J Kyle Roberts
Biographic Data
| ID | 4973028 |
|---|---|
| NAME | J Kyle Roberts |
| GIVEN NAMES | J Kyle |
| FAMILY NAME | Roberts |
| SIGNATURE | ROBERTS J K |
| AFFILIATIONS | Baylor College of Medicine |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2005 |
| LATEST PUBLICATION YEAR | 2015 |
| H-INDEX | 1 |
Explained Variance in Multilevel Models
Handbook of Advanced Multilevel Analysis
Hierarchical Linear Models
The history of hierarchical linear modeling (also called multilevel modeling) can be linked to the seminal work of Robinson (1950) in recognizing contextual effects. The theory that Robinson illustrated is also sometimes referred to as the “frog/pond” theory and bears discussion here. Suppose that a biologist has two frogs lying on an examination table that both weigh 500 grams and are both 1 year old. Consider further that Frog A was drawn from …
Use of Exploratory Factor Analysis in Published Research
Given the proliferation of factor analysis applications in the literature, the present article examines the use of factor analysis in current published research across four psychological journals. Notwithstanding ease of analysis due to computers, the appropriate use of factor analysis requires a series of thoughtful researcher judgments. These judgments directly affect results and interpretations. The authors examine across studies (a) the decis…
A Proposed New “What if Reliability” Analysis for Assessing the Statistical Significance of Bivariate Relationships
In this article, the authors (a) illustrate how displaying disattenuated correlation coefficients alongside their unadjusted counterparts will allow researchers to assess the impact of unreliability on bivariate relationships and (b) demonstrate how a proposed new “what if reliability” analysis can complement null hypothesis significance tests of bivariate relationships
A Proposed New “What if Reliability” Analysis for Assessing the Statistical Significance of Bivariate Relationships
In this article, the authors (a) illustrate how displaying disattenuated correlation coefficients alongside their unadjusted counterparts will allow researchers to assess the impact of unreliability on bivariate relationships and (b) demonstrate how a proposed new “what if reliability” analysis can complement null hypothesis significance tests of bivariate relationships
A Proposed New “What if Reliability” Analysis for Assessing the Statistical Significance of Bivariate Relationships
In this article, the authors (a) illustrate how displaying disattenuated correlation coefficients alongside their unadjusted counterparts will allow researchers to assess the impact of unreliability on bivariate relationships and (b) demonstrate how a proposed new “what if reliability” analysis can complement null hypothesis significance tests of bivariate relationships
Use of Exploratory Factor Analysis in Published Research
Given the proliferation of factor analysis applications in the literature, the present article examines the use of factor analysis in current published research across four psychological journals. Notwithstanding ease of analysis due to computers, the appropriate use of factor analysis requires a series of thoughtful researcher judgments. These judgments directly affect results and interpretations. The authors examine across studies (a) the decis…
Hierarchical Linear Models
The history of hierarchical linear modeling (also called multilevel modeling) can be linked to the seminal work of Robinson (1950) in recognizing contextual effects. The theory that Robinson illustrated is also sometimes referred to as the “frog/pond” theory and bears discussion here. Suppose that a biologist has two frogs lying on an examination table that both weigh 500 grams and are both 1 year old. Consider further that Frog A was drawn from …
Handbook of Advanced Multilevel Analysis
Explained Variance in Multilevel Models
Computer Science (3 works) · Advanced Statistical Modeling Techniques (2 works) · Data mining (2 works) · Econometrics (2 works) · Mathematics (2 works) · Psychology (2 works) · Statistics (2 works) · Advanced Statistical Methods and Models (1 work) · Affect (linguistics) (1 work) · Biology (1 work)