Analysis & Interpretation: Multiple Variables Simultaneously

Analysis & Interpretation: Multiple Variables Simultaneously Chapter 13, Student Edition MR/Brown & Suter

Learning Objectives • Discuss why a researcher might conduct a multivariate analysis • Explain the purpose and importance of cross tabulation • Describe a technique for comparing groups on a continuous dependent variable • Explain the difference between an independent sample t-test for means and a paired sample t-test for means • Discuss the Pearson product-moment correlation coefficient • Discuss a technique for examining the influence of one or more predictor variables on an outcome variable MR/Brown & Suter

Learning Objective 1 • Multivariate analyses allow researchers a closer look at their data than is possible with univariate analyses • Univariate analyses provide insights about the data while multivariate analyses can often provide further illumination of those insights MR/Brown & Suter

Learning Objective 1 MR/Brown & Suter

Learning Objective 2 • Cross tabulation – a multivariate technique used for studying the relationship between two or more categorical variables (i.e., nominal- or ordinal-level) • Cross tabs consider the joint distribution of sample elements across variables • It is the most used multivariate data analysis technique in applied marketing research MR/Brown & Suter

Learning Objective 3 • When there are only two groups, the independent samples t-test is used to determine if the mean score on the dependent variable for one group is significantly different than the mean score for the second group • The dependent variable must be a continuous variable for mean scores to be used MR/Brown & Suter

Learning Objective 4 • Independent Samples t-test for Means • A commonly used technique used to determine whether two groups differ on some characteristic assessed on a continuous measure • Paired Sample t-test for Means • A technique for comparing two means when scores for both variables are provided by the same sample MR/Brown & Suter

Learning Objective 4 • Independent Samples t-test for Means • Respondent Group Same or Different? Different • Mean scores on the dependent variable are compared group to group • Example: Group 1 is more favorable toward the brand than Group 2 • Paired Sample t-test for Means • Respondent Group Same or Different? Same • Mean scores on the dependent variable are compared variable to variable • Example: Group 1 is more favorable toward the brand at time period 1 than it was at time period 2 MR/Brown & Suter

Learning Objective 5 • Pearson Product-Moment Correlation Coefficient • A statistic that indicates the degree of linear association between two continuous variables • The correlation coefficient can range from -1 (inverse relationship) to +1 (direct relationship) • Note: Correlation ≠ Causation; Correlation = Relationship • Examples • Relationship between number of times exercising with weights and number of times exercising in a fitness class during the month • Relationship between the number of hours studied and number of chapters read during exam preparation MR/Brown & Suter

Learning Objective 6 • Regression Analysis • A statistical technique used to derive an equation that relates a single continuous dependent variable to a single independent variable • Yi = the level of a dependent variable (e.g., sales) • α = the intercept (think of a graph with an X- and Y-axis) • β = the slope coefficient (think of a line on a graph) • Xi = the level of an independent variable (e.g., number of sales reps) • εi = an error term MR/Brown & Suter

Analysis & Interpretation: Multiple Variables Simultaneously