subject:(intensive longitudinal data)

University of Manchester

1. Carter, Lesley-Anne. Rigorous methods for the analysis, reporting and evaluation of ESM style data.

Degree: 2016, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:305878

► Experience sampling methodology (ESM) is a real-time data capture method that can be used to monitor symptoms and behaviours as they occur during everyday life.… (more)

▼ Experience sampling methodology (ESM) is a real-time data capture method that can be used to monitor symptoms and behaviours as they occur during everyday life. With measures completed multiple times a day, over several days, this intensive longitudinal data collection method results in multilevel data with observations nested within days, nested within subjects. The aim of this thesis was to investigate the optimal use of multilevel models for ESM in the design, reporting and analysis of ESM data, and apply these models to a study in people with psychosis.A methodological systematic review was conducted to identify design, analysis and statistical reporting practices in current ESM studies. Seventy four studies from 2012 were reviewed, and together with the analysis of a motivating example, four significant areas of interest were identified: power and sample size, missing data, momentary variation and predicting momentary change. Appropriate multilevel methods were sought for each of these areas, and were evaluated in the three-level context of ESM.Missing data was found to be both underreported and rarely considered when choosing analysis methods in practice. This work has introduced a more detailed understanding of nonresponse in ESM studies and has discussed appropriate statistical methods in the presence of missing data. This thesis has extended two-level statistical methodology for data analysis to accommodate the three-level structure of ESM. Novel applications of time trends have been developed, were time can be measured at two separate levels. The suitability of predicting momentary change in ESM data has been questioned; it is argued that the first-difference and joint modelling methods that are claimed in the literature to remove bias possibly induce more in this context. Finally, Monte Carlo simulations were shown to be a flexible option for estimating empirical power under varying sample sizes at levels 3, 2 and 1, with recommendations made for conservative power estimates when a priori parameter estimates are unknown. In summary, this work demonstrates how multilevel models can be used to examine the rich data structure of ESM and fully utilize the variation in measures captured at all levels. Advisors/Committee Members: EMSLEY, RICHARD RA, Roberts, Christopher, Emsley, Richard.

Subjects/Keywords: Experience sampling; Multilevel; Intensive longitudinal data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Carter, L. (2016). Rigorous methods for the analysis, reporting and evaluation of ESM style data. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:305878

Chicago Manual of Style (16^th Edition):

Carter, Lesley-Anne. “Rigorous methods for the analysis, reporting and evaluation of ESM style data.” 2016. Doctoral Dissertation, University of Manchester. Accessed January 21, 2021. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:305878.

MLA Handbook (7^th Edition):

Carter, Lesley-Anne. “Rigorous methods for the analysis, reporting and evaluation of ESM style data.” 2016. Web. 21 Jan 2021.

Vancouver:

Carter L. Rigorous methods for the analysis, reporting and evaluation of ESM style data. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2021 Jan 21]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:305878.

Council of Science Editors:

Carter L. Rigorous methods for the analysis, reporting and evaluation of ESM style data. [Doctoral Dissertation]. University of Manchester; 2016. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:305878

University of Manchester

2. Carter, Lesley-Anne. Rigorous methods for the analysis, reporting and evaluation of ESM style data.

Degree: PhD, 2016, University of Manchester

URL: https://www.research.manchester.ac.uk/portal/en/theses/rigorous-methods-for-the-analysis-reporting-and-evaluation-of-esm-style-data(5c022c50-c399-4388-b76f-a02af55be4ad).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701110

► Experience sampling methodology (ESM) is a real-time data capture method that can be used to monitor symptoms and behaviours as they occur during everyday life.… (more)

▼ Experience sampling methodology (ESM) is a real-time data capture method that can be used to monitor symptoms and behaviours as they occur during everyday life. With measures completed multiple times a day, over several days, this intensive longitudinal data collection method results in multilevel data with observations nested within days, nested within subjects. The aim of this thesis was to investigate the optimal use of multilevel models for ESM in the design, reporting and analysis of ESM data, and apply these models to a study in people with psychosis. A methodological systematic review was conducted to identify design, analysis and statistical reporting practices in current ESM studies. Seventy four studies from 2012 were reviewed, and together with the analysis of a motivating example, four significant areas of interest were identified: power and sample size, missing data, momentary variation and predicting momentary change. Appropriate multilevel methods were sought for each of these areas, and were evaluated in the three-level context of ESM.Missing data was found to be both underreported and rarely considered when choosing analysis methods in practice. This work has introduced a more detailed understanding of nonresponse in ESM studies and has discussed appropriate statistical methods in the presence of missing data. This thesis has extended two-level statistical methodology for data analysis to accommodate the three-level structure of ESM. Novel applications of time trends have been developed, were time can be measured at two separate levels. The suitability of predicting momentary change in ESM data has been questioned; it is argued that the first-difference and joint modelling methods that are claimed in the literature to remove bias possibly induce more in this context. Finally, Monte Carlo simulations were shown to be a flexible option for estimating empirical power under varying sample sizes at levels 3, 2 and 1, with recommendations made for conservative power estimates when a priori parameter estimates are unknown. In summary, this work demonstrates how multilevel models can be used to examine the rich data structure of ESM and fully utilize the variation in measures captured at all levels.

Subjects/Keywords: 001.4; Experience sampling; Multilevel; Intensive longitudinal data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Carter, L. (2016). Rigorous methods for the analysis, reporting and evaluation of ESM style data. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/rigorous-methods-for-the-analysis-reporting-and-evaluation-of-esm-style-data(5c022c50-c399-4388-b76f-a02af55be4ad).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701110

Chicago Manual of Style (16^th Edition):

Carter, Lesley-Anne. “Rigorous methods for the analysis, reporting and evaluation of ESM style data.” 2016. Doctoral Dissertation, University of Manchester. Accessed January 21, 2021. https://www.research.manchester.ac.uk/portal/en/theses/rigorous-methods-for-the-analysis-reporting-and-evaluation-of-esm-style-data(5c022c50-c399-4388-b76f-a02af55be4ad).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701110.

MLA Handbook (7^th Edition):

Carter, Lesley-Anne. “Rigorous methods for the analysis, reporting and evaluation of ESM style data.” 2016. Web. 21 Jan 2021.

Vancouver:

Carter L. Rigorous methods for the analysis, reporting and evaluation of ESM style data. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2021 Jan 21]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/rigorous-methods-for-the-analysis-reporting-and-evaluation-of-esm-style-data(5c022c50-c399-4388-b76f-a02af55be4ad).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701110.

Council of Science Editors:

Carter L. Rigorous methods for the analysis, reporting and evaluation of ESM style data. [Doctoral Dissertation]. University of Manchester; 2016. Available from: https://www.research.manchester.ac.uk/portal/en/theses/rigorous-methods-for-the-analysis-reporting-and-evaluation-of-esm-style-data(5c022c50-c399-4388-b76f-a02af55be4ad).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.701110

Penn State University

3. Ji, Linying. Handling Missing Data in the Modeling of Intensive Longitudinal Data.

Degree: 2016, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/28928

► The availability of intensive longitudinal data has helped spur the use more sophisticated methods for studying change. Unfortunately, missing data issues also arise frequently in… (more)

Subjects/Keywords: intensive longitudinal data; missing data; multiple imputation; full-information maximum likelihood

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ji, L. (2016). Handling Missing Data in the Modeling of Intensive Longitudinal Data. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/28928

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ji, Linying. “Handling Missing Data in the Modeling of Intensive Longitudinal Data.” 2016. Thesis, Penn State University. Accessed January 21, 2021. https://submit-etda.libraries.psu.edu/catalog/28928.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ji, Linying. “Handling Missing Data in the Modeling of Intensive Longitudinal Data.” 2016. Web. 21 Jan 2021.

Vancouver:

Ji L. Handling Missing Data in the Modeling of Intensive Longitudinal Data. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Jan 21]. Available from: https://submit-etda.libraries.psu.edu/catalog/28928.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ji L. Handling Missing Data in the Modeling of Intensive Longitudinal Data. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/28928

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universiteit Utrecht

4. Schuurman, N.K. Multilevel Autoregressive Modeling in Psychology: Snags and Solutions.

Degree: 2016, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/337475

► Psychological processes are of interest in all areas of psychology, and all such processes occur within individuals over time. Some examples of psychological processes are… (more)

▼ Psychological processes are of interest in all areas of psychology, and all such processes occur within individuals over time. Some examples of psychological processes are the regulation of daily mood, the effect of job motivation on job performance and vice versa, or social interactions between a parent and child. In order to study these processes it is necessary to take many repeated measures for each individual. Multilevel autoregressive models are statistical models than can be used to analyze this kind of data - data that consist of many repeated measures, for many individuals. The approach of autoregressive models is summarized well with the saying ``The best predictor of future behavior is past behavior'': In autoregressive models, current observations are used to predict future observations. By extending the autoregressive model to a multilevel autoregressive model, it becomes possible to model the repeated measures for many individuals at the same time, while also modeling the differences between the processes of each individual. Multilevel autoregressive models are increasing in popularity within psychology, however, the methods for analyzing psychological data with these models are still being developed. The aim for this dissertation was to further investigate, explicate, and if possible remedy certain difficulties in fitting and interpreting multilevel autoregressive models in the context of psychological science. Specifically, in Chapter 1 it is discussed why it is important to collect many repeated measures for studying psychological processes, and why it is important to model these processes on an individual level, as well as the similarities and differences between the individuals' processes. In Chapter 2 a difficulty with specifying an Inverse-Wishart prior distribution for the covariance matrix of the random parameters is explored, in the context of fitting the multilevel autoregressive model in a Bayesian framework. In Chapter 3 it is discussed how to standardize the model parameters, such that we can make meaningful comparisons of the strength of the cross-lagged effects in a multivariate model. In Chapters 4 and 5 the consequences of ignoring measurement errors for the estimation of the model parameters are investigated for respectively a single-subject autoregressive model and the multilevel autoregressive model, as well as how to account for measurement errors in these models. The final chapter of this dissertation contains a summary of the work presented in the previous chapters, and a discussion of some limitations of the multilevel autoregressive modeling approach. Advisors/Committee Members: Hoijtink, H.J.A., Hamaker, E.L..

Subjects/Keywords: time series; multilevel autoregressive modeling; intensive longitudinal data; bayesian modeling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Schuurman, N. K. (2016). Multilevel Autoregressive Modeling in Psychology: Snags and Solutions. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/337475

Chicago Manual of Style (16^th Edition):

Schuurman, N K. “Multilevel Autoregressive Modeling in Psychology: Snags and Solutions.” 2016. Doctoral Dissertation, Universiteit Utrecht. Accessed January 21, 2021. http://dspace.library.uu.nl:8080/handle/1874/337475.

MLA Handbook (7^th Edition):

Schuurman, N K. “Multilevel Autoregressive Modeling in Psychology: Snags and Solutions.” 2016. Web. 21 Jan 2021.

Vancouver:

Schuurman NK. Multilevel Autoregressive Modeling in Psychology: Snags and Solutions. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2016. [cited 2021 Jan 21]. Available from: http://dspace.library.uu.nl:8080/handle/1874/337475.

Council of Science Editors:

Schuurman NK. Multilevel Autoregressive Modeling in Psychology: Snags and Solutions. [Doctoral Dissertation]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/337475

Penn State University

5. Trail, Jessica Brooke. Dynamic Models for Intensive Longitudinal Data: New Models, Statistical Procedures, and Applications.

Degree: 2015, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/24818

► Functional data analysis (FDA) of intensive longitudinal data is becoming increasingly popular in the behavioral sciences to study time-varying processes. This dissertation focuses on integrating… (more)

▼ Functional data analysis (FDA) of intensive longitudinal data is becoming increasingly popular in the behavioral sciences to study time-varying processes. This dissertation focuses on integrating two methods which are useful in FDA: dynamical systems models and time-varying coefficient models. Dynamic models provide a more detailed description of a time-varying process than some traditional longitudinal data methods, including effects that characterize the shape, magnitude, and speed of the outcome’s response to a change in inputs, or predictors. Allowing these effects to vary as functions of time uses the intensive nature of the data to describe even more complex change. In the behavioral sciences, a better understanding of the dynamics of a process could be used to inform the design of behavioral interventions. For example, this dissertation was motivated by a study designed to examine the trajectories of change in students’ smoking behavior during the freshman year of college. A better understanding of the trajectory of smoking onset will help inform the development of behavioral interventions to alter this trajectory and prevention smoking. This is important because smoking is the leading preventable cause of death in the US. The repeated measurements used to describe a dynamical system frequently contain measurement error, and so statistical methods are needed to estimate the parameters of the differential equation model. In this dissertation, we propose a two-step estimation method. The first step uses spline smoothers to smooth the noisy observed data and estimate the derivatives. The second step uses penalized splines to estimate the time-varying effects in the differential equation. We use simulated data to describe the performance of the proposed estimation method. To test hypotheses about the time-varying effects, for example, whether or not an effect is significantly time-varying or determining if a covariate is significant in the model, we propose a generalized likelihood ratio test statistic. We use bootstrap methods and a Monte Carlo simulation study to assess the finite sample properties of the proposed test statistic. The proposed methodology is applied to data from the UpTERN study (Tiffany, et al., 2004). In this empirical example, we examine and test hypotheses about the relations between covariates such as alcohol use and gender and the dynamics of cigarette smoking. Advisors/Committee Members: Linda Marie Collins, Dissertation Advisor/Co-Advisor, Runze Li, Dissertation Advisor/Co-Advisor, John Fricks, Committee Member, Donna Coffman, Committee Member.

Subjects/Keywords: intensive longitudinal data; dynamic systems; time-varying effects

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Trail, J. B. (2015). Dynamic Models for Intensive Longitudinal Data: New Models, Statistical Procedures, and Applications. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/24818

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Trail, Jessica Brooke. “Dynamic Models for Intensive Longitudinal Data: New Models, Statistical Procedures, and Applications.” 2015. Thesis, Penn State University. Accessed January 21, 2021. https://submit-etda.libraries.psu.edu/catalog/24818.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Trail, Jessica Brooke. “Dynamic Models for Intensive Longitudinal Data: New Models, Statistical Procedures, and Applications.” 2015. Web. 21 Jan 2021.

Vancouver:

Trail JB. Dynamic Models for Intensive Longitudinal Data: New Models, Statistical Procedures, and Applications. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Jan 21]. Available from: https://submit-etda.libraries.psu.edu/catalog/24818.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Trail JB. Dynamic Models for Intensive Longitudinal Data: New Models, Statistical Procedures, and Applications. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/24818

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

6. Wood, Julie Katharine. Integrating Multiple Timescales and Process Models: An Illustrative Application to Affect Dynamics.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/15142jfw5255

► Process models are robust tools for developing, testing, and refining models of psychological processes. However, process models are typically applied only to intensive data collected… (more)

▼ Process models are robust tools for developing, testing, and refining models of psychological processes. However, process models are typically applied only to intensive data collected over relatively short periods of time (seconds, minutes). A key postulate of many developmental theories is that these short-term processes also change over longer time scales (months, years). Measurement burst designs combine collection of intensive longitudinal data that provides for description of short-term process with collection of longitudinal panel data that provide for description of how different aspects of those processes change over time. This paper illustrates an application of a multilevel process model to measurement-burst data, to examine interindividual differences and intraindividual change in intraindividual process dynamics. Ratings of affect arousal were collected from 150 adults age 18 to 90 years during three 21-day measurement bursts of intensive data collection, spaced approximately evenly over the course of a year. A 3-level autoregressive model with heterogeneous variance was used to examine how three key parameters of arousal dynamics (attractor point, inertia, and “reactivity” to biopsychosociocultural (BPSC) inputs) were related to interindividual differences and/or intraindividual changes in depressive symptoms, life events, perceived control, and relative age. Both interindividual differences and intraindividual burst-to-burst changes in depression were associated with lower arousal attractor positions. Older age (relative to others) was related to higher arousal attractor positions but less demonstrated BPSC-related reactivity. Experience of major life events was associated with higher arousal inertia, as well as higher BPSC-related reactivity at both the inter- and intraindividual level. These analysis and results illustrate how a multilevel model, cast in a process model framework, can be utilized by researchers to identify sources of inter- and intraindividual differences on multiple timescales. These kinds of models move developmental research to closer representations of plastic, multideterminant developmental theory. Advisors/Committee Members: Nilam Ram, Thesis Advisor/Co-Advisor, Zita Oravecz, Committee Member.

Subjects/Keywords: Process modeling; Multiple Timescales; Intensive longitudinal data; Affect Dynamics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wood, J. K. (2018). Integrating Multiple Timescales and Process Models: An Illustrative Application to Affect Dynamics. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15142jfw5255

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wood, Julie Katharine. “Integrating Multiple Timescales and Process Models: An Illustrative Application to Affect Dynamics.” 2018. Thesis, Penn State University. Accessed January 21, 2021. https://submit-etda.libraries.psu.edu/catalog/15142jfw5255.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wood, Julie Katharine. “Integrating Multiple Timescales and Process Models: An Illustrative Application to Affect Dynamics.” 2018. Web. 21 Jan 2021.

Vancouver:

Wood JK. Integrating Multiple Timescales and Process Models: An Illustrative Application to Affect Dynamics. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Jan 21]. Available from: https://submit-etda.libraries.psu.edu/catalog/15142jfw5255.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wood JK. Integrating Multiple Timescales and Process Models: An Illustrative Application to Affect Dynamics. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15142jfw5255

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Notre Dame

7. Lauren A. Trichtinger. Bootstrap Methods for Testing P-Technique Factor Analysis</h1>.

Degree: Psychology, 2020, University of Notre Dame

URL: https://curate.nd.edu/show/vt150g38p7r

► New data collection methods like smartphone applications afford researchers the opportunity to study intra-individual differences with finer details. Data from these types of studies… (more)

Subjects/Keywords: P-technique; factor analysis; intensive longitudinal data; nonnormal; time series; bootstrap

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Trichtinger, L. A. (2020). Bootstrap Methods for Testing P-Technique Factor Analysis</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/vt150g38p7r

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Trichtinger, Lauren A.. “Bootstrap Methods for Testing P-Technique Factor Analysis</h1>.” 2020. Thesis, University of Notre Dame. Accessed January 21, 2021. https://curate.nd.edu/show/vt150g38p7r.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Trichtinger, Lauren A.. “Bootstrap Methods for Testing P-Technique Factor Analysis</h1>.” 2020. Web. 21 Jan 2021.

Vancouver:

Trichtinger LA. Bootstrap Methods for Testing P-Technique Factor Analysis</h1>. [Internet] [Thesis]. University of Notre Dame; 2020. [cited 2021 Jan 21]. Available from: https://curate.nd.edu/show/vt150g38p7r.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Trichtinger LA. Bootstrap Methods for Testing P-Technique Factor Analysis</h1>. [Thesis]. University of Notre Dame; 2020. Available from: https://curate.nd.edu/show/vt150g38p7r

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

8. Yoo, Mina. MEASURES OF AGREEMENT IN METHOD COMPARISON STUDIES FOR INTENSIVE LONGITUDINAL DATA .

Degree: 2011, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/12131

► This dissertation is concerned with assessment of measurement agreement for intensive longitudinal data. Assessment of the measurement agreement encompasses a variety of applications. A number… (more)

Subjects/Keywords: concordance correlation coefficient; varying coefficient models; partially linear models; mixed effects models; measurement agreement; intensive longitudinal data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yoo, M. (2011). MEASURES OF AGREEMENT IN METHOD COMPARISON STUDIES FOR INTENSIVE LONGITUDINAL DATA . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/12131

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yoo, Mina. “MEASURES OF AGREEMENT IN METHOD COMPARISON STUDIES FOR INTENSIVE LONGITUDINAL DATA .” 2011. Thesis, Penn State University. Accessed January 21, 2021. https://submit-etda.libraries.psu.edu/catalog/12131.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yoo, Mina. “MEASURES OF AGREEMENT IN METHOD COMPARISON STUDIES FOR INTENSIVE LONGITUDINAL DATA .” 2011. Web. 21 Jan 2021.

Vancouver:

Yoo M. MEASURES OF AGREEMENT IN METHOD COMPARISON STUDIES FOR INTENSIVE LONGITUDINAL DATA . [Internet] [Thesis]. Penn State University; 2011. [cited 2021 Jan 21]. Available from: https://submit-etda.libraries.psu.edu/catalog/12131.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yoo M. MEASURES OF AGREEMENT IN METHOD COMPARISON STUDIES FOR INTENSIVE LONGITUDINAL DATA . [Thesis]. Penn State University; 2011. Available from: https://submit-etda.libraries.psu.edu/catalog/12131

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

9. Liao, Yue. Understanding the dynamic relationships between physical activity and affective states using real-time data capture techniques.

Degree: PhD, Preventive Medicine (Health Behavior), 7, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/588570/rec/7683

► This dissertation examined the relationships between physical activity and affective states using real‐time data capture techniques. Specifically, the (1) acute effects (i.e., the bi‐directional relationships… (more)

Subjects/Keywords: physical activity; affect; free‐living; ecological momentary assessment; intensive longitudinal data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liao, Y. (7). Understanding the dynamic relationships between physical activity and affective states using real-time data capture techniques. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/588570/rec/7683

Chicago Manual of Style (16^th Edition):

Liao, Yue. “Understanding the dynamic relationships between physical activity and affective states using real-time data capture techniques.” 7. Doctoral Dissertation, University of Southern California. Accessed January 21, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/588570/rec/7683.

MLA Handbook (7^th Edition):

Liao, Yue. “Understanding the dynamic relationships between physical activity and affective states using real-time data capture techniques.” 7. Web. 21 Jan 2021.

Vancouver:

Liao Y. Understanding the dynamic relationships between physical activity and affective states using real-time data capture techniques. [Internet] [Doctoral dissertation]. University of Southern California; 7. [cited 2021 Jan 21]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/588570/rec/7683.

Council of Science Editors:

Liao Y. Understanding the dynamic relationships between physical activity and affective states using real-time data capture techniques. [Doctoral Dissertation]. University of Southern California; 7. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/588570/rec/7683

10. Schuurman, N.K. Multilevel Autoregressive Modeling in Psychology: Snags and Solutions.

Degree: 2016, University Utrecht

URL: https://dspace.library.uu.nl/handle/1874/337475 ; URN:NBN:NL:UI:10-1874-337475 ; 1874/337475 ; urn:isbn:9789039365854 ; URN:NBN:NL:UI:10-1874-337475 ; https://dspace.library.uu.nl/handle/1874/337475

► Psychological processes are of interest in all areas of psychology, and all such processes occur within individuals over time. Some examples of psychological processes are… (more)

▼ Psychological processes are of interest in all areas of psychology, and all such processes occur within individuals over time. Some examples of psychological processes are the regulation of daily mood, the effect of job motivation on job performance and vice versa, or social interactions between a parent and child. In order to study these processes it is necessary to take many repeated measures for each individual. Multilevel autoregressive models are statistical models than can be used to analyze this kind of data - data that consist of many repeated measures, for many individuals. The approach of autoregressive models is summarized well with the saying ``The best predictor of future behavior is past behavior'': In autoregressive models, current observations are used to predict future observations. By extending the autoregressive model to a multilevel autoregressive model, it becomes possible to model the repeated measures for many individuals at the same time, while also modeling the differences between the processes of each individual. Multilevel autoregressive models are increasing in popularity within psychology, however, the methods for analyzing psychological data with these models are still being developed. The aim for this dissertation was to further investigate, explicate, and if possible remedy certain difficulties in fitting and interpreting multilevel autoregressive models in the context of psychological science. Specifically, in Chapter 1 it is discussed why it is important to collect many repeated measures for studying psychological processes, and why it is important to model these processes on an individual level, as well as the similarities and differences between the individuals' processes. In Chapter 2 a difficulty with specifying an Inverse-Wishart prior distribution for the covariance matrix of the random parameters is explored, in the context of fitting the multilevel autoregressive model in a Bayesian framework. In Chapter 3 it is discussed how to standardize the model parameters, such that we can make meaningful comparisons of the strength of the cross-lagged effects in a multivariate model. In Chapters 4 and 5 the consequences of ignoring measurement errors for the estimation of the model parameters are investigated for respectively a single-subject autoregressive model and the multilevel autoregressive model, as well as how to account for measurement errors in these models. The final chapter of this dissertation contains a summary of the work presented in the previous chapters, and a discussion of some limitations of the multilevel autoregressive modeling approach. Advisors/Committee Members: Hoijtink, Herbert, Hamaker, Ellen.

Subjects/Keywords: time series; multilevel autoregressive modeling; intensive longitudinal data; bayesian modeling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Schuurman, N. K. (2016). Multilevel Autoregressive Modeling in Psychology: Snags and Solutions. (Doctoral Dissertation). University Utrecht. Retrieved from https://dspace.library.uu.nl/handle/1874/337475 ; URN:NBN:NL:UI:10-1874-337475 ; 1874/337475 ; urn:isbn:9789039365854 ; URN:NBN:NL:UI:10-1874-337475 ; https://dspace.library.uu.nl/handle/1874/337475

Chicago Manual of Style (16^th Edition):

Schuurman, N K. “Multilevel Autoregressive Modeling in Psychology: Snags and Solutions.” 2016. Doctoral Dissertation, University Utrecht. Accessed January 21, 2021. https://dspace.library.uu.nl/handle/1874/337475 ; URN:NBN:NL:UI:10-1874-337475 ; 1874/337475 ; urn:isbn:9789039365854 ; URN:NBN:NL:UI:10-1874-337475 ; https://dspace.library.uu.nl/handle/1874/337475.

MLA Handbook (7^th Edition):

Schuurman, N K. “Multilevel Autoregressive Modeling in Psychology: Snags and Solutions.” 2016. Web. 21 Jan 2021.

Vancouver:

Schuurman NK. Multilevel Autoregressive Modeling in Psychology: Snags and Solutions. [Internet] [Doctoral dissertation]. University Utrecht; 2016. [cited 2021 Jan 21]. Available from: https://dspace.library.uu.nl/handle/1874/337475 ; URN:NBN:NL:UI:10-1874-337475 ; 1874/337475 ; urn:isbn:9789039365854 ; URN:NBN:NL:UI:10-1874-337475 ; https://dspace.library.uu.nl/handle/1874/337475.

Council of Science Editors:

Schuurman NK. Multilevel Autoregressive Modeling in Psychology: Snags and Solutions. [Doctoral Dissertation]. University Utrecht; 2016. Available from: https://dspace.library.uu.nl/handle/1874/337475 ; URN:NBN:NL:UI:10-1874-337475 ; 1874/337475 ; urn:isbn:9789039365854 ; URN:NBN:NL:UI:10-1874-337475 ; https://dspace.library.uu.nl/handle/1874/337475

Penn State University

11. McDaniel, Brandon Talmage. Understanding Stability and Change in Daily Coparenting: Predictors and Outcomes in Families with Young Children.

Degree: 2016, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/d217qp48j

► Coparenting consists of the ways parents work together in rearing their children. The ability of parents to cooperate, support one another, and avoid undermining or… (more)

▼ Coparenting consists of the ways parents work together in rearing their children. The ability of parents to cooperate, support one another, and avoid undermining or criticizing each other influences the quality of their couple relationship across time as well as can spill over into their children’s behavior and well-being. Coparenting quality can influence children directly through compromising the emotional security that children feel in regards to their parents and indirectly as the quality of coparenting spills over into the quality of parenting children receive from each individual parent. Coparenting can also be a source of strain or support for parents, as they provide assistance to one another in parenting. Therefore, examining the development of coparenting contributes in important ways to efforts to enhance family and child well-being. Some studies have reported a moderate degree of stability in the quality of coparenting during the early years after birth—with those parents who start off working well together continuing to work well together later—yet researchers have often left relatively large gaps in between assessments of coparenting quality. For example, many longitudinal studies of coparenting tend to assess coparenting every 6 months to 1 year. These large gaps in between assessments leave us with an inadequate understanding of the complex family processes that are experienced by parents and children on a daily basis. Furthermore, the so-called “stability” that is observed in these studies tells nothing of what happens in between these various snapshots of family life. Indeed, coparenting quality likely fluctuates within families over shorter periods of time, as parents and families deal with the stresses of everyday life and seek for equilibrium. Moreover, these fluctuations likely hold meaning for relationships, parents, and children. Therefore, the current dissertation fills this gap in the research by examining coparenting quality on a daily basis. Data for this dissertation were drawn from the Daily Family Life Project (DFLP), a longitudinal and daily diary study of parenting and family relationships in 183 couples with a young child under age 5. In Study I, I developed and validated the Daily Coparenting Scale (D-Cop), a 10 item measure of parents’ perceptions of daily coparenting quality. Utilizing multilevel factor analysis, I identified two daily coparenting factors at both the between- and within-person level: positive and negative daily coparenting. The reliabilities for assessing within-person change of the overall D-Cop and individual positive and negative subscales were good, and I confirmed that parents' reports of coparenting quality fluctuated on a daily basis. Also, I established the initial validity of the D-Cop, as scores related as expected to (a) an established measure of coparenting in the field and to (b) couple relationship quality, parent depressive symptoms, and child behavior problems. Further, fluctuations in daily couple relationship feelings related to fluctuations… Advisors/Committee Members: Douglas Michael Teti, Dissertation Advisor/Co-Advisor, Douglas Michael Teti, Committee Chair/Co-Chair, Mark E Feinberg, Committee Member, David Manuel Almeida, Committee Member, Alysia Yvonne Blandon, Outside Member, Gregory M Fosco, Committee Member.

Subjects/Keywords: Coparenting; Daily Diary; Dyadic Data; Intensive Longitudinal Designs; Couple Relationships; Parenting; Family Relationships; Child Behavior Problems; Variability; Intraindividual Variability; Mothers; Fathers; Daily Coparenting; Daily Parenting; Parenting Stress; Daily Relationship Satisfaction; Daily Child Mood; Childcare Burden; Multilevel Modeling; Parent Depression; Undermining; Support

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

McDaniel, B. T. (2016). Understanding Stability and Change in Daily Coparenting: Predictors and Outcomes in Families with Young Children. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/d217qp48j

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

McDaniel, Brandon Talmage. “Understanding Stability and Change in Daily Coparenting: Predictors and Outcomes in Families with Young Children.” 2016. Thesis, Penn State University. Accessed January 21, 2021. https://submit-etda.libraries.psu.edu/catalog/d217qp48j.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

McDaniel, Brandon Talmage. “Understanding Stability and Change in Daily Coparenting: Predictors and Outcomes in Families with Young Children.” 2016. Web. 21 Jan 2021.

Vancouver:

McDaniel BT. Understanding Stability and Change in Daily Coparenting: Predictors and Outcomes in Families with Young Children. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Jan 21]. Available from: https://submit-etda.libraries.psu.edu/catalog/d217qp48j.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

McDaniel BT. Understanding Stability and Change in Daily Coparenting: Predictors and Outcomes in Families with Young Children. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/d217qp48j

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. Bailly, Sébastien. Utilisation des antifongiques chez le patient non neutropénique en réanimation : Antifungal use on non neutropenic patients in Intensive Care Unit.

Degree: Docteur es, Modèles, méthodes et algorithmes en biologie, santé et environnement, 2015, Université Grenoble Alpes (ComUE)

URL: http://www.theses.fr/2015GREAS013

►

Les levures du genre Candida figurent parmi les pathogènes majeurs isolés chez les patients en soins intensifs et sont responsables d'infections systémiques : les candidoses… (more)

▼

Les levures du genre Candida figurent parmi les pathogènes majeurs isolés chez les patients en soins intensifs et sont responsables d'infections systémiques : les candidoses invasives. Le retard et le manque de fiabilité du diagnostic sont susceptibles d'aggraver l'état du patient et d'augmenter le risque de décès à court terme. Pour respecter les objectifs de traitement, les experts recommandent de traiter le plus précocement possible les patients à haut risque de candidose invasive. Cette attitude permet de proposer un traitement précoce aux malades atteints, mais peut entraîner un traitement inutile et coûteux et favoriser l'émergence de souches de moindre sensibilité aux antifongiques utilisés.Ce travail applique des méthodes statistiques modernes à des données observationnelles longitudinales. Il étudie l'impact des traitements antifongiques systémiques sur la répartition des quatre principales espèces de Candida dans les différents prélèvements de patients en réanimation médicale, sur leur sensibilité à ces antifongiques, sur le diagnostic des candidémies ainsi que sur le pronostic des patients. Les analyses de séries de données temporelles à l'aide de modèles ARIMA (moyenne mobile autorégressive intégrée) ont confirmé l'impact négatif de l'utilisation des antifongiques sur la sensibilité des principales espèces de Candida ainsi que la modification de leur répartition sur une période de dix ans. L'utilisation de modèles hiérarchiques sur données répétées a montré que le traitement influence négativement la détection des levures et augmente le délai de positivité des hémocultures dans le diagnostic des candidémies. Enfin, l'utilisation des méthodes d'inférence causale a montré qu'un traitement antifongique préventif n'a pas d'impact sur le pronostic des patients non neutropéniques, non transplantés et qu'il est possible de commencer une désescalade précoce du traitement antifongique entre le premier et le cinquième jour après son initiation sans aggraver le pronostic.

Candida species are among the main pathogens isolated from patients in intensive care units (ICUs) and are responsible for a serious systemic infection: invasive candidiasis. A late and unreliable diagnosis of invasive candidiasis aggravates the patient's status and increases the risk of short-term death. The current guidelines recommend an early treatment of patients with high risks of invasive candidiasis, even in absence of documented fungal infection. However, increased antifungal drug consumption is correlated with increased costs and the emergence of drug resistance whereas there is yet no consensus about the benefits of the probabilistic antifungal treatment.The present work used modern statistical methods on longitudinal observational data. It investigated the impact of systemic antifungal treatment (SAT) on the distribution of the four Candida species most frequently isolated from ICU patients', their susceptibilities to SATs, the diagnosis of candidemia, and the prognosis of ICU patients. The use of autoregressive integrated moving…

Advisors/Committee Members: Timsit, Jean-François (thesis director).

Subjects/Keywords: Traitement antifongique systémique; Unités de soins intensifs; Inférence causale; Candidoses invasives; Données observationnelles longitudinales; Modèles structurels marginaux; Systemic antifungal treatment; Intensive care units; Causal inference; Invasive candidiasis; Observational longitudinal data analysis; Structural marginal models; 610

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Bailly, S. (2015). Utilisation des antifongiques chez le patient non neutropénique en réanimation : Antifungal use on non neutropenic patients in Intensive Care Unit. (Doctoral Dissertation). Université Grenoble Alpes (ComUE). Retrieved from http://www.theses.fr/2015GREAS013

Chicago Manual of Style (16^th Edition):

Bailly, Sébastien. “Utilisation des antifongiques chez le patient non neutropénique en réanimation : Antifungal use on non neutropenic patients in Intensive Care Unit.” 2015. Doctoral Dissertation, Université Grenoble Alpes (ComUE). Accessed January 21, 2021. http://www.theses.fr/2015GREAS013.

MLA Handbook (7^th Edition):

Bailly, Sébastien. “Utilisation des antifongiques chez le patient non neutropénique en réanimation : Antifungal use on non neutropenic patients in Intensive Care Unit.” 2015. Web. 21 Jan 2021.

Vancouver:

Bailly S. Utilisation des antifongiques chez le patient non neutropénique en réanimation : Antifungal use on non neutropenic patients in Intensive Care Unit. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); 2015. [cited 2021 Jan 21]. Available from: http://www.theses.fr/2015GREAS013.

Council of Science Editors:

Bailly S. Utilisation des antifongiques chez le patient non neutropénique en réanimation : Antifungal use on non neutropenic patients in Intensive Care Unit. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); 2015. Available from: http://www.theses.fr/2015GREAS013

13. Rietdijk, S. Time Will Tell : Time Series Modeling in Psychology.

Degree: 2018, University Utrecht

URL: https://dspace.library.uu.nl/handle/1874/371385 ; URN:NBN:NL:UI:10-1874-371385 ; 1874/371385 ; urn:isbn:9789463323949 ; URN:NBN:NL:UI:10-1874-371385 ; https://dspace.library.uu.nl/handle/1874/371385

► The increasing popularity of intensive longitudinal research designs in psychology, such as the experience sampling method (ESM), calls for methodological research into optimal ways of… (more)

▼ The increasing popularity of intensive longitudinal research designs in psychology, such as the experience sampling method (ESM), calls for methodological research into optimal ways of analyzing the resulting intensive longitudinal data (ILD). Due to the high number of repeated measurements in this type of study, it is possible to look beyond the traditional toolbox of statistical techniques in psychology and to use time series analysis approaches. Time series models can be used to uncover the dynamics of a process as it unfolds over time and to investigate how individual people may differ in these process dynamics. The goal of this dissertation was to broaden our understanding of the possibilities as well as the challenges for applying time series techniques to ILD in psychology. The first chapter presented an exploratory study, which considered the usefulness of mixed (i.e., multilevel) Markov models for the ILD context, and which aimed to demonstrate the potential of this modeling approach for the study of state-switching processes. The chapter brought together information and examples concerning Markov modeling found in literature scattered across various fields of science, while focusing on those aspects most relevant to modeling ILD in psychology. Chapters 3 to 5 focused on a different class of time series models, which has already gained popularity with psychological researchers and which is often applied to ILD, namely the class of autoregressive models. In Chapter 3, a multilevel threshold-autoregressive (TAR) model for affect regulation was developed, building on existing applications of multilevel AR(1) models and the TAR model for a single time series. Although the substantive focus of the chapter is on affect dynamics, the modeling approach can be seen as a basic framework that could be applied to any psychological process involving (potentially) state-dependent inertia or autocorrelation. Chapters 4 and 5 investigated two somewhat related issues in the analysis of ESM data with time series techniques such as autoregressive modeling. Both have to do with the typical design of ESM studies and how this affects the resulting data: Typically, measurements are taken at semi-random times throughout the day and over multiple days. The individual data points are nested within persons, but can also be considered to be nested within days, which in turn are nested within persons. In Chapter 4, a novel three-level AR(1) model was proposed, which enables us to study the inertia of affect from day to day as well as from moment to moment within a day. Moreover, it was demonstrated that the question of the number of levels in ESM data deserves careful consideration, because misspecification of the number of levels can distort conclusions based on AR(1) modeling. Chapter 5 focused on an issue caused by the varying measurement intervals in many ESM studies, namely, that they result in unequally spaced data that violate an assumption of discrete-time (DT) models. In this chapter it was explained why bias in the parameters… Advisors/Committee Members: Hamaker, Ellen, Kuppens, P..

Subjects/Keywords: intensive longitudinal data; time series analysis; autoregression; Markov models; Bayesian estimation; multilevel analysis; Experience Sampling Methodology; affect dynamics; emotion regulation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rietdijk, S. (2018). Time Will Tell : Time Series Modeling in Psychology. (Doctoral Dissertation). University Utrecht. Retrieved from https://dspace.library.uu.nl/handle/1874/371385 ; URN:NBN:NL:UI:10-1874-371385 ; 1874/371385 ; urn:isbn:9789463323949 ; URN:NBN:NL:UI:10-1874-371385 ; https://dspace.library.uu.nl/handle/1874/371385

Chicago Manual of Style (16^th Edition):

Rietdijk, S. “Time Will Tell : Time Series Modeling in Psychology.” 2018. Doctoral Dissertation, University Utrecht. Accessed January 21, 2021. https://dspace.library.uu.nl/handle/1874/371385 ; URN:NBN:NL:UI:10-1874-371385 ; 1874/371385 ; urn:isbn:9789463323949 ; URN:NBN:NL:UI:10-1874-371385 ; https://dspace.library.uu.nl/handle/1874/371385.

MLA Handbook (7^th Edition):

Rietdijk, S. “Time Will Tell : Time Series Modeling in Psychology.” 2018. Web. 21 Jan 2021.

Vancouver:

Rietdijk S. Time Will Tell : Time Series Modeling in Psychology. [Internet] [Doctoral dissertation]. University Utrecht; 2018. [cited 2021 Jan 21]. Available from: https://dspace.library.uu.nl/handle/1874/371385 ; URN:NBN:NL:UI:10-1874-371385 ; 1874/371385 ; urn:isbn:9789463323949 ; URN:NBN:NL:UI:10-1874-371385 ; https://dspace.library.uu.nl/handle/1874/371385.

Council of Science Editors:

Rietdijk S. Time Will Tell : Time Series Modeling in Psychology. [Doctoral Dissertation]. University Utrecht; 2018. Available from: https://dspace.library.uu.nl/handle/1874/371385 ; URN:NBN:NL:UI:10-1874-371385 ; 1874/371385 ; urn:isbn:9789463323949 ; URN:NBN:NL:UI:10-1874-371385 ; https://dspace.library.uu.nl/handle/1874/371385

14. Ryan, Oisín. Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory.

Degree: 2020, University Utrecht

URL: https://dspace.library.uu.nl/handle/1874/400005 ; URN:NBN:NL:UI:10-1874-400005 ; 10.33540/258 ; 1874/400005 ; urn:isbn:9789464161458 ; URN:NBN:NL:UI:10-1874-400005 ; https://dspace.library.uu.nl/handle/1874/400005

► Psychological phenomena are inherently dynamic in nature. Our cognitions, emotions, dispositions and abilities all evolve and vary over time within an individual. This perspective has… (more)

▼ Psychological phenomena are inherently dynamic in nature. Our cognitions, emotions, dispositions and abilities all evolve and vary over time within an individual. This perspective has become mainstream in the fields of clinical psychiatry and psychology in recent years, where the overarching aim of research is to understand the causal mechanisms which underlie psychological disorder. Typically, researchers aim to study those mechanisms by collecting and analyzing non-experimental data. These data may consist of measurements of many individuals at a single time-point (so-called cross-sectional data) or one or more individuals at many closely spaced points in time (so-called intensive longitudinal data). Researchers generally fit relatively simple statistical models to those data and interpret the parameters of those models as reflecting direct causal relationships between psychological processes. There are two problems with this approach. The first of these is that current popular statistical modeling approaches are relatively limited in how they treat the passage of time. For example, the relationship between aspirin intake and headache strength will depend on whether we measure headache levels a minute, an hour, three hours, or a day after an aspirin pill is taken. However, the dependency of statistical relationships on the time-interval between measurements is typically ignored in current practice. The second problem is that of inferring causal relationships from non-experimental data: If we cannot randomly assign some individuals to take aspirin and others not, how do we know if a statistical dependency between aspirin and headache levels is really causal or not? Luckily psychology is not the first field to grapple with these problems. First, dynamical systems theory and specifically differential equation models have been used in fields as diverse as physics, ecology, climatology and biology to understand and describe phenomena that vary over time. Second, the interventionist causal inference framework has been developed by researchers in fields such as epidemiology, econometrics and computer science to help us understand if, how, when and why causal relationships can be inferred from non-experimental data. Inspired by these methodological frameworks, in my dissertation I develop new tools to address these problems in psychological research. In chapter 2 I develop a tool which allows researchers to explore a range of causal structures which produce the same statistical model in cross-sectional data. In chapter 3 I describe how a simple differential equation model for intensive longitudinal data can be used to explore time-interval dependency, and in chapter 4 I show how this approach could potentially be used to better identify targets for psychological interventions. In chapter 5 I explore how existing statistical tools can best be utilized to recover an underlying dynamical system. Finally, in chapter 6 I argue that formal theories of psychological phenomena are urgently needed if we are to understand… Advisors/Committee Members: Hamaker, E.L., Kuiper, R.M..

Subjects/Keywords: dynamical systems; causal modeling; psychology; psychopathology; continuous-time models; differential equations; formal theory; graphical models; intensive longitudinal data; network approach

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ryan, O. (2020). Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory. (Doctoral Dissertation). University Utrecht. Retrieved from https://dspace.library.uu.nl/handle/1874/400005 ; URN:NBN:NL:UI:10-1874-400005 ; 10.33540/258 ; 1874/400005 ; urn:isbn:9789464161458 ; URN:NBN:NL:UI:10-1874-400005 ; https://dspace.library.uu.nl/handle/1874/400005

Chicago Manual of Style (16^th Edition):

Ryan, Oisín. “Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory.” 2020. Doctoral Dissertation, University Utrecht. Accessed January 21, 2021. https://dspace.library.uu.nl/handle/1874/400005 ; URN:NBN:NL:UI:10-1874-400005 ; 10.33540/258 ; 1874/400005 ; urn:isbn:9789464161458 ; URN:NBN:NL:UI:10-1874-400005 ; https://dspace.library.uu.nl/handle/1874/400005.

MLA Handbook (7^th Edition):

Ryan, Oisín. “Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory.” 2020. Web. 21 Jan 2021.

Vancouver:

Ryan O. Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory. [Internet] [Doctoral dissertation]. University Utrecht; 2020. [cited 2021 Jan 21]. Available from: https://dspace.library.uu.nl/handle/1874/400005 ; URN:NBN:NL:UI:10-1874-400005 ; 10.33540/258 ; 1874/400005 ; urn:isbn:9789464161458 ; URN:NBN:NL:UI:10-1874-400005 ; https://dspace.library.uu.nl/handle/1874/400005.

Council of Science Editors:

Ryan O. Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory. [Doctoral Dissertation]. University Utrecht; 2020. Available from: https://dspace.library.uu.nl/handle/1874/400005 ; URN:NBN:NL:UI:10-1874-400005 ; 10.33540/258 ; 1874/400005 ; urn:isbn:9789464161458 ; URN:NBN:NL:UI:10-1874-400005 ; https://dspace.library.uu.nl/handle/1874/400005

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Open Access Theses and Dissertations