A01=Mitchell Sanders
A01=William Berry
ADA Score
analysis
Author_Mitchell Sanders
Author_William Berry
Average Annual Percentage Change
Average Daily Energy Expenditure
Bivariate Regression
Bivariate Regression Analysis
Bivariate Regression Model
Category=JHB
causal inference
coefficient
Dichotomous Dependent Variables
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
estimate
food
GROSS DOMESTIC INVESTMENT
independent
intake
Interactive Regression Model
Interval Level Variables
introduction to quantitative social science
logit models
Mitchell S. Sanders
model
Multi-equation Model
multicollinearity
Multivariate Regression Model
Negative Relationships
OLS Regression
OLS Regression Analysis
OLS Regression Procedure
Partial Slope Coefficient
path analysis
Person's Food Consumption
Person’s Food Consumption
probit analysis
Random Assignment
regression
Regression Assumptions
Regression Model
slope
Slope Coefficient
Slope Coefficient Estimate
Social Science Research
statistical modeling
variables
William D. Berry
Wu Article
Product details
- ISBN 9780367098940
- Weight: 453g
- Dimensions: 138 x 216mm
- Publication Date: 07 May 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Although nearly all major social science departments offer graduate students training in quantitative methods, the typical sequencing of topics generally delays training in regression analysis and other multivariate techniques until a student's second year. William Berry and Mitchell Sanders's Understanding Multivariate Research fills this gap with a concise introduction to regression analysis and other multivariate techniques. Their book is designed to give new graduate students a grasp of multivariate analysis sufficient to understand the basic elements of research relying on such analysis that they must read prior to their formal training in quantitative methods. Berry and Sanders effectively cover the techniques seen most commonly in social science journals--regression (including nonlinear and interactive models), logit, probit, and causal models/path analysis. The authors draw on illustrations from across the social sciences, including political science, sociology, marketing and higher education. All topics are developed without relying on the mathematical language of probability theory and statistical inference. Readers are assumed to have no background in descriptive or inferential statistics, and this makes the book highly accessible to students with no prior graduate course work.
William Berry
