Bias and equivalence provide a framework for methodological aspects of cross-cultural studies. Bias is a generic term for any systematic errors in the measurement that endanger the comparability of cross-cultural data; bias results in invalid comparative conclusions. The demonstration of equivalence (i.e., absence of bias) is a prerequisite for any cross-cultural comparison. Based on the source of incomparability, three types of bias, namely construct, method, and item bias, can be distinguished. Correspondingly, three levels of equivalence, namely, construct, metric, and scalar equivalence, can be distinguished. One of the goals in cross-cultural research is to minimize bias and enhance comparability. The definitions and manifestations of these types of bias and equivalence are described and remedies to minimize bias and enhance equivalence at the design, implementation, and statistical analysis phases of a cross-cultural study are provided. These strategies involve different research features (e.g., decentering and convergence), extensive pilot and pretesting, and various statistical procedures to demonstration of different levels of equivalence and detections of bias (e.g., factor analysis based approaches and differential item functioning analysis). The implications of bias and equivalence also extend to instrument adaptation and combining etic and emic approaches to maximize the ecological validity. Instrument choices in cross-cultural research and the categorization of adaptations stemming from considerations of the concept, culture, language, and measurement are outlined. Examples from cross-cultural research of personality are highlighted to illustrate the importance of combining etic and emic approaches. The professionalization and broadening of the field is expected to increase the validity of conclusions regarding cross-cultural similarities and differences.
Fons J.R. Van de Vijver and Jia He
Karyna Pryiomka and Joshua W. Clegg
Like science in general, psychological research has never had a method. Rather, psychologists have deployed many methods under quite variable justifications. The history of these methods is thus a history of contestation. Psychology’s method debates are many and varied, but they mostly constellate around two interconnected concerns: psychology’s status as a science, and psychology’s proper subject matter. On the first question, the majority position has been an attempt to establish psychology as scientific, and thus committed to quantification and to objective, particularly experimental, methods. Challenging this position, many have argued that psychology cannot be a science, or at least not a natural one. Others have questioned the epistemic privilege of operationalization, quantification, experimentation, and even science itself. Connecting epistemic concerns with those of ethics and morality, some have pointed to the dehumanizing and oppressive consequences of objectification. In contrast to the debates over psychology’s status as a science, the question of its proper subject matter has produced no permanent majority position, but perennial methodological debates. Perhaps the oldest of these is the conflict over whether and how self, mind, or consciousness can be observed. This conflict produced famous disagreements like the imageless thought controversy and the behaviorist assault on “introspection.” Other recurrent debates include those over whether psychologists study wholes or aggregates, structures or functions, and states or dynamic systems.
Alexandra Rutherford and Tal Davidson
As a conceptual and analytic framework, intersectionality has informed, and can transform, how scholars approach psychology and its history. Intersectionality provides a framework for examining how multiple social categories combine in systems characterized by both oppression and privilege to affect the experiences of those occupying the intersections of these social categories. The concept has its origins in the writings of Black feminists and critical race theorists in the 1970s and 1980s. Since that time, many critical debates about the definition, uses, and even misuses of intersectionality have been put forward by scholars in many fields. In psychology, the uptake of intersectionality as a methodological and epistemological framework has been undertaken largely by feminist psychologists. In this context, intersectionality has been used as both a logic for designing research, and as a perspective from which to critique the perpetuation of intersectional oppression latent in mainstream psychological research. In addition, intersectionality has also been applied to writing histories of psychology that attend to the operation of multiple intersecting forms of oppression and privilege. For example, historians of psychology have taken up intersectionality as a way to approach the intersections of scientific racism, sexism, and heterocentrism in the history of psychology’s concepts and theories. Intersectionality also has the potential for generating a more sophisticated historical understanding of social activism by psychologists. Finally, given that extant histories of psychology focusing on the American context have rendered the contributions of women of color largely invisible, intersectional analysis can serve to re-instantiate and foreground their experiences and contributions.
Vicente González-Romá and Ana Hernández
Human behavior takes place in different contexts (e.g., organizations, schools, families, sports teams, and communities) whose properties (e.g., climate, culture, cohesion, leadership, communication networks, and structure) influence human behavior. To estimate this influence, researchers need appropriate methods that avoid the problems associated with the application of standard Ordinary Least Square (OLS) regression. Multilevel modeling methods offer researchers a way to estimate the aforementioned influence. These methods take into account that the variables involved reside at different levels. For instance, in the relationship between work unit climate and employee job satisfaction, the former variable resides at the work unit level (level 2) whereas the latter resides at the individual one (level 1). Moreover, multilevel modeling methods also take into account that the data analyzed to estimate this type of relationships have a nested structure in which individuals (e.g., employees) are nested into collectives (e.g., work units). Finally, these methods decompose variance into between-group and within-group components and allow researchers to model variability at the between and within levels. Specifically, multilevel modeling methods allow researchers to test hypotheses that involve, among others: 1. A relationship between a higher-level predictor (e.g., work unit climate) and a lower-level outcome (e.g., employee job satisfaction); a so-called “direct cross-level effect”, and 2. An influence of a higher-level moderator (e.g., work unit climate) on an individual level relationship (e.g., the relationship between employee job stress and job satisfaction); a so-called “cross-level interaction”. Multilevel modeling methods can also be used to test more complex models involving mediation (e.g., 2-2-1, 2-1-1, or 1-1-1 models, depending on whether the antecedent and the mediator are level 1 or level 2 variables) and moderated mediation. We show how to test these models by presenting examples with real data and the corresponding SPSS syntax that readers can use to practice.
Alexandre J.S. Morin and David Litalien
As part of the Generalized Structural Equation Modeling framework, mixture models are person-centered analyses seeking to identify distinct subpopulations, or profiles, of participants differing quantitatively and qualitatively from one another on a configuration of indicators and/or relations among these indicators. Mixture models are typological (resulting in a classification system), probabilistic (each participant having a probability of membership into all profiles based on prototypical similarity), and exploratory (the optimal model is typically selected based on a comparison of alternative specifications) in nature, and can take different forms. Latent profile analyses seek to identify subpopulations of participants differing from one another on a configuration of indicators and can be extended to factor mixture analyses allowing for the incorporation of latent factors to the model. In contrast, mixture regression analyses seek to identify subpopulations of participants’ differing from one another in terms of relations among profile indicators. These analyses can be extended to the multiple-group and/or longitudinal analyses, allowing researchers to conduct tests of profile similarity across different samples of participants or time points, and latent transition analyses can be used to assess probabilities of profiles transition over time among a sample of participants (i.e., within person stability and change in profile membership). Finally, growth mixture analyses are built from latent curve models and seek to identify subpopulations of participants following quantitatively and qualitatively distinct trajectories over time. All of these models can accommodate covariates, used either as predictors, correlates, or outcomes, and can even be extended to tests of mediation and moderation.
Johnson Ching Hong Li and Virginia Man Chung Tze
In behavioral, social, and developmental research, researchers often begin with a fundamental question that examines whether there is a significant relationship between an independent variable (IV; e.g., video games) and a dependent variable (DV; e.g., aggression). However, examining this simple IV-DV relationship is not sufficient in most research scenarios given that this relationship may differ across the levels of a third variable, which is known as a moderator. For example, researchers may examine the degree to which the relationship between an independent variable and a dependent variable differs across the levels of a moderator or moderators (e.g., gender, ethnicity, socioeconomic status, intervention) to provide a more complete picture of the IV-DV effect and how this effect is or is not applicable to certain groups of participants. In lifespan developmental research, a key component lies in the study of change, growth, or trajectory of one’s life over time. Undoubtedly, not all individuals may follow the same developmental change or growth over time and examining moderators (e.g., gender, intervention, etc.) that may explain these individual changes is crucial for researchers to better understand the effects on their research investigation and for practical implications. The existing literature shows that conceptual and methodological strategies for moderation analysis have been developed and evolved in lifespan developmental psychology. In particular, researchers in lifespan developmental psychology have used various types of moderation analyses, including assessing whether moderators can explain the pretest and posttest difference based on the conventional analysis of variance (ANOVA) framework and evaluating whether moderators may explain how different individuals follow or deviate from the general growth and trajectory based on advanced latent growth curve modeling (LGCM). Researchers who study lifespan development have realized the importance of moderation effects in their work. In light of the complexity of current biological, psychological, and social factors embedded in lifespan developmental research, the trend of utilizing more sophisticated LGCM than ANOVA to understand the growth trajectories will receive more attention in the future.
Kimberly L. Fine and Kevin J. Grimm
Multilevel modeling is a data analytic framework that is appropriate when analyzing data that are dependent due to the clustering of observations in higher-level units. Clustered data appear in a variety of disciplines, which makes multilevel modeling a necessary data analytic tool for many researchers. Longitudinal data are a special kind of clustered data as the repeated observations are clustered within individuals. Multilevel models can be applied to longitudinal data to examine how individuals change over time and how individuals differ in their change processes over time. For longitudinal data, linear multilevel models, where the fixed- and random-effects parameters enter the model in a linear fashion, and nonlinear multilevel models, where at least one fixed-and/or random-effect parameter enters the model in a nonlinear fashion are commonly estimated to examine different forms of the individual change process. Multilevel structural equation modeling is an extension of multilevel modeling that allows for multivariate outcomes, and this framework is very useful for modeling multivariate longitudinal data (e.g., multivariate growth models and second-order growth models).
Gizem Hülür and Elisa Weber
Lifespan development is embedded in multiple social systems and social relationships. Lifespan developmental and relationship researchers study individual codevelopment in various dyadic social relationships, such as dyads of parents and children or romantic partners. Dyadic data refers to types of data for which observations from both members of a dyad are available. The analysis of dyadic data requires the use of appropriate data-analytic methods that account for such interdependencies. The standard actor-partner interdependence model, the dyadic growth curve model, and the dyadic dual change score model can be used to analyze data from dyads. These models allow examination of questions related to dyadic associations such as whether individual differences in an outcome can be predicted by one’s own (actor effects) and the other dyad member’s (partner effects) level in another variable, correlated change between dyad members, and cross-lagged dyadic associations, that is, whether one dyad member’s change can be predicted by the previous levels of the other dyad member. The choice of a specific model should be guided by theoretical and conceptual considerations as well as by features of the data, such as the type of dyad, the number and spacing of observations, or distributional properties of variables.
Rebecca K. Dickinson, Tristan J. Coulter, and Clifford J. Mallett
As a basic psychological framework, humanistic theory emphasizes a strong interest in human welfare, values, and dignity. It involves the study and understanding of the unique whole person and how people can reach a heightened sense of self through the process of self-actualization. The focus within humanism to encourage and foster people to be “all they can be” and develop a true sense of self links to a strengths-based approach in sports coaching and the defining principles of positive psychology. In the field of sport and performance psychology, positive psychology has been influential as a discipline concerned with the optimal functioning and human flourishing of performers. Since the 2000s, many sport and performance psychologists have embraced positive psychology as a theoretical basis for examining consistent and superior human performance. However, in the modern history of psychological science, positive psychology is not a new phenomenon; rather, it stems from humanism—the traditional “third wave” in psychology (after the dominance of psychoanalytic and behaviorist approaches). Sport is recognized as a potentially influential context through which people at all levels and backgrounds can thrive. The tendency to focus on performance outcomes, however—winning and losing—often overshadows the potential of sport to achieve this aspirational goal. As evidence of this view, many high-performing athletes are commenting on their distressing experiences to reach the top and the “culture of fear” they have been exposed to as they pursue their own and others’ (e.g., institutional) ambitions (e.g., medaling at the Olympic Games). Humanism concerns itself with the quality of a person’s life, which includes, but also extends beyond such objective and classifying achievements. It is a person-centered approach to understanding the individual and his or her psychological, emotional, and behavioral reality. It seeks to help people define this reality more clearly in such a way that will help them feel good and perform at a high level. Humanism has been, therefore, an important school of thought for improving the lives and experiences of people who play sport as well as those who perform in various other contexts.
Philip Parker and Robert Brockman
Longitudinal structural equation modeling (LSEM) is used to answer lifespan relevant questions such as (a) what is the effect of one variable on change in and other, (b) what is the average trajectory or growth rate of some psychological variable, and (c) what variability is there in average trajectories and what predicts this variability. The first of these questions is often answered by a LSEM called an autoregressive cross-lagged (ACL) model. The other two questions are most typically answered by an LSEM called a latent growth curve (LGC). These models can be applied to a few time waves (measured over several years) or to many time waves (such as present in diary studies) and can be altered, expanded, or even integrated. However, decisions on what model to use must be driven by the research question. The right tool for the job is not always the most complex. And, more importantly, the right tool must be matched to the best possible research design. Sometimes in lifespan research the right tool is LSEM. However, researchers should prioritize research design as well as careful specification of the processes and mechanisms they are interested in rather than simply choosing the most complicated LSEM they can find.