Which Statistical Test?

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]

Which Test: Factor Analysis (FA, EFA, PCA, CFA)

April 4, 2017

Confused about when to use FA, EFA, PCA, or CFA? Well, all of them are interdependence methods in which no single variable or group of variables are defined as being independent or dependent. The statistical procedure involves the analysis of all variables in the data set simultaneously so the goal of these interdependence procedures is to uncover structure by grouping of variables (as in factor analysis) rather than respondents (typically in cluster analysis) or objects (typically in perceptual mapping). So interdependence methods do not attempt to predict one or more variable by others as [READ MORE]

Repeated Measures ANOVA versus Linear Mixed Models.

March 9, 2017

You want to measure performance of the same individual measured over a period of time (repeated observations) on an interval scale dependant variable, but, which procedure to use?  So we are looking for an equivalent of the paired samples t-test, but we want to allow for two or more levels of the categorical variable i.e. pre, during, post. The Repeated Measures ANOVA [SPSS: ANALYZE / GENERAL LINEAR MODEL / REPEATED MEASURES] is simpler to use but sadly its often not as accurate and flexible as using Linear Mixed Models (SPSS: ANALYZE / MIXED MODELS / LINEAR). Reminder that the Linear [READ MORE]

Statistical Modeling: A Primer (by Kevin Gray)

March 7, 2017

Interesting article by Kevin Gray at Cannon Gray (http://cannongray.com) Model means different things to different people and different things at different times. As I briefly explain in A Model’s Many Faces, I often find it helpful to classify models as conceptual, operational or statistical. In this post we’ll have a closer look at the last of these, statistical models. First, it’s critical to understand that statistical models are simplified representations of reality and, to paraphrase the famous words of statistician George Box, they’re all wrong but some of them [READ MORE]

Is my Likert-scale data fit for parametric statistical procedures?

April 8, 2016

We’re all very familiar with the “Likert-scale” but do we know that a true Likert-scale consists not of a single item, but of several items which under the right conditions – i.e. subjected to an assessment of its reliability (e.g. intercorrelations between all pairs of items) and validity (e.g. convergent, discriminant, construct etc.) can be summed into a single score. The Likert-scale is a unidimensional scaling method (so it measures a one-dimensional construct), is bipolar, and in its purest form consists of only 5 scale points, though often we refer to a [READ MORE]

Which Test: Logistic Regression or Discriminant Function Analysis

October 8, 2015

Discriminant Function Analysis (DFA) and Logistic Regression (LR) are so similar, yet so different. Which one when, or either at any time? Lets see….   DISCRIMINANT FUNCTION ANALYSIS (DFA): Is used to model the value (exclusive group membership) of a either a dichotomous or a nominal dependent variable (outcome) based on its relationship with one or more continuous scaled independent variables (predictors). A predictive model consisting of one or more discriminant functions (based on the linear combinations of the predictor variables that provide the best discrimination between the groups) [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]

Revisiting the basics of data and measurement scales (Part 2)

July 28, 2015

The statistical procedures we choose depend on the type of data we collected with the different types of measurement scales we employed. We should be careful to understand the constructs we measure and the type of scales we employ as this will determine what statistical procedures are appropriate for analysis.   This post (in following-up to part 1) is partly based on what S.S. Stevens told us in 1946 (see “Further Reading” below) about data and scales. Please note that this classification is no science as there is a lingering debate about the classification [READ MORE]

Which Test: Chi-Square, Logistic Regression, or Log-linear analysis

November 19, 2013

In a previous post I have discussed the differences between logistic regression and discriminant function analysis, but how about log-linear analysis? Which, and when, to choose between chi-square, logistic regression, and log-linear analysis?   Lets briefly review each of these statistical procedures: The chi-square test (χ²) is a descriptive statistic, just as correlation is descriptive of the association between two variables. Chi-square is not a modeling technique, so in the absence of a dependent (outcome) variable, there is no prediction of either a value (such as in ordinary [READ MORE]

Why ANOVA and not multiple t-tests? Why MANOVA and not multiple ANOVA’s, etc.

September 9, 2013

ANOVA reigns over the t-test and the MANOVA reigns over the ANOVA. Why?   If we want to compare several predictors with a single outcome variable, we can either do a series of t-tests, or a single factorial ANOVA.   Not only is a factorial ANOVA less work, but conducting several t-tests for each predictor separately will result in a higher probability of making Type I errors. In fact, with every single t-test there is a chance of a Type I error. Conducting several t-tests compounds this probability. In contrast, a single factorial ANOVA controls for this error so that the probability [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]

Which test: Quantify the association / relationship / correlation / dependence between two variables

July 20, 2012

When the research objective is to understand the association / relationship / correlation / dependence between two variables, we have a choice among different statistical procedures, depending on the following variable characteristics:   Number of variables:  One dependent variable and one independent variable (technically we don’t distinguish between a dependent and independent variable in these procedures).    Examples:  To what extent are two variables related with each other? Is one variable dependent on another variable? When the variables are BINOMIAL / BINARY / [READ MORE]
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