P- Technique Comes of Age

Nesselroade, John R.; Ford, Donald H.

doi:10.1177/0164027585007001003

Cited by 109 publications

(28 citation statements)

References 18 publications

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“…The analysis can then be repeated for a number of single subjects drawing upon research strategies and designs pertaining to e.g. P-technique (Nesselroade and Ford, 1985). However this type of analysis will not be further pursued here.…”

Section: Outline Of Isoamentioning

confidence: 99%

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Studying Individual Patterns of Development Using I-States as Objects Analysis (ISOA)

Bergman

El-Khouri

1999

Biom. J.

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An approach is presented for studying individual pattern development in person‐oriented terms focusing on the concept of i‐state, i.e. an individual's configuration of information at a specific point in time. The procedure is called I‐States as Objects Analysis (ISOA). First common i‐states (typical states) are identified using cluster analysis of subindividuals and then this information is used for describing typical developmental patterns. Both a general procedure and a specific procedure used on a demonstration data set were developed. Using ISOA, change and stability can be studied both with regard to structure and with regard to individual variation. An empirical example was given which concerned longitudinal data about school grades at four different ages for 333 boys and girls. The data were split into a test sample and a replication sample of equal sizes. It was contended from the empirical study that ISOA functioned reasonably well on the sample studied. In the discussion, it was pointed out that ISOA can be a powerful method to use for small samples with many measurement occasions and that the method is optimal for studying short‐term change.

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Section: Outline Of Isoamentioning

confidence: 99%

“…a person's configuration of information at a specific time. For overviews of methods for studying dynamic systems the reader is referred to Casti (1989), Kelso (in press) and Percival and Richards (1989) and for examples within developmental psychology to Barton (1994) and Smith and Thelen (1993).…”

Section: Introductionmentioning

confidence: 99%

Studying Individual Patterns of Development Using I-States as Objects Analysis (ISOA)

Bergman

El-Khouri

1999

Biom. J.

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“…Nesselroade and Ford (1985) discussed the case for multivariate, single-subject research designs with built-in replications as a promising approach to studying behavior. In brief, their proposal was to conduct several multivariate, single-subject factor analytic research designs in concert in order (a) to identify patterns of individual behavior that might then (b) be shown to be replicable over individuals rather than to simply pool individuals’ data initially and define common patterns ipso facto on the pooled data.…”

Section: Scientifically Studying Human Behaviormentioning

confidence: 99%

“…Fitting the common factor model to the MTS of one individual was labeled P-technique factor analysis by Cattell (see e.g., Cattell, Cattell, & Rhymer, 1947; Cattell, 1963), a term that seems now well-ensconced in the multivariate literature. 4 Nesselroade and Ford (1985) called this configuration the MRSRM design (Multivariate Replicated Single-subject Repeated Measurement design) and recommended it for conducting research that falls within the tradition contrasted above with the experimental and differential traditions. Use of MRSRM designs has been well-illustrated in, for example, the domain of self-reported affect (e.g., Lebo & Nesselroade, 1978; Zevon & Tellegen, 1982).…”

Section: Scientifically Studying Human Behaviormentioning

confidence: 99%

Some Behaviorial Science Measurement Concerns and Proposals

Nesselroade

Molenaar

2016

Multivariate Behavioral Research

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101

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Primarily from a measurement standpoint, we question some basic beliefs and procedures characterizing the scientific study of human behavior. The relations between observed and unobserved variables are key to an empirical approach to building explanatory theories and we are especially concerned about how the former are used as proxies for the latter. We believe that behavioral science can profitably reconsider the prevailing version of this arrangement because of its vulnerability to limiting idiosyncratic aspects of observed/unobserved variable relations. We describe a general measurement approach that takes into account idiosyncrasies that should be irrelevant to the measurement process but can intrude and may invalidate it in ways that distort and weaken relations among theoretically important variables. To clarify further our major concerns, we briefly describe one version of the measurement approach that fundamentally supports the individual as the primary unit of analysis orientation that we believe should be preeminent in the scientific study of human behavior.

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“…In particular, it was Molenaar and Nesselroade (Molenaar, 1985;Nesselroade & Ford, 1985a;Nesselroade et al, 2002;Nesselroade & Molenaar, 2004) who advanced the person-centered approach in subsequent years. Typically, this was done by using time-delayed embedding to create block-Toeplitz covariance matrices that could be used as input for standard SEM programs.…”

Section: Summary and Implicationsmentioning

confidence: 99%

Maximum Likelihood Dynamic Factor Modeling for ArbitraryNandTUsing SEM

Voelkle

Oud²,

Oertzen

et al. 2012

Structural Equation Modeling: A Multidisciplinary Journal

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This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary T and N by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time series analysis (T large and N D 1) and conventional SEM (N large and T D 1 or small) by integrating both approaches. The resulting combined model offers a variety of new modeling options including a direct test of the ergodicity hypothesis, according to which the factorial structure of an individual observed at many time points is identical to the factorial structure of a group of individuals observed at a single point in time. Third, we illustrate the flexibility of SEM time series modeling by extending the approach to account for complex error structures. We end with a discussion of current limitations and future applications of SEM-based time series modeling for arbitrary T and N.Keywords: dynamic factor analysis, factorial invariance, maximum likelihood estimation, time series analysis Structural equation models have a long history in the social sciences and related disciplines and are well established in present-day research. Since their earliest use almost a century ago (Wright, 1920), structural equation modeling (SEM) has been extended in various ways, making it a powerful and highly general data analytic approach (cf. Muthén, 2002). The vast majority of research using SEM has focused on the variation among individuals. This applies equally to cross-sectional studies (e.g., confirmatory factor analysis) and longitudinal studies (e.g., latent growth curve models). A typical example of such a between-person structural equation model for N independent individuals with p D 2 orthogonal factors and q D 6 indicators, along with

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P- Technique Comes of Age

Cited by 109 publications

References 18 publications

Studying Individual Patterns of Development Using I-States as Objects Analysis (ISOA)

Studying Individual Patterns of Development Using I-States as Objects Analysis (ISOA)

Some Behaviorial Science Measurement Concerns and Proposals

Maximum Likelihood Dynamic Factor Modeling for ArbitraryNandTUsing SEM

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