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Academic literature on the topic 'Configural frequency analysis CFA'

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Published: 10 March 2023

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Journal articles on the topic "Configural frequency analysis CFA"

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Von Eye, Alexander, Wolfgang Wiedermann, and Stefan Von Weber. "Configural analysis of oscillating progression." Journal for Person-Oriented Research 7, no. 1 (August 26, 2021): 14–21. http://dx.doi.org/10.17505/jpor.2021.23448.

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Oscillating series of scores can be approximated with locally optimized smoothing functions. In this article, we describe how such series can be approximated with locally estimated (loess) smoothing, and how Configural Frequency Analysis (CFA) can be used to evaluate and interpret results. Loess functions are often hard to describe because they cannot be represented by just one function that has interpretable parameters. In this article, we suggest that specification of the CFA base model be based on the width of the window that is used for local curve optimization, the weight given to data points in the neighborhood of the approximated one, and by the function that is used to locally approximate observed data. CFA types indicate that more cases were found than expected from the local optimization model. CFA antitypes indicate that fewer cases were found. In a real-world data example, the development of Covid-19 diagnoses in France is analyzed for the beginning period of the pandemic.
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Lienert, Gustav A., and Hans Zur Oeveste. "Configural Frequency Analysis as a Statistical Tool for Developmental Research." Educational and Psychological Measurement 45, no. 2 (July 1985): 301–7. http://dx.doi.org/10.1177/001316448504500214.

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Configural frequency analysis (CFA) is suggested as a technique for longitudinal research in developmental psychology. Stability and change in answers to multiple choice items and in Yes-No item-patterns obtained with measurements repeated two or three times are identified by CFA and illustrated by developmental analysis of an item from Gorham's Proverb Test.
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Von Eye, Alexander, and Wolfgang Wiedermann. "Configural Analysis in Component Space." Journal for Person-Oriented Research 8, no. 1 (June 1, 2022): 1–9. http://dx.doi.org/10.17505/jpor.2022.24217.

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Unless very large samples are available, the number of variables and variable categories that can be simultaneously used in categorical data analysis is small when models are estimated. In this article, an approach is proposed that can help remedy this problem. Specifically, it is proposed to perform, in a first step, principal component analysis or factor analysis. These methods help reduce the dimensionality of the data space without loss of important information. In a second step, sectors are created in the component or factor space. These sectors can, in a third step, be subjected to Configural Frequency analysis (CFA). CFA identifies those sectors that contradict a priori-specified hypotheses. It is also proposed to take into account the ordinal nature of the sectors. In addition, distributional assumptions can be considered. This is illustrated in data examples. Possible extensions of the proposed approach are discussed.
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Zhang, Li, Ziqiang Xin, Cody Ding, and Chongde Lin. "An Application of Configural Frequency Analysis." Swiss Journal of Psychology 72, no. 2 (January 2013): 61–70. http://dx.doi.org/10.1024/1421-0185/a000096.

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Development of class reasoning was investigated using configural frequency analysis (CFA). We administered class inclusion, vicariant inclusion, and law of duality tasks to a sample of 540 Chinese second through fifth graders. In each task, children were asked to compare two classes and make a choice from four alternative answers while the number of classes was not given. Results showed that (1) children’s performance on both class inclusion and vicariant inclusion tasks improved significantly from Grade 2 to Grade 3 and from Grade 3 to Grade 4, but children did not tend to give correct answers to class inclusion items until Grade 4 and to vicariant inclusion items until Grade 5; (2) children from Grades 2 to 5 performed poorly on the law of duality task, but fifth graders were more likely to respond correctly than the general population; and (3) second graders tended to give wrong answers such as “equal number” and “not sure.” A discussion of the development of class reasoning followed.
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Kieser, Meinhard, and Norbert Victor. "Configural Frequency Analysis (CFA) Revisited — a New Look at an Old Approach." Biometrical Journal 41, no. 8 (December 1999): 967–83. http://dx.doi.org/10.1002/(sici)1521-4036(199912)41:8<967::aid-bimj967>3.0.co;2-l.

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Perli, H. G., G. Hommel, and W. Lehmacher. "Test Procedures in Configural Frequency Analysis (CFA) Controlling the Local and Multiple Level." Biometrical Journal 29, no. 3 (1987): 255–67. http://dx.doi.org/10.1002/bimj.4710290302.

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Lienert, G. A., and Petra Netter. "Nonparametric Analysis of Treatment-Response Tables by Bipredictive Configurai Frequency Analysis." Methods of Information in Medicine 26, no. 02 (April 1987): 89–92. http://dx.doi.org/10.1055/s-0038-1635489.

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SummaryBipredictive configurai frequency analysis (CFA) is introduced as a complement to predictive CFA as a method for analyzing r X c contingency tables with r treatment modalities and c response patterns. It is illustrated by an example from dose effect regression evaluation in a randomized block design.
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Bergman, Lars R. "Scandinavian Society for Person-Oriented Research (SPOR) Young Researcher Award 2021." Journal for Person-Oriented Research 8, no. 1 (June 1, 2022): 37. http://dx.doi.org/10.17505/jpor.2022.24220.

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The award is given to Wolfgang Wiedermann, University of Missouri, Columbia, for his many contributions to new valuable methods for analyzing data in a person-oriented context.Wiedermann has often, but not exclusively, carried out his research within the framework of the configural frequency analysis (CFA) tradition, in many cases in collaboration with Alexander von Eye. The CFA framework is eminently well suited to provide a methodological toolbox for the researcher carrying out person-oriented analyses. Wiedermann has been a driving force in developing many new methods that not only extend the CFA approach but which also in a clever and creative way makes use of other types of statistical methods to find new types of solutions to methodological challenges.
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Heine, Jörg-Henrik, and Mark Stemmler. "Analysis of Categorical Data with the R Package confreq." Psych 3, no. 3 (September 7, 2021): 522–41. http://dx.doi.org/10.3390/psych3030034.

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The person-centered approach in categorical data analysis is introduced as a complementary approach to the variable-centered approach. The former uses persons, animals, or objects on the basis of their combination of characteristics which can be displayed in multiway contingency tables. Configural Frequency Analysis (CFA) and log-linear modeling (LLM) are the two most prominent (and related) statistical methods. Both compare observed frequencies (foi…k) with expected frequencies (fei…k). While LLM uses primarily a model-fitting approach, CFA analyzes residuals of non-fitting models. Residuals with significantly more observed than expected frequencies (foi…k>fei…k) are called types, while residuals with significantly less observed than expected frequencies (foi…k<fei…k) are called antitypes. The R package confreq is presented and its use is demonstrated with several data examples. Results of contingency table analyses can be displayed in tables but also in graphics representing the size and type of residual. The expected frequencies represent the null hypothesis and different null hypotheses result in different expected frequencies. Different kinds of CFAs are presented: the first-order CFA based on the null hypothesis of independence, CFA with covariates, and the two-sample CFA. The calculation of the expected frequencies can be controlled through the design matrix which can be easily handled in confreq.
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Stemmler, Mark, Jörg-Henrik Heine, and Susanne Wallner. "Person-centered data analysis with covariates and the R-package confreq." Methodology 17, no. 2 (June 30, 2021): 149–67. http://dx.doi.org/10.5964/meth.2865.

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Configural Frequency Analysis (CFA) is a useful statistical method for the analysis of multiway contingency tables and an appropriate tool for person-oriented or person-centered methods. In complex contingency tables, patterns or configurations are analyzed by comparing observed cell frequencies with expected frequencies. Significant differences between observed and expected frequencies lead to the emergence of Types and Antitypes. Types are patterns or configurations which are significantly more often observed than the expected frequencies; Antitypes represent configurations which are observed less frequently than expected. The R-package confreq is an easy-to-use software for conducting CFAs; another useful shareware to run CFAs was developed by Alexander von Eye. Here, CFA is presented based on the log-linear modeling approach. CFA may be used together with interval level variables which can be added as covariates into the design matrix. In this article, a real data example and the use of confreq are presented. In sum, the use of a covariate may bring the estimated cell frequencies closer to the observed cell frequencies. In those cases, the number of Types or Antitypes may decrease. However, in rare cases, the Type-Antitype pattern can change with new emerging Types or Antitypes.
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Dissertations / Theses on the topic "Configural frequency analysis CFA"

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von, Eye Alexander, and Patrick Mair. "A Functional Approach to Configural Frequency Analysis." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/1488/1/document.pdf.

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Standard Configural Frequency Analysis (CFA) is a one-step procedure that determines which cells of a cross-classification contradict a base model. Selecting these cells out does not guarantee that the base model fits. Therefore, the role played by these cells for the base model is unclear, and interpretation of types and antitypes can be problematic. In this paper, functional CFA is proposed. This model of CFA pursues two goals simultaneously. First, cells are selected out that constitute types and antitypes. Second, the base model is fit to the data. This is done using an iterative procedure that blanks out individual cells one at a time, until the base model fits or until there are no more cells that can be blanked out. In comparison to standard CFA, functional CFA is shown to be more parsimonious, that is, fewer types and antitypes need to be selected out. In comparison to Kieser and Victor's CFA which focuses exclusively on optimizing the fit of the base model, functional CFA needs, in most cases, more iteration steps, but the overall goodness-of-fit for the base model is better. The methods are illustrated and compared using data examples from the literature. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Tam, Hak Ping. "Preliminary variable selection and data preparation strategies for configural frequency analysis and other categorical multivariate techniques /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487779439845611.

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PAGNOTTA, GABRIELE. "Lo sviluppo rurale tra evoluzione delle forme di conduzione, antropizzazione e valorizzazione dei prodotti locali." Doctoral thesis, 2015. http://hdl.handle.net/2158/1001713.

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La tesi ha analizzato tre tematiche legate allo sviluppo rurale. La prima è relativa alla diffusione del contoterzismo. La seconda si riferisce all'indagine del fenomeno della perdita di superficie agricola. Infine, è stata utilizzata una metodologia innovativa per approfondire la conoscenza dell’attitudine dei consumatori verso un prodotto locale e tradizionale, come l'olio extra-vergine di oliva. La crescente diffusione del contoterzismo è un fenomeno strutturale dell'agricoltura italiana. Infatti, negli ultimi dieci anni, è aumentata la richiesta di lavorazioni in conto terzi ed oggi un terzo delle aziende agricole si rivolge ad imprese contoterziste per l'affidamento parziale o completo delle lavorazioni ordinarie. Pertanto, data la rilevanza del fenomeno, sono state svolte due analisi complementari: una macro che indaga sulla diffusione nazionale del contoterzismo ed una micro, che studia le caratteristiche delle aziende che utilizzano contoterzismo passivo o che svolgono lavorazioni agro-meccaniche attivamente. L’analisi dei dati evidenzia che il contoterzismo si è sviluppato in aree prevalentemente pianeggianti, omogenee, con superfici a seminativo ed è adottato principalmente da aziende il cui conduttore svolge un'altra occupazione, extra-agricola. In molti casi, il contoterzista stesso assume un ruolo fondamentale nella gestione aziendale: infatti una parte sempre più consistente della SAU nazionale è affidata in maniera completa ad imprese esterne. Di fatto, il contoterzismo contribuisce a mantenere l'attività primaria in molte aree della penisola. A tal proposito, è stato altresì studiato il fenomeno della perdita di superficie agricola. In Italia, negli ultimi quaranta anni, si è registrata una diminuzione di SAU di circa 5 milioni di ettari. Tale riduzione comporta delle conseguenze negative sul territorio, a causa della contrazione dell’offerta delle esternalità positive e dei servizi non di mercato prodotti dal settore primario (come la salvaguardia dell'equilibrio idrogeologico, la tutela della biodiversità o il mantenimento del paesaggio). Le principali cause di riduzione della superficie agricola sono due: da una parte l'abbandono dell'attività agricola, dall'altra l'aumento dell'antropizzazione. Riguardo la prima, dal 2000 al 2010 si è verificata una riduzione del 32,4% del numero di aziende agricole (anche se, mediamente, sono aumentate di dimensione). Per l'antropizzazione, invece, dal 1950 al 2000 la superficie artificiale è aumentata del 166%. La distinzione dei territori nei quali è prevalso come causa principale uno dei due fenomeni non è stata compiuta in letteratura in quanto non di facile stima. Pertanto, lo scopo dell’analisi è stato quello di distinguere i territori nei quali la perdita di SAU è riconducibile o ai processi di antropizzazione, oppure alla ristrutturazione del settore primario. La perdita di SAU per antropizzazione si è verificata maggiormente nelle aree costiere, nell'Italia centrale e nel Nord-Est, mentre l'abbandono dell'attività agricola è stato più intenso nelle aree montane dell’Italia Centrale. Il mantenimento delle attività agricole passa anche dalla valorizzazione delle sue produzioni. In particolare crescente attenzione negli ultimi anni è stata data al forte legame delle produzioni alimentari con il territorio. In questo ambito diventa cruciale conoscere gli aspetti e gli elementi che influenzano il processo decisionale del consumatore. Il caso studio ha riguardato l'analisi delle motivazioni di acquisto di un prodotto tradizionale ad alta familiarità, come l'olio extra-vergine di oliva tra i consumatori italiani, al fine di suggerire alle aziende alcune marketing recommendations per la commercializzazione di questa categoria di prodotti. La produzione di olio, infatti, riveste un ruolo rilevante per numerose zone ed aziende agricole della penisola: oltre il 50% delle aziende possiede oliveti i quali, nell'insieme, costituiscono circa il 7% della SAU nazionale. Di conseguenza, data l'importanza dell'olivicoltura a livello nazionale e in quanto l'olio è un alimento cardine della dieta mediterranea, sono state analizzate tramite il modello CUB le preferenze dei consumatori, in modo da comprendere quali migliori strategie le aziende possono adottare per ampliare le proprie quote di mercato. Più precisamente, è stato analizzato il ruolo ricoperto dalle caratteristiche del prodotto, dalle motivazioni personali dei consumatori e dall'influenza dei canali di comunicazione per le decisioni d'acquisto. I risultati indicano che per gli alimenti la cui familiarità è determinata dal reiterato consumo nel tempo all’interno delle comunità di riferimento, prevalgono le motivazioni di acquisto personali, come la ricerca di un particolare gusto, rispetto agli stimoli provenienti dall’esterno (come la pubblicità e le altre campagne di informazione). La tesi è composta da tre articoli, una introduzione generale e delle conclusioni.
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Books on the topic "Configural frequency analysis CFA"

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von Eye, Alexander, and Wolfgang Wiedermann. Configural Frequency Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-64008-1.

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Eye, Alexander von. Advances in configural frequency analysis. New York: Guilford Press, 2010.

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Eye, Alexander von. Advances in configural frequency analysis. New York: Guilford Press, 2010.

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Eye, Alexander von. Introduction to configural frequency analysis: The search for types and antitypes in cross-classifications. Cambridge [England]: Cambridge University Press, 1990.

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Fife-Schaw, Chris. Analysing categorical data. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0016.

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This chapter explores the analysis of categorical data and the chi-squared test. This includes logistic regression (how it works, and goodness of fit), the importance of predictors, and cautionary notes about the use of regression analysis. The chapter also discusses the prediction of multi-category outcome variables (multinomial logistic regression, and configural frequency analysis (CFA)).
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von Eye, Alexander, Eun-Young Mun, Patrick Mair, and Stefan von Weber. Configural Frequency Analysis. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199934898.013.0005.

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von Eye, Alexander. Configural Frequency Analysis. Psychology Press, 2003. http://dx.doi.org/10.4324/9781410606570.

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Eye, Alexander von, Patrick Mair, and Eun-Young Mun. Advances in Configural Frequency Analysis. Guilford Publications, 2010.

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Eye, Alexander von, Eun Young Mun, and Patrick Mair. Advances in Configural Frequency Analysis. Guilford Publications, 2010.

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Eye, Alexander von. Configural Frequency Analysis: Methods, Models, and Applications. Taylor & Francis Group, 2003.

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Book chapters on the topic "Configural frequency analysis CFA"

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Röhmel, Joachim, Bernd Streitberg, and Christian Tismer. "A Permutation Approach to Configural Frequency Analysis (CFA) and the Iterated Hypergeometric Distribution." In Contributions to Statistics, 355–78. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57991-2_21.

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Ihm, Peter, and Ingeborg Küchler. "Alternatives to Configural Frequency Analysis." In From Data to Knowledge, 186–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-79999-0_18.

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"Methods of longitudinal CFA." In Introduction to Configural Frequency Analysis, 143–44. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.008.

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"Applications and strategies of CFA." In Introduction to Configural Frequency Analysis, 59–62. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.005.

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"CFA of change over time." In Introduction to Configural Frequency Analysis, 145–204. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.009.

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"CFA and log-linear models." In Introduction to Configural Frequency Analysis, 216–21. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.011.

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"Models of CFA: concepts and assumptions." In Introduction to Configural Frequency Analysis, 46–58. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.004.

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"Global models of CFA: applications and examples." In Introduction to Configural Frequency Analysis, 63–81. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.006.

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"Regional models of CFA: applications and examples." In Introduction to Configural Frequency Analysis, 82–142. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.007.

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"The analysis of contingency tables." In Introduction to Configural Frequency Analysis, 3–15. Cambridge University Press, 1990. http://dx.doi.org/10.1017/cbo9780511629464.002.

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Conference papers on the topic "Configural frequency analysis CFA"

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Schwarz, Aubriana, Patricia Goodhines, Amelia Wedel, Lisa LaRowe, and Aesoon Park. "Sleep-Related Cannabis Expectancies Questionnaire (SR-CEQ): Replication and Psychometric Validation among College Students using Cannabis for Sleep Aid." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.45.

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Introduction: Emerging evidence suggests that cannabis is commonly used to aid sleep among college students. Although outcome expectancies have been associated with the progression of cannabis use, sleep-related expectancies have not been included in widely-used cannabis expectancy measures. Recently, the Sleep-Related Cannabis Expectancies Questionnaire (SR-CEQ; Goodhines et al., 2020) was developed and initial evidence for its 2-factor structure was obtained in a general college sample (including non-cannabis users). However, the SR-CEQ’s associations with sleep and cannabis use behaviors among cannabis sleep aid users is unknown. This study aimed to replicate the previous factor structure and test construct and concurrent validity of the SR-CEQ among college students using cannabis for sleep aid. Method: Cross-sectional data were drawn from 94 college students reporting at least bimonthly cannabis use for sleep aid. Five multivariate outliers on the SR-CEQ were excluded, resulting in an analytic sample of 89 (Mage=19.92 [SD=1.19; range=18-22]; 66% female; 72% White, 12% Multiracial, 7% Asian, 5% Black or African-American, 1% self-reported Other and 3% did not disclose; 14% Hispanic/Latinx). Students completed an online survey of sleep and substance use behaviors. A confirmatory factor analysis (CFA) replicated the 2-factor structure (Positive and Negative Sleep-Related Cannabis Expectancies), bivariate correlations tested associations with related constructs (sleep and cannabis use behaviors/beliefs), and independent-samples t-tests further explicated relevant group differences. Results: After dropping item 5 (factor loading<.40), CFA with a 2-factor structure indicated good fit to the data (χ2(41)=66.76, p=.01; CFI=0.94; SRMR=0.07; RMSEA=0.08 [90% CI=0.05, 0.12]). Positive sleep-related cannabis expectancies (α=.84) were associated with dysfunctional beliefs about sleep (r=.24, p=.02), but not insomnia symptoms, poor sleep quality, or frequencies of cannabis use (ps>.05). Students who used cannabis more frequently in general (≥36 of 60 days, per median split) reported more positive sleep-related cannabis expectancies (t[86]=1.99, p=.05, Cohen’s d=0.42). Negative sleep-related cannabis expectancies (α=.80) were not associated with any cannabis or sleep variables assessed (ps>.05). Negative sleep-related cannabis expectancies were marginally lower among students with greater frequency of general cannabis use (t[87]=-1.89, p=.06, Cohen’s d=0.40) and cannabis use for sleep aid (≥3 times/week, per median split; t[87]=-1.87, p=.06, Cohen’s d=0.40). Further, greater negative sleep-related cannabis expectancies were reported among male (versus female) students (t[87]=2.30, p=.02, Cohen’s d=0.51). Conclusion: Overall, replication of this 2-factor structure showed good fit to the data and both subscales demonstrated good internal consistency. Although replication is needed, results suggest that college students using cannabis for sleep aid may have less negative sleep-related expectancies about sleep. Positive sleep-related cannabis expectancies were associated with dysfunctional beliefs about sleep, but not sleep behaviors or cannabis use. Current novel findings extend existing knowledge of general non-sleep related cannabis expectancies among cannabis users in terms of cannabis use correlates. Findings can help identify at-risk students and modifiable risk factors that can be targeted to minimize harm with cannabis sleep aid use. Future research is needed among larger samples to (a) assess generalizability to varied populations and (b) clarify temporal sequencing of potential consequences through longitudinal designs.
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