Abstract Purpose This paper aims to present the fundamental aspects for the development and validation (D&V) of attitudes’ measurement scale, as well as its practical aspects that are not deeply explored in books and manuals. These aspects are the results of a long experience of the authors and arduous learning with errors and mistakes. Design/methodology/approach The nature of this paper is methodological and can be very useful for an initial reading on the theme that it rests. This paper presents four D&V stages: literature review or interviews with experts; theoretical or face validation; semantic validation or validation with possible respondents; and statistical validation. Findings This is a methodological paper, and its main finding is the usefulness for researchers. Research limitations/implications The main implication of this paper is to support researchers on the process of D&V of measurement scales. Practical implications Became a step-by-step guide to researchers on the D&V of measurement scales. Social implications Support researchers on their data collection and analysis. Originality/value This is a practical guide, with tips from seasoned scholars to help researchers on the D&V of measurement scales.

Abstract Purpose This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used. Design/methodology/approach The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation. Findings From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level. Originality/value Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.