Chapter 16 Social Statistics. Chapter Outline The Origins of the Elaboration Model The Elaboration Paradigm Elaboration and Ex Post Facto Hypothesizing.

Slides:

Advertisements

Similar presentations

Lesson 10: Linear Regression and Correlation

Advertisements

Causal-Comparative Research Designs

Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and l Chapter 12 l Multiple Regression: Predicting One Factor from Several Others.

CHAPTER 8 MULTIPLE REGRESSION ANALYSIS: THE PROBLEM OF INFERENCE

6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.

Ch11 Curve Fitting Dr. Deshi Ye

QUANTITATIVE DATA ANALYSIS

Chapter 13 Conducting & Reading Research Baumgartner et al Data Analysis.

Multivariate Data Analysis Chapter 4 – Multiple Regression.

Chapter 14 Conducting & Reading Research Baumgartner et al Chapter 14 Inferential Data Analysis.

Chapter Topics Types of Regression Models

Overview of STAT 270 Ch 1-9 of Devore + Various Applications.

Chapter 11 Multiple Regression.

Ch. 14: The Multiple Regression Model building

Today Concepts underlying inferential statistics

Data Analysis Statistics. Levels of Measurement Nominal – Categorical; no implied rankings among the categories. Also includes written observations and.

Statistics 350 Lecture 17. Today Last Day: Introduction to Multiple Linear Regression Model Today: More Chapter 6.

Chapter 14 Inferential Data Analysis

Simple Linear Regression Analysis

1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS & Updated by SPIROS VELIANITIS.

Correlation & Regression

The Practice of Social Research

Leedy and Ormrod Ch. 11 Gray Ch. 14

LIS 570 Summarising and presenting data - Univariate analysis continued Bivariate analysis.

Introduction to Linear Regression and Correlation Analysis

Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 12 Analyzing the Association Between Quantitative Variables: Regression Analysis Section.

Chapter 13: Inference in Regression

Chapter 11 Simple Regression

Chapter 8 Introduction to Hypothesis Testing

Understanding Statistics

1 1 Slide © 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Chapter 16 The Elaboration Model Key Terms. Descriptive statistics Statistical computations describing either the characteristics of a sample or the relationship.

Educational Research: Competencies for Analysis and Application, 9 th edition. Gay, Mills, & Airasian © 2009 Pearson Education, Inc. All rights reserved.

1 1 Slide © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Statistical Methods Statistical Methods Descriptive Inferential

Inference for Regression Simple Linear Regression IPS Chapter 10.1 © 2009 W.H. Freeman and Company.

Chapter 4 Linear Regression 1. Introduction Managerial decisions are often based on the relationship between two or more variables. For example, after.

Regression Chapter 16. Regression >Builds on Correlation >The difference is a question of prediction versus relation Regression predicts, correlation.

CORRELATION: Correlation analysis Correlation analysis is used to measure the strength of association (linear relationship) between two quantitative variables.

© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.

Chapter 16 Data Analysis: Testing for Associations.

Foundations of Sociological Inquiry Statistical Analysis.

Agresti/Franklin Statistics, 1 of 88 Chapter 11 Analyzing Association Between Quantitative Variables: Regression Analysis Learn…. To use regression analysis.

I271B QUANTITATIVE METHODS Regression and Diagnostics.

CHAPTER OVERVIEW Say Hello to Inferential Statistics The Idea of Statistical Significance Significance Versus Meaningfulness Meta-analysis.

Introducing Communication Research 2e © 2014 SAGE Publications Chapter Seven Generalizing From Research Results: Inferential Statistics.

Lesson 14 - R Chapter 14 Review. Objectives Summarize the chapter Define the vocabulary used Complete all objectives Successfully answer any of the review.

Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Simple Linear Regression Analysis Chapter 13.

1 1 Slide The Simple Linear Regression Model n Simple Linear Regression Model y =  0 +  1 x +  n Simple Linear Regression Equation E( y ) =  0 + 

Chapter 8 Relationships Among Variables. Outline What correlational research investigates Understanding the nature of correlation What the coefficient.

Chapter 13 Understanding research results: statistical inference.

Jump to first page Inferring Sample Findings to the Population and Testing for Differences.

Univariate Point Estimation Confidence Interval Estimation Bivariate: Linear Regression Multivariate: Multiple Regression 1 Chapter 4: Statistical Approaches.

Chapter 22 Inferential Data Analysis: Part 2 PowerPoint presentation developed by: Jennifer L. Bellamy & Sarah E. Bledsoe.

Chapter 15 The Elaboration Model. Chapter Outline The Origins of the Elaboration Model The Elaboration Paradigm Elaboration and Ex Post Facto Hypothesizing.

Criminal Justice and Criminology Research Methods, Second Edition Kraska / Neuman © 2012 by Pearson Higher Education, Inc Upper Saddle River, New Jersey.

Chapter 8 Introducing Inferential Statistics.

Correlation and Linear Regression

REGRESSION G&W p

Introduction to Regression Analysis

Regression Analysis Part D Model Building

The Nature of Probability and Statistics

Lecture Slides Elementary Statistics Twelfth Edition

Wednesday, September 23 Descriptive v. Inferential statistics.

CHAPTER 22: Inference about a Population Proportion

Correlation and Regression

Simple Linear Regression

Lecture Slides Elementary Statistics Twelfth Edition

Chapter 13 Additional Topics in Regression Analysis

Chapter Outline Inferences About the Difference Between Two Population Means: s 1 and s 2 Known.

Presentation transcript:

Chapter 16 Social Statistics

Chapter Outline The Origins of the Elaboration Model The Elaboration Paradigm Elaboration and Ex Post Facto Hypothesizing

Descriptive Statistics Used to summarize data being studied. Can be used to summarize the following: – Distribution of attributes on a single variable. – Associations between variables.

Measures of Association Descriptive statistics that summarize the relationships between variables. Based on a proportionate reduction of error (PRE) model.

Proportionate Reduction of Error Model Comparison: 1. Number of errors we would make if we knew the distribution of attributes on that variable. 2. Number of errors we would make if we knew the joint distribution overall and were told for each case the attribute of one variable each time we were asked to guess the attribute of the other.

Regression Analysis Relationships between variables in the form of equations, which can predict the value of a dependent variable on the basis of values of independent variables. Computed on the basis of a regression line, the actual location of points in a scattergram.

Types of Regression Analysis Linear regression analysis Multiple regression analysis Partial regression analysis Curvilinear regression analysis

Other Multivariate Techniques Time series analysis - study of processes occurring over time. Path analysis - a method of presenting graphically the networks of causal relationships among several variables. Factor analysis - a method of discovering the general dimensions represented by a collection of actual variables.

Inferential Statistics Used to estimate generalizability of findings arrived at through analysis of a sample to the larger population from which the sample was selected. Inferences about some characteristic of a population must indicate a confidence interval and a confidence level.

Statistical and Substantive Significance Substantive - observed association is strong, important and meaningful. Statistical - make assumptions about data and methods that are almost never completely satisfied by social research.