A review of the application of logistic regression in educational research: common issues, implications, and suggestions

Niu, Lian

doi:10.1080/00131911.2018.1483892

Cited by 47 publications

(29 citation statements)

References 120 publications

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“…This method reveals how variables (predictor variables) affect the likelihood that persons belong to different groups (dependent variable). The effect size is typically reported in the form of odds ratios (Field 2009) but it should be noted that this value cannot be directly interpreted as relative probabilities (Niu 2020). Here, the statistical modelling was based on the attitude model in Fig.…”

Section: Discussionmentioning

confidence: 99%

Students’ attitudes toward technology: exploring the relationship among affective, cognitive and behavioral components of the attitude construct

Svenningsson

Höst

Hultén

et al. 2021

Int J Technol Des Educ

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When studying attitudes toward technology education, the affective attitudinal component has primarily been the focus. This study focuses on how the affective, cognitive and behavioral attitudinal components of technology education can be incorporated using a two-step survey: the traditional PATT-questionnaire (PATT-SQSE) and the recently developed Mitcham Score questionnaire. The aim of this study is to explore the relationship among the cognitive, affective and potential behavioural components of students’ attitudes toward technology in a Swedish context, using the PATT-SQ-SE instrument including the Mitcham score open items. Results of the analyses show that relationships among the attitudinal components are observable. The results also imply that relationships among the attitudinal components are different for girls than boys. A key factor for the participating students’ attitudinal relations was interest (affective component) in technology education. An individual interest in technology education was related to both the cognitive component and behavioral intention. Another key relationship, for girls, was that the cognitive component had a strong relationship with behavioral intention, which was not the case for boys. Based on the observed relations between the cognitive, affective and behavioural components we have identified two key implications for educational practice: Girls should learn a broader conception of technology in technology education, if we want them to pursue technology-related careers to a higher degree; Students’ interest in technology should be stimulated through engaging tasks in technology education.

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Section: Discussionmentioning

confidence: 99%

Students’ attitudes toward technology: exploring the relationship among affective, cognitive and behavioral components of the attitude construct

Svenningsson

Höst

Hultén

et al. 2021

Int J Technol Des Educ

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“…In reference to the way the data is processed, it is defined as correlational-causal. Also, it has a non-experimental-observational cross-sectional approach in relation to the collecting of data (Niu, 2020).…”

Section: Type Of Researchmentioning

confidence: 99%

“…The appropriate sample size for the study was determined using the sample size formula for a finite population (Laurencelle, 2009;Niu, 2020), with a known population of 1 million participants, a confidence level of 95%, an error of 5% and a probability p of 0.5. Obtaining an estimated sample of n=385.…”

Section: Data Collection and Processingmentioning

confidence: 99%

Perception of Institutional Quality as a Determining Factor in Academic Performance in Higher Middle School Students

Lima-Vargas¹,

Obaya²,

Lima-Vargas³

et al. 2021

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Higher middle education is within compulsory education in most Latin American countries. In Mexico, higher middle education presents the lowest indicator in relation to terminal efficiency, this is due to the low academic performance of students. Meeting student expectations have been found to have a direct influence on their academic performance. Other authors indicate that the measurement of satisfaction in education should be considered from a service perspective. The study aims to assess the impact of students' perception of quality towards their institution (teachers and administrators) on their academic performance and gender differences. This study was conducted using the SERVQUAL data collection tool, in upper middle-level students in Mexico. The findings suggest that the ability to convey confidence during teacher-student or institution-student interaction is the variable that most influences students' academic performance, followed by tangibility and empathy with significant variations in gender. These results will help higher middle-level educational institutions develop not only educational plans, but also more efficient administrative plans to improve academic performance.

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“…pass the external test of the Secondary School of Economics. Niu (2020) also considered the gender variable relevant when it comes to academic development, according to the author women are 33% more likely than men to receive a university degree.…”

Section: Time and Gender Influence On Student's Performancementioning

confidence: 99%

Test time assessment on student's performance of statistics subjects by generalized linear models

Brum¹,

Liska

Santos

2021

RSD

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Can the time it takes a student to complete a test influence his / her performance? To answer this question, the logistic regression model was considered. In its development, evaluation was considered as a way of quantifying the performance of a student reflecting his degree of knowledge in a given content. For this we use records of the initial and final moments when developing an evaluation. The records of time spent were obtained from five different undergraduate classes, with subjects taught by the same teacher, with the same theoretical content, at the same university. The results confirm statistically that each additional minute that the student remains taking the test, implies in greater chances of obtaining good performance, as well as differences of performance between the feminine and masculine genders, although not statistically different, demonstrating that feminine students have greater chances of reaching the average. The model also confirms, according to the odds ratios that during the evaluations the students' performance decreases, having the best score in the first test. Through the references consulted, we understand that the difference in the grades of each student is influenced by several factors, the result of their own experiences.

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A review of the application of logistic regression in educational research: common issues, implications, and suggestions

Cited by 47 publications

References 120 publications

Students’ attitudes toward technology: exploring the relationship among affective, cognitive and behavioral components of the attitude construct

Students’ attitudes toward technology: exploring the relationship among affective, cognitive and behavioral components of the attitude construct

Perception of Institutional Quality as a Determining Factor in Academic Performance in Higher Middle School Students

Test time assessment on student's performance of statistics subjects by generalized linear models

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