Two Multivariate Methods for Analyzing Small Group Interaction

This article attempts to fill two gaps in the literature on individual party choice. First, it deals with hitherto unanswered questions about relationships between social cleavages and party choice in Israel. Second, the article attempts to overcome methodological problems arising in the multivariate analysis of multiparty systems by utilizing Goodman's method of log-linear contingency table analysis. In the sample, occupation is not as strongly related to party choice as is a nonhierarchical dimension of economic position, sector of the economy. Ethnicity is modestly related to party choice, but hypotheses that the relationship is affected by place of birth, age, or other variables are disconfirmed. Hypotheses that the relationship between religiosity and party choice is affected by economic position are also disconfirmed. The advantages of using log linear contingency table analysis are demonstrated.

Although the existence of higher order associations has been proved, interspecific association is generally treated as a pair-wise phenomenon. Its possible reason is that although pair-wise association is only an imperfect description of the relationships among species, its methods are simple and well known. Unfortunately, the complexity of vegetation can not be described by such simplex methods. This paper shows two methods which enable detailed analysis of higher-order associations: Juhasz-Nagy's information theory functions and the log-linear contingency table analysis. From mathematical point of view, the two methods are closely related (both methods measure the non-randomness in the multi-way contingency tables). On the other hand, their theoretical backgrounds are different. The log-linear contingency table analysis was developed by statisticians to solve general statistical problems, while Juhasz-Nagy's approach was developed by a biologist to solve biological problems. The aim of this paper is to show how these two approaches decompose the total association, to point at the similarities and differences between the two approaches, and by this way to facilitate the analysis of higher order associations.

Symptom Severity Cut Points for the MD Anderson Symptom Inventory for CML (MDASI-CML)

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.

Objectives The purposes of this study were to investigate the attitudes toward physical education and physical activity levels of middle school students in Jiangxi Province, China, and explore the relationship between these two factors. Methods The subjects consisted of 722 middle school students (male: 371 and female: 351) from 7th to 9th grade at four public middle schools in Jiangxi Province, China. The measurement tools were used the Chinese version of Students' Attitude Toward Physical Education (Hu et al., 2014) and the Chinese version of Physical Activity Questionnaire for Adolescents (Li, 2015). For data analysis, confirmatory factor analysis (CFA), descriptive statistics, univariate analysis of variance (ANOVA), multivariate analysis of variance (MANOVA) were performed, using SPSS 26 and AMOS 27 statistical program. Results The results showed that first, participants' attitudes toward physical education were generally positive across all grades and genders, with average scores above 3.0 for all sub-factors. According to the result of MANOVA, gender had a significant impact on all dependent variables [CE: F(1, 716)=21.795, p<.05; CU: F(1, 716)=14.818, p<.05; TE: F(1, 716)=18.150, p<.05; TU: F(1, 716)=8.130, p<.05]; however, grade did not significantly affect attitudes. Second, physical activity levels varied by gender and grade, with males generally exhibiting higher (2.34) scores than females (1.88). According to a multivariate analysis of variance (MANOVA), both gender [F(1, 716)=80.342, p＜.05] and grade [F(2, 716)=24.351, p＜.05) had significant effects on physical activity scores, while the interaction effect between grade and gender was not significant [F(2, 716)=1.659, p＞.05]. Third, correlation analysis revealed slightly positive relationships between physical activity and the four sub-scales of attitude [CE (r=.290, p<.05); CU (r=.299, p<.05); TE (r=.326, p<.05); TU (r=.283, p<.05)]. Conclusions The results of this study are expected to enhance the understanding of middle school students by schools and physical education teachers for the development of physical education in China, and to contribute to the development of future physical education programs by providing theoretical evidence.

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.

In obesity research it is common to have repeated measures on subjects. Traditional statistical analyses of repeated measures data are analysis of variance (ANOVA) for random effects, and multivariate analysis of variance (MANOVA). Each assume that every subject was measured (i) the same number of times, and (ii) at the same time points. Another typical complication of many research designs is the presence of time-varying covariates. The usual ANOVA approach to repeated measures does not allow such covariates, and the MANOVA approach usually treats them as dependent variables. Hierarchical linear models deal with all of the above issues in a natural manner, making them an important tool for obesity research. This paper discusses some simple hierarchical models, and shows their application using two real data sets.

To assess the relationship between nutritional status and some factors related with poverty (acute diarrhea and overcrowding) in children less than 6 years of age in Bogotá. A community-based, cross-sectional study, was conducted during 2000 in the urban area of Bogotá, using a household interview survey. 3081 adults having in their homes children under five years were interviewed. This number of participants was calculated to obtain a representative sample of the city for estimating a binomial population parameter. A multistage sampling was carried out. The sample size was reduced to 2833 with the elimination of cases missing scores on dependent variables (DVs). Cephalic and brachial perimeters (continuous variables), used as a proxy of nutritional status (DVs), were selected as the main outcome measures. A 2 x 2 x 2 multivariate analysis of variance was performed on two DVs. Independent variables were overcrowding (as a measure of marginalization) and recent acute diarrhea. The analyses included sequential adjustment for nonorthogonality. DVs showed a low correlation (r=0.3) and a symmetric distribution. Evaluation of assumptions of linearity and multicollinearity were satisfactory. The omnibus test, using Wilk's criterion, showed that combined DVs were significantly affected by recent acute diarrhea (F=9.65 p=0.000), age less than 1 year (F=350.81 p=0.000) and the interaction acute diarrhea x overcrowding (F=6.25 p=0.000). In order to evaluate the impact of each main effect on the individual DVs, a Roy-Bargmann stepdown analysis was carried out. Homogeneity of regression was achieved for all components of the stepdown analysis. The brachial perimeter has a unique contribution to predict recent acute diarrhea (Roy-Bargmann F(1,1)=13.1 p=0.000). The cephalic perimeter has a unique contribution to predict acute diarrhea and overcrowding (Roy-Bargmann F(1,1)=10.22 p=0.001). Recent acute diarrhea and overcrowding are related with poor nutritional status. Brachial and cephalic perimeters could be attractive indicators of different varieties of nutritional deficiency.

Means comparisons using multivariate analysis of variance (MANOVA) is a useful though somewhat complex statistical approach for comparing independent groups across multiple dependent measures. In the field of applied linguistics, studies using MANOVA remain few, though its application has been seen across many areas including language assessment, acquisition, psycholinguistics, and textual analysis. As with any statistical method of this relative complexity, MANOVA relies upon several empirical assumptions that need to be examined prior to analysis, though the interpretations that it can provide in terms of both statistical significance and effect can offer a unique approach to examining the effects of group differences across a number of separate but related measured variables. To that end, this entry will cover the main considerations necessary for setting up, running, and interpreting MANOVA in practical and theoretical terms.

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

Morphometrics is a quantitative analysis depending on the shape and size of several specimens. Morphometric quantitative analyses are commonly used to analyse fossil record, shape and size of specimens and others. The aim of the study is to find the differences between rocky mountain wolves and arctic wolves based on gender. The sample utilised secondary data which included seven variables as independent variables and two dependent variables. Statistical modelling was used in the analysis such was the analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). The results showed there exist differentiating results between arctic wolves and rocky mountain wolves based on independent factors and gender.

Since the 1970s, the Malian Textile Development Company’s (CMDT) introduction of cotton has transformed South Mali's agriculture, driving intensification, mechanisation, and farm diversification. While small, poorly equipped farms have declined, some subsistence-focused practices persist. The study aimed to characterise the typology of family farms in the context of anthropogenic pressures and territorial changes. To achieve this, semi-structured surveys were conducted on 180 farms across seven randomly selected villages. Farms were classified into six types (T1 to T6) based on cultivated area, crop distribution, livestock, equipment, and agricultural workforce. Multivariate analysis of variance (MANOVA) was used to examine the effect of different farm types on several dependent variables. The results showed a predominance of type T4 (49%), T5 (20%), and T6 (17%) farms, which were mostly medium-scale to large-scale. Wilks' lambda test (0.473) indicated that 52.7% of the variance in dependent variables is attributed to differences between farm types. The F value of 6.478, with a p-value of 0.000, showed that these differences were statistically significant (p < 0.05). The Partial Eta Squared of 0.171 reflected a moderate effect size, suggesting that farm types account for 17.1% of the total variance in dependent variables. Significant differences were observed for each variable across the different farm types (T1 to T6). Equipment accounted for the highest proportion of explained variance (37.1%), while agricultural labor showed the lowest (12.2%). The typology highlights a marked diversity in farming systems, shaped by socioeconomic and environmental factors, within a context of gradual modernisation and pressure on resources.

Back to table of contents Previous article Next article LetterFull AccessLetterRonald E. Hellman M.D.Eileen Klein Ph.D.Thomas Uttaro Ph.D.Ronald E. Hellman M.D.Search for more papers by this authorEileen Klein Ph.D.Search for more papers by this authorThomas Uttaro Ph.D.Search for more papers by this authorPublished Online:1 Apr 2008https://doi.org/10.1176/ps.2008.59.4.447AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Update on a Program for LGBT Persons With Major Mental IllnessTo the Editor: There is little documentation in the literature on culturally focused services for lesbian, gay, bisexual, and transgendered (LGBT) persons with major mental illness, despite the unique sociocultural issues and identities that they bring into long-term mental health care settings ( 1 , 2 ). We report here on a sample of individuals attending a culturally focused program for LGBT patients with a diagnosis of major mental illness. Participants attended the Rainbow Heights Club (RHC) in Brooklyn, New York ( 3 ), which was created in 2002 in association with the LGBT Affirmative Program of South Beach Psychiatric Center in Brooklyn ( 4 ). Seventy-five individuals were assessed during May and June 2006. After informed consent was obtained, participants completed a multiple-choice questionnaire measuring level of participation and impact of the program. Data were analyzed through response frequencies and multivariate analysis of variance (MANOVA). Study participants had been in psychiatric treatment an average of 16 years. The percentages reported here are based on the number of participants who completed each item. Among the 69 participants who completed the item about diagnosis, affective disorder was the most common diagnosis (32 participants, or 46%), followed by psychotic disorders (24 participants, or 35%), anxiety disorders (seven participants, or 10%), and other disorders (six participants, or 9%). Information about medical conditions and about substance abuse was available for 72 participants. Thirty-six (50%) reported having significant medical conditions. Three (4%) admitted to current abuse of alcohol or other drugs.Of the 73 participants who answered the question about duration of program attendance, 31 (43%) had attended for more than two years, 22 (30%) had attended for one to two years, and 20 (27%) had attended for less than a year. Thirty-four (47% of 72 respondents) attended more than once a week, and 13 (18%) attended less than once a month. Fifty-five participants (76% of 72 respondents) stated that they found the RHC program very helpful. Thirty-six participants (54% of 67 respondents) reported being more adherent to their mental health treatment because of their involvement in the RHC. Fifty-three participants (80% of 66 respondents) reported that their psychiatric symptoms were better or much better since they began attending the RHC. Forty-one (61% of 67 respondents) reported that their relationships were better or much better since they began attending, and 26 (39%) said that their relationships were the same as before program entry.Thirty-six participants (51% of 70 respondents) reported spending time with other members outside the club, and 66 (92% of 72 respondents) reported that it was somewhat or very helpful to think about the club when they were not there. Fifty-three participants (78% of 68 respondents) reported better or much better self-esteem since they began attending RHC. Forty-three participants (63% of 68 respondents) reported better or much better ability to manage stress, and 53 (78% of 68 respondents) reported that their sense of hope was improved or much improved. The MANOVA tested the relationship between five independent variables (substance abuse, helpfulness of the RHC, spending time with members outside RHC, sexual identity, and age) and five dependent variables (symptom improvement, relationships, self-worth, stress, and sense of hope and purpose). A significant association was found between spending time with RHC members outside the club and the dependent variables (Wilks' λ =.719, F=3.360, df=5 and 43, p<.012). The between-subjects test indicated a significant association between spending time with RHC members outside the club and a general improvement in social relationships since joining the RHC (F=5.68, df=1 and 1, p<.021). These findings indicate that the members of a culturally focused program for LGBT individuals with major mental illness experienced improvements in symptoms, mental well-being, and socialization. Further research is necessary to validate these findings and to determine whether they are causally related to specific program elements and why program members benefit. Studies of this minority population are rare and have been limited by small samples. These preliminary findings hold promise for culturally sensitive LGBT programs for persons from sexual minority groups who have chronic and disabling psychiatric disorders.Acknowledgments and disclosuresThe authors report no competing interests.The authors are affiliated with South Beach Psychiatric Center, Brooklyn, New York.

How has the COVID Pandemic Impacted the Clinical Volume and Variety of an Academic Oral and Maxillofacial Surgery Program?

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