scholarly journals Dangers of including outcome at baseline as a covariate in latent change score models

2021 ◽  
Author(s):  
Kimmo Sorjonen ◽  
Gustav Nilsonne ◽  
Bo Melin
Keyword(s):  
Structural Equation ◽  
Change Score ◽  
Published Data ◽  
Regression To The Mean ◽  
Initial Value ◽  
Equation Modelling ◽  
Measuring Change ◽  
The Mean ◽  
Latent Change Score ◽  
Matrix Reasoning

Latent change score modelling is a version of structural equation modelling for measuring change between measurements. It seems quite common to regress change on the initial value included in the calculation of the change score (i.e. ΔY (= Y2 – Y1) is regressed on Y1). However, similarly as in simpler regression analyses, this procedure may make findings susceptible to the influence of regression to the mean. This suspicion was verified in the present simulations. An empirical application, including re-analyses of previously published data, indicated that previously claimed reciprocal promoting effects of vocabulary and matrix reasoning on each other’s longitudinal development may actually be due to regression to the mean. Researchers are recommended not to regress change on the initial value included in the calculation of the change score when employing latent change score modelling, or at least to verify findings with analyses omitting this parameter.

scholarly journals Regression to the mean in latent change score models: an example involving breastfeeding and intelligence

2021 ◽  
Author(s):  
Kimmo Sorjonen ◽  
Gustav Nilsonne ◽  
Michael Ingre ◽  
Bo Melin
Keyword(s):  
Observational Data ◽  
Observational Studies ◽  
Positive Association ◽  
Change Score ◽  
Longitudinal Survey ◽  
Regression To The Mean ◽  
Intergenerational Change ◽  
The Mean ◽  
Latent Change Score ◽  
Child Intelligence

Latent change score models are often used to study change over time in observational data. However, latent change score models may be susceptible to regression to the mean. In the present study, we investigate regression to the mean in the case of breastfeeding and intelligence of children. Earlier observational studies have identified a positive association between breastfeeding and child intelligence, even when adjusting for maternal intelligence. Here, we used latent change score modeling to analyze intergenerational change in intelligence, both from mothers to children and backward from children to mothers, in the 1979 National Longitudinal Survey of Youth (NLSY79) dataset (N = 6283). When analyzing change from mothers to children, breastfeeding was found to have a positive association with intergenerational change in intelligence, whereas when analyzing backward change from children to mothers, a negative association was found. These discrepant findings highlight a hidden flexibility in the analytical space and call into question the reliability of earlier studies of breastfeeding and intelligence using observational data.

Full for Første Gang: En Longitudinell Studie

1995 ◽  
Vol 12 (3) ◽  
pp. 121-131
Author(s):  
Willy Pedersen ◽  
Henrik Aas
Keyword(s):  
Alcohol Consumption ◽  
Structural Equation ◽  
Religious Involvement ◽  
Alcohol Problems ◽  
Peer Influences ◽  
Independent Effect ◽  
Friendship Networks ◽  
Equation Modelling ◽  
The Mean ◽  
Effect Of Age

In a longitudinal study with a prospective of five waves of data, the predictors and consequences of alcohol and intoxication debut were investigated. The sample consisted of 465 Norwegian adolescents, who were followed up over six years. Median age of alcohol debut was 15 years, and the mean age 14.8 years. By using structural equation modelling, parental and peer influences on alcohol and intoxication debut were estimated. While controlling for parental and peer influences, the consequences of age of debut were studied in terms of subsequent alcohol consumption and the development of alcohol problems. The findings revealed that there was an independent effect of age of alcohol debut both as regards further alcohol consumption and development of alcohol problems. But no such effect was reported for the age of first intoxication. Five percent of the sample had not had their alcohol debut by the end of their teens. Logistic regression analyses were conducted to predict abstainer status. The findings indicated that the abstainers were heterogeneous. Low parental substance use, high religious involvement and school adaptation increased the probability of abstaining. However, weak friendship networks also had the same effects.

scholarly journals Structural equation modelling the relationship between anti-fungal prophylaxis and Pseudomonas bacteremia in ICU patients.

2021 ◽  
Author(s):  
James Hurley
Keyword(s):  
Latent Variables ◽  
Structural Equation ◽  
Bacterial Infections ◽  
Structural Equation Models ◽  
Model Fit ◽  
Published Data ◽  
Icu Patients ◽  
Equation Modelling ◽  
Anti Fungal ◽  
The Relationship

Abstract Purpose Animal models implicate candida colonization facilitating invasive bacterial infections. The clinical relevance of this microbial interaction remains undefined. Observations from studies of anti-septic, antibiotic, anti-fungal, and non-decontamination-based interventions to prevent ICU acquired infection collectively serve as a natural experiment. Methods Three candidate generalized structural equation models (GSEM), with Candida and Pseudomonas colonization as latent variables, were confronted with blood culture and respiratory tract isolate data derived from 460 groups from 279 studies including studies of combined antibiotic and antifungal exposures within selective digestive decontamination (SDD) interventions. Results Introducing an interaction term between Candida colonization and Pseudomonas colonization substantially improved GSEM model fit. Model derived coefficients for singular exposure to anti-septic agents (-1.23; -2.1 to -0.32), amphotericin (-1.78; -2.79 to -0.78) and topical antibiotic prophylaxis (TAP; +1.02; +0.11 to + 1.93) versus Candida colonization were similar in magnitude but contrary in direction. By contrast, the model-derived coefficients for singular exposure to TAP, as with anti-septic agents, versus Pseudomonas colonization were weaker or non-significant. Singular exposure to amphotericin would be predicted to more than halve candidemia and Pseudomonas bacteremia incidences versus literature benchmarks for absolute differences of approximately one percentage point or less. Conclusion GSEM modelling of published data supports the postulated interaction between Candida and Pseudomonas colonization towards promoting bacteremia among ICU patients. The model implicates that anti-fungal agents have greater impact in preventing bacteremia versus TAP, which has no impact.

scholarly journals Lord’s paradox in latent change score modeling: an example involving facilitating longitudinal effects between intelligence and academic achievement

2021 ◽  
Author(s):  
Kimmo Sorjonen ◽  
Bo Melin ◽  
Gustav Nilsonne
Keyword(s):  
Academic Achievement ◽  
Change Score ◽  
Outcome Variable ◽  
Initial Value ◽  
Longitudinal Effects ◽  
Effect Regression ◽  
Subsequent Measurement ◽  
The Mean ◽  
Regression Toward The Mean ◽  
Over Time

It has been claimed that intelligence causes academic achievement to increase over time, and that also, conversely, academic achievement causes intelligence to increase over time. This bidirectional facilitating longitudinal effect between intelligence and academic achievement rests on observed associations between initial intelligence and the change in academic achievement between an initial and a subsequent measurement, and vice versa. Here, we demonstrate, through simulating empirical data used in previous research, that such longitudinal associations may be due to regression toward the mean rather than a true facilitating effect. Regression toward the mean occurs due to the conditioning of change on the initial value on the outcome variable. Researchers should be aware of this fallacy and are recommended to verify their findings with analyses without adjustment for an initial value on the outcome.

scholarly journals What to do Without a Control Group: You have to go latent, but not all latents are equal

2021 ◽  
Author(s):  
John Protzko ◽  
Jan te Nijenhuis ◽  
Khaled Ziada ◽  
Hanaa Abdelazim Mohamed Metwaly ◽  
salaheldin Bakhiet
Keyword(s):  
Measurement Invariance ◽  
Latent Variable ◽  
Structural Equation ◽  
Change Score ◽  
Equation Modeling ◽  
Control Group ◽  
Longitudinal Structural Equation Modeling ◽  
Latent Ability ◽  
Latent Change Score ◽  
The One

The One-Group Pretest-Posttest Design, where the same group of people is measured before and after some event, can be fraught with statistical problems and issues with causal inference. Still, these designs are common from political science to developmental neuropsychology to economics. In cases with cognitive data, it has long been known that a second test, with no treatment or an ineffective manipulation between testings, leads to increased scores at time 2 without an increase in the underlying latent ability. We investigate several analytic approaches involving both manifest and latent variable modeling to see which methods are able to accurately model manifest score changes with no latent change. Using data from 600 schoolchildren given an intelligence test twice, with no intervention between, we show using manifest test scores, either directly or through univariate latent change score analysis, falsely leads one to believe an underlying increase has occurred. Latent change score models on latent data also show a spurious significant effect on the underlying latent ability. Multigroup Confirmatory Factor Analysis only shows the correct answer when measurement invariance is tested, imposed (if viable), and the means of both time points are tested constricting time 2 to zero. Longitudinal structural equation modeling with measurement invariance correctly shows no change at the latent level when measurement invariance is tested, imposed, and model fit tested. When dealing with the One-Group Pretest-Posttest Design, analyses must occur at the latent level, measurement invariance tested, and change parameters explicitly tested. Otherwise, one may see change where none exists.

Novelty and Threat in the Weapon Focus Effect: A Study with Structural Equation Modelling

2013 ◽  
Author(s):  
William Blake Erickson ◽  
James Michael Lampinen ◽  
Juliana Leding ◽  
Christopher S. Peters
Keyword(s):  
Structural Equation Modelling ◽  
Structural Equation ◽  
Equation Modelling ◽  
Weapon Focus ◽  
Focus Effect ◽  
Weapon Focus Effect

PENGARUH TANGGUNG JAWAB PIMPINAN DAN IKLIM DEMOKRASI TERHADAP PEMBENTUKAN ORGANISASI CERDAS DAN DAMPAKNYA TERHADAP SIKAP PROFESIONAL DOSEN

ALQALAM ◽  
2007 ◽  
Vol 24 (2) ◽  
pp. 178
Author(s):  
Tata Rosita
Keyword(s):  
Path Analysis ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Equation Modelling

Tujuan pokok penelitian ini adalah untuk memperoleh bukti empirik mengenai pembentukan organisasi cerdas pada lembaga pendidikan tinggi. Paradigma baru organisasi adalah keharusan untuk menyesuaikan dengan lingkungan sehingga memerlukan perubahan melalui kepemimpinan dan demokratisasi. Lebih jauh, kita ingin melihat bagaimana pengaruh organisasi cerdas terhadap sikap profesional dosen.Penelitian ini dilaksanakan dengan menggunakan metode survei penjelasan pada Perguruan Tinggi Swasta di D KI Jakarta. Data yang dikumpulkan terdiri dari data sekunder yaitu melalui penyebaran kuesioner. Teknik Analisis data menggunakan  Structural Equation Modelling (SEM) dengan pendekatan Path Analysis (Analisis Jalur).Berdasarkan analisis dan pembahasan hasil penelitian ini, maka dapat disimpulkan beberapa hal sebagai berikut: (1) Tanggung jawab pimpinan memiliki pengaruh yang sangat signifikan terhadap organisasi cerdas, (2) Iklim demokrasi memiliki pengaruh yang signifikan terhadap organisasi cerdas, (3) Tanggung jawab pimpinan tidak memiliki pengaruh yang signifikan terhadap Iklim demokrasi, dan (4) Organisasi cerdas memiliki pengaruh yang signifikan terhadap sikap profesional.

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