Comparing Groups with Multiple Dependent Variables

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent variables. However, this univariate approach decreases power, increases the risk for Type 1 error, and contradicts the rationale for conducting multivariate tests in the first place. Purpose: The purpose of this study was to provide a user-friendly primer on conducting descriptive discriminant analysis (DDA), which is a post-hoc strategy to MANOVA that takes into account the complex relationships among multiple dependent variables. Method: A real-world example using the Statistical Package for the Social Sciences syntax and data from 1,095 middle school students on their body composition and body image are provided to explain and interpret the results from DDA. Results: While univariate post hocs increased the risk for Type 1 error to 76%, the DDA identified which dependent variables contributed to group differences and which groups were different from each other. For example, students in the very lean and Healthy Fitness Zone categories for body mass index experienced less pressure to lose weight, more satisfaction with their body, and higher physical self-concept than the Needs Improvement Zone groups. However, perceived pressure to gain weight did not contribute to group differences because it was a suppressor variable. Conclusion: Researchers are encouraged to use DDA when investigating group differences on multiple correlated dependent variables to determine which variables contributed to group differences.

Historically, behavior analytic research and practice have been grounded in the single subject examination of behavior change. Within the behavior analytic community, there remains little doubt that the graphing of behavior is a powerful strategy for demonstrating functional control by the independent variable; however, during the past thirty years, various statistical techniques have become a popular alternative form of evidence for demonstrating a treatment effect. Concurrently, a mounting number of behavior analytic investigators are measuring multiple dependent variables when conducting statistical analyses. Without employing strategies that protect the experimentwise error rate, evaluation of multiple dependent variables within a single experiment is likely to inflate the Type I error rate. In fact, with each additional dependent variable examined in univariate fashion, the probability of “incorrectly” identifying statistical significance increases exponentially as a function of chance. Multivariate analysis of variance (MANOVA) and several other statistical techniques can preclude this common error. We provide an overview of the procedural complications arising from methodologies that might inflate the Type I error rate. Additionally, we provide a sample of reviewer comments and suggestions, and an enrichment section focusing on this somewhat contentious issue, as well as a number of statistical and neural network techniques that enhance power and preclude the inflation of Type I error rates

Objective To investigate the statistical.method for analyzing nurse job burnout Data. Methods Hotelling T2 test and multivariate analysis of variance(MANOVA)were used to analyze nurse job burnout datum. Results There was statistical significance between different cities,different ethnieities in the personal achievements,hut from three dimensions of the whole job burnout,there are no statistical sig-nificant differences among different cities, different ethnicities and different ages. Conclusions The use of Hotelling T2 test and multivariate analysis of variance to analyzing nurse job burnout data, not only ob-tain overall conclusion, and further use of single-variable analysis may also gain comparative results of each dimensions. Key words: Job burnout; Hotelling T2 test; Multivariate analysis of variance

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after “controlling” for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Background: Deficiency of sleep among nurses working in shifts, results in sleepiness, fatigue, health, and safety problem, and it also can deteriorate the quality of care provision. The present study aimed to compare sleepiness and fatigue between night-shift nurses who have a 3-h nap and day-shift nurses. Methods: A prospective cohort study was done on 23 night-shift nurses with a 3-h nap and 24 day-shift nurses in Sina Hospital in Tehran (Iran) during 2015–2016. Subjective sleepiness and fatigue before and after their shifts were measured by Stanford Sleepiness Scale (SSS) and Swedish Occupational Fatigue Inventory (SOFI-20), respectively. Statistical analysis including independent t-test, Mann–Whitney test, multivariate analysis of variance, and discriminant analysis were performed to compare the two groups. Results: The difference in means of subjective sleepiness before and after the shifts was significantly greater among night-shift nurses having a 3-h nap (1.43±1.50) than the day-shift nurses (0.37±0.92) (P=0.007). Although after their working shifts, the night-shift nurses (1.40±2.30) were more fatigued than the day-shift ones (0.96±1.46), this was not statistically significant (P=0.81). Moreover, day or night shift working with a 3-h nap was an effective factor (P=0.015) while considering the interrelationship between sleepiness and fatigue in multivariate analysis. Discriminant analysis revealed the dominant role of subjective sleepiness in differentiating the two groups. Conclusions: Despite the 3-h nap, subjective sleepiness was seen more among the night-shift nurses than the day-shift ones; however, the mean difference scores of fatigue did not differ between the two groups. Presumably, getting a 3-h nap during night shift and a heavy work load for day-shift nurses might result in insignificant fatigue between the groups. Furthermore, studies with new strategies for napping among nurses are recommended.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Bidin Yatim, Suzilah Ismail; MANOVA versus alternative methods. AIP Conf. Proc. 4 December 2014; 1635 (1): 934–939. https://doi.org/10.1063/1.4903694 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

Assessments by Patients With Schizophrenia and Psychiatrists of Relative Risk of Research Procedures

The aim of the study is to determine the factors of individuals’ skills with regard to internet usage in Turkey by Confirmatory Factor Analysis, and to analyze the factor indicators in order to clarify if they vary by gender, age and education level, i.e. if a digital divide is in question, by Multivariate Analysis of Variance (MANOVA) method. For that purpose, the data on “IT Usage – 2016” of Turkish Statistical Institute was selected as baseline. All data used in this study is categorical. Though, since Confirmatory Factor Analysis and MANOVA are methods using continuous variables, firstly, the variables were transferred to quantified variables by Optimal Scaling method, and before employing Confirmatory Factor Analysis and MANOVA methods, the validity of both multivariate normality and equality of variance-covariance matrices hypotheses was checked. The results obtained showed that the skill with regard to internet usage consists of four factors: personal intended internet activities, e-learning, e-government services, and software related activities. According to the MANOVA results, these factors significantly vary by gender, age, and education level, and thus, there’s a second and a third level digital divide between individuals in Turkey.

Estimating significant causes of variation in emulsions’ droplet size distributions obtained by the electrical sensing zone and laser low angle light scattering techniques

Samples (0.5 m 2 ) were taken from the South Llano River, Kimble County, Tex., to determine inter- and intraspecific differences in microhabitat association for the three most abundant species of Naucoridae. Various aspects of substratum condition, current speed, temperature, depth, as well as complex key hydraulic features, were used to characterize the microhabitat for each sample. Eight species of naucorids occur in the river; however, only Ambrysus circumcinctus Montandon, Cryphocricos hungerfordi Usinger, and Limnocoris lutzi La Rivers occurred with sufficient frequency in our samples to allow statistical analysis. None of the three species exhibited age-related differences in microhabitat association based on multivariate analysis of variance (MANOVA), and none of the examined abiotic variables yielded significant differences between nymphs and adults when analyzed from a univariate perspective except Froude number for L. lutzi . Both A. circumcinctus and C. hungerfordi exhibited highly significant differences (MANOVA) in abiotic characteristics between quadrats of occurrence and nonoccurrence. Results of individual analyses of variance (ANOVAs) indicated that 9 of the 10 abiotic variables were significant for A. circumcinctus and 8 for C. hungerfordi . In contrast, quadrats where L. lutzi occurred could not be distinguished from those in which it was absent based on MANOVA, and only two abiotic variables exhibited significance in the ANOVAs. Associations between age groups (nymphs and adults) within a species and between all possible species pairs (combined age groups) also were evaluated with contingency X2 tests using presence-absence data and with Pearson's product–moment correlations using density data. In all cases, significant negative associations were not detected, suggesting that neither age groups nor species exhibit microhabitat segregation. Discriminant function analysis for each species supported MANOVA results on presence–absence data. Separate multiple step-up regressions were used to evaluate which abiotic factors were most related to the density for each species. Significant variables included mean current speed, which accounted for 23.8% of the variation in density of A. circumcinctus ; rock standard error of the mean, which accounted for 43.1% of the variation in density of C. hungerfordi ; Froude number and depth range, which together accounted for 39.0% of the variation in densitiy of L. lutzi . In summary, each common naucorid clearly shows microhabitat associations, but the evidence of interspecific differences is indirect.

Clinical psychology is a prominent career goal among psychology undergraduates, yet the mechanisms shaping this preference remain understudied. Guided by Social Cognitive Career Theory (SCCT), this study examined how personality traits, psychological distress, and motivational dimensions influence students’ intentions to pursue graduate studies in clinical psychology within the Turkish context. A total of 1,072 undergraduate psychology students (86.6% women; M = 21.97, SD = 3.08) completed an online survey assessing their motivations for pursuing clinical psychology, personality traits (as measured by the Big Five), and psychological distress (GHQ-12). Exploratory factor analysis (EFA) was conducted to identify motivational dimensions, followed by Pearson’s correlations to examine associations among variables. One-way multivariate analysis of variance (MANOVA) was conducted to find the prevalent motivating themes and compare students intending to specialize in clinical psychology with those preferring other subfields. EFA showed eight motivational factors (e.g., personal growth, helping others, professional prestige, becoming a psychotherapist). MANOVA indicated significant group differences in personality traits and psychological distress. Higher psychological distress among those interested in clinical psychology did not translate into a self-healing motivation but may reflect heightened empathic sensitivity. Preliminary construct validity of the newly developed Reasons for Choosing Clinical Psychology Questionnaire (RCCPQ) was supported through theoretically consistent correlations with personality traits and distress. Results underscore the interplay of personality dispositions, contextual factors, and goal-oriented motivations, consistent with SCCT. Clinical psychology appears to attract students not merely for self-oriented reasons but for culturally and socially shaped aspirations related to caregiving and professional meaning. Findings refining the wounded healer conceptualization, revealing a more complex and non-linear approach toward becoming a psychotherapist, are discussed.

This book describes a practical aproach to univariate and multivariate analysis of variance. It starts with a general non-mathematical account of the fundamental theories and this is followed by a discussion of a series of examples using real data sets from the authors' own work in clinical trials, psychology and industry. Included are discussions of factorial and nested designs, structures on the multiple dependent variables measured on each subject, repeated measures analyses, covariates, choice of text statistic and simultaneous test procedures.

The objectives of this investigation was to determine the effectiveness of Mindfulness Based Stress Reduction (MBSR) intervention for reducing anxiety, enhancing self-control and improving academic performance among adolescent students. The present study consisted of 300 adolescent students in the age group of 15-18 years, selected through random sampling. State-Trait Anxiety Inventory by Spiel berger et al. and Self-control Scale by Tangney et al. were administered to 150 boys and 150 girls and their academic scores were collected from their performance in continuous assessment. Pre-test and post-test with randomized control group design was used for the present study. MBSR was given to the students in the experimental group for a period of 8 weeks while the control group did not receive the intervention, following which a post-test was carried out. Statistical analyses such as Independent sample't' test, Paired t test, Multi variate Analysis of Variance (MANOVA) and Multi variate Analysis of Covariance (MANCOVA) were used for this study. The results indicate that MBSR is effective for reducing anxiety, enhancing self-control and improving academic performance among adolescent students. This intervention ensured that students could use it for their well-being not only at the present, but also in the future circumstances. Therefore, MBSR may be included in the school curriculum in order to help adolescent students who cope with the expectations and challenges of educational demands.

This paper presents a simplified approach to Bayesian multivariate analysis of variance (MANOVA) using a cell mean parametrization with specific application to growth curve analysis. With the proposed approach for MANOVA, all cell mean vectors and multivariate treatment contrasts are estimable regardless of the nature of the design when appropriate prior distributions are specified. With the usual multivariate normal model, the distributions of linear contrasts of vector cell means are shown to be obtainable from a matric-variate t distribution. Polynomial effects of time are also considered in order to provide a model for the analysis of longitudinal data.

The roles and functions of 52 school psychologists from Iowa and Tennessee were examined. A multivariate analysis of variance (MANOVA) was used to test differences between reported time spent on prereferral, assessment, intervention, consultation, and curriculum-based assessment. Significant differences were found for the time spent in all areas. A second one-way MANOVA was used to test the differences between the two groups in reported actual time spent and desired time spent. A significant difference was found for consultation. A third MANOVA was utilized to test the differences between the two groups' desired time spent on the five variables. A significant difference was found for curriculum-based assessment. The results suggest that school psychologists in Tennessee and Iowa occupy different roles. School psychologists in Tennessee reported spending the majority of their time on assessment activities, whereas the Iowa sample balances their time between the five functions. The Iowa role exemplifies alternative functions for school psychologists. The Tennessee role is oriented more toward the refer, test, place model.