basic.bib

 @Manual{tinytable,
    title = {tinytable: Simple and Configurable Tables in 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', and 'Typst' Formats},
    author = {Vincent Arel-Bundock},
    note = {R package version 0.3.0.3},
    url = {https://vincentarelbundock.github.io/tinytable/},
  }


@article{russell_search_2002,
	title = {In {Search} of {Underlying} {Dimensions}: {The} {Use} (and {Abuse}) of {Factor} {Analysis} in {Personality} and {Social} {Psychology} {Bulletin}},
	volume = {28},
	copyright = {http://journals.sagepub.com/page/policies/text-and-data-mining-license},
	issn = {0146-1672, 1552-7433},
	shorttitle = {In {Search} of {Underlying} {Dimensions}},
	url = {http://journals.sagepub.com/doi/10.1177/014616702237645},
	doi = {10.1177/014616702237645},
	abstract = {An examination of the use of exploratory and confirmatory factor analysis by researchers publishing in Personality and Social Psychology Bulletin over the previous 5 years is presented, along with a review of recommended methods based on the recent statistical literature. In the case of exploratory factor analysis, an examination and recommendations concerning factor extraction procedures, sample size, number of measured variables, determining the number of factors to extract, factor rotation, and the creation of factor scores are presented. These issues are illustrated via an exploratory factor analysis of data from the University of California, Los Angeles, Loneliness Scale. In the case of confirmatory factor analysis, an examination and recommendations concerning model estimation, evaluating model fit, sample size, the effects of non-normality of the data, and missing data are presented. These issues are illustrated via a confirmatory factor analysis of data from the Revised Causal Dimension Scale.},
	language = {en},
	number = {12},
	urldate = {2024-06-13},
	journal = {Personality and Social Psychology Bulletin},
	author = {Russell, Daniel W.},
	month = dec,
	year = {2002},
	pages = {1629--1646},
}

@article{van_der_eijk_risky_2015,
	title = {Risky {Business}: {Factor} {Analysis} of {Survey} {Data} – {Assessing} the {Probability} of {Incorrect} {Dimensionalisation}},
	volume = {10},
	issn = {1932-6203},
	shorttitle = {Risky {Business}},
	url = {https://dx.plos.org/10.1371/journal.pone.0118900},
	doi = {10.1371/journal.pone.0118900},
	language = {en},
	number = {3},
	urldate = {2024-06-13},
	journal = {PLOS ONE},
	author = {Van Der Eijk, Cees and Rose, Jonathan},
	month = mar,
	year = {2015},
	pages = {e0118900},
}
@book{fabrigar_exploratory_2012,
	address = {Oxford, New York},
	series = {Understanding statistics},
	title = {Exploratory factor analysis},
	isbn = {9780199734177},
	publisher = {Oxford University Press},
	author = {Fabrigar, Leandre R. and Wegener, Duane Theodore},
	year = {2012},
	keywords = {Explanatory factor analysis, Psychology, Mathematical models, Social sciences, Mathematical models},
}

	
@article{widaman_thinking_2023,
	title = {Thinking {About} {Sum} {Scores} {Yet} {Again}, {Maybe} the {Last} {Time}, {We} {Don}’t {Know}, {Oh} {No} . . .: {A} {Comment} on},
	issn = {0013-1644, 1552-3888},
	shorttitle = {Thinking {About} {Sum} {Scores} {Yet} {Again}, {Maybe} the {Last} {Time}, {We} {Don}’t {Know}, {Oh} {No} . . .},
	url = {http://journals.sagepub.com/doi/10.1177/00131644231205310},
	doi = {10.1177/00131644231205310},
	abstract = {The relative advantages and disadvantages of sum scores and estimated factor scores are issues of concern for substantive research in psychology. Recently, while championing estimated factor scores over sum scores, McNeish offered a trenchant rejoinder to an article by Widaman and Revelle, which had critiqued an earlier paper by McNeish and Wolf. In the recent contribution, McNeish misrepresented a number of claims by Widaman and Revelle, rendering moot his criticisms of Widaman and Revelle. Notably, McNeish chose to avoid confronting a key strength of sum scores stressed by Widaman and Revelle—the greater comparability of results across studies if sum scores are used. Instead, McNeish pivoted to present a host of simulation studies to identify relative strengths of estimated factor scores. Here, we review our prior claims and, in the process, deflect purported criticisms by McNeish. We discuss briefly issues related to simulated data and empirical data that provide evidence of strengths of each type of score. In doing so, we identified a second strength of sum scores: superior cross-validation of results across independent samples of empirical data, at least for samples of moderate size. We close with consideration of four general issues concerning sum scores and estimated factor scores that highlight the contrasts between positions offered by McNeish and by us, issues of importance when pursuing applied research in our field.},
	language = {en},
	urldate = {2024-06-18},
	journal = {Educational and Psychological Measurement},
	author = {Widaman, Keith F. and Revelle, William},
	month = oct,
	year = {2023},
	pages = {00131644231205310},
}

@book{kline_principles_2016,
	address = {New York},
	edition = {Fourth edition},
	series = {Methodology in the social sciences},
	title = {Principles and practice of structural equation modeling},
	isbn = {9781462523351 9781462523344},
	abstract = {"Emphasizing concepts and rationale over mathematical minutiae, this is the most widely used, complete, and accessible structural equation modeling (SEM) text. Continuing the tradition of using real data examples from a variety of disciplines, the significantly revised fourth edition incorporates recent developments such as Pearl\&\#39;s graphing theory and structural causal model (SCM), measurement invariance, and more. Readers gain a comprehensive understanding of all phases of SEM, from data collection and screening to the interpretation and reporting of the results. Learning is enhanced by exercises with answers, rules to remember, and topic boxes. The companion website supplies data, syntax, and output for the book\&\#39;s examples--now including files for Amos, EQS, LISREL, Mplus, Stata, and R (lavaan). New to This Edition *Extensively revised to cover important new topics: Pearl\&\#39;s graphing theory and SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more. *Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping. *Expanded coverage of psychometrics. *Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan). *Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models. Pedagogical Features *Exercises with answers, plus end-of-chapter annotated lists of further reading. *Real examples of troublesome data, demonstrating how to handle typical problems in analyses. *Topic boxes on specialized issues, such as causes of nonpositive definite correlations. *Boxed rules to remember. *Website promoting a learn-by-doing approach, including syntax and data files for six widely used SEM computer tools"--},
	publisher = {The Guilford Press},
	author = {Kline, Rex B.},
	year = {2016},
	keywords = {Structural equation modeling, Social sciences, Statistical methods Data processing, SOCIAL SCIENCE / Research, MEDICAL / Psychiatry / General, PSYCHOLOGY / Statistics, EDUCATION / Statistics, BUSINESS \& ECONOMICS / Statistics},
}

@article{hu_cutoff_1999,
	title = {Cutoff criteria for fit indexes in covariance structure analysis: {Conventional} criteria versus new alternatives},
	volume = {6},
	issn = {1070-5511, 1532-8007},
	shorttitle = {Cutoff criteria for fit indexes in covariance structure analysis},
	url = {http://www.tandfonline.com/doi/abs/10.1080/10705519909540118},
	doi = {10.1080/10705519909540118},
	language = {en},
	number = {1},
	urldate = {2024-06-18},
	journal = {Structural Equation Modeling: A Multidisciplinary Journal},
	author = {Hu, Li‐tze and Bentler, Peter M.},
	month = jan,
	year = {1999},
	pages = {1--55},
}

@book{byrne_structural_1994,
	edition = {1},
	title = {Structural Equation Modelling with EQS and EQS/Windows: Basic Concepts, Applications, and Programming},
	isbn = {9781135809607},
	publisher = {Sage},
	author = {Byrne, Barbara M.},
	year = {1994},
}

@article{groskurth_why_2023,
	title = {Why we need to abandon fixed cutoffs for goodness-of-fit indices: {An} extensive simulation and possible solutions},
	volume = {56},
	issn = {1554-3528},
	shorttitle = {Why we need to abandon fixed cutoffs for goodness-of-fit indices},
	url = {https://link.springer.com/10.3758/s13428-023-02193-3},
	doi = {10.3758/s13428-023-02193-3},
	abstract = {Abstract
            
              To evaluate model fit in confirmatory factor analysis, researchers compare goodness-of-fit indices (GOFs) against fixed cutoff values (e.g., CFI {\textgreater} .950) derived from simulation studies. Methodologists have cautioned that cutoffs for GOFs are only valid for settings similar to the simulation scenarios from which cutoffs originated. Despite these warnings, fixed cutoffs for popular GOFs (i.e., χ
              2
              , χ
              2
              /
              df
              , CFI, RMSEA, SRMR) continue to be widely used in applied research. We (1) argue that the practice of using fixed cutoffs needs to be abandoned and (2) review time-honored and emerging alternatives to fixed cutoffs. We first present the most in-depth simulation study to date on the sensitivity of GOFs to model misspecification (i.e., misspecified factor dimensionality and unmodeled cross-loadings) and their susceptibility to further data and analysis characteristics (i.e., estimator, number of indicators, number and distribution of response options, loading magnitude, sample size, and factor correlation). We included all characteristics identified as influential in previous studies. Our simulation enabled us to replicate well-known influences on GOFs and establish hitherto unknown or underappreciated ones. In particular, the magnitude of the factor correlation turned out to moderate the effects of several characteristics on GOFs. Second, to address these problems, we discuss several strategies for assessing model fit that take the dependency of GOFs on the modeling context into account. We highlight tailored (or “dynamic”) cutoffs as a way forward. We provide convenient tables with scenario-specific cutoffs as well as regression formulae to predict cutoffs tailored to the empirical setting of interest.},
	language = {en},
	number = {4},
	urldate = {2024-06-18},
	journal = {Behavior Research Methods},
	author = {Groskurth, Katharina and Bluemke, Matthias and Lechner, Clemens M.},
	month = aug,
	year = {2023},
	pages = {3891--3914},
}


@article{mcneish_dynamic_2023,
	title = {Dynamic fit index cutoffs for confirmatory factor analysis models.},
	volume = {28},
	issn = {1939-1463, 1082-989X},
	url = {https://doi.apa.org/doi/10.1037/met0000425},
	doi = {10.1037/met0000425},
	language = {en},
	number = {1},
	urldate = {2024-06-18},
	journal = {Psychological Methods},
	author = {McNeish, Daniel and Wolf, Melissa G.},
	month = feb,
	year = {2023},
	pages = {61--88},
}

@article{asparouhov_bayesian_2015,
	title = {Bayesian {Structural} {Equation} {Modeling} {With} {Cross}-{Loadings} and {Residual} {Covariances}: {Comments} on {Stromeyer} et al.},
	volume = {41},
	issn = {0149-2063, 1557-1211},
	shorttitle = {Bayesian {Structural} {Equation} {Modeling} {With} {Cross}-{Loadings} and {Residual} {Covariances}},
	url = {http://journals.sagepub.com/doi/10.1177/0149206315591075},
	doi = {10.1177/0149206315591075},
	abstract = {A recent article in the Journal of Management gives a critique of a Bayesian approach to factor analysis proposed in Psychological Methods. This commentary responds to the authors’ critique by clarifying key issues, especially the use of priors for residual covariances. A discussion is also presented of cross-loadings and model selection tools. Simulated data are used to illustrate the ideas. A reanalysis of the example used by the authors reveals a superior model overlooked by the authors.},
	language = {en},
	number = {6},
	urldate = {2024-06-19},
	journal = {Journal of Management},
	author = {Asparouhov, Tihomir and Muthén, Bengt and Morin, Alexandre J. S.},
	month = sep,
	year = {2015},
	pages = {1561--1577},
}

@article{bollen_latent_2002,
	title = {Latent {Variables} in {Psychology} and the {Social} {Sciences}},
	volume = {53},
	issn = {0066-4308, 1545-2085},
	url = {https://www.annualreviews.org/doi/10.1146/annurev.psych.53.100901.135239},
	doi = {10.1146/annurev.psych.53.100901.135239},
	abstract = {▪ Abstract  The paper discusses the use of latent variables in psychology and social science research. Local independence, expected value true scores, and nondeterministic functions of observed variables are three types of definitions for latent variables. These definitions are reviewed and an alternative “sample realizations” definition is presented. Another section briefly describes identification, latent variable indeterminancy, and other properties common to models with latent variables. The paper then reviews the role of latent variables in multiple regression, probit and logistic regression, factor analysis, latent curve models, item response theory, latent class analysis, and structural equation models. Though these application areas are diverse, the paper highlights the similarities as well as the differences in the manner in which the latent variables are defined and used. It concludes with an evaluation of the different definitions of latent variables and their properties.},
	language = {en},
	number = {1},
	urldate = {2024-06-28},
	journal = {Annual Review of Psychology},
	author = {Bollen, Kenneth A.},
	month = feb,
	year = {2002},
	pages = {605--634},
}

@article{zinbarg_cronbachs_2005,
	title = {Cronbach’s α, {Revelle}’s β, and {Mcdonald}’s ω{H}: their relations with each other and two alternative conceptualizations of reliability},
	volume = {70},
	copyright = {http://www.springer.com/tdm},
	issn = {0033-3123, 1860-0980},
	shorttitle = {Cronbach’s α, {Revelle}’s β, and {Mcdonald}’s ω{H}},
	url = {http://link.springer.com/10.1007/s11336-003-0974-7},
	doi = {10.1007/s11336-003-0974-7},
	language = {en},
	number = {1},
	urldate = {2024-06-28},
	journal = {Psychometrika},
	author = {Zinbarg, Richard E. and Revelle, William and Yovel, Iftah and Li, Wen},
	month = mar,
	year = {2005},
	pages = {123--133},
}