Regression Lab Exercises. Crosstabs Good to give a general picture Good to give a general picture Good to compare dependent varialbes between groups (countries,

Slides:



Advertisements
Similar presentations
Hypothesis Testing and Comparing Two Proportions Hypothesis Testing: Deciding whether your data shows a “real” effect, or could have happened by chance.
Advertisements

2013/12/10.  The Kendall’s tau correlation is another non- parametric correlation coefficient  Let x 1, …, x n be a sample for random variable x and.
SPSS Session 5: Association between Nominal Variables Using Chi-Square Statistic.
Personal Response System (PRS). Revision session Dr David Field Do not turn your handset on yet!
Welfare Attitudes. Outline Review of Welfare Policies Review of Welfare Policies Discussion of Svallfors’ methodology and the relationship between attitudes.
Bivariate Analyses.
Basic Statistics The Chi Square Test of Independence.
1 9. Logistic Regression ECON 251 Research Methods.
Bivariate Analysis Cross-tabulation and chi-square.
Multiple Regression Fenster Today we start on the last part of the course: multivariate analysis. Up to now we have been concerned with testing the significance.
Chapter 11 Contingency Table Analysis. Nonparametric Systems Another method of examining the relationship between independent (X) and dependant (Y) variables.
By Wendiann Sethi Spring  The second stages of using SPSS is data analysis. We will review descriptive statistics and then move onto other methods.
Econ 140 Lecture 131 Multiple Regression Models Lecture 13.
Lab CPA1 LAB CPA. CORPORAL PUNISHMENT AND APPROVAL OF VIOLENCE The Research Questions 1. Do nations where corporal punishment is used by many parents have.
Multiple Regression Models
Matching level of measurement to statistical procedures
Inferential Statistics  Hypothesis testing (relationship between 2 or more variables)  We want to make inferences from a sample to a population.  A.
Correlations and T-tests
Data Analysis Statistics. Levels of Measurement Nominal – Categorical; no implied rankings among the categories. Also includes written observations and.
Multiple Regression 2 Sociology 5811 Lecture 23 Copyright © 2005 by Evan Schofer Do not copy or distribute without permission.
Summary of Quantitative Analysis Neuman and Robson Ch. 11
Crosstabs. When to Use Crosstabs as a Bivariate Data Analysis Technique For examining the relationship of two CATEGORIC variables  For example, do men.
Correlation Question 1 This question asks you to use the Pearson correlation coefficient to measure the association between [educ4] and [empstat]. However,
Dummies (no, this lecture is not about you) POL 242 Renan Levine February 13/15, 2007.
Statistics for the Social Sciences Psychology 340 Fall 2013 Tuesday, November 19 Chi-Squared Test of Independence.
This Week: Testing relationships between two metric variables: Correlation Testing relationships between two nominal variables: Chi-Squared.
Inferential Statistics: SPSS
LIS 570 Summarising and presenting data - Univariate analysis continued Bivariate analysis.
LEARNING PROGRAMME Hypothesis testing Intermediate Training in Quantitative Analysis Bangkok November 2007.
Bivariate Relationships Analyzing two variables at a time, usually the Independent & Dependent Variables Like one variable at a time, this can be done.
Copyright © 2012 Pearson Education. All rights reserved Copyright © 2012 Pearson Education. All rights reserved. Chapter 15 Inference for Counts:
Regression. Regression  y= output, such as income, support for welfare  c= constant, does not change  b= coefficient (an increase of one year of education.
Statistics and Quantitative Analysis U4320 Segment 12: Extension of Multiple Regression Analysis Prof. Sharyn O’Halloran.
Chi-square (χ 2 ) Fenster Chi-Square Chi-Square χ 2 Chi-Square χ 2 Tests of Statistical Significance for Nominal Level Data (Note: can also be used for.
1 rules of engagement no computer or no power → no lesson no SPSS → no lesson no homework done → no lesson GE 5 Tutorial 5.
Data Analysis Lab 02 Using Crosstabs to compare percentages.
Interactions POL 242 Renan Levine March 13/15, 2007.
Multiple Linear Regression. Purpose To analyze the relationship between a single dependent variable and several independent variables.
Multiple Regression Lab Chapter Topics Multiple Linear Regression Effects Levels of Measurement Dummy Variables 2.
Hypothesis testing Intermediate Food Security Analysis Training Rome, July 2010.
Multiple Regression. Multiple Regression  Usually several variables influence the dependent variable  Example: income is influenced by years of education.
Recap of data analysis and procedures Food Security Indicators Training Bangkok January 2009.
CATEGORICAL VARIABLES Testing hypotheses using. When only one variable is being measured, we can display it. But we can’t answer why does this variable.
 Relationship between education level, income, and length of time out of school  Our new regression equation: is the predicted value of the dependent.
4 normal probability plots at once par(mfrow=c(2,2)) for(i in 1:4) { qqnorm(dataframe[,1] [dataframe[,2]==i],ylab=“Data quantiles”) title(paste(“yourchoice”,i,sep=“”))}
1. Tables, Charts, and Graphs Microsoft Word & Excel 2003.
Inferential Statistics. Explore relationships between variables Test hypotheses –Research hypothesis: a statement of the relationship between variables.
PSY6010: Statistics, Psychometrics and Research Design Professor Leora Lawton Spring 2007 Wednesdays 7-10 PM Room 204.
UNIT 2 BIVARIATE DATA. BIVARIATE DATA – THIS TOPIC INVOLVES…. y-axis DEPENDENT VARIABLE x-axis INDEPENDENT VARIABLE.
BPS - 3rd Ed. Chapter 61 Two-Way Tables. BPS - 3rd Ed. Chapter 62 u In prior chapters we studied the relationship between two quantitative variables with.
Chapter 7 Calculation of Pearson Coefficient of Correlation, r and testing its significance.
DTC Quantitative Research Methods Regression I: (Correlation and) Linear Regression Thursday 27 th November 2014.
PART 2 SPSS (the Statistical Package for the Social Sciences)
6.7 Scatter Plots. 6.7 – Scatter Plots Goals / “I can…”  Write an equation for a trend line and use it to make predictions  Write the equation for a.
Significance Tests for Regression Analysis. A. Testing the Significance of Regression Models The first important significance test is for the regression.
UNIT 2 BIVARIATE DATA. BIVARIATE DATA – THIS TOPIC INVOLVES…. y-axis DEPENDENT VARIABLE x-axis INDEPENDENT VARIABLE.
Chapter 11 Linear Regression and Correlation. Explanatory and Response Variables are Numeric Relationship between the mean of the response variable and.
RESEARCH METHODS Lecture 32. The parts of the table 1. Give each table a number. 2. Give each table a title. 3. Label the row and column variables, and.
The  2 (chi-squared) test for independence. One way of finding out is to perform a  2 (chi-squared) test for independence. We might want to find out.
Seven Steps for Doing  2 1) State the hypothesis 2) Create data table 3) Find  2 critical 4) Calculate the expected frequencies 5) Calculate  2 6)
Introduction to Marketing Research
Dr. Siti Nor Binti Yaacob
Bi-variate #1 Cross-Tabulation
Summarising and presenting data - Bivariate analysis
The Chi-Square Distribution and Test for Independence
Hypothesis Testing and Comparing Two Proportions
15.1 Goodness-of-Fit Tests
BIVARIATE ANALYSIS: Measures of Association Between Two Variables
The 2 (chi-squared) test for independence
BIVARIATE ANALYSIS: Measures of Association Between Two Variables
Presentation transcript:

Regression Lab Exercises

Crosstabs Good to give a general picture Good to give a general picture Good to compare dependent varialbes between groups (countries, sex) Good to compare dependent varialbes between groups (countries, sex) Can visually see connection between depedent and independent variabls Can visually see connection between depedent and independent variabls

Crosstab to Compare Countries

Choose the variables (country and dependent variable)

Click ”Cells...” then click percentages

Then click ”continue” then ”OK”

How to read the crosstab In the columns we have the countries In the columns we have the countries In the rows we have the numbers and percentages of people giving a certain response to the question of whether they think the government should regulate industry less. In the rows we have the numbers and percentages of people giving a certain response to the question of whether they think the government should regulate industry less. In the first row we see that 28 people or 2.5% of Czechs were strongly against less government regulation, while 123 or 11% of the Danes were strongly against. In the first row we see that 28 people or 2.5% of Czechs were strongly against less government regulation, while 123 or 11% of the Danes were strongly against. In the second row we see that 114 Czechs or 10% were against less government regulation, while 148 Danes or 22.1% were against less government regulation. In the second row we see that 114 Czechs or 10% were against less government regulation, while 148 Danes or 22.1% were against less government regulation.

Making your own table You must decide what you are measuring You must decide what you are measuring For this example it is ”degree of market liberalism” which is why we recoded to make 5 = strongly agree, rather than 1 = strongly agree as it originally was coded For this example it is ”degree of market liberalism” which is why we recoded to make 5 = strongly agree, rather than 1 = strongly agree as it originally was coded If we were measuring degree of support for social democratic policies, we would have kept the original coding for this question, but changed it for the other questions that gave the lowest score (1) for supporting state policies If we were measuring degree of support for social democratic policies, we would have kept the original coding for this question, but changed it for the other questions that gave the lowest score (1) for supporting state policies Calculate the % in favor or strongly in favor of LESSREG for 2 countries and make a table in Word Calculate the % in favor or strongly in favor of LESSREG for 2 countries and make a table in Word These are the last two responses. The first two that we already discussed measured the % against or strongly against, that is it measured OPPOSITION to market liberalism, while the table we will make now will show SUPPORT for market liberalism. These are the last two responses. The first two that we already discussed measured the % against or strongly against, that is it measured OPPOSITION to market liberalism, while the table we will make now will show SUPPORT for market liberalism.

This is what the table would look like

Your Next Step Choose 5 questions that measure the issue you are interested in Choose 5 questions that measure the issue you are interested in In the last session you recoded questions so that they are all in the same direction In the last session you recoded questions so that they are all in the same direction So use these questions again So use these questions again It could be anything, like support for welfare, tolerance toward immigrants, etc. It could be anything, like support for welfare, tolerance toward immigrants, etc. Make a table based on combining these 5 crosstabs Make a table based on combining these 5 crosstabs

Crosstab showing dependent and independent variables Now we will go back to one question, like LESSREG Now we will go back to one question, like LESSREG We will see if women are more or less market liberal than men in the Czech Republic We will see if women are more or less market liberal than men in the Czech Republic We must first add the Czech filter, so we only get answers for the Czech Republic We must first add the Czech filter, so we only get answers for the Czech Republic Then we replace the variable for countries with the variable for gender Then we replace the variable for countries with the variable for gender

First the filter

We no longer have to use the ”if” function, because we have already created the Czech filter from it, so instead we choose the Czech filter from the ”selected cases” and move it over to ”Use filter variable” then press OK

Go back now to the crosstab

Replace ”country” with ”sex” and click OK

This is what the result looks like

Make your own table for the questions measuring your topic. We see men are more market liberal than women, but the difference is small. That question is whether the difference is big enough to be more than a random difference.

Now we will do a bivariate regression with the same two variables

Choose the depedent variable and choose ”sex” as the independent variable

The Model summary: R-square is very low. The model only explains 0.2% of the total variance in LESSREG

Df total shows that there were 1134 cases, which shows you that your country filter is working, otherwise it would have been around 44,000. Sig. =.120 means the model is only significant at the 12% level which is much higher than the 5% level that is normally acceptable

Here we see there is a negative correlation between being a woman and supporting less regulation (B= -.094), but the correlation is very small and is only on a scale ׀of 0-1׀ (the standardized coefficient. Furthermore, t < ׀1.96 ׀ and is only significant at the 12% level. The t- significance for this variable (SEX) and the significance for the entire model is the same, since we only have one independent variable.

Your Next Step at This Lab Now choose the questions that you have for measuring the attitude you chose. Now choose the questions that you have for measuring the attitude you chose. They should be at least 5 questions. They should be at least 5 questions. Choose any independent variable, such as SEX, or INCOME, EDUCATION or AGE Choose any independent variable, such as SEX, or INCOME, EDUCATION or AGE Run bivariate regressions on each of the questions using the same independent variable and think about why some might have been significant or not. Run bivariate regressions on each of the questions using the same independent variable and think about why some might have been significant or not. Today choose only one independent variable, so you can see whether this variable is significant for some questions but not for others. Today choose only one independent variable, so you can see whether this variable is significant for some questions but not for others. When discussing multivariate regressions we will compare the importance of different indepedent variables and start to comtemplate whether, for example gender can explain attitudes better or worse than income, age or education. When discussing multivariate regressions we will compare the importance of different indepedent variables and start to comtemplate whether, for example gender can explain attitudes better or worse than income, age or education.