Review of a Quantitative Article: Educational Research and Assessment

This report provides a comprehensive review of a quantitative research article by Balkis, Arslan, and Duru (2016), which investigates the factors contributing to school absenteeism among high school students in Turkey. The study explores the relationship between absenteeism and personal factors such as student attitudes and motivation, as well as family factors including parental income and education levels. The research employed a quantitative approach, utilizing the School Assessment Survey-Revised (SAAS-R) and statistical analysis techniques like SPSS to analyze data collected from 423 students. The findings reveal negative correlations between absenteeism and both personal factors and parental education levels. The report critically analyzes the article, discussing opportunities for further research, potential threats to validity, the insights provided, and the implications of the findings for teachers and student performance. The review highlights the strengths of the study's data analysis while also acknowledging limitations in sampling and providing suggestions for future studies, including the use of qualitative and mixed-methods approaches. The review concludes by emphasizing the practical implications of the research for improving teacher-student relationships and student academic outcomes.

Running head: UNDERSTANDING EDUCATIONAL RESEARCH AND ASSESSMEENT
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Understanding Educational Research and Assessment
Review of a quantitative article
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Review of a quantitative article
Balkis, M., Arslan, G., & Duru, E. (2016). The School Absenteeism among High School
Students: Contributing Factors. Educational Sciences: Theory and Practice, 16(6),
1819-1831.
Summary of the article
Purpose of study
The article aims to evaluate and examine the relationship between the absenteeism
registered in high schools and the related factors contributing to these incidences. The author
illustrates that these factors are classified into personal and family factors. The examples of the
individual factors include student attitudes towards the schools and teachers, motivation, and
academic self-perception among others whereas the family factors include the income and the
level of education among the parents (Balkis, Arslan, and Duru, 2016, pp.1819-1831).
Sampling and selection of respondents
The sample of the participants was selected from two schools in an urban town in
Turkey where the target students were from grades 9 to 12. Concisely, 423 students were
sampled out where 58.4 percent were boys while 41.6% were boys ranging between 15 and 18
years of age. However, the sample had students from the entire similar ethnic group although
they had different social and economic backgrounds. The participation was on a voluntary basis
as required by the code of ethics in any research and lasted 25 minutes approximately (Harriss,
MacSween, and Atknison, 2017, pp.1126-1131).

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Data collection
The use of demographic information sheet was used to gather data. These were
administered using the School Assessment Survey-Revised (SAAS-R) to collect the students’
information such as their age, parent’s income, their academic qualification, and current
absenteeism among other aspects as acknowledged by (Suldo et al., 2014, pp.3-14).
Research design and Statistical analysis
The study utilized the use of the Statistical Package for Social Sciences technique to
perform the correlations among the variables from the data obtained by the SAAS-R tool. The
SPSS was also used to draw the relationships among the variances using a one-way ANOVA to
examine whether there could be the change in the dependent variable with alterations in the
independent variable. Additionally, the SPSS AMOS was used in the estimation of the likelihood
of the parameters alongside the Chi-square and other error approximations in the SPSS software
(Jatnika, 2015, pp.818).
Results
The correlation between the variables established using the Pearson product-moment
of correlation analysis indicated that the personal factors and the level of education of the parents
were statistically associated negatively to the current and previous absenteeism among the
students. The results of the correlations analysis are displayed in the table below.

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The results from the analysis of variance (ANOVA) indicated that absenteeism was
higher among the students whose parents had a lower income than those whose parents had high-
income rates. Concisely, absenteeism was higher among students whose parents had lower levels
of education whereas the academic performances were negatively related to absenteeism. The
author summarized the direct and indirect relationships among various variables as shown in the
diagram below.

UNDERSTANDING EDUCATIONAL RESEARCH AND ASSESSMENT
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Critical analysis of the article
In the effort to understand educational research and assessment, it is important to
analyze some aspects from the article for future improvement of similar studies and utilization of
the findings (Morse, 2015, pp.1212-1222). This can be achieved through critical analysis of the
following.
Opportunities for further studies
Some of the potential limitations of this research provide an opportunity for further
researches. The future researchers could utilize the intensive use of qualitative methods, which
are not mentioned by the author (Flick, 2014). Another similar study could use mixed methods

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where it could combine both the qualitative and the quantitative methods in fostering the
understanding of educational assessment and researches (Mertens, 2014). Additional
opportunities for this research could explore the sampling of the students from both the public
and the private schools in Turkey unlike the author in this article who only explored the public
schools.
Threats to validity rivalry of the hypotheses
The possible hypothesis that could be examined to compete the stated rationale in the
article is the evaluation of the students who have no parents or those cared for by the well-
wishers concerning the current and present absenteeism. The author based the research on the
assumption that the entire students lived with their parents, which might be the case across all the
respondents. This hypothesis could challenge the author’s thesis in the article.
Insights and critics
The author provides insightful analysis of the collected data. This is evident due to
the extensive use of the correlation and SEM analysis tools among other modules. Through these
techniques, the author draws clear results and well-presented direct and indirect relationships
among the variables. The level of data analysis displayed by the article can be recommended for
use by similar researches in the future due to high reliability presented. However, the research
findings from the report can be criticized due to the sampling method used. With the limitation
of the samples to two public schools, the data could vary across other schools. Additionally, the
sample size was minimal to be projected across a large population as illustrated in the studies of
(Kenny, Kaniskan, and McCoach, 2015, pp.486-507), who acknowledges the statistical power in

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using large data samples. These areas could be improved in future studies to carter for the
limitations of the sample sizes in the research findings.
Implications of the findings
The findings from the article can be beneficial for the high school teachers in
understanding the behavioral trends among different students. With this background knowledge
by the teachers, it could increase the chances of building better and more professional
relationships between the teachers and the learners in a classroom environment (Gruenewald,
2014, pp.161-178). Additionally, the improved relationship between the teachers and the
students is more likely to motivate the student to reduce the rates and chances of absenteeism
among the student. This could precipitate improvements in the academics of the student.
Therefore, the findings from the article are likely to boost the students’ performance by reducing
absents among students.

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References
Balkis, M., Arslan, G., & Duru, E. (2016). The School Absenteeism among High School
Students: Contributing Factors. Educational Sciences: Theory and Practice, 16(6), 1819-
1831.
Flick, U. (2014). An introduction to qualitative research. Sage.
Gruenewald, D. A. (2014). Place-based education: Grounding culturally responsive teaching in
geographical diversity. In Place-based education in the global age (pp. 161-178).
Routledge.
Harriss, D. J., MacSween, A., & Atkinson, G. (2017). Standards for ethics in sport and exercise
science research: 2018 update. International journal of sports medicine, 38(14), 1126-
1131.
Jatnika, R. (2015). The effect of SPSS course to students attitudes toward statistics and
achievement in statistics. International Journal of Information and Education
Technology, 5(11), 818.
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models
with small degrees of freedom. Sociological Methods & Research, 44(3), 486-507.
Mertens, D. M. (2014). Research and evaluation in education and psychology: Integrating
diversity with quantitative, qualitative, and mixed methods. Sage publications.
Morse, J. M. (2015). Critical analysis of strategies for determining rigor in qualitative
inquiry. Qualitative health research, 25(9), 1212-1222.

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Suldo, S. M., McMahan, M. M., Chappel, A. M., & Bateman, L. P. (2014). Evaluation of the
teacher–student relationship inventory in American high school students. Journal of
Psychoeducational Assessment, 32(1), 3-14.