This chapter explores the promise and pitfalls of using the Internet as a tool to collect data, with a focus on practical and conceptual concerns relevant to social-personality psychologists. It discusses some of the benefits and challenges of collecting data over the Internet, and focuses primarily on using the Internet to recruit participants and collect data. The chapter reviews a practical guidance for implementing online studies. It provides detailed examples of how common study designs within social-personality psychology can be successfully implemented via the Internet. Using an experimental design in an Internet-based study requires translating the random assignment process and the experimental manipulation(s) to a Web-based format. The chapter highlights a few unique options and challenges of Internet-based study design. It also discusses a few but unique ethical concerns which the Internet data collection presents before ending with a discussion on the future opportunities for Internet use in research.

This chapter focuses on what has become known as the psychometric or nonrepresentational approach to measurement. It considers the issues traditionally discussed under the heading of reliability, and reviews several still persistent definitions or types of reliability coefficients. The chapter discusses the problems and misuses of coefficient alpha, the most commonly used psychometric index in social-personality psychology. It suggests the generalizability theory as a broader and more heuristic perspective. The chapter examines issues related to construct validation. It also considers the construct validation as the crucial issue in psychological measurement and includes a broad range of validity evidence, focusing on convergent and discriminant aspects. The chapter describes model testing in construct validation and scale construction. It also focuses on the measurement models in structural equation modeling. The chapter reviews three classical strategies (external criterion, rational-intuitive, and internal-factor analytic) and also suggests an integrated model adopting the construct-oriented approach.

This chapter reviews the statistical methods used to explore causal (directional) and noncausal research questions. Researchers have often regarded some statistical procedures as experimental and other statistical procedures as nonexperimental (e.g., correlation, structural equation modeling). The chapter discusses practical issues that influence the implementation of the analysis and design features of the study relevant to that statistical procedure. The most widely used noncausal analysis in social-personality psychology is exploratory factor analysis (EFA). There are two major issues that should be taken into account when designing studies to be analyzed using EFA: selection of measured variables and selection of sample. The chapter overviews the major types of causal hypotheses. It explains the conditions necessary for establishing causal relations and comments on study design features and statistical procedures that assist in establishing these conditions. The chapter also reviews the statistical procedures used to test different types of causal hypotheses.

This chapter introduces the use of confirmatory factor analysis (CFA) and item response theory (IRT) modeling, particularly as each is used to evaluate measurement invariance of assessment devices. It covers CFA, which is a special case of the common factor model. The chapter discusses basic ideas with regard to model specification and evaluation. It explains how measurement invariance is pursued in CFA models. The chapter also discusses the parallel issues with regard to IRT models, including both basic forms of IRT model and the study of measurement invariance within such models. It provides illustrations of analyses with empirical data to demonstrate how to fit such models and evaluate results. While good introductory presentations are available for both CFA and IRT, the authors look forward to the increased use of the two techniques for investigating measurement invariance and improving the nature of measurements used in psychological sciences.