Structural equation modeling (SEM) is a powerful and flexible modeling framework for testing complex relationships among observed and latent variables. However, its methodological complexity and analytical flexibility also increase the risk of questionable research practices (QRPs), especially in fields like applied linguistics, where training in advanced statistics may be limited. This article synthesizes the literature on QRPs and applies it to SEM by identifying seven categories of problematic practices: not checking assumptions, not validating a measurement model, not testing competing models, not sufficiently justifying modeling decisions, relying on post hoc model modification, overemphasizing global fit indices, and incomplete or nontransparent reporting. Each practice is described with examples and linked to broader issues in research ethics and transparency. The paper concludes with concrete recommendations for improving the credibility and reproducibility of SEM research, emphasizing the integration of best practices with the principles of open science.

Recent fraud cases in psychological and medical research have emphasized the need to pay attention to Questionable Research Practices (QRPs). Deliberate or not, QRPs usually have a deteriorating effect on the quality and the credibility of research results. QRPs must be revealed but prevention of QRPs is more important than detection. I suggest two policy measures that I expect to be effective in improving the quality of psychological research. First, the research data and the research materials should be made publicly available so as to allow verification. Second, researchers should more readily consider consulting a methodologist or a statistician. These two measures are simple but run against common practice to keep data to oneself and overestimate one’s methodological and statistical skills, thus allowing secrecy and errors to enter research practice.

We comment on Sijtsma’s (2014) thought-provoking essay on how to minimize questionable research practices (QRPs) in psychology. We agree with Sijtsma that proactive measures to decrease the risk of QRPs will ultimately be more productive than efforts to target individual researchers and their work. In particular, we concur that encouraging researchers to make their data and research materials public is the best institutional antidote against QRPs, although we are concerned that Sijtsma’s proposal to delegate more responsibility to statistical and methodological consultants could inadvertently reinforce the dichotomy between the substantive and statistical aspects of research. We also discuss sources of false-positive findings and replication failures in psychological research, and outline potential remedies for these problems. We conclude that replicability is the best metric of the minimization of QRPs and their adverse effects on psychological research.

We respond to the commentaries Waldman and Lilienfeld (Psychometrika, 2015) and Wigboldus and Dotch (Psychometrika, 2015) provided in response to Sijtsma’s (Sijtsma in Psychometrika, 2015) discussion article on questionable research practices. Specifically, we discuss the fear of an increased dichotomy between substantive and statistical aspects of research that may arise when the latter aspects are laid entirely in the hands of a statistician, remedies for false positives and replication failure, and the status of data exploration, and we provide a re-definition of the concept of questionable research practices.

Ageism has become a social problem in an aged society. This study re-examines an ageism affirmation strategy; the designs and plans for this study were pre-registered. Participants were randomly assigned to either an experimental group (in which they read an explanatory text about the stereotype embodiment theory and related empirical findings) or a control group (in which they read an irrelevant text). The hypothesis was that negative attitudes toward older adults are reduced in the experimental group compared with the control group. Bayesian analysis was used for hypothesis testing. The results showed that negative attitudes toward older adults were reduced in the experimental group. These findings contribute to the development of psychological and gerontological interventions aimed at affirming ageism. In addition, continued efforts to reduce questionable research practices and the spread of Bayesian analysis in psychological research are expected.

We surveyed 348 Psychology and Education researchers within Spain, on issues such as their perception of a crisis in Science, their confidence in the quality of published results, and the use of questionable research practices (QRP). Their perceptions regarding pressure to publish and academic competition were also collected. The results indicate that a large proportion of the sample of Spanish academics think there is a crisis in Science, mainly due to a lack of economic investment, and doubts the quality of published findings. They also feel strong pressure to publish in high impact factor journals and a highly competitive work climate.

Criticism of null hypothesis significance testing, confidence intervals, and frequentist statistics in general has evolved into advocacy of Bayesian analyses with informative priors for strong inference. This paper shows that Bayesian analysis with informative priors is formally equivalent to data falsification because the information carried by the prior can be expressed as the addition of fabricated observations whose statistical characteristics are determined by the parameters of the prior. This property of informative priors makes clear that only the use of non-informative, uniform priors in all types of Bayesian analyses is compatible with standards of research integrity. At the same time, though, Bayesian estimation with uniform priors yields point and interval estimates that are identical or nearly identical to those obtained with frequentist methods. At a qualitative level, frequentist and Bayesian outcomes have different interpretations but they are interchangeable when uniform priors are used. Yet, Bayesian interpretations require either the assumption that population parameters are random variables (which they are not) or an explicit acknowledgment that the posterior distribution (which is thus identical to the likelihood function except for a scale factor) only expresses the researcher’s beliefs and not any information about the parameter of concern.