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Journal Article•DOI•

A Partial Simulation Study of Phantom Effects in Multilevel Analysis of School Effects: The Case of School Socioeconomic Composition:

Hao Zhou1, Xin Ma1•
University of Kentucky1
01 Feb 2021-Sociological Methods & Research (SAGE PublicationsSage CA: Los Angeles, CA)-pp 004912412098619
TL;DR: This paper used hierarchical linear modeling (HLM) to estimate the effects of socioeconomic status (SES) on academic achievement at different levels of an educational system and found that if a prior acad...
Abstract: Hierarchical linear modeling (HLM) is often used to estimate the effects of socioeconomic status (SES) on academic achievement at different levels of an educational system. However, if a prior acad...

Summary (4 min read)

Jump to: [1.2 Phantom Effects] – [1.3 Contextual Effects] – [1.4 Purpose of Research] – [1.5.1 Informing Policy Change] – [1.5.2 Promoting Pioneer Research] – [2.2 School socioeconomic composition] – [2.3 Effects of School SES on Academic Achievement] – [2.5 Phantom effects of School SES] – [3.5 Partial Simulation] – [3.6 Working with Plausible Values in Partial Simulation] – [4.1 The Null Model] – [4.2 The Absolute Effects Models] – [4.3 The Relative Effects Models] – [5.3 Implications for Empirical Research] – [5.4 Implications for Policy and Practice] – [5.6 Suggestions for Further Research] and [5.7 Conclusion]

1.2 Phantom Effects

  • Phantom effects can also occur at other levels of an educational system.
  • At the student level, in the absence of SES, the racial-ethnic background often indicates statistically significant effects on academic achievement of students; however, in the presence of SES, such significant effects often disappear, which makes the racial-ethnic effects phantom effects.
  • With a focus on school SES, the present study investigates school contextual effects as a potential source of phantom effects in the school effectiveness research literature.

1.3 Contextual Effects

  • At the level 2, school SES was the mean SES for each school.
  • A two-level random intercept HLM was applied.
  • The results showed that after controlling for student's prior academic achievement, school SES effect disappeared in both cases (i.e., SES_1 and SES_2).
  • The effects of school SES were phantom effects.

1.4 Purpose of Research

  • The purpose of the present study is to examine the extent to which the effects.
  • The combination of empirical answers to both questions will provide evidence to address the issue of the extent to which the effects of school SES on science achievement of students are phantom effects.

1.5.1 Informing Policy Change

  • As argued earlier, many researchers have shown that school SES largely affects students ' academic achievement (e.g., OECD, 2015) .
  • The phenomenon of phantom effects associated with school SES may threaten the credibility of claims like this.
  • To some degree, education policymakers may have been misinformed on research evidence due to the complexity concerning school contextual effects, especially school SES.
  • This study aims to provide empirical evidence on whether phantom effects of school SES on students' academic achievement exist and, if yes, the extent to which school SES produces phantom effects on students' academic achievement.
  • The significance of this study is that it may promote policy change through a revisiting of educational policies and practices concerning school SES.

1.5.2 Promoting Pioneer Research

  • Among many important school characteristics, school socioeconomic status (SES)-a school background variable-plays a critical role in many educational policies and practices.
  • In New Zealand and the United Kingdom, schools adopt a funding model that provides similar resources to all schools and provides additional funding to schools with high needs (e.g. rural school, high percentage of students from low SES, etc.) (Perry and McConney, 2010) .
  • In the United States, policymakers issued different polices aimed to adjust school SES for better distribution of educational resources, such as magnet schools and school assignment policy.
  • The present study considers this important school characteristic.

2.2 School socioeconomic composition

  • At school level, school SES related with student academic achievement (e.g., Ma, 2010) .
  • School SES is often measured in two ways, either as the proportion of students enrolled in a reduced-price or free lunch program (Sirin 2005).
  • In general, School SES is dependent upon student SES.

2.3 Effects of School SES on Academic Achievement

  • If the coefficient is positive, school SES improves student academic achievement.
  • If the coefficient is negative, school SES hinders student academic achievement.
  • All studies referenced earlier showed evidence to support that students in high SES schools performed better than students in low SES schools.
  • Some of them, however, indicated that the effects of school SES can be conditional.
  • Lam and Lau's study (2014) , after controlling school size on school's level, showed that the school SES effects disappeared.

2.5 Phantom effects of School SES

  • The first method is that MLM includes variables that highly correlated with students' present academic achievement (Harker & Tymms, 2004) .
  • The above illustration pertains to this approach.
  • In other words, the effects of school SES are phantom effects because the model cannot adequately control for measurement errors.
  • There are few empirical studies to support this view.
  • Showed the same pattern as taking Year 5 student achievement as prior ability.

3.5 Partial Simulation

  • Simulated data would then work with actual data to address a statistical issue, thus named partial simulation.
  • The partial simulation procedure can generate a random variable with a defined correlation to an existing variable.
  • Once these prior measures of science achievement were generated, a separate multilevel analysis was performed with models that were discussed in the previous section.
  • As a result, 10 sets of multilevel analyses were conducted for each of the ten correlation conditions.

3.6 Working with Plausible Values in Partial Simulation

  • A short discussion on the variables employed at the student level and at the school level is in order before the modeling activities.
  • In addition, 26percent of the students are native, and 81 percent of the students speak English at home.
  • At the school level, the average school size is 1,251 students with a standard deviation of 887 students Meanwhile, 38 percent of the schools are located in city areas, 49 percent of the schools are located in town areas, and 13 percent of the schools are located in rural areas.
  • The average school SES is .07 with a standard deviation of .54.
  • Disciplinary climate is an index, and the average disciplinary climate is 0.28 with a standard deviation of 0.38.

4.1 The Null Model

  • The null model (see Chapter 3) provides the background for all the subsequent analyses.
  • The results of the null model show that the average science achievement of U.S. students is 494 points.
  • Therefore, according to the PISA science scale (M = 500, SD = 100), U.S. students scored a little lower than the international average.
  • The variance in science achievement at the student level is 7727.50, and variance in science achievement at the school level is 1876.65.
  • Intra-class correlation is approximately 0.20, which indicates that 20 percent of the total variance in science achievement is due to the school level.

4.2 The Absolute Effects Models

  • It is important to emphasize that, although all effects are statistically significant at the alpha level of .05 in Table 4 .2, some effects have rather small effect sizes.
  • If 25 percent of a SD can be considered practically important (e.g., Cohen, 1988) , then phantom effects of school SES appear when a prior measure has a correlation of .65 (even .55) with the present measure.
  • Compared with the base model, student SES effects and school SES effects in the model with .75 correlation between prior and present measures are decreased by 51 percent and 53 percent respectively.

4.3 The Relative Effects Models

  • As discussed in Chapter 3, the relative effects models examine student SES effects and school SES effects in the presence of student and school background variables (at student and school levels).
  • Meanwhile, with the increasing correlation between prior science achievement and present science achievement, the association between student SES and student science achievement decreases dramatically as well.
  • In the relative model, all effects associated with student SES are below .25 SD.
  • Phantom effects of student SES disappear when a prior science achievement reaches .35 (even .25) in correlation to present science achievement measurement.

5.3 Implications for Empirical Research

  • This study also offers a way to help create prior academic achievement measures when they are not available for data analysis.
  • Researchers are encouraged to conduct a thorough literature review to locate possible correlations between prior academic achievement measures and current academic achievement measures.
  • When these correlations are known, this study developed a procedure (in the programming language of R) to create prior academic achievement measurements, which will help researchers conduct data analysis based on correctly specified models.

5.4 Implications for Policy and Practice

  • The effectiveness of these policy practices is open to question based on the evidence in this and other studies.
  • The association between school SES and student academic achievement may be attenuated by misspecified contextual models.
  • In other words, student SES and school SES may not have as strong effects on student academic achievement as previous studies indicated, if the school contextual models are correctly specified.
  • In order to make appropriate policies, policymakers may want to encourage (e.g., fund) research projects that gather appropriate evidence with a fuller data collection from students and schools, particularly including prior student academic achievement measures.

5.6 Suggestions for Further Research

  • On the other hand, the approach that focuses on potential measurement errors may also be explored further.
  • Measurement error and model specification are often tangled up with each other to produce effects on parameter estimation.
  • Based on simulation study, the author gave a thumbs-up rule for applying each approach.
  • Pokropek (2015) provides only limited information to answer some of the questions, but more comprehensive studies should be conducted.

5.7 Conclusion

  • The result of this study can be summarized by several important points.
  • First, based on partial simulation procedure, phantom effects of school SES and student SES are real.
  • The stronger the correlation between prior science achievement measure and present science achievement measure, the greater the decrease in both student SES effects and school SES effects.
  • Third, the procedure of partial simulation provides a new angle to conduct theoretical studies (full simulation), which is entirely based on ideal assumption.
  • Finally, the procedure of partial simulation offers researchers a way to create prior student academic achievement measures when they are not available for data analysis.

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A PARTIAL SIMULATION STUDY OF PHANTOM EFFECTS IN MULTILEVEL
ANALYSIS OF SCHOOL EFFECTS: THE CASE OF SCHOOL SOCIOECONOMIC
COMPOSITION
________________________________________
DISSERTATION
________________________________________
A dissertation submitted in partial fulfillment of the
requirements for the degree of Doctor of Philosophy in the
College of Education
at the University of Kentucky
By
Hao Zhou
Lexington, Kentucky
Director: Dr. Xin Ma, Professor of Quantitative and Psychometric Methods
Lexington, Kentucky
2019
Copyright © Hao Zhou 2019
ABSTRACT OF DISSERTATION
A PARTIAL SIMULATION STUDY OF PHANTOM EFFECTS IN MULTILEVEL
ANALYSIS OF SCHOOL EFFECTS: THE CASE OF SCHOOL SOCIOECONOMIC
COMPOSITION
Socioeconomic status (SES) affects students’ academic achievement at different
levels of an educational system. However, misspecified Hierarchical Linear Model (HLM)
may bias school SES estimation. In this study, a partial simulation study was conducted
to examine how misspecified HLM model bias school and student SES estimation.
The result of this study can be summarized by four important points. First, based
on partial simulation procedure, phantom effects of school SES and student SES are real.
Second, characteristics of phantom effects are generalized. The stronger the correlation
between prior science achievement measure and present science achievement measure,
the greater the decrease in both student SES effects and school SES effects. Third, the
procedure of partial simulation provides a new angle to conduct theoretical studies (full
simulation), which is entirely based on ideal assumption. Finally, the procedure of partial
simulation offers researchers a way to create prior student academic achievement
measures when they are not available for data analysis.
KEYWORDS: Partial Simulation Study, School SES Effect, Student SES Effect
Hao Zhou
(Name of Student)
04/26/2019
Date
A PARTIAL SIMULATION STUDY OF PHANTOM EFFECTS IN MULTILEVEL
ANALYSIS OF SCHOOL EFFECTS: THE CASE OF SCHOOL SOCIOECONOMIC
COMPOSITION
By
Hao Zhou
Dr. Xin Ma
Director of Dissertation
Dr. Margaret Bausch
Director of Graduate Studies
04/26/2019
Date
Citations
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Journal Article•DOI•
Inadequacies in the SES–Achievement model: Evidence from PISA and other studies

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01 Oct 2021-Revista De Educacion
TL;DR: Marks et al. as mentioned in this paper found that SES has only moderate effects on student achievement, and its effects are especially weak when considering prior achievement, an important and relevant predictor.
Abstract: Correspondence Gary N. Marks, Social and Political Sciences, University of Melbourne, Parkville 3052, Victoria, Australia. Emails: gmarks@unimelb.edu.au; garymarks2030@gmail.com Abstract Students’ socioeconomic status (SES) is central to much research and policy deliberation on educational inequalities. However, the SES model is under severe stress for several reasons. SES is an illdefined concept, unlike parental education or family income. SES measures are frequently based on proxy reports from students; these are generally unreliable, sometimes endogenous to student achievement, only low to moderately intercorrelated, and exhibit low comparability across countries and over time. There are many explanations for SES inequalities in education, none of which achieves consensus among research and policy communities. SES has only moderate effects on student achievement, and its effects are especially weak when considering prior achievement, an important and relevant predictor. SES effects are substantially reduced when considering parent ability, which is causally prior to family SES. The alternative cognitive ability/genetic transmission model has far greater explanatory power; it provides logical and compelling explanations for a wide range of empirical findings from student achievement studies. The inadequacies of the SES model are hindering knowledge accumulation about student performance and the development of successful policies.

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Larry J. Grabau, Lesley Eblie Trudel, X Ma
27 May 2022-International journal of science education
TL;DR: This paper found that disciplinary climate was more often associated with science dispositions (epistemology, enjoyment, interest, instrumental, self-efficacy activities) in U.S. science classrooms, but an emphasis on teaching support in Canadian science classrooms.
Abstract: ABSTRACT While the United States (U.S.) and Canada share features in their secondary education systems (e.g. proportion of immigrants), these nations have differences (e.g. linguistic ). Given the primacy of Canada over the U.S. vis-à-vis science literacy [as measured by the Programme International for Student Assessment (PISA)], underlying differences in science dispositions and school climate variables could exist. The PISA 2015 science-focused dataset, including 5712 students in 172 schools in the U.S. and 20,058 students in 645 schools in Canada, was modelled via multilevel methods to assess associations among science dispositions (epistemology, enjoyment, interest, instrumental, self-efficacy activities) and science literacy. Associations of school climate variables of instructional leadership, disciplinary climate and teaching support (the latter two in the context of science classes) with the above measures were evaluated. Disciplinary climate was more often associated with science dispositions (as outcome measures) in the U.S., while teaching support was most often associated with these measures for Canada. Disciplinary climate was associated with science literacy (as an outcome measure) in the U.S.; in contrast, no school climate measures were associated with science literacy in Canada. These results support an emphasis on disciplinary climate in U.S. science classrooms, but an emphasis on teaching support in Canadian science classrooms.

1 citations

Journal Article•DOI•
Meta-analytical insights on school SES effects

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Cheng Yong Tan, Xiaoou Hong, Lin Gao, Qiongjiang Song
17 Mar 2023-Educational Review
TL;DR: This article used meta-analysis to synthesize findings involving 480 effect sizes from 97 studies (dated 2000-2020) to provide insights on associations between school socioeconomic status (SES) and student learning outcomes; schools' percentage of ethnic minority students and students' prior ability; and school processes in K-12 schools.
Abstract: The present study uses meta-analysis to synthesise findings involving 480 effect sizes from 97 studies (dated 2000-2020) to provide insights on associations between school socioeconomic status (SES) and (a) student learning outcomes; (b) schools’ percentage of ethnic minority students and students’ prior ability; and (c) school processes in K-12 schools. It makes three contributions to the school SES scholarship. First, it elucidates the magnitude (r = .58) and nature of school SES effects (e.g. larger effect sizes for achievement (vis-à-vis attainment) outcomes). Second, it clarifies the conceptual meaning of school SES, namely that school SES is less associated with school processes than it is with schools’ percentage of ethnic minority students or students’ prior ability. Third, the study shows that school SES is more strongly associated with specific school processes (school leadership and climate, teacher capacity, parental involvement benefiting student learning) than others (instructional programmes, educational resources).
Journal Article•DOI•
How much is too much time spent on homework: an exploratory study based on a Bayesian multilevel piecewise model with a random change point

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Hao Zhou, Yueyang Shao
09 May 2023-Educational studies
TL;DR: This article applied multilevel piecewise linear regression with a random effects model under a Bayesian estimation framework to a total of 5,072 7th grade students in 35 schools to search for an optimum time in terms of the effect of homework time on student academic achievement.
Abstract: In this study, we applied multilevel piecewise linear regression with a random-effects model under a Bayesian estimation framework to a total of 5,072 7th grade students in 35 schools to search for an optimum time in terms of the effect of homework time on student academic achievement. First, we found a nonlinear relationship between time spent on homework and student academic achievement. Second, we found an optimum duration of homework with two rather distinguishing phenomena of patterns while controlling only student socioeconomic status (SES) and gender. The optimum time spent on homework is 1.77 hours. However, after further controlling students’ prior academic achievement, the optimum time disappeared. We discuss possible explanations for the disappearance of the optimum time and the implications for further research directions.
References
More filters
Journal Article•DOI•
Effect of School Population Socioeconomic Status on Individual Academic Achievement

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Stephen J. Caldas, Carl L. Bankston
01 May 1997-Journal of Educational Research
TL;DR: The relationship between the socioeconomic status (SES) of peers and individual academic achievement was examined in this paper, while a variety of sociodemographic factors were being controlled, including a student's own SES.
Abstract: The relationship between the socioeconomic status (SES) of peers and individual academic achievement was examined in this study. This question was investigated while a variety of sociodemographic factors were being controlled, including a student's own SES. Student SES was measured by using participation in the federal free/reduced–price lunch program as an indicator of poverty status, and parental educational and occupational background as a measure of family social status. These measures were aggregated to the school level to define the SES of the peer population. Student achievement is a factor score of the three 10th–grade components of the Louisiana Graduation Exit Examination. Peer family social status in particular does have a significant and substantive independent effect on individual academic achievement, only slightly less than an individual's own family social status.

560 citations

Journal Article•DOI•
Using Hierarchical Linear Modeling to Study Social Contexts: The Case of School Effects

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Valerie E. Lee1•
University of Michigan1
01 Jun 2000-Educational Psychologist
TL;DR: In this article, the authors focus on a particular educational context, the school, and how characteristics of the structure and organization of high schools influence students' academic development and how teachers' attitudes, taken as a collective property of the social organization of schools, influence both learning and social distribution.
Abstract: This article focuses on a particular educational context, the school, and how characteristics of the structure and organization of high schools influence students' academic development. The emphasis is on a type of quantitative inquiry called school effects research. It describes a methodology that is most appropriate for conducting studies of school effects in particular and educational contexts in general: hierarchical linear modeling (HLM). Two previously published studies are used as heuristic examples of school effects studies conducted with HLM methods. Both studies use large and nationally representative longitudinal data from the National Education Longitudinal Study of 1988 to explore school effects on learning and its social distribution by student socioeconomic status. Study 1 focuses on the effects of high school size on learning. Study 2 focuses on how teachers' attitudes, taken as a collective property of the social organization of schools, influence both learning and its social distribution...

543 citations

Journal Article•DOI•
Doubly-Latent Models of School Contextual Effects: Integrating Multilevel and Structural Equation Approaches to Control Measurement and Sampling Error.

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Herbert W. Marsh1, Oliver Lüdtke2, Alexander Robitzsch, Ulrich Trautwein2, Tihomir Asparouhov, Bengt Muthén3, Benjamin Nagengast1 •
University of Oxford1, Max Planck Society2, University of California, Los Angeles3
11 Dec 2009-Multivariate Behavioral Research
TL;DR: Results are used to test the big-fish-little-pond effect (BFLPE), showing that individual student levels of academic self-concept (L1-ASC) are positively associated with individual level achievement and negatively associated with school-average achievement—a finding with important policy implications for the way schools are structured.
Abstract: This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2) levels and sampling error due the sampling of persons in the aggregation of L1 characteristics to form L2 constructs. We consider a set of models that are latent or manifest in relation to sampling items (measurement error) and sampling of persons (sampling error) and discuss when different models might be most useful. We demonstrate the flexibility of these 4 core models by extending them to include random slopes, latent (single-level or cross-level) interactions, and latent quadratic effects. Substantively we use these models to test the big-fish-little-pond effect (BFLPE), showing that individual student levels of academic self-concept (L1-ASC) are positively associated with individual level achievement (L1-ACH) and negatively associated with school-average achievement (L2-ACH)-a finding with important policy implications for the way schools are structured. Extending tests of the BFLPE in new directions, we show that the nonlinear effects of the L1-ACH (a latent quadratic effect) and the interaction between gender and L1-ACH (an L1 × L1 latent interaction) are not significant. Although random-slope models show no significant school-to-school variation in relations between L1-ACH and L1-ASC, the negative effects of L2-ACH (the BFLPE) do vary somewhat with individual L1-ACH. We conclude with implications for diverse applications of the set of latent contextual models, including recommendations about their implementation, effect size estimates (and confidence intervals) appropriate to multilevel models, and directions for further research in contextual effect analysis.

388 citations

Journal Article•DOI•
Capital at Home and at School: Effects on Student Achievement

[...]

Toby L. Parcel1, Mikaela J. Dufur2•
Ohio State University1, Brigham Young University2
01 Mar 2001-Social Forces
TL;DR: The authors investigated the effects of both family and school capital on student math and reading achievement using the National Longitudinal Survey of Youth (NLSY) merged Child-Mother Data for 1992 and 1994, to which indicators of capital in the children's schools for 1993-94 and 1994-95 have recently been added.
Abstract: We investigate the effects of both family and school capital on student math and reading achievement. We use the National Longitudinal Survey of Youth (NLSY) merged Child-Mother Data for 1992 and 1994, to which indicators of capital in the children's schools for 1993-94 and 1994-95 have recently been added. We study children who attended first through eighth grades in both 1992 and 1994, with samples of 2034 for math achievement and 2203 for reading recognition. Findings suggest that school capital effects are modest in size while family capital effects are stronger ; combinations of school and family capital boost or modify additive findings. We sketch directions for future research and discuss the usefulness of analyzing school and family capital as parallel concepts

318 citations

Journal Article•DOI•
Does the SES of the School Matter? An Examination of Socioeconomic Status and Student Achievement Using PISA 2003

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Laura B. Perry1, Andrew McConney1•
Murdoch University1
01 Apr 2010-Teachers College Record
TL;DR: In this paper, the authors examined the relationship between school SES and student outcomes by asking two research questions: 1) how does the association vary for students of different socioeconomic backgrounds? In other words, is the association stronger for students from lower SES backgrounds than for higher SES background? 2) How does the relationship vary across schools with different socioeconomic compositions? In particular, are increases in school socioeconomic composition consistently associated with increases in student academic achievement?
Abstract: Background/Context: It is well established in the research literature that socioeconomically disadvantaged students and schools do less well on standardized measures of academic achievement compared with their more advantaged peers. Although studies in numerous countries have shown that the socioeconomic profile of a school is strongly correlated with student outcomes, less is understood about how the relationship may vary if both individual student and school socioeconomic status (SES) are disaggregated. Purpose/Objective/Research Question/Focus of Study: This study examines the relationship between school SES and student outcomes in more detail by asking two research questions. First, how does the association vary for students of different socioeconomic backgrounds? In other words, is the association stronger for students from lower SES backgrounds than for students from higher SES backgrounds ? Second, how does the association vary across schools with different socioeconomic compositions? In other words, are increases in school socioeconomic composition consistently associated with increases in student academic achievement? Population/Participants/Subjects: This study uses data from the Australian 2003 Programme for International Student Assessment (PISA). The sample includes over 320 secondary schools and more than 12,000 students from Australia. Research Design: This study is a secondary analysis of data from the Australian 2003 PISA. Descriptive statistics are used to compare the average reading mathematics, and science achievement of secondary school students from different SES backgrounds in a variety of school SES contexts. Conclusions: The two main findings of the study are that increases in the mean SES of a school are associated with consistent increases in students' academic achievement, and that this relationship is similar for all students regardless of their individual SES. In the Australian case, the socio-economic composition of the school matters greatly in terms of students' academic performance.

316 citations

"A Partial Simulation Study of Phant..." refers background in this paper

  • ...In New Zealand and the United Kingdom, schools adopt a funding model that provides similar resources to all schools and provides additional funding to schools with high needs (e.g. rural school, high percentage of students from low SES, etc.) (Perry and McConney, 2010)....

    [...]

  • ...School socioeconomic composition is, perhaps, the most popular school contextual variable and school SES has been declared to have a large and persistent effect on students’ academic achievement (Perry & McConney, 2010; Willms, 2010)....

    [...]

  • ...In New Zealand and the United Kingdom, schools adopt a funding model that provides similar resources to all schools and provides additional funding to schools with high needs (e.g. rural school, high percentage of students from low SES, etc.) (Perry & McConney, 2010)....

    [...]

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The effect of school population socioeconomic status on individual student academic achievement.

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