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Ii International Colloquium On Languages, Cultures, Identity, In School And Society, International Colloquium

Proceedings of the International Colloquium on Languages, Cultures, Identity in School and Society

The influx of immigrants in countries worldwide, coupled with the challenges associated to the schooling of their children in host countries' schools, makes it more necessary than ever to broaden our knowledge of the linguistic, ethnic, and cultural realities derived from this phenomenon. This is the rationale for the present Colloquium, which revolves around the following topics:

• Individual, school, and societal bilingualism/multilingualism-related issues
• Multi/Transculturalism-related issues in families, schools, and society
• Impact of bi/multilingualism on individuals' and societies' language, culture, and identity
• Impact of multi/transculturalism on individuals' and societies' language, culture, and identity
• Language ideologies, policies, and practices
• Promotion, maintenance, and …

Best-Subset Selection Criteria For Multiple Linear Regression, Gordon P. Brooks, Porsche Ruengvirayudh

Grant Evaluation & Statistical Support

This study presents comparisons of subset selection criteria used to help determine the "best" regression model in multiple linear regression. No such criteria can replace a researcher's knowledge of theory to help choose useful models, but such criteria may help in exploratory research. Relationships among variables can be more complicated than expected and may require adjustment to theory based on empirical models. Knowing how well each subset selection method performs can be useful in such cases. Monte Carlo simulations were performed to compare a number of well-known criteria (e.g., AIC, BIC, PRESS, adjusted R² ) to some less well-known …

Comparing Stepwise Regression Models To The Best-Subsets Models, Or, The Art Of Stepwise, Pornchanok Ruengvirayudh, Gordon P. Brooks

Grant Evaluation & Statistical Support

Stepwise regression methods are widely recognized as undesirable for explanatory purposes. As exploratory methods, however, they may provide efficient means for researchers to examine multiple models for further investigation. This study used Monte Carlo methods to examine the use of forward and backward regression without stopping criteria (i.e., with customized stepping criteria) in order to create all possible stepwise models (i.e., from one predictor to the fully specified model). Using these stepwise methods without stopping criteria often produced the same models as best-subset regression when there was little multicollinearity. Agreement was still good, but it declined, as multicollinearity became stronger. …