references.bib

@article{henderson_stepwise_1989,
    title = {Stepwise {Regression} in {Social} and {Psychological} {Research}},
    volume = {64},
    issn = {0033-2941},
    url = {https://doi.org/10.2466/pr0.1989.64.1.251},
    doi = {10.2466/pr0.1989.64.1.251},
    abstract = {Researchers often invoke stepwise ordinary least squares regression to explain, predict or classify practical problems or theoretical constructs in psychological and social research. Unfortunately, this statistical technique is used without proper consideration for its inherent theoretical and practical limitations, a problem expected to grow even more serious with the proliferation of statistical packages for use on personal computers. Use of stepwise regression in social and psychological research is reconsidered here. Explanations of forward selection, backward elimination and combination stepwise procedures are provided; limitations of the technique, statistical and practical, are then addressed. Analysis shows that most of the current applications of stepwise regression should be rejected, or at least tempered with strong qualification to inference.},
    language = {en},
    number = {1},
    urldate = {2019-04-30},
    journal = {Psychological Reports},
    author = {Henderson, Douglas A. and Denison, Daniel R.},
    month = feb,
    year = {1989},
    pages = {251--257}
}

@article{streiner_regression_1994,
    title = {Regression in the {Service} of the {Superego}: {The} {Do}'s and {Don}'ts of {Stepwise} {Multiple} {Regression}},
    volume = {39},
    issn = {0706-7437},
    shorttitle = {Regression in the {Service} of the {Superego}},
    url = {https://doi.org/10.1177/070674379403900401},
    doi = {10.1177/070674379403900401},
    abstract = {Stepwise multiple regression is a very powerful but often misused technique. It can be used to find a set of independent variables which can predict some outcome. However, there are problems when the results of a stepwise solution are used to try to explain or understand the dependent variable. This paper discusses the different types of stepwise regressions, some of the legitimate and illegitimate uses of this technique, some of the difficulties encountered when trying to interpret the results and other solutions to the problems posed by using them.},
    language = {en},
    number = {4},
    urldate = {2019-04-30},
    journal = {The Canadian Journal of Psychiatry},
    author = {Streiner, David L.},
    month = may,
    year = {1994},
    pages = {191--196}
}

@article{antonakis_looking_2011,
    title = {Looking for validity or testing it? {The} perils of stepwise regression, extreme-scores analysis, heteroscedasticity, and measurement error},
    volume = {50},
    issn = {0191-8869},
    shorttitle = {Looking for validity or testing it?},
    url = {http://www.sciencedirect.com/science/article/pii/S0191886910004575},
    doi = {10.1016/j.paid.2010.09.014},
    abstract = {When researchers introduce a new test they have to demonstrate that it is valid, using unbiased designs and suitable statistical procedures. In this article we use Monte Carlo analyses to highlight how incorrect statistical procedures (i.e., stepwise regression, extreme scores analyses) or ignoring regression assumptions (e.g., heteroscedasticity) contribute to wrong validity estimates. Beyond these demonstrations, and as an example, we re-examined the results reported by Warwick, Nettelbeck, and Ward (2010) concerning the validity of the Ability Emotional Intelligence Measure (AEIM). Warwick et al. used the wrong statistical procedures to conclude that the AEIM was incrementally valid beyond intelligence and personality traits in predicting various outcomes. In our re-analysis, we found that the reliability-corrected multiple correlation of their measures with personality and intelligence was up to .69. Using robust statistical procedures and appropriate controls, we also found that the AEIM did not predict incremental variance in GPA, stress, loneliness, or well-being, demonstrating the importance for testing validity instead of looking for it.},
    number = {3},
    urldate = {2019-04-30},
    journal = {Personality and Individual Differences},
    author = {Antonakis, John and Dietz, Joerg},
    month = feb,
    year = {2011},
    keywords = {Emotional intelligence, Errors-in-variables, General intelligence, Heteroscedasticity, Monte Carlo, Personality, Truncation, Validity},
    pages = {409--415},
    file = {ScienceDirect Full Text PDF:/Users/ben/Zotero/storage/VY7ZSDAX/Antonakis and Dietz - 2011 - Looking for validity or testing it The perils of .pdf:application/pdf;ScienceDirect Snapshot:/Users/ben/Zotero/storage/5A9LTIML/S0191886910004575.html:text/html}
}


@article{cho_cronbachs_2015,
    title = {Cronbach’s {Coefficient} {Alpha}: {Well} {Known} but {Poorly} {Understood}},
    volume = {18},
    issn = {1094-4281, 1552-7425},
    shorttitle = {Cronbach’s {Coefficient} {Alpha}},
    url = {http://journals.sagepub.com/doi/10.1177/1094428114555994},
    doi = {10.1177/1094428114555994},
    abstract = {This study disproves the following six common misconceptions about coefficient alpha: 1) Alpha was first developed by Cronbach. 2) Alpha equals reliability. 3) A high value of alpha is an indication of internal consistency. 4) Reliability will always be improved by deleting items using “alpha if item deleted.” 5) Alpha should be greater than or equal to .7 (or, alternatively, .8). 6) Alpha is the best choice among all published reliability coefficients. This study discusses the inaccuracy of each of these misconceptions and provides a correct statement. This study recommends that the assumption of unidimensionality and tau-equivalency be examined before the application of alpha and that SEM-based reliability estimators be substituted for alpha when one of these conditions is not satisfied. This study also provides formulas for SEM-based reliability estimators that do not rely on matrix notation and step-by-step explanations for the computation of SEM-based reliability estimates.},
    language = {en},
    number = {2},
    urldate = {2019-04-30},
    journal = {Organizational Research Methods},
    author = {Cho, Eunseong and Kim, Seonghoon},
    month = apr,
    year = {2015},
    pages = {207--230},
    file = {Cho and Kim - 2015 - Cronbach’s Coefficient Alpha Well Known but Poorl.pdf:/Users/ben/Zotero/storage/YUI34M77/Cho and Kim - 2015 - Cronbach’s Coefficient Alpha Well Known but Poorl.pdf:application/pdf}
}