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Gratitude is a positive social emotion that involves recognizing that others have brought benefits into one's life. Loneliness, on the other hand, is an unpleasant emotion resulting from a perceived lack of social connectedness. Although previous studies have reported an inverse association between gratitude and loneliness, these studies have not been systematically examined in a single review. To address this gap in the literature, we conducted a random‐effects meta‐analysis to examine the association between gratitude and loneliness. Analysis of 26 studies revealed a moderate sized effect (mean Fisher's z transformed correlation, zr = −.406, 95% confidence interval [CI] = −.463, −.349; mean back‐transformed correlation, r = −.385, 95% CI = −.433, −.335). To complement these effect sizes, we calculated a probability‐based common language effect size for correlations. Random‐effects homogeneity testing suggested the presence of effect size heterogeneity. Analyses of both continuous and categorical moderators were non‐significant, indicating that these variables did not influence effect size magnitude. Furthermore, publication bias tests suggested that our results were not influenced by unpublished studies. Finally, we proposed several statistical and clinical recommendations for future research. Regarding the latter, we offered suggestions for modifying gratitude enhancement programs with the aim of reducing loneliness. [ABSTRACT FROM AUTHOR]

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared with its major alternatives, including the Spearman rank-order correlation, the bootstrap estimate, the Box-Cox transformation family, and a general normalizing transformation (i.e., rankit), as well as to various bias adjustments. Nonnormality caused the correlation coefficient to be inflated by up to +0.14, particularly when the nonnormality involved heavy-tailed distributions. Traditional bias adjustments worsened this problem, further inflating the estimate. The Spearman and rankit correlations eliminated this inflation and provided conservative estimates. Rankit also minimized random error for most sample sizes, except for the smallest samples (n = 10), where bootstrapping was more effective. Overall, results justify the use of carefully chosen alternatives to the Pearson correlation when normality is violated.

Previous research on sensation seeking (SS) was dominated by a variable-oriented approach indicating that SS level has a linear relation with a host of problem behaviors. Our aim was to provide a person-oriented methodology--a probabilistic clustering--that enables examination of both inter- and intra-individual differences in not only the level, but also in the pattern of SS. We have applied model-based clustering to a four-semester long longitudinal high school survey ("N" = 3334) and to a cross-sectional university survey ("N" = 438). The results indicated that impulsive patterns are linked to negative outcomes whereas non-impulsive patterns are associated with positive outcomes. Our study aims to serve as a methodological example on how to apply model-based clustering to examine different types of sensation-seeking patterns. This modern clustering method allows for probabilistic categorization, with continuous typicality scores besides cluster membership variables. These typicality scores turned out to have higher temporal stability than simple categorical membership variables did. (Contains 4 figures and 3 tables.)

The authors conducted a Monte Carlo simulation of 4 test statistics or comparing dependent correlations with no variables in common. Empirical Type 1 error rates and power estimates were determined for K. Pearson and L. N. G. Filon's (1898) z, O. J. Dunn and V. A. Clark's (1969) z, J. H. Steiger's (1980) original modification of Dunn and Clark's z, and Steiger's modification of Dunn and Clark's z using a backtransformed average z procedure for sample sizes of 10, 20, 50, and 100 under 3 different population distributions. For the Type 1 error rate analyses, the authors evaluated 3 different magnitudes of the predictor-criterion correlations (rho[subscript 12] = rho[subscript 34] = 0.10, 0.30, and 0.70). Likewise, for the power analyses, 3 different magnitudes of discrepancy or effect sizes between correlations with no variables in common (rho[subscript 12] and rho[subscript 14] were examined (values of 0.10, 0.40, and 0.60). All of the analyses were conducted at 3 different levels of predictor intercorrelation. Results indicated that the choice as to which test statistic is optimal, in terms of power and Type 1 error rate, depends not only on sample size and population distribution but also on (a) the predictor intercorrelations and (b) the effect size (for power) or the magnitude of the predictor-criterion correlations (for Type 1 error rate). For the conditions examined in the present study, Pearson and Filon's z had inflated Type 1 error rates when the predictor-criterion correlations were low to moderate. Dunn and Clark's z and the 2 Steiger procedures had similar, but conservative Type 1 error rates when the predictor-criterion correlations were low. Moreover, the power estimates were similar among the 3 procedures.

This study examined the association of gender-role orientation to creative accomplishments and cognitive styles in 127 college students. Results indicated that the gender role orientation of instrumentality was positively associated with creative accomplishments in the business venture domain and that androgynous, versus non-androgynous, individuals were more creatively productive in the domains of literature, theater, and video-photography. (Contains references.) (Author/DB)

A Monte Carlo evaluation of four test statistics for comparing dependent zero-order correlations was conducted with four sample sizes and three population distributions. Results indicate that choice of optimal test statistic depends on sample size and distribution, and predictor intercorrelation and effect size or magnitude of the predictor-criterion correlations. (SLD)

The ordinary gain score, g, is defined as g=[x.sub.2] - [x.sub.1], where [x.sub.1] is the pretest score and [x.sub.2] is the posttest score. The present study extends and refines previous research on the reliability and validity of gain scores. Using particular values as stated in the tables and graphs, the pre-and posttest reliabilities, pre- and posttest validities, ratios of pretest to posttest standard deviations, and correlations between the pretest and posttest were varied systematically to examine the effects of these parameter configurations on gain scores' reliability and validity. Results plotted graphically provide insight via visual interpretation not easily inferred using only values from a table. One interesting finding was that the reliability of a gain score can be at a maximum when the validity is at a minimum. Another is that a high correlation between pre- and posttest was beneficial to the validity of the gain score but detrimental to its reliability. By identifying the situations in which gain scores can be reliable and valid, findings inform researchers when gain scores should or should not be used. DOI 10.2466/03.PMS.111.5.399-406

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.addbeh.2005.11.004 Byline: James B. Hittner, Rhonda Swickert Keywords: Sensation seeking; Thrill seeking; Disinhibition; Alcohol; Drinking; Meta-analysis Abstract: A meta-analysis was conducted to investigate the association between sensation seeking and alcohol use. Analysis of 61 studies revealed a small to moderate size, heterogeneous effect (mean weighted r =.263) between alcohol use and sensation seeking total scale scores. Analysis of the four sensation seeking components indicated that disinhibition was most strongly correlated with alcohol use (mean weighted r =.368). Categorical moderator analyses revealed that studies that did, versus did not, analyze covariates yielded smaller effect sizes. Continuous moderator analyses indicated that earlier published studies and higher percentages of male and white Caucasian participants were associated with stronger effect sizes between sensation seeking and alcohol use. Novel statistical methodologies for analyzing the association between sensation seeking and alcohol use were proposed and recommendations for future research were suggested. Author Affiliation: Department of Psychology, College of Charleston, 66 George Street, Charleston, SC 29424, USA Article Note: (footnote) [star] Portions of this research were previously presented at the annual meeting of the American Association of Behavioral and Social Sciences, Las Vegas, NV (February, 2005), and the Society for Southeastern Social Psychologists Conference, Clemson, SC (November, 2004).

The potential problems which may arise from the use of difference (a.k.a., gain) scores in the measurement of change are well documented, including (a) difference scores are often negatively correlated with initial ability, and (b) they often tend to be unreliable. Conversely, they make excellent dependent variables in a true experiment since they tend to reduce variability due to individual differences among persons. This brief didactic paper presents a conclusion similar to Nicewander and Price, using a perhaps more straightforward argument based on difference scores. We argue that the same reason difference scores provide powerful significance tests, namely, reduction of 'true score' variance, is also the reason they tend to be unreliable. Further, we make the point that reducing true score variance will increase the power of a significance test (since it will reduce the denominator or 'error term' of the observed statistic) but will decrease reliability (since it is the numerator and a component of the denominator of the reliability coefficient).

An interactive FORTRAN 77 program is presented that computes the pooled correlation from independent subgroups via the formula provided by R. Charter and R. Alexander (1993). In addition, the means and standard deviations for each variable are also provided for the composite group. (Author/SLD)