1-10 of 18 Results  for:

  • Developmental Psychology x
  • Methods and Approaches in Psychology x
Clear all

Article

Noemi Pizarroso Lopez

Historical psychology claims that the mind has a history, that is, that our ways of thinking, reasoning, perceiving, feeling, and acting are not necessarily universal or invariable, but are instead subject to modifications over time and space. The theoretical and methodological foundations of this movement were laid in France by psychologist Ignace Meyerson in his book Les fonctions psychologiques et les œuvres, published in 1948. His program stressed the active, experimental, constructive nature of human behavior, spanning behavioral registers as diverse as the linguistic, the religious, the juridical, the scientific/technical, and the artistic. All these behaviors involve aspects of different mental functions that we can infer through a proper analysis of “works,” considered as consolidated testimonies of human activity. As humanity’s successive achievements, constructed over the length of all the paths of the human experience, they are the materials with which psychology has to deal. Meyerson refused to propose an inventory of functions to study. As unstable and imperfect products of a complex and uncertain undertaking, they can be analyzed only by avoiding the counterproductive prejudice of metaphysical fixism. Meyerson spoke in these terms of both deep transformations of feelings, of the person, or of the will, and of the so-called “basic functions,” such as perception and the imaginative function, including memory, time, space, and object. Before Meyerson the term “historical psychology” had already been used by historians like Henri Berr and Lucien Febvre, a founding member of the Annales school, who firmly envisioned a sort of collective psychology of times past. Meyerson and his disciples eventually vied with their fellow historians of the Annales school for the label of “historical psychology” and criticized their notions of mentality and outillage mental. The Annales historians gradually abandoned the label, although they continued to cultivate the idea that mental operations and emotions have a history through the new labels of a “history of mentalities” and, more recently at the turn of the century, a “history of emotions.” While Meyerson and a few other psychologists kept using the “historical psychology” label, however, mainstream psychology remained quite oblivious to this historical focus. The greatest efforts made today among psychologists to think of our mental architecture in terms of transformation over time and space are probably to be found in the work of Kurt Danziger and Roger Smith.

Article

The term “psychology” was applied for the first time in the 16th century. Yet the most interesting examples appeared in three different contexts. The Croatian poet and humanist Marko Marulić (ca. 1520), the German philosopher and Calvinist Johann Thomas Freig (1575), and the German Lutheran philosopher Rudolph Goclenius (1590). Marulić’s manuscript is likely lost, and neither of the other two defined the term. Even the interests of the three went apparently in different directions. Marulić focused on poetry and history, Freig on physica, and Goclenius on theological issues. Nevertheless, they had something in common, and this may represent the gate through which the ways they conceived the term can be understood. They all dealt with the soul, but also that it was a highly disputable concept and not uniformly understood. Another commonality was the avoidance or reinterpretation of Aristotle’s philosophy. The Florentines’ cultivation of Plato had influenced Marulić. Freig was a Ramist, thus, also a humanist who approached philosophical questions rhetorically. Goclenius belonged partly to the same movement. Consequently, they all shared a common interest in texts and language. This is just one, yet quite important aspect of the origin of psychology as a science. Thus, these text- and humanity-oriented aspects of psychology are traceable from the very beginning. This reaches a peak point when Alexander Baumgarten publishes his two volumes on aesthetics, as they were based on Christian Wolff’s Psychologia empirica (1732). They are also traceable in Kant’s critical phase, and even more in Wundt’s folkpsychology. Thus there is a more or less continuous line from the very first uses of the term psychology and some tendencies in social and cultural psychology. In other words, psychology is pursued along an historical line that ends up in the German, and not the British enlightenment.

Article

In the literature of mainstream scientific psychology, German scholar William Stern has been known primarily (if at all) as the inventor of the intelligence quotient (IQ). In fact, however, Stern’s contributions to psychology were much greater and more consequential than this. In this all-inclusive article, I have sought to provide readers with a fuller appreciation for the breadth and depth of Stern’s work, and, in particular, for that comprehensive system of thought that he elaborated under the name “critical personalism.” Drawing frequently on translated quotations from Stern’s published works, and on his personal correspondence with the Freiburg philosopher Jonas Cohn, I have endeavored to show how Stern was much more than “the IQ guy.” During the first 20 years of his academic career, spent at the University of Breslau in what is now the Polish city of Wroclaw, Stern founded that sub-discipline of psychology that would be concentrated on the study of individual differences in various aspects of human psychological functioning. He also made major contributions to that sub-discipline referred to at the time as “child” psychology, and laid the foundations for a comprehensive system of thought that he would name “critical personalism.” After relocating to Hamburg in 1916, Stern continued his scholarly efforts in these domains, taught courses both in psychology and in philosophy at the university that opened its doors there in 1919, and played major administrative roles there in the institutional homes of both disciplines until forced to flee Nazi Germany in 1934. The present chapter highlights ways in which, over the course of his scholarly career, Stern boldly opposed certain trends within mainstream thinking that were ascendant during his time.

Article

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.

Article

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.

Article

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).

Article

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.

Article

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.

Article

Gawon Cho, Giancarlo Pasquini, and Stacey B. Scott

The study of human development across the lifespan is inherently about patterns across time. Although many developmental questions have been tested with cross-sectional comparisons of younger and older persons, understanding of development as it occurs requires a longitudinal design, repeatedly observing the same individual across time. Development, however, unfolds across multiple time scales (i.e., moments, days, years) and encompasses both enduring changes and transient fluctuations within an individual. Measurement burst designs can detect such variations across different timescales, and disentangle patterns of variations associated with distinct dimensions of time periods. Measurement burst designs are a special type of longitudinal design in which multiple “bursts” of intensive (e.g., hourly, daily) measurements are embedded in a larger longitudinal (e.g., monthly, yearly) study. The hybrid nature of these designs allow researchers to address questions not only of cross-sectional comparisons of individual differences (e.g., do older adults typically report lower levels of negative mood than younger adults?) and longitudinal examinations of intraindividual change (e.g., as individuals get older, do they report lower levels of negative mood?) but also of intraindividual variability (e.g., is negative mood worse on days when individuals have experienced an argument compared to days when an argument did not occur?). Researchers can leverage measurement burst designs to examine how patterns of intraindividual variability unfolding over short timescales may exhibit intraindividual change across long timescales in order to understand lifespan development. The use of measurement burst designs provides an opportunity to collect more valid and reliable measurements of development across multiple time scales throughout adulthood.

Article

Intraindividual variability (IIV) refers to short-term fluctuations that may be more rapid, and are often conceptualized as more reversible, than developmental change that unfolds over a longer period of time, such as years. As a feature of longitudinal data collected on micro timescales (i.e., seconds, minutes, days, or weeks), IIV can describe people, contexts, or general processes characterizing human development. In contrast to approaches that pool information across individuals and assess interindividual variability in a population (i.e., between-person variability), IIV is the focus of person-centered studies addressing how and when individuals change over time (i.e., within-person variability). Developmental psychologists interested in change and how and when it occurs, have devised research methods designed to examine intraindividual change (IIC) and interindividual differences in IIC. Dispersion, variability, inconsistency, time-structured IIV, and net IIV are distinct operationalizations of IIV that, depending on the number of measures, occasions, and time of measurement, reflect unique information about IIV in lifespan developmental domains of interest. Microlongitudinal and measurement-burst designs are two methodological approaches with intensive repeated measurement that provide a means by which various operationalizations of IIV can be accurately observed over an appropriate temporal frame to garner clearer understanding of the dynamic phenomenon under investigation. When methodological approaches are theoretically informed and the temporal frame and number of assessments align with the dynamic lifespan developmental phenomenon of interest, researchers gain greater precision in their observations of within-person variability and the extent to which these meaningful short-term fluctuations influence important domains of health and well-being. With technological advancements fueling enhanced methodologies and analytic approaches, IIV research will continue to be at the vanguard of pioneering designs for elucidating developmental change at the individual level and scaling it up to generalize to populations of interest.