir. Öğrencil-

170

Hatice Gonca USTA & Celal Teyyar UĞURLU & Ahmet Salih UĞURLU

erdeki bu davranış değişikliklerinin kalıcı izli olması yaşantıya dayalı ve yapılandırmacı

ortamlardan etkilenir. Öğrencilerin okula devam durumları söz konusu davranış değişi-

kliğinin yaşanması bakımından önemlidir. Eğitim kurumları öğrencilerin okula de-

vamları ile yakından ilgilenmeli ve devamı sağlayan etkenlerin neler olduğu üzerinde

düşünmelidirler. Devamsızlık başarı, okul iklimi, Sosyo-ekonomik koşullar, öğretmen

desteği, akademik bilgi, iletişim sorunları, anksiyete gibi farklı olgu ya da durumlara

bağlı olarak değişebilmektedir. Bu çalışmada Cumhuriyet Üniversitesi’nde 2013-2014

eğitim öğretim yılında öğrenim gören öğrencilerin devamsızlık tutumları ile ilişkili

değişkenler (Cinsiyet, Sınıf düzeyi, Öğrenim zamanı, Genel Not Ortalaması, Bölümden

Memnuniyet, Haftalık Ders Saati, Haftalık Devamsızlık Saati ve İkamet) istatistiksel

olarak analiz edilmiştir. Araştırmada veri toplama aracı olarak öğrencilerin devamsızlık

tutumlarını belirlemek amacıyla geliştirilen “Devamsızlık Tutumu Ölçeği” (DTÖ)

kullanılmıştır. Veriler analize hazır hale getirmek için uç değer ve kayıp değer analizleri

yapılmıştır. Öğrencilerin Devamsızlık Tutumları düşük, orta ve yüksek tutum düzeyi

olarak üç gruba ayrılmıştır. Bu ayrım gerçekleştirilirken iki aşamalı kümeleme analizi

kullanılmıştır. Bağımlı değişkenin üç kategorili ve sıralı bir yapıda olmasından dolayı da

sıralı lojistik regresyon analizi kullanılmıştır. Sıralı lojistik regresyon analizi için gerekli

varsayımlar sınanmıştır. Analiz sonuçlarına göre öğrencilerin haftalık ders saati, haftalık

devamsızlık saati ve bölümden memnuniyet değişkenlerinin devamsızlığa ilişkin tutum

üzerinde manidar olduğu ortaya çıkmıştır.

Anahtar Kelimeler: Devamsızlık Tutumu, Devamsızlık Saati, Ders Saati,

Bölümden Memnuniyet, Lojistik Regresyon

INTRODUCTION

The quality of education is affected by

many different variables. Educational institu-

tions may induce behavioral changes in an

effective environment, and the motivation

level of students may have a positive effect on

the quality of education. Students’ attitudes

towards their teachers, administrators and

peers, their relationships and behavioral pat-

terns may have an impact on their commit-

ment and confidence. Inducing lasting beha-

vioral changes in students is largely depen-

dent on experience-based and constructivist

environments. Especially in formal education,

the students’ levels of attendance has a fairly

important effect on their level of learning.

Absenteeism can be defined as non-

attendance to school or courses without pa-

rental consent or any legal justification. Other

definitions of absenteeism include non-

attendance to classes on a regular basis (Kear-

ney and Silverman, 1990); unexcused non-

attendance without any legal reason (Stoll,

1990); and occasional or regular non-

attendance to school (Ministry of National

Education, 2009).

Absenteeism from school can be re-

garded as a factor that prevents in students

the behavioral changes induced by experien-

ce. According to Austin and Totaro (2011),

qualified attendance to educational activities

can improve performance outcomes. Signifi-

cant changes in student behaviors can also

affect their level of absenteeism (Uğurlu, Koç,

Usta and Şimşek, 2012). Educational instituti-

ons should pay close attention to student ab-

senteeism, and consider the factors that ensu-

re attendance. Schools have a close interest in

their students’ academic achievements. In

fact, academic achievement often precede

many other forms of achievement. According

to Silah (2003); by taking an interest in stu-

dents’ academic achievements, educational

institutions assume the task of ensuring that

students develop more qualified behaviors in

their areas of interest. It is often mentioned

that there are many variables that influence

students’ academic achievement.

Absenteeism may change according

to different conditions or situations (Yıldız

and Şanlı Kula, 2012; Altınkurt, 2008; Özbaş,

2010). For instance, the relationship between

Absenteeism Attitudes Of Universıty Students: Logistic Prediction Between Variables 171

absenteeism and academic achievement (Kab-

lan, 2009; Yalnızkurt, 2008; Rothman, 2001),

absenteeism and school performance (Snyder

et al., 2010; Scarpa, 1978), absenteeism and

sexual minorities (Bui, 2009), and absenteeism

and school climate (Gaziel, 2004) are several

examples of such conditions and situations.

Different factors can be mentioned in relation

to student absenteeism, such as socioecono-

mic conditions, teacher support, academic

knowledge, communication problems and

anxiety. Ingul et al. (2011) suggested that fac-

tors such as family structure, individual cha-

racteristics, socioeconomic status also affect

student absenteeism. Altınkurt (2008), on the

other hand, analyzed six dimensions of stu-

dent absenteeism by utilizing data from vari-

ous different studies. These dimensions are;

reasons associated with administrators, rea-

sons associated with teachers, reasons associa-

ted with family, reasons associated with envi-

ronment, reasons associated with academic

anxiety, and personal reasons. The disposition

of students to absenteeism can be evaluated

based on these reasons. Different reasons of

absenteeism can determine students’ disposi-

tions towards absenteeism. Özbaş (2010) and

Tutar (2002) also explained student absente-

eism based on a similar classification. The

absenteeism of university students is also

categorized in a similar manner (Bülbül,

2012).

It is possible to say that the problema-

tic nature of absenteeism gets much more

pronounced at different levels for students,

society, schools and families. Society, educa-

tors, psychologists and psychiatrists have

eventually started to consider the underlying

causes of student absenteeism as an important

problem (Nair, 2010). Reasons of absenteeism

problems include sickness (Hill, Standen and

Tattersfield, 2014), familial issues, low socioe-

conomic status, dissonance among peers, and

minority status (Moonie, Sterling, Figgs and

Costro, 2008). Skipping school, student absen-

teeism, and their outcomes with respect to

academic achievement can be considered as

evidence of a dysfunctional relationship

between the school and the student. Such

evidence are also indicative of an inferior

school climate, of student apathy, and of other

school-related factors. Most factors related

with absenteeism suggest that greater atten-

tion should be devoted to students, and strict

attention should be paid to satisfy their vari-

ous needs (Rothman, 2001). This is because

student absenteeism has been on the rise,

gradually becoming a more aggravated prob-

lem, and leading to low academic performan-

ce. It was determined that the rate of absente-

eism in higher education has reached nearly

40% (Arulampalam, Naylor and Smith, 2012).

In this context, this study investigated

through logistic estimation student absente-

eism attitude levels with respect to variables

such as gender, grade level, time of education,

cumulative grade point average, satisfaction

with the department, weekly course hours,

weekly hours of absence, and place of resi-

dence.

Knowing the underlying reasons of an

issue as important as absenteeism is key to

minimizing it and increasing the level of

school attendance in formal education. Even

though school attendance is important at all

levels, ensuring greater attendance in higher

education - which has a considerable impact

on the rest of an individual’s life - is relatively

more significant. Diplomas given by formal

education institutions to undergraduate stu-

dents are bestowed based on the assumptions

that the students have attended their classes;

interacted with other students, the class envi-

ronment and the professors; and passed

his/her classes as a result of these interactions.

Therefore, it is rather important to determine

the predictive power and level of importance

of variables such as gender, grade level, time

of education, cumulative grade point average,

weekly course hours, weekly hours of absence

172


and place of residence on university students’

attitudes towards absenteeism. The main

purpose of this study was to identify the vari-

ables associated with university students’

attitudes towards school attendance, since

there is no study in the current literature add-

ressing this problem.

METHOD

Research Model

This is a correlational study in which

the correlations between two or more variab-

les are investigated without any intervention.

The purpose of correlational studies is to re-

veal the covariation of or correlations among

variables (Karasar, 2003).

The logistic regression model estab-

lished within the scope of this research is

shown in Figure 1.

Figure 1. Logistic Regression Model

Figure 1 shows the predictor variables

of the regression model, which are Gender

(Male/Female), Grade Level (1-2-3-4), Time of

Education (Daytime Education/Evening Edu-

cation), Cumulative Grade Point Average

(Continuous), Level of Satisfaction with the

Department (Not Satisfied-Partly Satisfied-

Completely Satisfied), Weekly Course Hours

(Continuous), Weekly Hours of Absence

(Continuous), and Place of Residence. On the

other hand, the predicted variable of the mo-

del is Absenteeism Attitude (Low, Medium or

High).

Study Group

The study group comprised underg-

raduate students from four different depart-

ments (education, engineering, literature) at

Cumhuriyet University. Maximum variation

sampling - which is a purposeful sampling

method - was employed when forming the

study group. To ensure variation in the study

group; faculty, department, time of education

and gender variables were taken into account.

Although a larger number of students were

included into the study, a dataset that compri-

sed of 447 students was used in the logistic

regression analysis after extreme value and

missing value analyses.

Data Collection Tool

To assess absenteeism attitude, which

was the predicted variable of this study, the

“Absenteeism Attitude Scale” (AAS) develo-

ped by Usta, Uğurlu and Şimşek (2014) was

used. On the other hand, to determine the


predictor variables, a structured questionnaire

was employed. AAS is a three-dimensional

attitude scale consisting of 19 items that expla-

ins 54% of the variation in absenteeism attitu-

des among university students. The first order

CFA results regarding the validity of AAS

revealed that the scale has high validity (AGFI

= .91, CFI = .98, SRMR = 0.046, RMSEA = 0.06),

while the Cronbach’s Alpha analysis carried

out to determine the level of reliability indica-

ted that the scale had a high degree of reliabi-

lity (r = .91) (Usta, Uğurlu and Şimşek, 2014).

High AAS scores indicate positive attitudes

towards class attendance, whereas low scores

indicate negative attitudes towards class at-

tendance (in other words, positive attitudes

towards absenteeism).

Data Analysis

In order to use the predicted variable

(i.e. the absenteeism attitude) in the logistic

regression analysis, students were grouped as

low, medium and high groups with respect to

their absenteeism attitude levels. To this end,

a “Two-Stage Clustering Analysis” was emp-

loyed for the identification of students catego-

ries. Thus, homogeneous classes were obtai-

ned from the heterogeneous dataset (Kayri,

2007, p. 97). The results of two-stage cluste-

ring analysis are shown in Table 1.

Table 1.Results of two-stage clustering analysis

Variable Cluster F % Mean Standard

deviation

Dependent

variable

1 130 29.08 45.15 6.65

2 214 47.87 63.44 5.47

3 103 23.04 80.18 5.96

According to two-stage clustering

analysis results shown in Table 1, the mean

and standard deviation of absenteeism attitu-

de score of the 130 students (29.08%) in the

first group were 45.15 and 6.65, respectively;

the mean and standard deviation of absente-

eism attitude scores of the 214 students

(47.87%) in the second group were 63.44 and

5.47, respectively; and the mean and standard

deviation of absenteeism attitude scores of the

103 students (23.04%) in the third group were

80.18 and 5.96, respectively. The first cluster

indicated students with “low” level absente-

eism attitude, the second cluster indicated

those with “medium” level absenteeism atti-

tude, and the third cluster indicated those

with “high” level absenteeism attitude. Thus,

a dependent variable with three categories

was obtained.

Logistic regression analysis is named

according to the dependent variable on which

logit transformation is applied. In cases where

the dependent variable is an ordinal and cate-

gorical variable with at least three categories,

an “Ordinal (Ordered) Logistic Regression

Analysis” will be used (Çokluk, 2010, p. 1362;

Ayhan, 2006, p. 19). In ordinal logistic regres-

sion analysis, categories should be coded ac-

cording to an ascending order (Ayhan, 2006,

p. 19). In this study, “Ordinal (Ordered) Lo-

gistic Regression Analysis” was preferred,

since the categorical dependent variable – i.e.

absenteeism attitude – was transformed into a

variable with three categories.

174


In the study where “Ordinal (Orde-

red) Logistic Regression Analysis” was used

since the dependent variable had three cate-

gories, students with high absenteeism attitu-

de were taken as the “reference category”.

Therefore, the obtained coefficients indicate

the likelihood that students had a high level

of attendance attitude – or, in other words, a

negative attitude towards absenteeism beha-

vior.

Preparation of Data: Before starting

the analyses, extreme value and missing value

analyses were carried out primarily for each

variable within the scope of the study. Based

on the missing value analysis, missing values

related to academic achievement and the sa-

tisfaction level with the department were

excluded from the scope of analysis. As a

result of the extreme value analysis concer-

ning weekly hours of absence and weekly

course hours, data were identified outside the

[-3,+3] interval. The model was tested over 447

data points obtained through extreme value

and missing data analyses.

Since logistic regression analysis is

very sensitive to correlations between inde-

pendent variables, there should not be any

multicollinearity among variables. Multicolli-

nearity arises when correlations between va-

riables are high (r > .90) (Tabachnick and Fi-

dell, 1996). As correlation values were r < .90

in this study, a problem of multicollinearity

was not encountered in the current study.

In order to detect whether there was

multicollinearity between the predictor vari-

ables in the analysis, the value of the increase

in tolerance and variance were examined.

Having a VIF below 10 or tolerance value over

0.2 indicates that there is no multicollinearity

(Field, 2009; Green and Salkind, 2010). It was

observed that predictor variables employed in

the study did not have a multicollinearity

problem. Tolerance and VIF values for predic-

tor variables are given in Table 2.

Table 2. Results of Multicollinearity Assumption Between

Independent Variables

Variables TOLERANCE VIF

Gender .916 1.092

Cumulative Grade Point Average .890 1.124

Weekly Course Hours .835 1.197

Weekly Hours of Absence .925 1.081

Time of Education .889 1.125

Grade Level .875 1.143

Satisfaction with the Current De-

partment

.971 1.030

Place of Residence .794 1.259

The results shown in Table 2 indicate

that the tolerance value was .916 for the gen-

der variable, .814 for the cumulative grade

point average, .835 for the weekly course ho-

urs, .925 for weekly hours of absence, .889 for

time of education, .875 for grade level, .971 for

satisfaction with the current department, and

.794 for place of residence. All tolerance va-


lues were greater than .02. A look at the VIF

values reveals that it was 1.092 for the gender

variable, 1.124 for the cumulative grade point

average, 1.197 for weekly course hours, 1.081

for weekly hours of absence, 1.125 for time of

education, 1.143 for grade level, 1.030 for sa-

tisfaction with the current department, and

1.259 for place of residence. All VIF values

were smaller than 10. It was thus concluded

that relevant assumptions were satisfied.

Another significant assumption that

should be satisfied in Ordinal (Ordered) Lo-

gistic Regression analysis is the assumption of

parallelism. While determining the most app-

ropriate logit models, parallelisms between

sub-models are analyzed by defining the same

number of models equal to the binary combi-

nations of the number of categories (Özda-

mar, 1997, p. 463-464). The assumption of

parallelism requires estimated values of pa-

rameters to pass the same cut-off point for all

categories of the dependent variable (Akın

and Şentürk, 2012, p. 189). Chi-square test was

employed to test the validity of the assump-

tion of parallelism, and the obtained results

are shown in Table 3.

Table 3. Results of the Assumption of Parallelism in Ordinal

Logistic Regression

Model

-2 Loglikeli-

hood

(-2LL)

2 Sd P

Null Hypothe-

sis 860.055

Overall 845.731 14.324 10 .159

H0 = Parameter estimates pass from the same cut-off point.

H1 = Parameter estimates pass from different cut-off points.

Table 3 shows that the result of the as-

sumption of parallelism obtained with the chi-

square test (2= 14.324,p= .159> 0.05). In Ordi-

nal Logistic Regression, the significance level

of the assumption of parallelism should be

greater than .05 (Akın and Şentürk, 2012, p.

189; Şenel and Alatlı, 2014, p. 41). It is thus

possible to say that H0 hypothesis was accep-

ted, indicating that the assumption of paralle-

lism for the model was satisfied. This implies

that each category of the dependent variable – in

other words, of the absenteeism attitude variab-

le – are equal to each other. More generally, as

all assumptions were satisfied, it was decided to

apply ordinal logistic regression analysis in the

study.

FINDINGS

The findings of the ordinal logistic reg-

ression analysis are presented in this section.

The model fit table obtained from the analysis

provides -2 loglikelihood (-2 LL) values for the

model formed without independent variables

and the model formed with the inclusion of

independent variables. Model fit data are given

in the table below.

176


Table 4. Model Fit Data

Model -2 LL 2 Sd P

Only Intercept Point 934.578

Final 860.055 74.523 10 .000

Table 4 indicates that there is a signifi-

cant difference between the model formed

with the inclusion of independent variables

and the null model formed without including

independent variables (2=934.578-860.055=

74.523, p=.000). This indicates a correlation

between the dependent variable and inde-

pendent variables. As the following next step,

the goodness of fit of the model was analyzed.

Pearson’s chi-square and deviation statistics

evaluate the model fit using the difference

between observed and expected values (Şenel

and Alatlı, 2014, p. 40). The results of the go-

odness of fit test are presented in Table 5

Table 5. Goodness of Fit Test Results

2 Sd P

Pearson 896.126 .868 .247

Deviation 855.896 .868 .609

H0 = The model represents the data.

H1 = The model does not represent the data.

Table 5 provides the significance values,

Pearson 2= 896.126,p= .247 > 0.05) and Devia-

tion (2= 855.896, p= .609> 0.05) for the mo-

del’s goodness of fit. The model’s goodness of

fit indicates the fit of the data with the model,

and it should have a significance value greater

than .05 (Akın and Şentürk, 2012, p. 189; Şenel

and Alatlı, 2014, p. 41). Hence, it can be said

that the model fits the data and that the H0

hypothesis was accepted.

In this study, the model’s goodness of

fit was analyzed through the pseudo-R2 value.

Pseudo-R2 is a tool for measuring and asses-

sing the strength of the relationship between

independent variables. The most commonly

used pseudo-R2 statistics are McFadden, Cox-

Snell and Nagelkerke R2 statistics. Outcomes

of the analysis are shown in Table 6 below.

Table 6. Pseudo-R2 Statistics

Cox and Snell Nagelkerke McFadden

.154 .175 .079


As shown in Table 6, the pseudo-R2 va-

lues were obtained as Cox and Snell (.154),

Nagelkerke (.175) and McFadden (.079). Since

the interpretation of Cox and Snell value

among the different pseudo-R2 values is diffi-

cult, it is common practice to check the Na-

gelkerke value (Field, 2009, p. 269), which

indicates the percentage of dependent variab-

le explained by independent variables (Oruç

and Özen Kutanis, 2015, p. 41). As R2 values

are not a good criteria for logistic regression

analysis, their values tend to be low in this

type of regression analysis (Akın and Şentürk,

2012, p. 190). As such, it was determined that

independent variables of the model explained

17.5% of the dependent variable. In this study;

gender, cumulative grade point average, we-

ekly course hours, weekly hours of absence,

time of education, grade level, satisfaction

with the department and place of residence

were taken into consideration as independent

variable. It is possible to define in this study

the factors that are likely to have an effect on

absenteeism as exogenous factors. However,

endogenous factors (motivation, interest, affi-

nity to school etc.) that might affect students’

absenteeism attitudes should not be ignored.

Furthermore, the characteristics of the city

where the university is located, the university

campus and the academic staff are also impor-

tant. In this context, we suggest that the

explanatory power of the model could be

improved by diversifying the independent

variables.

In this study, the Wald test was used

to determine whether the independent variab-

les were significant or not. Testing logistic

regression analysis with the Wald test provi-

des the advantage of concluding the analysis

with less biased parameter findings (Çokluk,

2010: 1394). To interpret the model, the odds

ratio should be calculated by taking ”the

exponential” of the Wald statistic. The results

obtained by using this approach are shown in

Table 7.

Table 7. Expressing the Significance of Model Parameters

Variables Β Wald Odds ratio

(eβ) P

Dependent

variable

1 (Low) -.048 .003 - .957

2 (Medium) 2.334 6.778 - .009

Independent

variable

GEND (1) -.311 2.100 0.733 .147

GEND (2) 0 - - -

GPA .369 2.417 1.446 .120

CLASS_HOUR .056 4.232 1.058 .040

ABSENT_HOUR -.379 34.450 .685 .000

TIME_EDUC (1) -.295 1.992 .745 .158

178


TIME_EDUC (2) 0 - - -

GRD_LEV (2) .299 1.166 1.349 .280

GRD_LEV (3) -.071 .098 .931 .754

GRD_LEV (4) 0 - - -

SATIS (1) -1.526 13.024 .217 .000

SATIS (2) -.304 2.436 .738 .119

SATIS (3) 0 - - -

RES (1) .054 .071 1.055 .790

RES (2) 0 - - -

Based on an examination of the fin-

dings obtained through the significance

analysis of the model parameters, it was ob-

served that weekly course hours (p=.040),

weekly hours of absence (p=.000) and satisfac-

tion with the current department (p=.000)

variables had significant effects on students’

absenteeism attitudes. The parameter signifi-

cance values corresponding to these statistics

should be smaller than .05 (Akın and Şentürk,

2012: 190; Field, 2009: 259). It is described that

the interpretation of ordinal logistic regres-

sion analysis parameters is more difficult and

complicated than binary and multinomial

logistic regression, and it is necessary to

“exponentiate” the estimated parameter va-

lues and also to specify reference categories in

order to interpret them properly. In other

words, interpretation should be made accor-

ding to the reference category. Such an

examination of parameter significance is cal-

led “interpretation in terms of odds ratio”

(Akın and Şentürk, 2012: 190; Garson, 2012:

44). Field (2009, p. 942) recommended interp-

retation in terms of odds ratio as well. In this

respect, “exponential” values were calculated

for interpretation in Table 7.

In the interpretation of odds ratio, Field (2009:

786) referred to a resulting rate of increase

when the odds ratio is greater than 1 and a

resulting rate of decrease when the odds ratio

is smaller than 1. In this respect, examination

of Table 7 reveals that a unit increase in

students’ weekly course hours increases the

odds of having a high level attitude of

attendance by .056 units Looking at the odds

ratio of the weekly course hours variable, it

can be seen that it is equal to 1.058 – in other

words, larger than 1. This implies that a unit

increase in students’ weekly course hours

increases their attitude level towards

attendance by 1.06. It thus appears that

increasing the weekly course hours at school

is a factor that reduces student absenteeism. A

unit increase in students’ weekly hours of

absence, in turn, reduces the odds of having a

high level attitude of attendance by .379 units

An evaluation of the odds ratio of weekly

hours of absence shows that it is equal to .685

– in other words, smaller than 1 – and that a

unit increase in students’ weekly hours of

absence reduces the degree of having a high

level attitude regarding attendance by .69. As

such, students who had a high absence rate


were also those who had a lower tendency to

adopt a positive attitude towards attending

classes. Among the different variables

considered within the scope of the study, the

last variable that appeared to be significant

was satisfaction with the department. This is a

categorical variable, in which the category

“Fairly Satisfied” was chosen as the reference

category. In this case, being “Not Satisfied At

All” from the current department was

determined to be significant. In other words,

the odds of having a high attitude level

towards attendance among students who

were not satisfied at all from their current

departments were 1.526 times lower than

students who were satisfied with their

departments. The Odds ratio of the

satisfaction with the current department

variable was .217 – in other words, smaller

than 1 – and it is possible to say that the

attitudes towards attendance of students who

were not satisfied with their current

departments were .22 times lower compared

to satisfied students.

Pearson’s correlation coefficient

was calculated to analyze the relationship

between the CGPA (Cumulative Grade Point

Average) and the weekly hours of absence

variables with absenteeism attitude. It was

observed that there was a low, positive

correlation between Absenteeism Attitude

(AA) and Cumulative Grade Point Average

(CGPA) (r=.15), and a low negative correlation

between AA and Hours of Absence (HA) (r=-

.33). In addition, the correlation between

students’ hours of absence and their CGPA

was low and negative (r=-.19). These findings

indicate that greater hours of absence were

associated with lower cumulative grade point

average and that low cumulative grade point

average was associated with low attitude

towards attendance. Since cumulative grade

point average occurs after the behavior of

absenteeism, it was suggested that the

relationship between cumulative grade point

average and absenteeism attitude was due to

the mediation effect of hours of absence. Since

the necessary assumption for forming a

structural equation model to analyze the

mediation effect of the hours of absence were

not satisfied, the partial correlation coefficient

between CGPA and AA was estimated while

taking the hours of absence under control.

The estimated partial correlation coefficient

indicated that the relationship between CGPA

and AA was not significant (r=.09; p>.05)

when controlling the hours of absence. This

implies that the relationship between CGPA

and AA occurs through the mediation of the

hours of absence.

CONCLUSION, DISCUSSION AND

RECOMMENDATIONS

In this study, data on absenteeism at-

titudes obtained from students studying at

Cumhuriyet University during the 2013-2014

academic year were analyzed statistically.

Using two-stage clustering analysis,

absenteeism attitudes of students were sepa-

rated into three groups as low, medium and

high attitude level. Analysis results revealed

that students’ weekly course hours, weekly

hours of absence and satisfaction with the

current department variables had a significant

effect on the attitude towards absenteeism.

There are various studies in the litera-

ture on the variables that explain student ab-

senteeism. These studies evaluate the variab-

les associated with, or which can predict, exp-

lain or affect, the students’ academic achie-

vement. In other words, academic achieve-

ment was the main focus of these studies.

Hours of absence and satisfaction with the

current department are also among significant

variables that have an effect on students’ aca-

demic achievement.

In their study, Özer and Sarı (2009)

investigated the factors that affect university

students’ academic achievement. The study

concluded that an increase in the hours of

180


absence have a negative effect on academic

achievement. Rençber (2012) and Ullah (2007)

obtained similar results in their studies. Ac-

cording to the results of the current study,

there is a close relationship between academic

achievement and the degree of class attendan-

ce, while class attendance has a positive effect

on students’ academic achievement. Further-

more, Kablan (2009) determined that students

who do not believe that class attendance is

important for academic achievement are ab-

sent from school much more frequently,

which in turn leads to lower academic achie-

vement. However, in this study which took

absenteeism as the predicted variable, cumu-

lative grade point average was not found to

be a significant variable.

Satisfaction with the current depart-

ment is another variable which affects stu-

dents’ academic achievement. It is observed

that students who like the department they

are currently enrolled in are more successful

(Özer and Sarı, 2009). Investigating variables

that affect attitudes towards school, Adıgüzel

and Karadaş (2013) indicated that individuals

who have a low attitude towards school were

absent from school more frequently. This

study also concluded that students who were

not satisfied with their departments had a

higher absenteeism attitude compared to tho-

se who were satisfied with their departments.

Various studies (Belloc, Maruotti and

Petrella, 2010; Gury, 2011; Lassibille and

Gómez, 2008; Tinto, 1993) concluded that

students who start to university without a

genuine goal or who get enrolled to a prog-

ram they are not interested in have a higher

frequency of absence from school, which in

turn leads to a higher probability of dropping

out from school.

Considering all these, the results of

the study indicate that, apart from academic

achievement, it is rather important to identify

the factors which determine students’ attitu-

des towards absenteeism. According to the

results of this study, variables that affect atti-

tude towards absenteeism are weekly hours

of absence, weekly course hours and satisfac-

tion with the department.

Since one of the significant variables

affecting attitude towards absenteeism is we-

ekly hours of absence, class environments that

lead to less absenteeism can be created. Furt-

hermore, if students observe that class atten-

dance leads to greater academic achievement,

they may acquire a more positive attitude

towards attendance. In addition, weekly cour-

se hours is another variable that predicts ab-

senteeism attitude. It was observed that ha-

ving more weekly course hours lead to higher

attitudes towards attendance. This might be

associated with having a regular weekly class

schedule. As such, designing more continuous

class schedules in blocks on the weekdays

might serve as a factor that increases the ten-

dency to attend classes.

REFERENCES

Adıgüzel, A., KAradaş, H. (2013). Ortaöğretim

öğrencilerinin okula ilişkin tutumları-

nın devamsızlık ve okul başarıları

arasındaki ilişki. YYÜ Eğitim Fakültesi

Dergisi (YYU Journal Of Education Fa-

culty), X (I) ,49-66

http://efdergi.yyu.edu.tr

Akın, H. B. ve Şentürk, E. (2012). Bireylerin

mutluluk düzeylerinin ordinal lojistik

regresyon analizi ile incelenmesi.

Öneri 10 (37), 183-192.

Altınkurt, Y. (2008). Ög renci devamsızlıkları-

nın nedenleri ve devamsızlıg ın aka-

demik bas arıya olan etkisi. Dumlupı-

nar Üniversitesi, Sosyal Bilimler Dergisi,

20, 129-142.

Arulampalam,W., Robin A. Naylor, R.A. ve

Smith, J. (2012). Am I missing somet-

hing? The effects of absence from

class on student performance. Econo-

mics of Education Review, 31, 363–375.

Austin, W.A. ve Totaro, M.W. (2011). Gender

differences in the effects of internet

usage on high school absenteeism. Jo-

urnal of Socio-Economics, 40 (2), 192-

198.

http://www.sciencedirect.com/science/journal/10535357

http://www.sciencedirect.com/science/journal/10535357


Ayhan, S. (2006). Sıralı lojistik regresyon ana-

liziyle Türkiye’deki hemşirelerin iş bı-

rakma niyetini etkileyen faktörlerin

belirlenmesi. (Yüksek lisans tezi. Os-

mangazi Üniversitesi Fen Bilimleri

Enstitüsü, Eskişehir.)

https://tez.yok.gov.tr/UlusalTezMerke

zi/ adresinden edinilmiştir.

Belloc, F., Maruotti, A. & Petrella, L. (2010).

University drop-out: an Italian expe-

rience. Higher Education, 60 (2) 127-

138.

Bui, H.X. (2009). School absenteeism among

aexual ainorty aale youth:An exploration

of risk and protective factors. Doctor of

Philosophy, New York University,

USA.

Bülbül, T. (2012). Yükseköğretimde okul terki:

Nedenler ve çözümler. Egitim ve Bi-

lim, 37 (166), 219-235.

Çokluk. Ö. (2010). Lojistik regresyon analizi:

kavram ve uygulama. Kuram ve Uygu-

lamada Eğitim Bilimleri, 10 (3), 1357-

1407.

Field, A. (2009). Discovering statistics using Spss

(and sex and drugs and rock 'n' roll)

(third edition). London: Sage

publications.

Garson, D. G. (2012). Ordinal regression. Ashe-

boro: Statistical Associates Publishing.

Gaziel, H.H. (2004) Predictors of absenteeism

among primary school teachers. Social

Psychology of Education, 7, 421–434.

Green, B. S., Salkınd J.N. (2010). Using SPSS

for Windows and Macintosh: Analyzing

and Understanding Data. Prentice Hall

Press Upper Saddle River, NJ USA.

Gury, N. (2011). Dropping out of higher edu-

cation in France: A micro-economic

approach using survival analysis.

Education Economics, 19(1), 51–64.

Hill, R.A., Standen, P.J. ve Tattersfield, A.E.

(2014). Asthma, wheezing, and school

absence in primary schools. Archives of

Disease in Childhood, 64, 246-251.

Ingul, J.M., Klöcner, C.A., Silverman, W.K. ve

Nordhal, H.M. (2011). Adolescent

school absenteeism: modelling social and

individual risk factors, Child and Adoles-

cent Mental Health. Published by

Blackwell Publishing, 9600 Garsing-

ton Road.

Kearney, C. A. ve Silverman, W. K. (1990). A

preliminary analysis of a functional

model of assessmentand treatmentfor

school refusal behavior. Behavior

Modification, 1, 340-366.

Kablan, Z. (2009). Öğretmen adaylarının derse

devamının öğrenme başarısına etkisi.

Pamukkale Üniversitesi Eğitim Fakültesi

Dergisi, 1 (25), 22-32.

Karasar, N. (2003). Bilimsel araştırma yöntemi

(12. Baskı). Ankara: Nobel Yayın Da-

ğıtım.

Kayri, M. (2007). Araştırmalarda iki aşamalı

kümeleme analizi (two step cluste-

ring) ve bir uygulaması. Eğitim Araş-

tırmaları, 7 (28), 89-99.

Lassibille, G. & Gómez, N. L. (2008). Why do

higher education students drop out?

(Evidence from Spain). Education Eco-

nomics, 16(1), 89-105.

MEB. (2009). Devamsızlık ve okulu terk riski

durum saptaması ve ihtiyaç analizi

raporu. Ankara.

04 Ekim 2014 tarihinde

http://ysop.meb.gov.tr/dosyalar/adey/

ihtiyacanaliziraporu.pdf adresinden

erişildi.

Moonie, S., Sterling, D.A., Figgs,L.W. ve

Costro, M. (2008). The Relationship

between school absence, academic

performance, and asthma status. Jour-

nal of School Health, 78 (3), 140-148.

Nair, MKC, (2010) School Absenteeism

Among Children. Indian Pediatricts,

47,(17) 921-922.

Oruç, E. ve Özen Kutanis, R. (2015). Pozitif

psikolojik sermayenin örgüt içi politik

davranışlara etkisi: akademisyenler

https://tez.yok.gov.tr/UlusalTezMerkezi/

https://tez.yok.gov.tr/UlusalTezMerkezi/

http://ysop.meb.gov.tr/dosyalar/adey/ihtiyacanaliziraporu.pdf

http://ysop.meb.gov.tr/dosyalar/adey/ihtiyacanaliziraporu.pdf

182


üzerine bir araştırma. İşletme Araştır-

maları Dergisi, 7 (3), 36-58.

Özbaş, M. (2010). İlköğretim okullarında öğ-

renci devamsızlığının nedenleri. Er-

zincan Üniversitesi Eğitim ve Bilim Der-

gisi, 35 (156), 32-44.

Özdamar, K. (1997). Paket programlar ile ista-

tistiksel veri analizi. Eskişehir: Kaan

Kitabevi.

Özer, H., Sarı, A. (2009). Kovaryans analizi

modelleriyle üniversite öğrencilerinin

başarılarını etkileyen faktörlerin belir-

lenmesi: Atatürk Üniversitesi İİBF öğ-

rencileri için bir uygulama. Dokuz Ey-

lül Üniversitesi İktisadi ve İdari Bilimler

Fakültesi Dergisi, 24 (2), 105-126.

Cumhuriyet Üniversitesi önlisans ve lisans

eğitim öğretim ve sınav yönetmeliği.

Rençber (2012). Üniversite öğrencilerinin aka-

demik başarılarını etkileyen faktörler.

Çankırı Karatekin Üniversitesi Sosyal Bi-

limler Enstitüsü Dergisi, 3(1) 191-198.

Rothman, S. (2001). School absence and stu-

dent background factors: A multilevel

analysis. International Educational Jo-

urnal, 4 (1), 59-68.

Scarpa, E. (1978). The relationship between ab-

senteeism and achievement in reading and

matheematichs among secondary school

senior. Master Thesis, Southern Con-

necticut State College, United State.

Silah, M. (2003). Üniversite öğrencilerinin

akademik basarılarını etkileyen çesitli

nedenler arasından süreksiz durum-

luk kaygısının yeri ve önemi. Eğitim

Araştırmaları Dergisi, 10, 102-115.

Snyder, F., Flay, B., Vuchinich, S., Acock, A.,

Washburn, I, Beats, M, Li, K.K. (2010).

Impact of a social-emotional and cha-

racter development program on

school-level indicators of academic

achievement, absenteeism, and discip-

linary outcomes: A Matched-pair,

cluster-randomized, controlled trial,

Journal of Research on Educational Effec-

tiveness, 3, 26–55.

Stoll, P. (1990). Absent pupils who are Offici-

ally Present. Education Today, 40 (3),

22-25.

Şenel, S. ve Alatlı, B. (2014). Lojistik regresyon

analizinin kullanıldığı makaleler üze-

rine bir inceleme. Eğitimde ve Psiko-

lojide Ölçme ve Değerlendirme Dergi-

si, 5 (1), 35-52.

Tabachnick, B. G. & Fidell, L. S. (1996). Using

multivariate statistics. New York,

USA: Harper Collins College Publis-

hers.

Tinto, V. (1975). Dropout from higher educa-

tion: Theoretical synthesis of recent

research. Review of Educational Rese-

arch, 45(1), 89-125.

Tutar, H. (2002). Türk Cumhuriyetleri ve akraba

topluluklarından gelen öğrencilerin başa-

rısızlık nedenleri. (Yayımlanmamış

yüksek lisans tezi). Kırıkkale Üniver-

sitesi Sosyal Bilimler Enstitüsü, Kırık-

kale.

Uğurlu, C.T., Koç, C., Usta Sayın, H.G., Şim-

şek, A.S. (2012) Faculty of education

students’ views on absence attitudes.

Procedia-Socialand Behavioral Sciences,

46, 3401-3408.

Ullah, H., Mardell, A. W. (2007). Students’

academic success and its association

to student ınvolvement with learning

and relationships with faculty and

pers. College Student Journal, 41(4),

1192-1202

Usta, H. G., Uğurlu, C. T., Şimşek, A. S. (2014).

Üniversite öğrencilerinde devamsızlık

davranışları: nedenler ve tutum dü-

zeyleri. Yüksek Öğretim ve Bilim Dergi-

si. 4(3). 182-190.

Yalnızkurt, Y. (2008). Öğrenci devamsızlıkla-

rının nedenleri ve devamsızlığın aka-

demik başarıya olan etkisi. Dumlupı-

nar Üniversitesi Sosyal Bilimler Dergisi,

20, 129-142.

Yıldız, M ve Şanlı Kula, K. (2012). Öğretmen-

lerin öğrenci devamsızlıkları hakkın-

daki görüşleri. DPUJSS, 32 (4) 19-28.

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