Point Value: 10

After considerable reflection, I have decided to rework this exercise and focus upon the immigration questions introduced in Exercise 1. Fortunately, there are somewhat parallel questions in the California June 2023 and the Texas December 2023 surveys asking about attitudes toward providing a path to citizenship for undocumented immigrants. Consequently we can compare both the univariate and bivariate results of the two surveys. Different labels for these dependent variables are used to avoid confusion between the data sets.To complete the exercise you will need to extract and record results from the two studies. Nevertheless, the dependent variables ‘Path’ and ‘Imm2cit’ have both been recoded, relabeled and rescored so that they range from zero to one, where zero indicates the least support and one the most. It perhaps makes sense to work with one data set at a time, recording in turn the results from each. Once you have all the results for both surveys recorded in a table, it will be easier to answer the questions comparting the results.

This exercise deals with nominal and ordinal summary statistics. Those for interval level data measures will be used in subsequent exercises. This exercise also employs basic data handling procedures such as SPSS’s missing values and recode commands which facilitate the use of summary measures.

Before proceeding read Labs 4, 6 & 7. Then review the syntax files in light of what you have learned from the Labs.

As you did in HW1, access the California data set and its respective syntax file. Then after you have obtained the required results, turn you attention to the Texas data and syntax. Again, it will likely be easier to work with one data set at a time. Record the results for each in the tables provided below. Once you have completed the tables, answer the questions. Submit both your answers and your tables via Canvas.

The dependent variables ‘Path’ and ‘Imm2cit’ have all been recoded, relabeled and rescored so that they range from zero to one, where zero indicates the least support and one the most.

Part 1 – Comparing Univariate Measures
Record the relevant Univariate Summary Statistics. These can be obtained by analyzing the California and Texas data files with their respective syntax files. Below the table paste the question wordings for Path and Imm2Cit.

Univariate Summary Measures regarding attitudes toward undocumented immigrants

	Calif	Texas
	DV: Path	DV: Imm2Cit
	Coefficients	Coefficients
minimum
maximum
range
mean
mode
std dev
variance
skew
kurtosis

Please answer these questions about the Univariate measures (Univ 1 – Univ 10) using only brief or numeric answers:

Univ 1: What is the minimum score of the variables ‘path’ and ‘immig2cit’?

Univ 2: What is their maximum score?

Univ 3: What is their range?

Univ 4: With reference to the measures of central tendency, in which state is the support for its respective DV greater?

Univ 5: How much greater? (round to 3 decimal places)

Univ 6: Looking at the measures of dispersion, in which state is opinion more divided?

Univ 7: By how many standard deviation units? (round to 3 decimal places before substraction)

Univ 8: In which state is opinion more skewed?

Univ 9: Do the negative skew scores suggest more low scores or high scores on the variables?

Univ 10: Do the negative kurtosis scores suggest a relatively peaked or flat distribution?

Part 2: Bivariate Summary Measures
Summary measures of bivariate association provide a metric with which to calibrate the degree of association between two variables. Comparable indicators IV’s used in these analyses are available in the California and Texas surveys. Use the syntax provided to calculate the relevant coefficients for California and Texas. Use the following chart to report each state’s coefficient value and the measure of association used.

Looking at the California and Texas output files, complete the table and answer the questions. Submit both your completed table and your answers.

Comparing Bivariate Measures

Below the table paste the question wordings for Path and Imm2Cit.

Bivariate Coefficients for 2023 California and Texas surveys:
Attitudes toward Undocumented immigrants by demographic and political variables

	Calif-DV Path		Texas-DV Imm2Cit
IVs	Coefficient	Measure used	Coefficient	Measure used
female
ethnicity
Dem3
age
educ
income
interest
liberal5

.

Please answer the following questions

Biv 1: Which is the strongest nominal level predictor of ‘Path’ in the California data set?

Biv 2: Which is the strongest nominal level predictor of ‘Imm2Cit’ in the Texas data set?

Biv 3: Do these two predictors differ appreciably, i.e., in the first two decimal places?

Biv 4: Which is the strongest ordinal level predictor of ‘Path’ in the California data set?

Biv 5: Which is the strongest ordinal level predictor of ‘Imm2Cit’ in the Texas data set?

Biv 6: Which of these two is stronger?

Biv 7: Is ethnicity (ethn) a better predictor in California or Texas?

Biv 8: Is age a better predictor in California or Texas?

Biv 9: Is education positively or negatively associated with income?

Biv 10: Is education’s relationship with income stronger in California or Texas?

Take care to submit both the tables and answers to the questions for both Univariate and Bivariate analyses.