Predicting Values

Which test / procedures? How do we decide?

July 2, 2017

With so many statistical procedures available, how do we decide which tests are best to address our research objectives? (several posts deal with this topic).   First and foremost, the decision as to which statistical procedures to apply to the data should be made BEFORE the design of the data collection instrument (e.g. the questionnaire), and not AFTER data has been collected. Plan ahead so that your analysis are entirely focused on addressing your research objectives and NOT to address your data.  Too many researchers remain guilty of waiting to see the data so they can decide what to [READ MORE]

So many regression procedures. Confused?

September 11, 2015

Regression is the work-horse  of research analytics. It has been around for a long time and it probably will be around for a long time to come. Whether we always realise it or not, most of our analytical tools are in some way or another based on the concept of correlation and regression.   Lets look at a few regression based procedures in the researchers’ toolbox:   1. Simple and multiple linear regression: Applicable if both the single dependent variable (outcome or response variable) and one or many independent variables (predictors) are measured on an interval scale. If we [READ MORE]

When the regression work-horse looks sick

June 19, 2013

Regression, in particular simple bivariate and multiple regression (and to a much lesser extent multivariate regression which is a “multivariate general linear model” procedure) is the work-horse of many researchers. For some, it is a horse exploited to the bone when other statistical (or even non-statistical) procedures would have done a better job!  Also, many statistical procedures are based on linear regression models (often without us realising it such as the fact that the ANOVA can be explained as a simple regression model).    At the core of many statistical analytics is [READ MORE]

Building statistical models: Linear (OLS) regression

October 17, 2012

Everyday, researchers around the world collect sample data to build statistical models to be as representative of the real world as possible so these models can be used to predict changes and outcomes in the real world. Some models are very complex, while others are as basic as calculating a mean score by summating several observations and then dividing the score by the number of observations. This mean score is a hypothetical value so it is just a simple model to describe the data.   The extent to which a statistical model (e.g. the mean score) represents the randomly collected [READ MORE]

Which test: Predict the value (or group membership) of one variable based on the value of another based on their relationship / association

July 21, 2012

When the research objective is to use one or more predictor variables to predict the values (or group membership) of one or more outcome variables, we have a choice among different statistical procedures, depending on the following variable characteristics:   Number of variables:  One (or more) dependent / outcome variable(s) and one (or more) independent / predictor variable(s)   Examples:  To what extent can we use the values of a predictor variable to predict the values of an outcome variable? (predict the values) Which predictor variables best predict whether a respondent will be [READ MORE]