Comparing a Binary Dependent Variable Across Cultural Groups Using Applied Logistic Regression

2021 ◽  
pp. 131-232
Author(s):  
Thanh V. Tran ◽  
Keith T. Chan
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Cross Cultural ◽  
Cultural Groups ◽  
Logistic Regression Models ◽  
Cross Cultural Comparison ◽  
Binary Dependent Variable ◽  
Basic Ideas ◽  
Cultural Data Analysis ◽  
Cultural Data

This chapter reviews the basic ideas of logistic regression involving a binary dependent regressed on independent variables, along with assumptions for analysis and interpretations of results. It provides strategies and practical guides for data analysis using Stata and explains the basic assumptions of logistic regression and its applications for cross-cultural data analysis. The chapter also provides examples of logistic regression models for cross-cultural comparison, and outlines the techniques for testing the equivalence of effects across groups. The text includes examples of charts and graphs that can be used to explain differences in effects across cultural groups.

Introduction to Applied Cross-Cultural Data Analysis

2021 ◽  
pp. 1-6
Author(s):  
Thanh V. Tran ◽  
Keith T. Chan
Keyword(s):  
Data Analysis ◽  
Cultural Analysis ◽  
Cross Cultural ◽  
Cultural Identification ◽  
Social Work Research ◽  
Race Ethnicity ◽  
Definition Of ◽  
Cultural Data Analysis ◽  
Cultural Data ◽  
Cross Cultural Analysis

This chapter introduces applied cross-cultural data analysis and addresses the concepts of culture and how culture can be integrated into social work research. We review the definition of culture and how it has been understood and examined in research across various disciplines. We present an overview of the theories and frameworks of cross-cultural analysis, and provide the lens through which culture is examined by means of the techniques and approaches that are used in this book. Cross-cultural analysis can be viewed as comparisons based on key demographic variables such as countries of origin, race, ethnicity, language, sex, religion, and related cultural identifications. The assumption is that people who share the same cultural identification also share similar values and behaviors.

Applied Cross-Cultural Data Analysis for Social Work

2021 ◽  
Author(s):  
Thanh V. Tran ◽  
Keith T. Chan
Keyword(s):  
Social Work ◽  
Data Analysis ◽  
Large Scale ◽  
Cultural Analysis ◽  
Cross Cultural ◽  
Bivariate Analysis ◽  
Practical Applications ◽  
Statistical Approaches ◽  
Cultural Data Analysis ◽  
Cultural Data

Applied Cross-Cultural Data Analysis for Social Work is a research guide which provides a hands-on approach for learning and understanding data analysis techniques for examining and interpreting data for the purpose of cultural group comparisons. This book aims to provide practical applications in statistical approaches of data analyses that are commonly used in cross-cultural research and evaluation. Readers are presented with step-by-step illustrations in the use of descriptive, bivariate, and multivariate statistics to compare cross-cultural populations using large-scale, population-based survey data. These techniques have important applications in health, mental health, and social science research relevant to social work and other helping professions, especially in providing a framework of evidence to examine health disparities using population-health data. For each statistical approach discussed in this book, we explain the underlying purpose, basic assumptions, types of variables, application of the Stata statistical package, the presentation of statistical findings, and the interpretation of results. Unlike previous guides on statistical approaches and data analysis in social work, this book explains and demonstrates the strategies of cross-cultural data analysis using descriptive and bivariate analysis, multiple regression, additive and multiplicative interaction, mediation, and SEM and HLM for subgroup analysis and cross-cultural comparisons. This book also includes sample syntax from Stata for social work researchers to conduct cross-cultural analysis with their own research.

Adolescents’ Attribution and Outcome Expectancies Regarding Relational Aggression: A Cross-Cultural Comparison and Cultural Value Associations

2020 ◽  
pp. 027243162097767
Author(s):  
Yan Li ◽  
Michelle F. Wright ◽  
Danae Rollet
Keyword(s):  
Cultural Values ◽  
Relational Aggression ◽  
Romantic Relationship ◽  
Social Cognitive ◽  
Outcome Expectancies ◽  
Cross Cultural ◽  
Chinese Adolescents ◽  
Outcome Expectancy ◽  
Cultural Groups ◽  
Cross Cultural Comparison

This study compares 477 Chinese and 342 American adolescents’ responses to open-ended questions regarding attribution and outcome expectancies of relational aggression, and investigates how cultural values were related to these social cognitive processes. Results revealed cross-cultural similarities and differences. In particular, American adolescents attributed romantic relationship competition, which was absent in Chinese adolescents’ responses. Furthermore, American adolescents demonstrated a stronger instrumental orientation in their social cognition (e.g., gain status), whereas Chinese adolescents tended to hold the blaming the victim attribution, and the socially harm the victim outcome expectancy. Finally, this study revealed that in both cultural groups, higher collectivism was linked to the blaming the aggressor attribution, as well as escalated peer conflict and aggression as outcome expectancies, whereas individualism was linked to the blaming the victim attribution. Findings of this study enriched our knowledge about the cultural construal of adolescents’ attribution and outcome expectancy regarding relational aggression.

Current Issues in Bilingualism: an Update of Directions in Research

1984 ◽  
Vol 5 ◽  
pp. 10-34 ◽  
Author(s):  
Braj B. Kachru
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Cross Cultural ◽  
Research Activity ◽  
Social Commitment ◽  
Linguistic Research ◽  
Basic Concepts ◽  
Tools And Techniques ◽  
Intense Research ◽  
Cultural Data

This survey is primarily an update of the review published inARAL I, (Kaplan 1981:2-24). The research on various aspects of bilingualism during the last five years shows three main characteristics. First, there is questioning of some basic concepts which are still considered, as it were, sacrosanct by researchers for the description and analysis of bilingualism--individual and societal--and for actual fieldwork (cf., section 2 below). In such questioning--however mute at present--several theoretical and methodological sacred cows are under attack. Second, one notices a shift in the areas of research towards experimentation, with more precise methodology and techniques, to answer questions concerning the bilingual's brain (cf., sections 5 and 6). Third, there are insightful breakthroughs in crosscultural and cross-linguistic research with serious applied orientation (cf., sections 10 and 11) and, equally important, there is concern for a social commitment in such research. True, in this intense research activity there are very few questions asked which may be considered as breaking new ground. However, the newness lies in the answers which are provided to old questions. In these answers, we notice many fresh insights gained through a wealth of cross-cultural data, through the results obtained from longitudinal studies (e.g., Hakuta and Diaz in press, Rosier and Holm 1980), through the experiments conducted with highly refined and sophisticated tools and techniques of data collection and data analysis, and through increasing understanding of the bilinguals' and monolinguals' neuropsychological processes.

scholarly journals Neural Network: An Improved FCM for Multimodal Cultural Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yan Wang ◽  
Di Liu ◽  
Lingling Tian ◽  
Aiping Tan
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Bp Neural Network ◽  
Clustering Algorithm ◽  
Cultural Analysis ◽  
Fuzzy C Means ◽  
Correlation Degree ◽  
Feature Information ◽  
Cultural Data Analysis ◽  
Cultural Data

With the development of cloud computing, big data, and artificial intelligence (AI) technology, there is a growing interest in “cultural analysis.” Cultural analysis requires different types of data such as texts, pictures, and videos. The richness and differences of resources in the cultural field lead to diverse modalities of cultural data. Traditional text analysis methods can no longer meet the data analysis needs of current multimedia cultural resources. This article starts from cultural data’s feature information to solve the heterogeneity problem faced by massive multimodal cultural data analysis. It analyzes it from geography, time, art, and thematic character, classified and aggregated to form a multimodal cultural feature information matrix. The corresponding correlation measurement methods for different matrices from the above dimensions are proposed, solved in turn, and substituted into the optimized training back propagation (BP) neural network to obtain the final correlation degree. The improved fuzzy C-means (FCM) clustering algorithm is used to aggregate the high correlation cultural data based on the degree. The algorithm proposed in this study is compared with the existing algorithm. The experimental results show that the optimized BP neural network is at least 58% more accurate than the current method for calculating different matrices’ correlation degrees. In terms of accuracy, the improved fuzzy C-means algorithm effectively reduces the random interference in the selection of the initial clustering center, which is significantly higher than other clustering algorithms.

scholarly journals Rein Tension Signals Elicit Different Behavioral Responses When Comparing Bitted Bridle and Halter

2021 ◽  
Vol 8 ◽  
Author(s):  
Marie Eisersiö ◽  
Anna Byström ◽  
Jenny Yngvesson ◽  
Paolo Baragli ◽  
Antonio Lanata ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Response Latency ◽  
Regression Models ◽  
Negative Reinforcement ◽  
Behavioral Responses ◽  
Logistic Regression Models ◽  
Young Horses ◽  
And Training ◽  
Head Neck

When a rider maintains contact on the reins, rein tension will vary continuously in synchronicity with the horse's gait and stride. This continuous variation makes it difficult to isolate the rein tension variations that represent a rein tension signal, complicating interpretation of rein tension data from the perspective of horse-rider interaction. This study investigated (1) the characteristics of a rein tension signal and (2) horse response to a rein tension signal for backing, comparing pressure applied by a bit (bridle), or by a noseband (halter). Twenty Warmblood horses (10 young, 10 adult) wearing a rein tension meter were trained to step back in the aisle of a stable. The handler stood next to the horse's withers, applying tension on the reins until the horse stepped back. This was repeated eight times with the bridle and eight times with the halter. Data analysis was performed using mixed linear and logistic regression models. Horses displaying behaviors other than backing showed significantly increased response latency and rein tension. Inattentive behavior was significantly more common in the halter treatment and in young horses, compared with the bridle treatment and adult horses. Evasive behaviors with the head, neck, and mouth were significantly more common in the bridle treatment than in the halter treatment and the occurrence of head/neck/mouth behaviors increased with increasing rein tension and duration of the rein tension signal. When controlling for behavior, the horses responded significantly faster and to a lighter rein tension signal in the bridle treatment than in the halter treatment. By scrutinizing data on rein tension signals in relation to horse behavior and training exercise, more can be learnt about the horse's experience of the pressures applied and the timing of the release. This can assist in developing ways to evaluate rein tension in relation to correct use of negative reinforcement.

Applied Hierarchical Linear Modeling for Cross-Cultural Comparison

2021 ◽  
pp. 268-290
Author(s):  
Thanh V. Tran ◽  
Keith T. Chan
Keyword(s):  
Hierarchical Linear Modeling ◽  
Neighborhood Effects ◽  
Cross Cultural ◽  
Linear Modeling ◽  
Social Work Research ◽  
Cross Cultural Comparison ◽  
Cultural Research ◽  
Cross Cultural Research ◽  
Cultural Data Analysis ◽  
Neighborhood Level

We explain and demonstrate the application of Hierarchical Linear Modeling (HLM) in cross-cultural research. This method of analysis has not been sufficiently explored in social work research, and it can be a highly useful and appropriate statistical approach for making cross-cultural comparisons. We explain the rationale for HLM or multilevel modeling for cross-cultural data analysis, and we provide an example in which we use Stata to test for neighborhood effects across race groups using survey data. We provide Stata commands and examples of testing for invariance of effects across groups while controlling for heteroscedasticity due to neighborhood level effects. Finally, we included geomaps based on the data to provide visualization of neighborhood effects.

scholarly journals Residual analysis in regression mixture model

2019 ◽  
pp. 8-16
Author(s):  
V. O. Miroshnychenko
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Generalized Estimating Equations ◽  
Regression Models ◽  
Regression Coefficients ◽  
Nonlinear Regression Model ◽  
Logistic Regression Models ◽  
Continuous Response ◽  
True Number ◽  
Regression Mixture

We consider data in which each observed subject belongs to one of different subpopulations (components). The true number of component which a subject belongs to is unknown, but the researcher knows the probabilities that a subject belongs to a given component (concentration of the component in the mixture). The concentrations are different for different observations. So the distribution of the observed data is a mixture of components’ distributions with varying concentrations. A set of variables is observed for each subject. Dependence between these variables is described by a nonlinear regression model. The coefficients of this model are different for different components. An estimator is proposed for these regression coefficients estimation based on the least squares and generalized estimating equations. Consistency of this estimator is demonstrated under general assumptions. A mixture of logistic regression models with continuous response is considered as an example. It is shown that the general consistency conditions are satisfied for this model under very mild assumptions. Performance of the estimator is assessed by simulations and applied for sociological data analysis. Q-Q diagrams are built for visual comparison of residuals’ distributions.

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