Current Practices in Data Analysis Procedures in Psychology: What Has Changed?

This paper analyzes current practices in psychology in the use of research methods and data analysis procedures (DAP) and aims to determine whether researchers are now using more sophisticated and advanced DAP than were employed previously. We reviewed empirical research published recently in prominent journals from the USA and Europe corresponding to the main psychological categories of Journal Citation Reports and examined research methods, number of studies, number and type of DAP, and statistical package. The 288 papers reviewed used 663 different DAP. Experimental and correlational studies were the most prevalent, depending on the specific field of psychology. Two-thirds of the papers reported a single study, although those in journals with an experimental focus typically described more. The papers mainly used parametric tests for comparison and statistical techniques for analyzing relationships among variables. Regarding the former, the most frequently used procedure was ANOVA, with mixed factorial ANOVA being the most prevalent. A decline in the use of non-parametric analysis was observed in relation to previous research. Relationships among variables were most commonly examined using regression models, with hierarchical regression and mediation analysis being the most prevalent procedures. There was also a decline in the use of stepwise regression and an increase in the use of structural equation modeling, confirmatory factor analysis, and hierarchical linear modeling. Overall, the results show that recent empirical studies published in journals belonging to the main areas of psychology are employing more varied and advanced statistical techniques of greater computational complexity

Comparison of the procedures of Fleishman and Ramberg et al. for generating non normal data in simulation studies

Simulation techniques must be able to generate the types of distributions most commonly encountered in real data, for example, non-normal distributions. Two recognized procedures for generating non-normal data are Fleishman's linear transformation method and the method proposed by Ramberg et al. that is based on generalization of the Tukey lambda distribution. This study compares these procedures in terms of the extent to which the distributions they generate fit their respective theoretical models, and it also examines the number of simulations needed to achieve this fit. To this end, the paper considers, in addition to the normal distribution, a series of non-normal distributions that are commonly found in real data, and then analyses fit according to the extent to which normality is violated and the number of simulations performed. The results show that the two data generation procedures behave similarly. As the degree of contamination of the theoretical distribution increases, so does the number of simulations required to ensure a good fit to the generated data. The two procedures generate more accurate normal and non-normal distributions when at least 7000 simulations are performed, although when the degree of contamination is severe (with values of skewness and kurtosis of 2 and 6, respectively) it is advisable to perform 15000 simulations

The effect of skewness and kurtosis on the Kenward-Roger approximation when group distributions differ

This study examined the independent effect of skewness and kurtosis on the robustness of the linear mixed model (LMM), with the Kenward-Roger (KR) procedure, when group distributions are different, sample sizes are small, and sphericity cannot be assumed. Methods: A Monte Carlo simulation study considering a split-plot design involving three groups and four repeated measures was performed. Results: The results showed that when group distributions are different, the effect of skewness on KR robustness is greater than that of kurtosis for the corresponding values. Furthermore, the pairings of skewness and kurtosis with group size were found to be relevant variables when applying this procedure. Conclusions: With sample sizes of 45 and 60, KR is a suitable option for analyzing data when the distributions are: (a) mesokurtic and not highly or extremely skewed, and (b) symmetric with different degrees of kurtosis. With total sample sizes of 30, it is adequate when group sizes are equal and the distributions are: (a) mesokurtic and slightly or moderately skewed, and sphericity is assumed; and (b) symmetric with a moderate or high/extreme violation of kurtosis. Alternative analyses should be considered when the distributions are highly or extremely skewed and samples sizes are small

Using the linear mixed model to analyze non-normal data distributions in longitudinal designs

Using a Monte Carlo simulation and the Kenward-Roger (KR) correction for degrees of freedom this paper analyzes the application of the linear mixed model (LMM) to a mixed repeated measures design. The LMM was first used to select the covariance structure with three types of data distribution: normal, exponential and log-normal. This showed that with ho mogeneous between-groups covariance, and when the distribution was normal, the covariance structure with the best fit was the unstructured population matrix. Wit h heterogeneous between-groups covariance and when the pairing between covariance matrices and group sizes was null the best fit was shown by the between-subjects heterogeneous unstructured population matrix, this being the case for all the distributions analyzed. By contrast, with posit ive or negative pairing the within-subject and between-subjects heterogeneous first-order autoregressive structure produced the best fit. In the second stage of the study, the robustness of the LMM was tested. This showed that the KR method provided adequate control of Type I error rates for the time effect with normally distributed data. However, as skewness increased, as occurs, for example, in the log-normal distribution, robustness was null, especially when the assumption of sphericity was violated. As regards the influence of kurtosis the analysis showed that the degree of robustness increased in line with the amount of kurtosis

Analyzing longitudinal data and use of the generalized linear model in health and social sciences

In the health and social sciences, longitudinal data have often been analyzed without taking into account the dependence between observations of the same subject. Furthermore, consideration is rarely given to the fact that longitudinal data may come from a non-normal distribution. In addition to describing the aims and types of longitudinal designs this paper presents three approaches based on generalized estimating equations that do take into account the lack of independence in data, as well as the type of distribution. These approaches are the marginal model (population-average model), the random effects model (subject-specific model), and the transition model (Markov model or auto-correlation model). Finally, these models are applied to empirical data by means of specific procedures included in SAS, namely GENMOD, MIXED, and GLIMMIX

Non-normal Distributions Commonly Used in Health, Education, and Social Sciences: A Systematic Review

Statistical analysis is crucial for research and the choice of analytical technique should take into account the specific distribution of data. Although the data obtained from health, educational, and social sciences research are often not normally distributed, there are very few studies detailing which distributions are most likely to represent data in these disciplines. The aim of this systematic review was to determine the frequency of appearance of the most common non-normal distributions in the health, educational, and social sciences. The search was carried out in the Web of Science database, from which we retrieved the abstracts of papers published between 2010 and 2015. The selection was made on the basis of the title and the abstract, and was performed independently by two reviewers. The inter-rater reliability for article selection was high (Cohen's kappa = 0.84), and agreement regarding the type of distribution reached 96.5%. A total of 262 abstracts were included in the final review. The distribution of the response variable was reported in 231 of these abstracts, while in the remaining 31 it was merely stated that the distribution was non-normal. In terms of their frequency of appearance, the most-common non-normal distributions can be ranked in descending order as follows: gamma, negative binomial, multinomial, binomial, lognormal, and exponential. In addition to identifying the distributions most commonly used in empirical studies these results will help researchers to decide which distributions should be included in simulation studies examining statistical procedures

Bias, precision and accuracy of skewness and kurtosis estimators for frequently used continuous distributions

Several measures of skewness and kurtosis were proposed by Hogg (1974) in order to reduce the bias of conventional estimators when the distribution is non-normal. Here we conducted a Monte Carlo simulation study to compare the performance of conventional and Hogg's estimators, considering the most frequent continuous distributions used in health, education, and social sciences (gamma, lognormal and exponential distributions). In order to determine the bias, precision and accuracy of the skewness and kurtosis estimators for each distribution we calculated the relative bias, the coefficient of variation, and the scaled root mean square error. The effect of sample size on the estimators is also analyzed. In addition, a SAS program for calculating both conventional and Hogg's estimators is presented. The results indicated that for the non-normal distributions investigated, the estimators of skewness and kurtosis which best reflect the shape of the distribution are Hogg's estimators. It should also be noted that Hogg's estimators are not as affected by sample size as are conventional estimators

Sphericity estimation bias for repeated measures designs in simulation studies

In this study, we explored the accuracy of sphericity estimation and analyzed how the sphericity of covariance matrices may be affected when the latter are derived from simulated data. We analyzed the consequences that normal and nonnormal data generated from an unstructured population covariance matrix with low (ε = .57) and high (ε = .75) sphericity can have on the sphericity of the matrix that is fitted to these data. To this end, data were generated for four types of distributions (normal, slightly skewed, moderately skewed, and severely skewed or log-normal), four sample sizes (very small, small, medium, and large), and four values of the within-subjects factor (K = 4, 6, 8, and 10). Normal data were generated using the Cholesky decomposition of the correlation matrix, whereas the Vale-Maurelli method was used to generate nonnormal data. The results indicate the extent to which sphericity is altered by recalculating the covariance matrix on the basis of simulated data. We concluded that bias is greater with spherical covariance matrices, nonnormal distributions, and small sample sizes, and that it increases in line with the value of K. An interaction was also observed between sample size and K: With very small samples, the observed bias was greater as the value of K increased

Life Satisfaction and Character Strengths in Women With Breast Cancer: Zest and Hope as Predictors.

Background:Empirical evidence shows that life satisfaction is positively related to character strengths, and although this association has been observed in different populations, it is scarce in breast cancer patients. This study analyzes the relationship between character strengths and life satisfaction in Spanish women diagnosed with breast cancer. Methods:A sample of 117 women completed the Satisfaction with Life Scale (SWLS) and the Spanish version of the VIA Inventory of Strengths (VIA-IS). Correlation analysis and regression modeling were performed to determine which strengths predict life satisfaction. Results:The results of the correlation analysis showed that 15 strengths were positively and significantly associated with life satisfaction, with the highest correlations corresponding to zest, hope, curiosity, social intelligence, love, gratitude, and judgment. Regression modeling indicated that of these, zest and hope were key strengths for predicting life satisfaction. Conclusions:These findings suggest that intervention programs based on the development of zest and hope could help to improve life satisfaction and, therefore, the psychological well-being of women with breast cancer.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported Grupo de investigación consolidado CTS 110, Junta de Andalucí

The Flourishing Scale: Psychometric Properties in Breast Cancer Patients.

Background/Objective. Promoting well-being is a key goal of cancer care, and it needs to be assessed using appropriate instruments. Flourishing is considered part of psychological well-being and it is commonly assessed with the Flourishing Scale (FS). To our knowledge, no studies have analyzed the psychometric properties of the FS in breast cancer patients. Our aim here was to provide validity evidence for use of the FS in this context. Method. Participants were 217 Spanish women with breast cancer who completed the FS and other scales assessing positive psychology constructs and indicators of psychological maladjustment. The internal structure of the FS was analyzed using confirmatory factor analysis (CFA). We calculated the average variance extracted (AVE) to evaluate convergent validity, and both McDonald's omega and Cronbach’s alpha coefficients to estimate reliability. Item analysis was performed by computing corrected item-total correlations. Validity evidence based on relationships with other variables was obtained through Pearson correlation analysis, controlling for age and cancer stage. Results. The CFA supported a single-factor structure, with adequate goodness-of-fit indices (CFI = .997, NNFI = .996, RMSEA = .069, and SRMR = .047) and standardized factor loadings ranging from .70 to .87. The value of the AVE was .63, and the reliability coefficient obtained with both procedures was .91. Corrected item-total correlations ranged from .62 to .78. Correlation analysis showed direct and strong associations between the FS score and scores on positive psychology constructs (range from .43 to .74). The FS score was inversely correlated with scores on depression, anxiety, stress, negative affect, and pessimism (range from -.14 to -.52). Discussion. The FS is a useful tool for exploring well-being in the breast cancer context, providing useful information for psychological assessment.This research was supported by funding from the Regional Government of Andalusia to Consolidated Research Group CTS110