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Published: 10 March 2023

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1

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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3

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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5

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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6

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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7

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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8

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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9

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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11

Mawardi, Muhamad Iqbal, Bertho Tantular, and Resa Septiani Pontoh. "PENERAPAN CONFIGURAL FREQUENCY ANALYSIS (CFA) UNTUK MENENTUKAN KARAKTERISTIK USER DAN NON USER MOTOR X DI KOTA CIREBON (Studi Kasus PT. XYZ)." Jurnal Ilmiah Matematika dan Pendidikan Matematika 9, no. 2 (December 29, 2017): 75. http://dx.doi.org/10.20884/1.jmp.2017.9.2.2868.

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ABSTRACT. PT. XYZ has a role as the main dealer of motor X in West Java with marketing area in three cities: Bandung, Bekasi and Cirebon. The company survey always held every year about Brand Awareness & Image to users and non user motor X to know the characteristics of consumers. Based on the company report survey there was a decrease in the indicators of Top Of Mind Unit in Cirebon City for the user of 30.8% and for non users of 9.8%. This study aims to determine the characteristics that cause the decline of the Top Of Mind Unit based on the configuration of user characteristics and non user motor X in Cirebon City by using Configural Frequency Analysis (CFA). From the results of data analysis can be seen that there are three configurations of user characteristics and non users that deviate from the base model is formed. These three configurations can be considered in determining the marketing strategy to maintain and improve the Top Of Mind Unit in Cirebon City.

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12

Kööts, Liisi, Anu Realo, and Jüri Allik. "Relationship Between Linguistic Antonyms in Momentary and Retrospective Ratings of Happiness and Sadness." Journal of Individual Differences 33, no. 1 (January 2012): 43–53. http://dx.doi.org/10.1027/1614-0001/a000061.

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Momentary ratings of affective states with a pair of strict antonyms (“happy” vs. “sad”) were studied with an experience-sampling method in a group of 110 participants during 14 consecutive days at 7 randomly determined occasions per day. Before and after the experimental session participants also retrospectively rated how happy or sad they had been during the previous 2 weeks. Multilevel analysis showed that, at the level of single measurement trials, the momentary ratings of happiness and sadness were moderately negatively correlated (r = –.32, p < .001). A between-subject correlation of the two antonyms, however, was in a positive direction (r = .13, p = .123). Participants experienced mixed feelings during a considerable number of measurement trials, whereas the tendency to feel mixed emotions was predicted by all Big Five personality traits except Agreeableness. A configural frequency analysis (CFA) demonstrated that, although there was no strict bipolarity between momentary ratings of happiness and sadness, they were nevertheless used in an exclusive manner in many occasions.

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13

Intrieri, Robert, and Paige Goodwin. "Confirmatory Factor Analysis of the Perceived Stress Scale-10 Across Age Groups." Innovation in Aging 4, Supplement_1 (December 1, 2020): 304–5. http://dx.doi.org/10.1093/geroni/igaa057.975.

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Abstract The Perceived Stress Scale-10 (PSS; Cohen et al, 1983) was developed to measure subjective elements of stress. Most measures focus on objective characteristics of stress (e. g., frequency of occurrence) and specific situations that produce stress (e. g., divorce) but ignore the cognitive appraisal associated with stressful stimuli. The PSS-10 assesses the interplay between stressor and appraisal-mediated coping ability. Factor analytic studies provide support for two factors: perceived helplessness and perceived self-efficacy (see Roberti et al. 2006). The current study presents data from 591 people across three groups: 221 young adults (mean age 19.31, SD = 1.21), 283 middle-age adults (mean age 48.27, SD = 5.14), and 109 older adults (mean age 72.95, SD = 7.22). An ordinal confirmatory factor analysis (CFA) using robust weighted least squares (WLSMV) evaluated invariance across age groups. Results showed CFI/TLI values of .964/.953, 965/.960, and .964/.969 for Configural (CI), Metric (MI), and Scalar (SI) models. The RMSEA for CI, MI, and SI models was .086, .081, and .071. Based upon recommendations of Cheung and Rensvold (2002), Sass (2011), and Chen (2007), a cutoff of ΔCFI ≥ 0.01 was established as evidence of invariance. The ΔCFI between CI and MI models was < .01 so analysis continued with the SI test. Once again, ΔCFI between MI and SI models was < 0.01 which did not justify rejection of the null hypothesis. Based on these analyses, PSS-10 scores are valid across multiple age groups. Further, results support the multidimensional nature of the PSS-10.

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14

von Eye, Alexander. "Configural Frequency Analysis." Methodology 3, no. 4 (January 2007): 170–72. http://dx.doi.org/10.1027/1614-2241.3.4.170.

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15

von Eye, A. "Introduction to Configural Frequency Analysis." Biometrics 49, no. 2 (June 1993): 674. http://dx.doi.org/10.2307/2532592.

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16

Stemmler, Mark, and Jörg-Henrik Heine. "Using Configural Frequency Analysis as a Person-centered Analytic Approach with Categorical Data." International Journal of Behavioral Development 41, no. 5 (July 9, 2016): 632–46. http://dx.doi.org/10.1177/0165025416647524.

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Configural frequency analysis and log-linear modeling are presented as person-centered analytic approaches for the analysis of categorical or categorized data in multi-way contingency tables. Person-centered developmental psychology, based on the holistic interactionistic perspective of the Stockholm working group around David Magnusson and Lars Bergman, is briefly revisited. According to person-centered theory, systems or individuals are seen as a whole and as inseparable units; individuals are embedded and strongly interconnected with their context; the individual and the environment influence each other, and the individual is seen as an active agent or producer of his or her own development. Four models of configural frequency analysis are presented: (1) First-order configural frequency analysis, which is basically the analysis of a main effects log-linear model; (2) prediction configural frequency analysis, which defines one or more dependent variables; (3) two-group configural frequency analysis, which proposes that there is no association between discrimination variables and group membership; and (4) functional configural frequency analysis, which allows us to blank out certain outlier cells in order to test for the quasi-independence of the rest of the cross-table. The use of the open source R-package confreq for computational analysis is demonstrated. The advantages, as well as the limitations, of configural frequency analysis are discussed.

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17

Intrieri, Robert, and Paige Goodwin. "The Life Orientation Test: A Confirmatory Factor Analysis Across Three Age Groups." Innovation in Aging 4, Supplement_1 (December 1, 2020): 459. http://dx.doi.org/10.1093/geroni/igaa057.1486.

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Abstract The Life Orientation Test (LOT; Scheier & Carver, 1985) was developed as a measure of dispositional optimism. Optimism has been linked to positive life outcomes and is associated with psychological (Carver & Gaines, 1987; Scheier & Carver, 1985) and physical (Scheier & Carver, 1987; Scheier et al. 1989) well-being. The current study assessed 520 people placed into three age groups: young adult (n =149), middle-age adult (n = 252), and older adult (n = 119). The mean age for the young group was 19.24 (SD = 2.01), middle-aged (47.68 (SD = 4.75), and old was 71.99 (SD = 7.21). Data were submitted for a confirmatory factor analysis (CFA) which tested for invariance across the age groups. Previous factor analyses have identified two distinct factors representing optimism and pessimism. Results from the CFA showed that both Configural and Metric invariance models demonstrated acceptable fit for the two factor model (□2(df=57) = 61.92, p = 0.3047; □2(df=69) = 78.77, p = 0.1974). In contrast, Scalar invariance resulted in a poor fit across the three age groups (□2(df=81) = 139.288, p < 0.0001). Model comparisons revealed no significant differences between Configural and Metric models (□2(df=12) = 16.996, p = 0.1498). Model comparisons between Configural and Scalar and Metric and Scalar were (□2(df=24) = 78.947, p < 0.0001; □2(df=12) = 61.764, p < 0.0001). These results confirm previous research that shows a correlated two factor model consistent with the concept that optimism and pessimism are correlated elements rather than two ends of a continuum.

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18

Krauth, Joachim. "Configural Frequency Analysis: Methods, Models, and Applications." Journal of the American Statistical Association 99, no. 465 (March 2004): 292–93. http://dx.doi.org/10.1198/jasa.2004.s316.

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von Eye, Alexander, Michael J. Rovine, and Christiane Spiel. "Concepts of nonindependence in Configural Frequency Analysis*." Journal of Mathematical Sociology 20, no. 1 (April 1995): 41–54. http://dx.doi.org/10.1080/0022250x.1995.9990151.

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Gutiérrez‐Peña, Eduardo, and Alexander von Eye. "A Bayesian approach to configural frequency analysis." Journal of Mathematical Sociology 24, no. 2 (February 2000): 151–74. http://dx.doi.org/10.1080/0022250x.2000.9990233.

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21

Eye, Alexander, Christiane Spiel, and Phillip K. Wood. "Configural Frequency Analysis in Applied Psychological Research." Applied Psychology 45, no. 4 (October 1996): 301–27. http://dx.doi.org/10.1111/j.1464-0597.1996.tb00770.x.

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Krauth, J. "Typological personality research by configural frequency analysis." Personality and Individual Differences 6, no. 2 (January 1985): 161–68. http://dx.doi.org/10.1016/0191-8869(85)90105-9.

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23

DOĞAN, İsmet, and Nurhan DOĞAN. "Configural Frequency Analysis as A Statistical Tool: Review." Turkiye Klinikleri Journal of Biostatistics 8, no. 2 (2016): 172–79. http://dx.doi.org/10.5336/biostatic.2016-51363.

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24

Koehler, Thomas, Kay Dulz, and Eckart Buck-Emden. "Headache Syndromes as Detected by Configural Frequency Analysis." Headache: The Journal of Head and Face Pain 31, no. 5 (May 1991): 325–28. http://dx.doi.org/10.1111/j.1526-4610.1991.hed3105325.x.

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Von Eye, Alexander, and Eduardo Gutiérrez Peña. "Configural Frequency Analysis: The Search for Extreme Cells." Journal of Applied Statistics 31, no. 8 (October 2004): 981–97. http://dx.doi.org/10.1080/0266476042000270545.

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Lienert, G. A., J. H. Reynolds, and W. Lehmacher. "Personality classification agreement identified by configural frequency analysis." Personality and Individual Differences 11, no. 5 (January 1990): 497–501. http://dx.doi.org/10.1016/0191-8869(90)90062-v.

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27

Schrepp, Martin. "The use of configural frequency analysis for explorative data analysis." British Journal of Mathematical and Statistical Psychology 59, no. 1 (May 2006): 59–73. http://dx.doi.org/10.1348/000711005x66761.

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Eye, Alexander Von, Christiane Spiel, and Phillip K. W. Ood. "Lead Article: Configural Frequency Analysis in Applied Psychological Research." Applied Psychology 45, no. 4 (October 1, 1996): 301–52. http://dx.doi.org/10.1080/026999496378576.

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29

Victor, N., and M. Kieser. "Configural frequency analysis and association analyses in contingency tables." Computational Statistics & Data Analysis 44, no. 1-2 (October 2003): 419–29. http://dx.doi.org/10.1016/s0167-9473(03)00038-0.

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Harloff, Joachim. "An efficient algorithm for Lindner’s test (configural frequency analysis)." Quality & Quantity 46, no. 1 (April 30, 2011): 371–78. http://dx.doi.org/10.1007/s11135-011-9499-9.

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31

Mun, Eun Young. "Book Review: Configural Frequency Analysis: Methods, Models, and Applications." Applied Psychological Measurement 30, no. 1 (January 2006): 72–74. http://dx.doi.org/10.1177/0146621605279207.

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32

Bergman, L. R., and A. von Eye. "Normal Approximations of Exact Tests in Configural Frequency Analysis." Biometrical Journal 29, no. 7 (1987): 849–55. http://dx.doi.org/10.1002/bimj.4710290714.

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Lautsch, Erwin, Gustav A. Lienert, and Alexander Von Eye. "Orthogonal Prediction Configural Frequency Analysis by Chi-Square Partitioning." Biometrical Journal 34, no. 6 (1992): 657–68. http://dx.doi.org/10.1002/bimj.4710340603.

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Wood, Phillip K., Kenneth J. Sher, and Alexander Von Eye. "Conjugate and Other Distributional Methods in Configural Frequency Analysis." Biometrical Journal 36, no. 4 (1994): 387–410. http://dx.doi.org/10.1002/bimj.4710360402.

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35

Raufelder, Diana, Danilo Jagenow, Frances Hoferichter, and Kate Mills Drury. "THE PERSON-ORIENTED APPROACH IN THE FIELD OF EDUCATIONAL PSYCHOLOGY." Problems of Psychology in the 21st Century 5, no. 1 (April 1, 2013): 79–88. http://dx.doi.org/10.33225/ppc/13.05.79.

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Individual differences are a fundamental component of psychology, but these differences are often treated as “noise” or “errors” in variable-oriented statistical analyses. Currently, there is a small but emerging body of research using the person-oriented approach. In this paper a brief theoretical and methodological overview of the person-oriented approach is given. A person-oriented approach is often preferable where the main theoretical and analytical unit is a pattern of operating factors, rather than individual variables. In order to illustrate the relevance of this approach to research in educational psychology several representative statistical methods are outlined, two of which employ a person-oriented approach (latent class analysis/ latent profile analysis, configural frequency analysis/ prediction configural frequency analysis) and one that combines person and variable-oriented approaches. Examples of data analyses are used to demonstrate that variable and person-oriented approaches provide the researcher with different information that can be complementary. Key words: configural frequency analysis, educational psychology, individual differences, latent class analysis, person-oriented approach.

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Harloff, Joachim. "Supplemental Application of Configural Frequency Analysis to Free Sorting Data." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 113, no. 1 (January 2012): 61–75. http://dx.doi.org/10.1177/0759106311426993.

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Rovine, Michael J. "Configural Frequency Analysis as a Method: Planned Comparisons in Contingency Table Analysis." Applied Psychology 45, no. 4 (October 1996): 327–28. http://dx.doi.org/10.1111/j.1464-0597.1996.tb00771.x.

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Tshilongamulenzhe, Maelekanyo C. "Testing measurement invariance of the learning programme management and evaluation (LPME) scale across gender using multi-group confirmatory factor analysis." Journal of Governance and Regulation 4, no. 2 (2015): 124–31. http://dx.doi.org/10.22495/jgr_v4_i2_c1_p5.

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The purpose of this study was to test measurement invariance of the LPME scale across gender using multi-group CFA. The LPME scale was developed to measure the effectiveness of management and evaluation practices pertaining to occupational learning programmes in the South African skills development context. A non-experimental cross-sectional survey was conducted with 389 human resource practitioners and apprentices/learners. The results indicate that the LPME scale is invariant between males and females at the levels of configural, metric and strong invariance. The number of factors/constructs, pattern of item factor loading, latent constructs variances and covariances, and the reliability of the LPME scale and its dimensions are equivalent between males and females.

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Stanton, Harrison C., and Cecil R. Reynolds. "Configural frequency analysis as a method of determining Wechsler profile types." School Psychology Quarterly 15, no. 4 (2000): 434–48. http://dx.doi.org/10.1037/h0088799.

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Bergman, L. R., and G. A. Lienert. "Evaluating Chessboard MANOVA Designs Using Predictive and Matching Configural Frequency Analysis." Biometrical Journal 33, no. 6 (1991): 655–60. http://dx.doi.org/10.1002/bimj.4710330603.

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von Eye, Alexander, and Michael J. Rovine. "Non-Standard Log-Linear Models for Orthogonal Prediction Configural Frequency Analysis." Biometrical Journal 36, no. 2 (1994): 177–84. http://dx.doi.org/10.1002/bimj.4710360210.

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42

Krebs, Harald, Marcus Ising, Wilhelm Janke, Michael Macht, Alexander von Eye, Heinz-Gerd Weijers, and Peter Weyers. "Response Curve Comparison by Pseudo-Multivariate Two-Sample Configural Frequency Analysis." Biometrical Journal 38, no. 2 (1996): 195–201. http://dx.doi.org/10.1002/bimj.4710380207.

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43

Jang, Seulki, Eun Sook Kim, Chunhua Cao, Tammy D. Allen, Cary L. Cooper, Laurent M. Lapierre, Michael P. O’Driscoll, et al. "Measurement Invariance of the Satisfaction With Life Scale Across 26 Countries." Journal of Cross-Cultural Psychology 48, no. 4 (March 22, 2017): 560–76. http://dx.doi.org/10.1177/0022022117697844.

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The Satisfaction With Life Scale (SWLS) is a commonly used life satisfaction scale. Cross-cultural researchers use SWLS to compare mean scores of life satisfaction across countries. Despite the wide use of SWLS in cross-cultural studies, measurement invariance of SWLS has rarely been investigated, and previous studies showed inconsistent findings. Therefore, we examined the measurement invariance of SWLS with samples collected from 26 countries. To test measurement invariance, we utilized three measurement invariance techniques: (a) multigroup confirmatory factor analysis (MG-CFA), (b) multilevel confirmatory factor analysis (ML-CFA), and (c) alignment optimization methods. The three methods demonstrated that configural and metric invariances of life satisfaction held across 26 countries, whereas scalar invariance did not. With partial invariance testing, we identified that the intercepts of Items 2, 4, and 5 were noninvariant. Based on two invariant intercepts, factor means of countries were compared. Chile showed the highest factor mean; Spain and Bulgaria showed the lowest. The findings enhance our understanding of life satisfaction across countries, and they provide researchers and practitioners with practical guidance on how to conduct measurement invariance testing across countries.

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Eye, Alexander von, and John R. Nesselroade. "Types of change: Application of configural frequency analysis in repeated measurement designs." Experimental Aging Research 18, no. 4 (December 1992): 169–83. http://dx.doi.org/10.1080/03610739208260357.

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45

Cohen, M. E. "Configural frequency analysis for exploring bacterial sets in periodontal health and disease." Community Dentistry and Oral Epidemiology 21, no. 5 (October 1993): 283–87. http://dx.doi.org/10.1111/j.1600-0528.1993.tb00775.x.

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Mafla, Ana Cristina, Mauricio Herrera-López, Karen España-Fuelagan, Iván Ramírez-Solarte, Carmen Gallardo Pino, and Falk Schwendicke. "Psychometric Properties of the SOC-13 Scale in Colombian Adults." International Journal of Environmental Research and Public Health 18, no. 24 (December 10, 2021): 13017. http://dx.doi.org/10.3390/ijerph182413017.

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The aim of this study was to determine the psychometric properties of the Spanish version of the SOC-13 in Colombian adults. The SOC-13 questionnaire was administered to 489 individuals aged ≥18 years who were in lockdown from March to July 2020 in Nariño County, Colombia. Psychometric properties of the scale were examined using a cross-validation method via exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Additionally, configural and metric invariance were tested. To determine the internal consistency of the questionnaire, McDonald’s omega (ω), Cronbach’s alpha (α), and composite reliability (CR) coefficients were estimated. The EFA determined that a three-factor structure best fit the data (comprehensibility, manageability, and meaningfulness) and CFA confirmed this three-factor model structure showing a good fit (χ2S-B = 188.530, χ2S-B/(62) = 3.615, p = 0.001; NNFI = 0.959; CFI = 0.968; RMSEA = 0.052 (90% CI [0.041–0.063]) and SRMR = 0.052).The invariance analysis indicated the same underlying theoretical structure between genders. Additionally, (ω), (α), and (CR) coefficients confirmed a high internal consistency of the instrument. The SOC-13 scale, reflecting comprehensibility, manageability, and meaningfulness, is a reliable and valid tool for assessing the sense of coherence in Colombian populations.

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Lock, Daniel, Daniel C. Funk, Jason P. Doyle, and Heath McDonald. "Examining the Longitudinal Structure, Stability, and Dimensional Interrelationships of Team Identification." Journal of Sport Management 28, no. 2 (March 2014): 119–35. http://dx.doi.org/10.1123/jsm.2012-0191.

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The propensity of strongly identified fans to contribute positive organizational outcomes for sport teams underpins why team identification maintains a central position in sport management. In the current study we examine the multidimensional structure, stability, and interrelationships between the dimensions of team identification, using longitudinal data (April 2011–April 2012) collected from fans of a new Australian Rules football team (N= 602). A Confirmatory Factor Analysis (CFA) of the team identification items included (measured using the Team*ID scale), supported a five-dimensional model structure. This model was subsequently computed as a longitudinal CFA to test the configural and metric invariance of the Team*ID scale. We used a cross-lagged panel model to examine the longitudinal stability of, and interrelationships between, the dimensions: affect, behavioral involvement, cognitive awareness, private evaluation, and public evaluation. Each dimension displayed relative stability over time. In addition, public evaluation and private evaluation in April 2011 displayed a positive relationship with behavioral involvement in April 2012. Similarly, cognitive awareness in April 2011 predicted increases in public evaluation in April 2012. We conclude with implications for theory and practice.

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Cerolini, Silvia, Andrea Zagaria, Mariacarolina Vacca, Philip Spinhoven, Cristiano Violani, and Caterina Lombardo. "Cognitive Emotion Regulation Questionnaire—Short: Reliability, Validity, and Measurement Invariance of the Italian Version." Behavioral Sciences 12, no. 12 (November 24, 2022): 474. http://dx.doi.org/10.3390/bs12120474.

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Objective: The Cognitive Emotion Regulation Questionnaire (CERQ) is a widely used instrument to assess cognitive emotion regulation strategies. The study aimed to test the psychometric properties of the Italian short version of the CERQ (CERQ-IS). Methods: Two separate samples of 442 young adults (Mage = 21.12; SD = 3.69) and 256 adolescents (Mage = 14.81; SD = 0.59) completed the CERQ, the Emotional Regulation Questionnaire (ERQ) and the Multidimensional Perfectionism Scale (MPS). A confirmatory factor analysis (CFA) was performed to evaluate the dimensionality of the CERQ-IS. Internal consistency was analysed by calculating model-based composite reliability coefficients. Criterion and discriminant validity were gathered through the correlations with the ERQ and the MPS, respectively. Factorial invariances tests across gender and age were computed by means of multiple-group CFA. Results: CFA confirmed the nine-factor structure showing an excellent fit to the data. Except for rumination which was minimally acceptable, all subscales had an acceptable to good reliability. Criterion validity was supported by significant correlations between CERQ-IS and ERQ subscales. Discriminant validity was confirmed by meaningless correlations with the MPS facets. Configural, metric and scalar invariance were established across both grouping variables. Conclusions: The brevity of this tool and its good psychometric properties suggest that CERQ-IS could be a useful screening tool in both clinical and research practice in adolescence and young adulthood.

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Chipuer, Heather M., and Alexander von Eye. "Suicide Trends in Canada and West Germany: An Application of Configural Frequency Analysis." Suicide and Life-Threatening Behavior 19, no. 3 (September 1989): 264–76. http://dx.doi.org/10.1111/j.1943-278x.1989.tb00212.x.

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Lienert, Gustav A., and Hans Zur Oeveste. "Change of Ratings and Ratings of Change as Identified by Configural Frequency Analysis." Educational and Psychological Measurement 48, no. 2 (April 1988): 331–39. http://dx.doi.org/10.1177/0013164488482006.

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