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We first describe the essentials of hypothesis testing and how testing helps make critical business decisions of statistical and practical significance. Without using difficult mathematical formulas, we discuss the steps involved in hypothesis testing, the types of errors that may occur, and provide strategies on how to best deal with these errors. We also discuss common types of test statistics and explain how to determine which type you should use in which specific situation. We explain that the test selection depends on the testing situation, the nature of the samples, the choice of test, and the region of rejection. Drawing on a case study, we show how to link hypothesis testing logic to empirics in SPSS. The case study touches upon different test situations and helps you interpret the tables and graphics in a quick and meaningful way.

Communicating the results of your study, project, or business case is crucial in market research. We discuss the core elements of a written research report, provide guidelines on how to structure its core elements, and how you can communicate the research findings to your audience in terms of their characteristics and needs. We show you how to organize and simplify complex and dense information in an efficient and reader-friendly way. Using Stata, and drawing on a case study, we show how you can combine and present several graphs and (regression) tables concisely and clearly. We also provide guidelines for oral presentations, suggestions for visual aids that facilitate the communication of difficult ideas, and ideas on how to best structure the oral presentation of results. Finally, we discuss ethical issues that may arise when communicating report findings to your client.

We first describe the essentials of hypothesis testing and how testing helps make critical business decisions of statistical and practical significance. Without using difficult mathematical formulas, we discuss the steps involved in hypothesis testing, the types of errors that may occur, and provide strategies on how to best deal with these errors. We also discuss common types of test statistics and explain how to determine which type you should use in which specific situation. We explain that the test selection depends on the testing situation, the nature of the samples, the choice of test, and the region of rejection. Drawing on a case study, we show how to link hypothesis testing logic to empirics in Stata. The case study touches upon different test situations and helps you interpret the tables and graphics in a quick and meaningful way.

Market research is key to understanding markets and requires the systematic gathering and interpreting of information about individuals and organizations. This will give you an essential understanding of your customers’ needs, a head start on your competitors, allow you to spot potential problems, and future growth. Drawing on real examples, we show the value of market research, describe its main purposes, and explain how market research differs from marketing research. We explain what makes, or breaks, a successful market research study and describe when market research is most needed. We also provide a description of the different types of market research providers.

When applying structural equation modeling methods, such as partial least squares (PLS) path modeling, in empirical studies, the assumption that the data have been collected from a single homogeneous population is often unrealistic. Unobserved heterogeneity in the PLS estimates on the aggregate data level may result in misleading interpretations. Finite mixture partial least squares (FIMIX-PLS) and PLS genetic algorithm segmentation (PLS-GAS) allow the classification of data in variance-based structural equation modeling. This research presents an initial application and comparison of these two methods in a computational experiment in respect of a path model which includes multiple endogenous latent variables. The results of this analysis reveal particular advantages and disadvantages of the approaches. This study further substantiates the effectiveness of FIMIX-PLS and PLS-GAS and provides researchers and practitioners with additional information they need to proficiently evaluate their PLS path modeling results by applying a systematic means of analysis. If significant heterogeneity were to be uncovered by the procedures, the analysis may result in group-specific path modeling outcomes, thus allowing further differentiated and more precise conclusions to be formed.

Mixture regression models have increasingly received attention from both marketing theory and practice, but the question of selecting the correct number of segments is still without a satisfactory answer. Various authors have considered this problem, but as most of available studies appeared in statistics literature, they aim to exemplify the effectiveness of new proposed measures, instead of revealing the performance of measures commonly available in statistical packages. The study investigates how well commonly used information criteria perform in mixture regression of normal data, with alternating sample sizes. In order to account for different levels of heterogeneity, this factor was analyzed for different mixture proportions. As existing studies only evaluate the criteria’s relative performance, the resulting success rates were compared with an outside criterion, so called chance models. The findings prove helpful for specific constellations.