Religion_1RR – Religious Mobility

Note to the Instructor: This is the first in a series of three exercises that focus on religious mobility.  We'll compare the religion in which respondents were raised with their current religious preference.  In these exercises we're going to analyze data from the Pew 2014 Religious Landscape Survey conducted by the Pew Research Center.  We're going to use SPSS to analyze the data.  A weight variable is automatically applied to the data set so it better represents the population from which the sample was selected.  You have permission to use this exercise and to revise it to fit your needs.  Please send a copy of any revision to the author so I can see how people are using the exercises. Please contact the author for additional information.

Goal of Exercise

The goal of this exercise is to explore religious mobility.  We'll compare the religion in which respondents were raised with their current religious preference to see how much mobility there is into and out of different religious groups.  In the next exercise (i.e.,Religion_2RR) we'll develop an overall measure of religious mobility and look to see where people go when they leave their religious group. 

Part I – The Data Set We'll be Using

The Pew Research Center has conducted a number of surveys that deal with religion.  Two of these surveys are the Religious Landscape Surveys conducted in 2007 and then repeated in 2014.  They were very large telephone surveys of about 35,000 adults in the United States.   For more information about the surveys, go to their website

We'll be using a subset of the 2014 survey in this exercise which I have named Pew_2014_Religious_Landscape_ Survey_subset_for_classes.sav.  For the purposes of these exercises I selected a subset of variables from the complete data set.  I recoded some of the variables, created a few new variables, and renamed the variables to make them easier for students to use.  There is a weight variable which should always be used so that the sample will better represent the population from which the sample was selected.  To open the data set in SPSS, just double click on the file name.[1]  Your instructor will tell you where the file is located.

Part II – Measuring Religious Preference

Religious preference refers to the religion with which respondents identify.  The Pew survey asked "What is your present religion, if any?  Are you Protestant, Roman Catholic, Mormon, Orthodox such as Greek or Russian Orthodox, Jewish, Muslim, Buddhist, Hindu, atheist, agnostic, something else, or nothing in particular?" 

Now that you have opened the data set, run a frequency distribution for the variable R1 which is the name of the variable.  The variable name starts with the letter R which tells you that this variable describes religious preference.  Some of you have used SPSS, the statistical package we're using, and know how to get a frequency distribution.  Others of you are new to SPSS.  There is a tutorial that you can use to learn how to get a frequency distribution.  The tutorial is freely available on the Social Science Research and Instructional Center's website.  Chapter 1 of the tutorial gives you a basic overview of SPSS and frequency distributions are covered in Chapter 4. 

It's very easy to get frequency distributions.  Once you have opened the data set in SPSS, look on the menu bar at the top and click on "Analyze."  This will open a drop-down menu.  Click on "Descriptive Statistics" and then on "Frequencies."  You screen should look like Figure 1.

Title: Figure 1 - Description: This is the SPSS dialog box for Frequencies.

Figure 1

Notice that the list of all variables is in the pane on the left.  I scrolled down to the variables that start with R (i.e., R1 through R7).  Select R1 by clicking on it and then click on the arrow pointing to the right.  This will move R1 into the "Variable(s)" box.  Your screen should look like Figure 2.

Title: Figure 2 - Description: This is the SPSS dialog box for Frequencies with R1 selected.

Figure 2

Now all you have to do is to click on "OK" to get your frequency distribution.  (Don't include this table in your paper.)  Your screen should look like Figure 3.  Note that I have only displayed the top part of the distribution because it's a very large table.[2]

Title: Figure 3 - Description: This is the SPSS output displaying the frequency distribution for R1.

Figure 3

Take a few minutes to familiarize yourself with the information in the table.

  • The first column is the value and the value label.  The value "1" refers to all people who answered Protestant.
  • The second column is the number of respondents who said they were Protestants (15,431).
  • The third column converts the frequencies to percents.  Notice that there are two types of missing information – responses that were uninterpretable and those who said they didn't know or refused to answer.  The percent column converts the frequency to a percent by dividing the frequency (15,431) by the total number of cases including those with missing values (35,071).  Carry out the computation for yourself and convince yourself that it is 44.0%.
  • The fourth column converts the frequencies to valid percents by dividing the frequency (15,431) by the number of cases with valid information (34,846).  In other words, it excludes the cases with missing information (225) from the denominator when computing the percent.  Carry out the computation for yourself and convince yourself that it is 44.3%.  This is called the valid percent.  The more missing information there is in the distribution, the greater could be the difference between the percent and the valid percent.  Normally we want to use the valid percents when describing the frequency distribution.
  • The fifth column is the cumulative percent.  Recall that the first twelve categories in the distribution were listed in Figure 3.  The cumulative percent for this twelfth category is 94.4%.  In other words, 94.4% of the cases with valid information selected one of the categories included in the first twelve categories.  You can see where this comes from if you add up the valid percents for the first twelve categories.

Now it's your turn.  The second category in the distribution is Roman Catholic.

  • What is the value for this category?
  • How many respondents said they were Roman Catholic?
  • What is the percent for this category?  What does this mean?
  • What is the valid percent for this category?  What does this mean?
  • Why aren't the percent and valid percents the same?
  • What is the cumulative percent for this category?  What does this mean?

Part III – Measuring Religious Preference for Protestants

One problem with the first question is that over 15,000 respondents said they were Protestant.  We know there are many different types of Protestants so we might want to break Protestants down more finely.  To do this the Pew survey asked another question – "As far as your present religion, what denomination or church, if any, do you identify with most closely?  Just stop me when I get to the right one. Are you Baptist, Methodist, Lutheran, Presbyterian, Pentecostal, Episcopalian or Anglican, Church of Christ or Disciples of Christ, Congregational or United Church of Christ, Holiness, Reformed, Church of God, nondenominational or independent church, something else, or none in particular?"

Run a frequency distribution for R2 which is the name of this variable.  (Don't include this table in your paper.)

This question was only asked of those who said they were Protestant in R1.  Notice that the number of cases with valid information was 15,403 and that there were 28 respondents who said they didn't know or refused to answer.  If you add these two numbers together, you get 15,431 which is the number who said Protestant in R1.  Those who didn't say they were Protestant in the previous question are included in the category that is labeled "system missing." 

The large number of categories in R2 makes it difficult to interpret.  R5 is an attempt to reduce the number of categories.  Run a frequency distribution for R5. (Include this table in your paper.)   Notice that this time Protestants are broken down by religious tradition.  Religious tradition is divided into three categories.

  • Evangelical Protestant tradition
  • Mainline Protestant tradition
  • Historically Black Protestant tradition
  • To find out what the Pew Center means by these traditions, read the following Pew reports:
    • Chapter 1 of the full report for the 2014 Religious Landscape Survey on "The Changing Religious Composition of the U.S. Population" and
    • Appendix B to this report on "Classification of Protestant Denominations."

For more information on the difference between the evangelical and the mainline Protestant traditions, read the article by John Green in the PBS Frontline article on "Evangelicals v. Mainline Protestants."  For a history of the black church, read Marilyn Mellowes' article on "The Black Church."

Now it's your turn again.  Write a paragraph explaining in your own words what is meant by these three religious traditions – the Evangelical Protestant tradition, the Mainline Protestant tradition, and the Historically Black Protestant tradition.  Study the frequency distribution carefully and be sure to answer the following questions.  Use the valid percents.

  • What are the five largest religious groups in R5?  Note that this table includes the religiously unaffiliated as a group.  What are the percents for each of these groups?
  • What percent of adults are Christian?  Non-Christian?  For this question, be sure to also include Orthodox Christians, Jehovah's Witness, Mormon, and Other Christian as Christian when you compute the percent of adults who are Christian.
  • Which non-Christian group is the largest?  What is the percent for that group?

Part IV – Exploring Religious Mobility

We'll going to compare the religion in which respondents were raised with their current religious preference to see how much mobility there is both into and out of different religious groups.  You already looked at the frequency distribution for R5 which describes respondent's current religious preference.  The variable RR3 refers to the religion in which respondents were raised.  The letters RR stand for "religion raised." 

The Pew survey asked, "Thinking about when you were a child, in what religion were you raised, if any? Were you Protestant, Roman Catholic, Mormon, Orthodox such as Greek or Russian Orthodox, Jewish, Muslim, Buddhist, Hindu, atheist, agnostic, something else, or nothing in particular?"  The respondents' answers to this question were recoded into the same categories as R5.

Now that we have figured out how to measure both current religious preference and the religious preference in which respondents were raised, let's turn to the question of religious mobility.  This represents a shift from what we typically call univariate (i.e., one-variable) analysis to bivariate (i.e., two-variable) analysis.  Frequency distributions look at variables one at a time.  Now we are going to look at the relationship between two variables – religious preference in which one was raised (RR3) and current preference (R5).  Crosstabulation looks at variables two at a time.

The statistical tools that we're going to use to explore the relationship between R5 and RR3 are crosstabulation, Chi Square, and measures of association.  We're not going to go into a lot of detail about these tools.  Your instructor will provide that information.   We will talk briefly about how to get SPSS to compute them and how to interpret them. 

Before we look at the relationship between variables, we need to talk about independent and dependent variables.  The dependent variable is whatever you are trying to explain.  In our case, that would be respondent's current religious preference (R5).  The independent variable is the religion in which they were raised (RR3).  We want to see whether the religious preference in which people are raised helps explain their current religious preference.  Another way to look at this is to ask if the religious preference in which people are raised helps us predict current religious preference.

To run a crosstabulation in SPSS click on "Analyze" in the menu bar at the top of the screen.  Now click on "Descriptive Statistics" in the drop-down menu and then on "Crosstabs."  (See Chapter 5, Cross Tabulations, in the online SPSS book cited on page 1 of this exercise.)   Your screen should look like Figure 4. 

Title: Figure 4 - Description: This is the SPSS dialog box for Crosstabs.

Figure 4

You're going to put your two variables (i.e., R5 and RR3) in the "Row(s)" and "Column(s)" boxes by clicking on the variable in the left-hand pane to select it and then clicking on the arrow that points to the right.  When you do that, the arrow will change so it points left.  If you click on it again, it will move the variable back to the left-hand pane.  That way you can correct errors you would make when you select the wrong variable.

But which variable goes in which box?  Typically, we put the independent variable in the column box and the dependent variable in the row box.  So in this case, we're going to put RR3 in the column box and R5 in the row box.  We're also going to click on the "Cells" box and check the box for "Column" percents.  If your independent variable is in the columns, then you want to use the column percents.  If it's in the rows, then you want to use the row percents.  Your screens should look like Figure 5 and 6.

Title: Figure 5 - Description: This is the Crosstabs dialog box with RR3 filled in as the column (independent) variable and R5 filled in as the row (dependent) variable.

Figure 5

Title: Figure 6 - Description: This is the second part of theCrosstabs dialog box with column percentages checked.

Figure 6

To get the table, click on "Continue" and then on "OK."  Your screen should look like Figure 7.  This is a big table so I'm only showing the top left part of the output.

Title: Figure 7 - Description: This is the SPSS crosstabs output for RR3 and R5.

Figure 7

There are two numbers in each cell of the table.  The top number is the number of cases in each cell and the bottom number is the column percent.  Notice that the column percents add down by column to 100.  Since the percent sum down to 100, you want to compare the percents across.  Always compare the percents in the direction opposite to the way they sum to 100.  This part of the table shows you that 65% of those who were raised Evangelical Protestant are still Evangelical today and that 45% of those raised Mainline Protestant are still Mainline today. 

This table is too large to conveniently print out or to copy into your paper so you will need to refer to the table in SPSS.  You're going to fill in the information in Figure 8 below by referring to the table in SPSS. 

What we want to know is the percent of respondents who were raised in each religious category that are still in that category today and the percent that have moved out of that category.

 

Title: Figure 8 - Description: Table set up to show percent of respondents who were raised in each religious category who are still in that category, and percent who are not

Figure 8

I filled in the first row for you so you would have an example to work from.  Notice that the second and third columns sum across to 100%.

Now that you have the data, write a paragraph or two describing religious mobility.  To help you write your paragraph(s), consider the following questions.

  •  Which religious groups have more than 50% out-mobility?  Out-mobility refers to those who have left the religion in which they were raised.
  •  Which religious groups have at least 60% staying in the religious group in which they were raised?
  • Look at those who grew up unaffiliated without any religious preference.  What percent of that group are still unaffiliated and what percent now have a religious preference?

Part V – Conclusions

Write a summary of what you learned about religious mobility.  Which religious groups have the most and the least mobility?  What does this tell you about religious mobility in the United States?

 


 

[1] This assumes that the proper associations have been set up on your computer so the computer knows that .sav files are SPSS data files

[2] SPSS allows you to change the way your output is displayed.  You can change these preferences by clicking on "Edit" in the menu bar at the top of the screen and then clicking on "Options" and finally on the "Output" tab.  Under "Variables in item labels shown as" select "Names and Labels" and then under "Variable values in item labels shown as" select "Values and Labels."  Then click on "OK."  You can also try out other options.