This paper is a critique of a psychology article titled, “Role of personality in parents’ education and children’s achievement: the role of personality.” The article sought to understand if a child’s personality and intelligence level affect his/her academic performance. The study investigated this relationship by exploring the relationship between a parent’s educational background and the academic performance of a student.
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A positive relationship between the parents’ education levels and the student’s academic performance sufficed. The researchers also established that a focus on the role of student characteristics (in academic achievement) undermined the relationship between the educational background of the parents and a student’s academic performance (Steinmayr et al., 2010). For example, the authors established that there was a significant reduction in the positive relationship between parents’ educational achievements and a student’s academic success when they assessed the personalities and intelligence levels of the student (Steinmayr et al., 2010).
Here, the researchers used parents’ education backgrounds as an indicator of socioeconomic status (SES) (although the role of student characteristics in influencing academic achievement played a significant role in determining a student’s educational success) (Steinmayr et al., 2010).
Independent of the above findings, this paper analyses the above study by evaluating important issues that support its arguments. In detail, this paper analyses the theoretical coherence of the article and the strengths and weaknesses of the arguments advanced. Some key highlights of this paper show that some parts of the study demonstrate inherent structural weaknesses because the researchers overly relied on German standards for measuring student intelligence, academic achievement, and student personalities.
The use of technical jargon to explain basic social relationships also makes it difficult for the readers to associate the “technical” findings of the study with its “social” nature. Comparatively, this paper shows that the researchers’ theoretical coherence is impressive. Based on this analogy, this paper recommends that future studies should avoid using “local” frameworks to measure intelligence, personality, and the level of academic achievement among students. Similarly, this paper proposes that future studies should use better samples, with a broader ability and social background range, to improve the quality of their findings.
The theoretical arguments presented by Steinmayr et al. (2010) show that there was an impressive theoretical coherence in the researchers’ arguments. A strong pillar of their argument is the elaboration of the nature of different and small relationships that explain the larger understanding of the research topic. For example, the researchers use the relationship between parents’ educational achievements and student academic performance and the analysis of intelligence as a mediator of the relationship between parents’ education backgrounds and academic achievement to explain the larger theoretical understanding of the research topic. This theoretical approach provides an informed understanding of the larger research topic by comprehending the smaller relationships that explain the larger relationship – the role of personality in understanding the parents’ educational backgrounds and a student’s academic achievement.
Besides the use of relational relevance, the level of theoretical organization in the study is also impressive. The use of flowcharts and diagrams especially help to paint a graphical understanding of the researchers’ arguments. The same effect is realized when the researchers use models to explain the theoretical arguments proposed by the researchers. For example, the researchers used structural equation models to test students’ intelligence levels as a mediator of the relationship between parents’ education backgrounds and the level of a student’s academic achievement (Steinmayr et al. 2010). The use of models and graphical representations make it easy for readers to comprehend the researchers’ arguments, thereby providing a better flow of thought for the readers.
Besides the organization of texts, it is also important to say that there are sufficient logic and evidence in most of the findings presented in the study. Indeed, there is a spirited attempt by the researchers to support their findings with experimental outcomes, and compare the same findings with previous research. For example, the researchers say that the strength of their arguments may be relatively weaker than previously published findings because they failed to use research inputs that have a strong internal variability (Steinmayr et al., 2010). This way, they compare the findings of the study to similar studies.
Despite the commendable theoretical coherence of arguments and thought, the use of equations and jargons in explaining the overlying relationships in the study creates an unwarranted technical “feel” to the paper. More so, this observation gains credence because the study is a non-technical paper that should focus on social relationships. By explaining these social relationships using equations and technical models, it becomes difficult for the readers to understand the relationship between the findings with their technical outcomes. Comprehensively, the above dynamics show the quality of theoretical coherence in the study.
Strengths and Weaknesses
The strengths and weaknesses of the analyzed article are obvious to the authors, especially in their admission of the limitations for their study. One limitation that they single out is the limitation of the investigated sample. This limitation manifested through its narrow selection of respondents. In other words, the researchers pre-selected the respondents to participate in the study, based on their abilities, personalities, and academic performances (Steinmayr et al., 2010).
This pre-selection led to the development of a narrow criterion for selecting respondents. Moreover, the researchers admit that there was a significant under-representation of students from low-income groups in the school sampled (Steinmayr et al., 2010). This under-representation may have undermined the accuracy of the findings since the sampled population group needed to have a wider internal variance. These weaknesses explain why the correlations found in the study differed from similar correlations found in other studies. The implication of this finding is that the correlations found in this study may have been stronger if there was a broader and more accurate representation of student dynamics.
The focus of parents’ education backgrounds as the main SES indicator was also a significant weakness of the study because the inclusion of other SES indicators may have provided a stronger indicator of SES. For example, other studies usually use a stronger composite index of SES variables (Steinmayr et al., 2010). In fact, most studies prefer to use at least two SES variables, such as income and educational qualifications. Therefore, the use of parents’ education backgrounds as the only SES indicator was a significant weakness of the paper (more so, because it failed to establish if there were differential associations between varied SES indicators and students’ academic performances).
Lastly, the data analysis method adopted by Steinmayr et al. (2010) did not present the proper causal relationship that should represent such a study. Instead, the researchers did not interpret the effect of students’ personality traits on their academic performance and the role of parents’ education backgrounds in this regard. Instead, the researchers preferred to use intelligence and student personalities as partial mediators between the relationships that the educational backgrounds of the parents have on students’ academic achievement. Instead, the researchers should have presented a causal relationship between students’ intelligence levels, the influence of parents’ education backgrounds and the academic achievements of the students.
The over-reliance on German-set standards to measure important variables in the study also provided a local approach to the study, which may make it difficult to extrapolate the findings to other non-German contexts. For example, the researchers used the German intelligence structure test and the German version of the NEO-FFI method to measure students’ intelligence levels and personalities respectively (Steinmayr et al., 2010).
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While both measures may accurately predict the personality and intelligence levels of the students, it is very difficult to rely on the same measures to provide a global understanding of the relationship between parents’ educational levels and students’ academic achievements. Instead, the findings of the study become more relevant to the German context or the European context only (by extension).
The same argument is true if we evaluate the use of the Grade Point Average (GPA) to measure students’ academic performances. The use of GPA to measure students’ academic performances may, therefore, create a localized approach to presenting the research findings because the use of other measures of academic achievement may lead to different results. Comprehensively, the above dynamics represent the main weaknesses of the study.
The use of the causal mediation analysis in the data analysis section is a great strength of the analyzed study because it improves the quality of the findings. Many studies have used mediation analyses to explain the causal mechanisms that underlie different dependent and independent relationships (Imai, Keele, & Tingley, 2013). One key contribution of mediation analysis is its ability to provide the researchers with different causal pathways that would explain the influence of the relationships between student intelligence, parents’ education backgrounds, and student personalities on students’ education achievements (Imai et al., 2013).
In addition, the use of the mediation analysis also helped to provide the researchers with an opportunity for conducting sensitivity analyses. The inclusion of these analyses helped the researchers to assess the robustness of their findings.
Another significant strength of the study was the use of a large population sample to conduct the study. The researchers used about 580 11th to 12th graders in the study (Steinmayr et al., 2010). Of this population group, there was a high participation rate of more than 90% (Steinmayr et al., 2010). Albeit the use of the high population samples may have slowed the collection and analysis of data, it improved the validity and reliability of the study because it provided a more representative sample of the population. Comparatively, the use of a lower population sample would undermine the reliability of the findings across a larger population sample.
This paper already establishes that a key weakness of the analyzed study was the lack of a proper representation of the variables to investigate. One key recommendation that emerges from this analysis is the inclusion of better samples, with a broader ability and social background range, to improve the representation of the sample population in future studies. Also, future studies should use more globalized scales for measuring students’ intelligence levels, personalities, and academic performances.
The reliance on German standards for doing so only presents a localized approach of conducting the study, thereby making it very difficult to generalize the findings beyond Germany. Future studies should also use less technical jargons for explaining social relationships because some readers may experience some difficulties trying to relate the outcomes of the study with the findings. Nonetheless, the logical presentations of the findings provide a more favorable picture of the study.
Imai, K., Keele, L., & Tingley, D. (2013). Causal Mediation Analysis Using R. Web.
Steinmayr, R., Dinger, F., & Spinath, B. (2010). Parents’ Education and Children’s Achievement: The Role of Personality. European Journal of Personality, 24(1), 535–550. Web.