When the research objective is to compare two independent groups, which means they are unpaired, unmatched, and thus different respondent groups, we have a choice among different statistical procedures, depending on the following variable characteristics: Number of variables: One dependent variable and one independent categorical variable (two levels or groups) Examples: Are the means / frequencies of two independent groups of respondents (e.g. males vs. females) significantly different on the scores of the dependent variable? When the dependent variable is BINOMIAL / [READ MORE]

When the research objective is to compare a single group distribution to a hypothetical / known distribution (goodness-of-fit tests), we have a choice among different statistical procedures, depending on the following variable characteristics: Number of variables: One dependent variable Examples: Does our sample data distribution fit the binomial / normal / poisson curve? Is our interval-measured sample distribution significantly different from a normal distribution (goodness-of-fit for normality)? Is the 10%/20%/20%/30%/20% age proportions in our sample significantly [READ MORE]

When the research objective is to compare a single group mean or frequency to a hypothetical / known value or proportion (such as an action standard or a norm), we have a choice among different statistical procedures, depending on the following variable characteristics: Number of variables: One dependent variable Examples: Is our mean customer satisfaction score significantly different from the industry average (or action standard) of e.g. 4.6? Is the 54/46 gender proportion in our sample significantly different from the population’s age proportions of 51/49? When [READ MORE]

BRIEF DESCRIPTION The One-way ANOVA is an extension of the Two-independent sample t-test as it compares the observed mean on the dependent variable among more than two groups as defined by the independent variable. For example, is the mean customer satisfaction score (on the dependent variable) significantly different among three customer groups: adult men, adult women, and children (on the independent variable). In addition to expressing group differences on the dependent variable, we can also express the findings in terms of relationship or association, e.g. “Age [READ MORE]

BRIEF DESCRIPTION The Two-independent sample t-test is for continuous scaled data and it compares the observed mean on the dependent variable between two groups as defined by the independent variable. For example, is the mean customer satisfaction score (on the dependent variable) significantly different between men and women (on the independent variable). The t-test is a parametric procedure. SIMILAR STATISTICAL PROCEDURES Non-parametric counterparts of the Two-independent t-test include the (Wilcoxon) Mann-Whitney U-test (non-parametric), Wald-Wolfowitz Runs [READ MORE]

BRIEF DESCRIPTION The One-Sample t-test is for continuous scaled data and it compares an observed sample mean with a predetermined value. For example, is our customer satisfaction sample mean significantly different from a pre-set figure such as an industry benchmark or an action standard. It also helps us to answer a question such as “Are we 95% confident that the mean score is between 7.5 and 8.5”. The t-test is a parametric procedure. SIMILAR STATISTICAL PROCEDURES One-sample z-test Non-parametric counterparts of the one-sample t-test include the Wilcoxon [READ MORE]