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Social and Behavioral Sciences Commons™
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- Quantitative Methods (2)
- ARMA and ARIMA models (1)
- Applied statistics (1)
- Characteristic roots (1)
- Cointegration (1)
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- Econometrics (1)
- Finance (1)
- Gifted Education (1)
- Hierarchical linear modeling (1)
- Literature Review (1)
- Literature review (1)
- Methodology (1)
- Multilevel models (1)
- Non-stationarity (1)
- Nutrition behavior (1)
- Quantitative methods (1)
- Reliability (1)
- Statistical analyses (1)
- Survey research (1)
- Unit root (1)
- WIC program (1)
- Publication
Articles 1 - 3 of 3
Full-Text Articles in Social and Behavioral Sciences
Time Series, Unit Roots, And Cointegration: An Introduction, Lonnie K. Stevans
Time Series, Unit Roots, And Cointegration: An Introduction, Lonnie K. Stevans
Lonnie K. Stevans
The econometric literature on unit roots took off after the publication of the paper by Nelson and Plosser (1982) that argued that most macroeconomic series have unit roots and that this is important for the analysis of macroeconomic policy. Yule (1926) suggested that regressions based on trending time series data can be spurious. This problem of spurious correlation was further pursued by Granger and Newbold (1974) and this also led to the development of the concept of cointegration (lack of cointegration implies spurious regression). The pathbreaking paper by Granger (1981), first presented at a conference at the University of Florida …
Statistical Methods Used In Gifted Education Journals, 2006-2010, Russell Warne, Maria Lazo, Tami Ramos, Nicola Ritter
Statistical Methods Used In Gifted Education Journals, 2006-2010, Russell Warne, Maria Lazo, Tami Ramos, Nicola Ritter
Russell T Warne
This article describes the statistical methods used in quantitative and mixed methods articles between 2006 and 2010 in five gifted education research journals. Results indicate that the most commonly used statistical methods are means (85.9% of articles), standard deviations (77.8%), Pearson’s r (47.8%), χ2 (32.2%), ANOVA (30.7%), t tests (30.0%), and MANOVA (23.0%). Approximately half (53.3%) of the articles included reliability reports for the data at hand; Cronbach’s alpha was the most commonly reported measure of reliability (41.5%). Some discussions of best statistical practice and implications for the field of gifted education are included.
Managing Clustered Data Using Hierarchical Linear Modeling, Russell Warne
Managing Clustered Data Using Hierarchical Linear Modeling, Russell Warne
Russell T Warne
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence assumption and lead to correct analysis of data, yet it is rarely used in nutrition research. The purpose of this viewpoint is to illustrate the benefits of hierarchical linear modeling within a nutrition research context.