Measures of Association for Cross Classifications

Goodman, Leo A.; Kruskal, William

doi:10.1007/978-1-4612-9995-0_1

Cited by 235 publications

(271 citation statements)

References 12 publications

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“…Post-hoc comparisons were made using Fisher's Protected t-test for continuous variables and chi-square for categorical variables. Measures of effect size (i.e., the amount of variance explained) were computed using etasquare (η 2 ) for continuous variables and Goodman and Kruskal's (1979) tau (τ) for categorical variables. In addition, multivariate comparisons of butches and femmes (in which controls were imposed for significant covariates) were made using multiple linear regression for continuous outcomes and multiple logistic regression for categorical outcomes.…”

Section: Discussionmentioning

confidence: 99%

The Coming-Out Process of Young Lesbian and Bisexual Women: Are There Butch/Femme Differences in Sexual Identity Development?

et al. 2007

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Research on lesbian and bisexual women has documented various biological and behavioral differences between butch and femme women. However, little research has examined whether differences exist in sexual identity development (i.e., the coming-out process). The present study examined longitudinally potential butch/femme differences in sexual identity formation and integration among an ethnically diverse sample of 76 self-identified lesbian and bisexual young women (ages 14-21 years). A composite measure of butch/femme identity classified 43% as butch and 51% as femme. Initial comparisons found butch/femme differences in sexual identity (i.e., nearly all butches identified as lesbian, but about half of femmes identified as bisexual), suggesting the need to examine this confound. Comparisons of lesbian butches, lesbian femmes, and bisexual femmes found that lesbian butches and femmes generally did not differ on sexual identity formation, but they differed from bisexual femmes. Lesbian butches and femmes had sexual behaviors and a cognitive sexual orientation that were more centered on women than those of bisexual femmes. With respect to sexual identity integration, lesbian butches were involved in more gay social activities, were more comfortable with others knowing about their homosexuality, and were more certain, comfortable, and accepting of their sexual identity than were bisexual femmes. Fewer differences were found between lesbian femmes and bisexual femmes or between lesbian butches and lesbian femmes. The findings suggest that sexual identity formation does not differ between butch or femme women, but differences are linked to sexual identity as lesbian or bisexual. Further, the findings that lesbian femmes sometimes differed from lesbian butches and at other times from bisexual femmes on sexual identity integration suggest that neither sexual identity nor butch/femme alone may explain sexual identity integration. Research examining the intersection between sexual identity and butch/femme is needed.

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Section: Discussionmentioning

confidence: 99%

The Coming-Out Process of Young Lesbian and Bisexual Women: Are There Butch/Femme Differences in Sexual Identity Development?

et al. 2007

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“…Goodman-Kruskal: The Goodman-Kruskal [59] correlation coefficient is a rank-based correlation coefficient. In order to introduce such correlation, let us define first three different types of pairs of values with respect to sequences x and y , namely: concordant, discordant and, neutral pairs.…”

Section: Methodsmentioning

confidence: 99%

On the selection of appropriate distances for gene expression data clustering

2014

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BackgroundClustering is crucial for gene expression data analysis. As an unsupervised exploratory procedure its results can help researchers to gain insights and formulate new hypothesis about biological data from microarrays. Given different settings of microarray experiments, clustering proves itself as a versatile exploratory tool. It can help to unveil new cancer subtypes or to identify groups of genes that respond similarly to a specific experimental condition. In order to obtain useful clustering results, however, different parameters of the clustering procedure must be properly tuned. Besides the selection of the clustering method itself, determining which distance is going to be employed between data objects is probably one of the most difficult decisions.Results and conclusionsWe analyze how different distances and clustering methods interact regarding their ability to cluster gene expression, i.e., microarray data. We study 15 distances along with four common clustering methods from the literature on a total of 52 gene expression microarray datasets. Distances are evaluated on a number of different scenarios including clustering of cancer tissues and genes from short time-series expression data, the two main clustering applications in gene expression. Our results support that the selection of an appropriate distance depends on the scenario in hand. Moreover, in each scenario, given the very same clustering method, significant differences in quality may arise from the selection of distinct distance measures. In fact, the selection of an appropriate distance measure can make the difference between meaningful and poor clustering outcomes, even for a suitable clustering method.

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“…The accuracy of participants' judgments on metamemory tasks is typically assessed using Gamma correlations (Goodman & Kruskal, 1954), which measure the association between individuals' predictions about their future ability to recognise the correct target with their actual subsequent recognition performance (see the Method section for a detailed description of how Gamma correlations are calculated).…”

Section: Metamemory Judgmentsmentioning

confidence: 99%

“…Similarly, recognition ability was calculated as the proportion of target words participants correctly recognised during the recognition test phase of the task. Gamma scores (Goodman & Kruskal, 1954) were calculated to provide an index of overall FOK judgement accuracy. This analysis is recommended by Nelson (1984) and is commonly used to analyse FOK tasks (e.g., Kelemen, Frost, & Weaver, 2000;Nelson & Narens, 1990;Nelson, Narens, & Dunlosky, 2004;Wojcik, et al, 2013).…”

Section: Scoringmentioning

confidence: 99%