Date

2-3-2023

Department

School of Education

Degree

Doctor of Philosophy in Education (PhD)

Chair

Jeffrey Savage

Keywords

social media, social networks, intense social media use, science and math test scores, middle school. at-risk, urban schools, ordinal logistic regression, quantitative correlational study, fail to reject the null hypothesis

Disciplines

Education

Abstract

The purpose of this quantitative correlational study was to investigate the intensity of social networking use, gender, and socioeconomic status as possible predictors of eighth grade at-risk middle school students’ math and science test scores. Because this study hypothesized a general linear model between an ordinal response variable and more than one explanatory variables, ordinal logistic regression analysis was used. Social media is pervasive in the daily lives of students and so it can have a possible deleterious influence on student achievement. This idea continues to animate practitioners, researchers, parents, and all others interested in the achievement and success of adolescents. The current study was carried out in an urban middle school in the MidAtlantic United States. A convenience sample of 68 students participated in the study by completing a survey using the SNAIS instrument to measure their intensity of social networking use. A test of the full model (with gender, SES, and SNAIS as the predictor variables) compared with a constant-only or null model showed no significant effect. These outcomes were explored based on the data analysis results. Some reasons for this apparently contradictory result are explored in the discussion, including the need to examine more accurate results of student achievement versus self-reported measures to ascertain the extent of potential errors in estimating achievement levels. The study suggests that the possible outcome of the hypothesized relationship between social media, social networking, and academic achievement are more complex than might be assumed. Further research is required to investigate the relationship between the hypothesized variables.

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