scholarly journals Gimme’s ability to recover group-level path coefficients and individual-level path coefficients

Methodology ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 58-91
Author(s):  
Steffen Nestler ◽  
Sarah Humberg
Keyword(s):  
Longitudinal Data ◽  
Statistical Methods ◽  
Simulation Studies ◽  
Group Level ◽  
Individual Level ◽  
Intensive Longitudinal Data ◽  
Path Coefficients ◽  
Specific Individual

The growing availability of intensive longitudinal data has increased psychological researchers' interest in ideographic-statistical methods that, for example, reveal the contemporaneous or lagged associations between different variables for a specific individual. However, when researchers assess several individuals, the results of such models are difficult to generalize across individuals. Researchers recently suggested an algorithm called GIMME, which allows for the identification of coefficients that exist across all individuals (group-level coefficients) or are specific to one or a subgroup of individuals (individual-level coefficients). In three simulation studies we investigated GIMME's performance in recovering group-level and individual-level coefficients. For the former, we found that GIMME performed well when the magnitude of the parameters was moderate to high and when the number of measurements was sufficiently large. However, GIMME had problems detecting individual-level coefficients or coefficients that occurred for a subset of individuals from the whole sample.

scholarly journals Uncovering general, shared, and unique temporal patterns in ambulatory assessment data

2018 ◽  
Author(s):  
Stephanie Lane ◽  
Kathleen Gates ◽  
Hallie Pike ◽  
Adriene Beltz ◽  
Aidan G.C. Wright
Keyword(s):  
Longitudinal Data ◽  
Structural Equation ◽  
Daily Diary ◽  
Ambulatory Assessment ◽  
Multiple Model ◽  
Assessment Data ◽  
Individual Level ◽  
Intensive Longitudinal Data ◽  
Diary Studies ◽  
Neuroimaging Data

Intensive longitudinal data provide psychological researchers with the potential to better understand individual-level temporal processes. While the collection of such data has become increasingly common, there are a comparatively small number of methods well-suited for analyzing these data, and many methods assume homogeneity across individuals. A recent development rooted in structural equation and vector autoregressive modeling, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME), provides one method for arriving at individual-level models composed of processes shared by the sample, processes shared by a subset of the sample, and processes unique to a given individual. As this algorithm was motivated and validated for use with neuroimaging data, its performance and utility is less understood in the context of ambulatory assessment data collected by psychologists. Here, we evaluate the performance of the S-GIMME algorithm across various conditions frequently encountered with daily diary (compared to neuroimaging) data; namely, a smaller number of variables, a lower number of time points, and smaller autoregressive effects. Importantly, we demonstrate for the first time the importance of the autoregressive effects in recovering data-generating connections and directions, and the ability to use S-GIMME with lengths of data commonly seen in daily diary studies. We demonstrate the use of the S-GIMME algorithm with an empirical example evaluating the general, shared, and unique temporal processes associated with a sample of individuals with borderline personality disorder (BPD). Finally, we underscore the need for methods such as S-GIMME moving forward given the increasing use of intensive longitudinal data in psychological research, and the potential for these data to provide novel insights into human behavior and mental health.

How to Perform Idiographic and a Combination of Idiographic and Nomothetic Approaches

2010 ◽  
Vol 218 (3) ◽  
pp. 166-174 ◽  
Author(s):  
Michaela Schmidt ◽  
Franziska Perels ◽  
Bernhard Schmitz
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Hierarchical Linear Modeling ◽  
Self Regulation ◽  
Training Group ◽  
Group Level ◽  
Linear Modeling ◽  
Individual Level ◽  
Cross Correlations ◽  
Time Series Analyses

The aim of the study is to combine and compare person-oriented and nomothetic approaches to analyze longitudinal data with time series analyses and hierarchical linear modeling (HLM). Based on the evaluation of an intervention study both approaches were used to compare individual and group data. In this study, a training was implemented to foster students’ self-regulation and selected results were presented at the individual and group level for the variables planning and motivation. To analyze data with time series analysis, cross-correlations and trend analyses were conducted. Cross-correlations revealed similar results on the aggregated and individual level whereas trend analysis indicated different results of these two levels. Results of HLM analyses for longitudinal data suggested that students’ motivation has more influence than the type of training group on students’ planning. The findings demonstrate that individual and group-level results differ and that both methods have different focuses. This means that it is useful to combine time series analyses and HLM approaches when analyzing longitudinal data.

scholarly journals Foundations of Idiographic Methods in Psychology and Applications for Psychotherapy

2019 ◽  
Author(s):  
Marilyn Piccirillo ◽  
Thomas Rodebaugh
Keyword(s):  
Longitudinal Data ◽  
Statistical Methods ◽  
Psychotherapy Research ◽  
Clinical Settings ◽  
Psychological Processes ◽  
Vector Autoregressive ◽  
Individual Level ◽  
Idiographic Approach ◽  
The Individual ◽  
Idiographic Methods

Researchers have long called for greater recognition and use of longitudinal, individual-level research in the study of psychopathology and psychotherapy. Much of our current research attempts to indirectly investigate individual-level, or idiographic, psychological processes via group-based, or nomothetic, designs. However, results from nomothetic research do not necessarily translate to the individual-level. In this review, we discuss how idiographic analyses can be integrated into psychotherapy and psychotherapy research. We examine and review key statistical methods for conducting idiographic analyses. These methods include factor-based and vector autoregressive approaches using longitudinal data. The theoretical framework behind each approach is reviewed and critically evaluated. Empirical examples of each approach are discussed, with the aim of helping interested readers consider how they may use idiographic methods to analyze longitudinal data and psychological processes. Finally, we conclude by citing key limitations of the idiographic approach, calling for greater development of these analyses to ease their successful integration into clinical settings.

scholarly journals Foundations of Idiographic Methods in Psychology and Applications for Psychotherapy

2019 ◽  
Author(s):  
Marilyn Piccirillo ◽  
Thomas Rodebaugh
Keyword(s):  
Longitudinal Data ◽  
Statistical Methods ◽  
Psychotherapy Research ◽  
Clinical Settings ◽  
Psychological Processes ◽  
Vector Autoregressive ◽  
Individual Level ◽  
Idiographic Approach ◽  
The Individual ◽  
Idiographic Methods

Researchers have long called for greater recognition and use of longitudinal, individual-level research in the study of psychopathology and psychotherapy. Much of our current research attempts to indirectly investigate individual-level, or idiographic, psychological processes via group-based, or nomothetic, designs. However, results from nomothetic research do not necessarily translate to the individual-level. In this review, we discuss how idiographic analyses can be integrated into psychotherapy and psychotherapy research. We examine and review key statistical methods for conducting idiographic analyses. These methods include factor-based and vector autoregressive approaches using longitudinal data. The theoretical framework behind each approach is reviewed and critically evaluated. Empirical examples of each approach are discussed, with the aim of helping interested readers consider how they may use idiographic methods to analyze longitudinal data and psychological processes. Finally, we conclude by citing key limitations of the idiographic approach, calling for greater development of these analyses to ease their successful integration into clinical settings.

Reply to Connor and Evers (2020): Critique of the Bias of Crowds Model Simply Restates the Model

2020 ◽  
Author(s):  
Keith Payne ◽  
Heidi A. Vuletich ◽  
Kristjen B. Lundberg
Keyword(s):  
Implicit Bias ◽  
Group Level ◽  
Systemic Racism ◽  
Individual Level ◽  
The Core ◽  
Psychological Science ◽  
Implicit Biases ◽  
The Individual

The Bias of Crowds model (Payne, Vuletich, & Lundberg, 2017) argues that implicit bias varies across individuals and across contexts. It is unreliable and weakly associated with behavior at the individual level. But when aggregated to measure context-level effects, the scores become stable and predictive of group-level outcomes. We concluded that the statistical benefits of aggregation are so powerful that researchers should reconceptualize implicit bias as a feature of contexts, and ask new questions about how implicit biases relate to systemic racism. Connor and Evers (2020) critiqued the model, but their critique simply restates the core claims of the model. They agreed that implicit bias varies across individuals and across contexts; that it is unreliable and weakly associated with behavior at the individual level; and that aggregating scores to measure context-level effects makes them more stable and predictive of group-level outcomes. Connor and Evers concluded that implicit bias should be considered to really be noisily measured individual construct because the effects of aggregation are merely statistical. We respond to their specific arguments and then discuss what it means to really be a feature of persons versus situations, and multilevel measurement and theory in psychological science more broadly.

New Statistical Methods Inspired by Data Collected from Alcohol and Substance Abuse Research

2018 ◽  
Author(s):  
Anne Buu ◽  
Runze Li
Keyword(s):  
Risk Factors ◽  
Substance Abuse ◽  
Longitudinal Data ◽  
Developmental Trajectories ◽  
Regression Splines ◽  
Simulation Studies ◽  
Process Data ◽  
Measurement Reactivity ◽  
Longitudinal Association ◽  
Highly Correlated

This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity and predictive validity using daily process data. The excellent statistical properties of the methods introduced have been supported by simulation studies. The applications in alcohol and substance abuse research have also been demonstrated by graphs on real longitudinal data.

scholarly journals Examining the Effects of Three Group-Level Metacognitive Scaffoldings on In-Service Teachers’ Knowledge Building

2021 ◽  
pp. 073563312110308
Author(s):  
Fan Ouyang ◽  
Si Chen ◽  
Yuqin Yang ◽  
Yunqing Chen
Keyword(s):  
Small Groups ◽  
Mixed Method ◽  
Peer Interactions ◽  
Knowledge Building ◽  
Group Level ◽  
Question Asking ◽  
Individual Level ◽  
The Individual ◽  
Task Oriented ◽  
Metacognitive Scaffolding

Group-level metacognitive scaffolding is critical for productive knowledge building. However, previous research mainly focuses on the individual-level metacognitive scaffoldings in helping learners improve knowledge building, and little effort has been made to develop group-level metacognitive scaffolding (GMS) for knowledge building. This research designed three group-level metacognitive scaffoldings of general, task-oriented, and idea-oriented scaffoldings to facilitate in-service teachers’ knowledge building in small groups. A mixed method is used to examine the effects of the GMSs on groups’ knowledge building processes, performances, and perceptions. Results indicate a complication of the effects of GMSs on knowledge building. The idea-oriented scaffolding has potential to facilitate question-asking and perspective-proposing inquiry through peer interactions; the general scaffolding does not necessarily lessen teachers’ idea-centered explanation and elaboration on the individual level; the task-oriented scaffolding has the worst effect. Pedagogical and research implications are discussed to foster knowledge building with the support of GMSs.

Missing Data in Research on Youth and Family Programs

2021 ◽  
pp. 003329412110268
Author(s):  
Jaime Ballard ◽  
Adeya Richmond ◽  
Suzanne van den Hoogenhof ◽  
Lynne Borden ◽  
Daniel Francis Perkins
Keyword(s):  
Missing Data ◽  
Statistical Methods ◽  
Self Report ◽  
Program Participation ◽  
Multilevel Data ◽  
Individual Level ◽  
Level Data ◽  
Before And After ◽  
Post Test ◽  
The Individual

Background Multilevel data can be missing at the individual level or at a nested level, such as family, classroom, or program site. Increased knowledge of higher-level missing data is necessary to develop evaluation design and statistical methods to address it. Methods Participants included 9,514 individuals participating in 47 youth and family programs nationwide who completed multiple self-report measures before and after program participation. Data were marked as missing or not missing at the item, scale, and wave levels for both individuals and program sites. Results Site-level missing data represented a substantial portion of missing data, ranging from 0–46% of missing data at pre-test and 35–71% of missing data at post-test. Youth were the most likely to be missing data, although site-level data did not differ by the age of participants served. In this dataset youth had the most surveys to complete, so their missing data could be due to survey fatigue. Conclusions Much of the missing data for individuals can be explained by the site not administering those questions or scales. These results suggest a need for statistical methods that account for site-level missing data, and for research design methods to reduce the prevalence of site-level missing data or reduce its impact. Researchers can generate buy-in with sites during the community collaboration stage, assessing problematic items for revision or removal and need for ongoing site support, particularly at post-test. We recommend that researchers conducting multilevel data report the amount and mechanism of missing data at each level.

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