Danielle J. Navarro

Bio: Danielle J. Navarro is an academic researcher from University of New South Wales. The author has contributed to research in topics: Inductive reasoning & Inference. The author has an hindex of 10, co-authored 32 publications receiving 387 citations.

The “Small World of Words” English word association norms for over 12,000 cue words

Abstract: Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms.

Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection

Abstract: Discussions of model selection in the psychological literature typically frame the issues as a question of statistical inference, with the goal being to determine which model makes the best predictions about data. Within this setting, advocates of leave-one-out cross-validation and Bayes factors disagree on precisely which prediction problem model selection questions should aim to answer. In this comment, I discuss some of these issues from a scientific perspective. What goal does model selection serve when all models are known to be systematically wrong? How might “toy problems” tell a misleading story? How does the scientific goal of explanation align with (or differ from) traditional statistical concerns? I do not offer answers to these questions, but hope to highlight the reasons why psychological researchers cannot avoid asking them.

The case for formal methodology in scientific reform

Abstract: Current attempts at methodological reform in sciences come in response to an overall lack of rigor in methodological and scientific practices in experimental sciences. However, most methodological reform attempts suffer from similar mistakes and over-generalizations to the ones they aim to address. We argue that this can be attributed in part to lack of formalism and first principles. Considering the costs of allowing false claims to become canonized, we argue for formal statistical rigor and scientific nuance in methodological reform. To attain this rigor and nuance, we propose a five-step formal approach for solving methodological problems. To illustrate the use and benefits of such formalism, we present a formal statistical analysis of three popular claims in the metascientific literature: (a) that reproducibility is the cornerstone of science; (b) that data must not be used twice in any analysis; and (c) that exploratory projects imply poor statistical practice. We show how our formal approach can inform and shape debates about such methodological claims.

Quantifying sentence acceptability measures : Reliability, bias, and variability

Abstract: Understanding and measuring sentence acceptability is of fundamental importance for linguists, but although many measures for doing so have been developed, relatively little is known about some of their psychometric properties. In this paper we evaluate within- and between-participant test-retest reliability on a wide range of measures of sentence acceptability. Doing so allows us to estimate how much of the variability within each measure is due to factors including participant-level individual differences, sample size, response styles, and item effects. The measures examined include Likert scales, two versions of forced-choice judgments, magnitude estimation, and a novel measure based on Thurstonian approaches in psychophysics. We reproduce previous findings of high between-participant reliability within and across measures, and extend these results to a generally high reliability within individual items and individual people. Our results indicate that Likert scales and the Thurstonian approach produce the most stable and reliable acceptability measures and do so with smaller sample sizes than the other measures. Moreover, their agreement with each other suggests that the limitation of a discrete Likert scale does not impose a significant degree of structure on the resulting acceptability judgments.

Handbook Of Psychological Testing

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Knowledge Of Language Its Nature Origin And Use

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How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables.

Abstract: Given that an effect size of d = .4 is a good first estimate of the smallest effect size of interest in psychological research, we already need over 50 participants for a simple comparison of two within-participants conditions if we want to run a study with 80% power. This is more than current practice. In addition, as soon as a between-groups variable or an interaction is involved, numbers of 100, 200, and even more participants are needed. As long as we do not accept these facts, we will keep on running underpowered studies with unclear results. Addressing the issue requires a change in the way research is evaluated by supervisors, examiners, reviewers, and editors. The present paper describes reference numbers needed for the designs most often used by psychologists, including single-variable between-groups and repeated-measures designs with two and three levels, two-factor designs involving two repeated-measures variables or one between-groups variable and one repeated-measures variable (split-plot design). The numbers are given for the traditional, frequentist analysis with p 10. These numbers provide researchers with a standard to determine (and justify) the sample size of an upcoming study. The article also describes how researchers can improve the power of their study by including multiple observations per condition per participant.