What is he best known for?
The fields of study he is best known for:
- Statistics
- Developmental psychology
- Social psychology
Patrick J. Curran mostly deals with Econometrics, Structural equation modeling, Statistics, Sample size determination and Statistical model.
His Econometrics research is multidisciplinary, incorporating elements of Latent variable model and Latent variable.
His biological study deals with issues like Applied mathematics, which deal with fields such as Non normality, Mathematical analysis and Normal distribution.
His Normal distribution research focuses on Monte Carlo method and how it connects with Statistical hypothesis testing.
His work in Sample size determination tackles topics such as Estimation theory which are related to areas like Ordinal data and Normality.
His studies in Statistical model integrate themes in fields like Salient, Spurious relationship, Outcome and Covariate.
His most cited work include:
- Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis. (3505 citations)
- The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. (2930 citations)
- An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. (1824 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Developmental psychology, Statistics, Econometrics, Structural equation modeling and Psychiatry.
His work on Item response theory, Covariate, Confidence interval and Monte Carlo method as part of general Statistics research is frequently linked to Estimation, thereby connecting diverse disciplines of science.
His Item response theory research integrates issues from Regression analysis and Set.
His studies deal with areas such as Sample size determination, Latent variable model, Latent class model, Latent variable and Latent growth modeling as well as Econometrics.
His Structural equation modeling research is multidisciplinary, incorporating perspectives in Algorithm, Linear regression, Applied mathematics and Statistical model.
Patrick J. Curran has researched Linear regression in several fields, including Interaction, Multilevel model and Mathematical model.
He most often published in these fields:
- Developmental psychology (24.77%)
- Statistics (22.94%)
- Econometrics (22.02%)
What were the highlights of his more recent work (between 2014-2021)?
- Integrative data analysis (8.26%)
- Demography (5.50%)
- Estimation (5.50%)
In recent papers he was focusing on the following fields of study:
Integrative data analysis, Demography, Estimation, Statistics and Sample size determination are his primary areas of study.
His research on Statistics often connects related topics like Econometrics.
In the subject of general Econometrics, his work in Ordinal data is often linked to Negative binomial distribution, thereby combining diverse domains of study.
The Sample size determination study combines topics in areas such as Sampling frame, Predictive validity, Pacific islanders and Missing data.
The various areas that Patrick J. Curran examines in his Covariate study include Monte Carlo method, Score and Measurement invariance.
His Statistical hypothesis testing research incorporates themes from Global health, Item analysis and Statistical power.
Between 2014 and 2021, his most popular works were:
- Improving Factor Score Estimation Through the Use of Observed Background Characteristics (21 citations)
- Advantages of Integrative Data Analysis for Developmental Research (20 citations)
- The Increasing Diversity and Complexity of Family Structures for Adolescents. (12 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Social psychology
- Developmental psychology
His scientific interests lie mostly in Integrative data analysis, Item response theory, Statistics, Covariate and Factor score.
Integrative data analysis is integrated with Drug, Epidemiology, Psychiatry, Adolescent substance and Life span in his research.
His biological study spans a wide range of topics, including Econometrics, Monte Carlo method, Score and Measurement invariance.
Sample size determination is the focus of his Statistics research.
He brings together Factor score and Best practice to produce work in his papers.
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