Religion_3CR – Comparing Religions – Religious Beliefs

Note to the Instructor: This is the third in a series of six exercises that focus on comparing religions.  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 compare two religions of your choice in terms of religious beliefs.  Religion_1CR guided you through the process of choosing the two religions to compare and selecting out respondents in those two religions.  In the next exercise (Religion_4CR) we'll compare the two religions you chose in terms of religious behavior. 

Part I—Concepts

We use concepts all the time.  We all know what a book is.  But when we use the word “book” we may not be talking about a particular book we’re reading. We could be talking about books in general.  In other words, we’re talking about the concept to which we have given the name “book.”  There are many different types of books – paperback, hardback, small, large, short, long, and so on.  But they all have one thing in common – they all belong to the category “book.”

Let’s look at some other examples.  Religious preference refers to the religion with which people identify.  Some people say they are Lutheran; others say they are Roman Catholic; still others say they are Muslim; and others say they have no religious preference.  Religiosity is another concept which refers to the degree of attachment that individuals have to their religious preference.  It’s different than religious preference.   Every religion has certain beliefs that are central to their religion.  These include, for example, beliefs about God, beliefs about life after death, and beliefs about the sacred books of their religion.  Religiosity, religious preference, and religious beliefs are all concepts.

In other words, a concept is an abstract idea.  There are the abstract ideas of book, religiosity, religious preference, religious beliefs, and many others.  Since concepts are abstract ideas and not directly observable, we must select measures or indicants of these concepts.  We call this process measurement.

Part II – 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.[2]

Part III – Choosing the Two Religions to Compare

Note:  If you have already chosen the two religions you want to compare, you can skip this part of the exercise and proceed to part 4.

In this exercise you're going to choose two religions which you will compare.  There are several ways you can do this.

  • You could compare your own religious group with another religious group of your choice.  If you don't have a religious preference, you could select "nothing in particular" to compare with another group.  For example, you could compare Lutherans with those who have no religious preference (i.e., nothing in particular).
  • You could compare two religious groups that you are interested in.  For example, you could compare Catholics with Lutherans.
  • Instead of comparing two religious groups, you could compare two religious traditions such as the Mainline Protestant tradition and the Evangelical Protestant tradition.
  • You could compare a Christian group with a non-Christian group.  For example, you could compare Catholics with Muslims.
  • You could compare two non-Christian groups.  For example, you could compare Muslims and Buddhists.

You want to compare two religions that have enough cases to make the comparisons meaningful.  Don't select religions that have fewer than 150 cases in them. 

Review Exercise_1CR to learn more about choosing the religions you will compare and how to select out respondents in those two religions.  In the rest of this exercise, I'm going to assume that you know how to do this.  If you're having trouble, talk with your instructor who will help you.

Part IV – Measuring Religious Beliefs

We want to compare our two religions to see if respondents in one religion hold different religious beliefs than respondents in the other religion.  There are many different beliefs that are central to religions.  The Pew survey asked about a number of these beliefs.  We're going to look at questions about God, life after death, and the sacred books of a person's religion. 

Let's start with beliefs about God.  The Pew survey asked "Do you believe in God or a universal spirit?"  This was followed by "How certain are you about this belief?  Are you absolutely certain, fairly certain, not too certain, or not at all certain?"  The names of these variables are RBL1 and RBL2.

The survey also included questions about life after death.  The Pew survey asked, "Do you think there is a heaven, where people who have lived good lives are eternally rewarded?" and "Do you believe there is a hell, where people who have lived bad lives are eternally punished?"  These variables are named RBL4 and RBL5.

Let's also look at beliefs about the sacred books of a person's religion.  For example, for Christians that would be the Bible and for Muslims that would be the Koran (Quran).   The Pew survey asked, "Which comes closest to your view?  Sacred text is the word of God OR sacred text is a book written by men and is not the word of God?"  This is what is referred to as a forced-choice question.  The respondent is asked to choose the response that comes closest to their own view.  This variable is named RBL6.

This question was followed up by another question which asked "And would you say?  Sacred text is to be taken literally, word for word OR not everything in the sacred text should be taken literally, word for word?"  For Christians, the sacred text is the Bible. Some Christians believe that the Bible is to be taken literally.  For example, they would say that the first two chapters in Genesis (the first book in the Bible) describe literally how the world was created. This is the basis for the disagreement over evolution and what should be taught in public schools (i.e., creationism or evolution).  This variable is named RBL7.

Run frequency distributions for all six variables.  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.  You could also review Exercise_1CR.  It goes into more detail on how to get and interpret frequency distributions.

Part V – Interpreting Frequency Distributions

Your frequency distributions for RBL1 and RBL2 should look like this.

Title: Figure 1 - Description: This shows the Frequencies output for both RBL1 and RBL2.

Figure 1

Take a few minutes to familiarize yourself with the information in the table.  It's particularly important to understand the difference between the percent and the valid percent columns. 

The percent column converts the frequencies to percents.  To compute the percent for those who answered "Yes" to RBL1, you would divide the frequency (31,065) by the total number of cases including those with missing values (35,071).  Carry out the computation and convince yourself that it is 88.6%.

The valid percent column converts the frequencies to valid percents by dividing the frequency by the number of cases with valid information.  Notice that in RBL1 some of the respondents said they didn't know if they believed in God and others refused to answer the question.  This is what we refer to as missing data and they are coded "9."  To compute the valid percent for those who answered "Yes," you would divide 31,065 by 34,520.  In other words, it excludes the cases with missing information (551) from the denominator when computing the percent.  Carry out the computation and convince yourself that it is 90.0%.  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.

For the second variable, RBL2, there are two types of missing data.  Some said they didn't know or refused to answer the question and they are coded "9."  But there's a second type of missing data that is labelled "System."  What does that mean?  The second question is a follow-up to the first question.  It asks how certain respondents are about their belief in God.  It wouldn't make any sense to ask this follow-up question to respondents who didn't answer "Yes" – they believed in God, to the first question.  Anyone who answered anything other than "Yes" to the first question is assigned a system missing code for the second question.  To verify this, add up the number of respondents who didn't answer "Yes" to the first question (i.e., 3,241 + 214 + 551) which equals 4,006.  That's the number of respondents who are given the system missing code in the second question.  So to get the valid percent for those who are absolutely certain there is a God, you would divide 22,062 by 30,801.  In other words, you would exclude those with missing information (263 + 4,006 which equals 4,270[3]).  Convince yourself that this equals 71.6%.

Write a paragraph describing respondents' beliefs about God.  Be sure to use the valid percents in your answer.  Keep in mind that when we talk about beliefs in God, we're referring to the God of many different religions.

Now look at the frequency distributions for RBL4 and RBL5.  What do these distributions tell you about respondents' beliefs in a heaven and a hell?  Again, be sure to use the valid percents.

Finally, look at the frequency distributions for RBL6 and RBL7.  Note that those who said that the sacred text of their religion was the word of God in RBL6 were asked the following follow up question.  "And would you say?  Sacred text is to be taken literally, word for word OR not everything in the sacred text should be taken literally, word for word?"  Be sure to use the valid percents.  Keep in mind that the sacred text that respondents are referring to depends on their religion.

Part VI – Comparing the Two Religions

Now we're going to compare the two religions that you chose earlier.  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.  Crosstabulation looks at variables two at a time.

The statistical tools that we're going to use 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 use crosstabulation 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 respondents' religious beliefs.  The independent variable is some variable that you think might help explain why some people have difference beliefs than others.  In our case, that would be whichever variable we used to select the two religions we want to compare. 

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 mentioned earlier.)   Your screen should look like Figure 2. 

Title: Figure 2 - Description: This is the SPSS dialog box for Crosstabs.

Figure 2

As an example, I'm going to compare Lutherans in the Evangelical Tradition with Lutherans in the Mainline Tradition and use RBL1 as my measure of religious beliefs.  In order to do this I selected all respondents who were value 4 (i.e., Lutherans in the Evangelical Tradition) and value 37 (Lutherans in the Mainline Tradition) on variable R4.[4]  (See Exercise_1CR for an explanation of how to select out particular respondents.)

To make sure that I did this correctly, I ran a frequency distribution for R4.  It should only include respondents in those two religious groups which is what the SPSS output shows in Figure 3.

Title: Figure 3 - Description: This is the SPSS  output showing the frequency distribution for R4 after selecting out values 4 and 37 on R4.

Figure 3

To run a crosstabulation, you're going to put your variables 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?  We're going to put the independent variable in the column box and the dependent variables in the row box.  Since we're trying to explain why some people hold different beliefs about God, we'll put RBL1 in the rows and whichever variable you used to select the two religions in the columns.  In my example, that would be R4.  Your screen should look like Figure 4.

Title: Figure 4 - Description: This is the SPSS Crosstabs dialog box with R4 in the columns and RBL1 in the rows.

Figure 4

Since your independent variable is in the columns, you want the column percents so click on the "Cells" button and check the box for the "Column" percents and then click on "Continue."  Now click on the "Statistics" button and check the boxes for Chi Square and Kendall's tau-b and then click on "Continue."  (We'll discuss these statistics in a little bit.)  Your screens should look like Figure 5 and 6.

Title: Figure 5 - Description: This is the Crosstabs: Cell Display dialog box with column percents selected.

Figure 5
 

Title: Figure 6 - Description: This is the Crosstabs: Statistics dialog box with Chi Square and Kendall's tau-b selected.

Figure 6

Now all you have to do is to click on "OK" and you should see your output.  Since the column percents sum down to 100, you should compare the percents straight across.  Always compare the percents in the direction opposite to the way they sum to 100. 

How would you describe your findings in a report?  Think of writing two sentences.  The first sentence should describe the pattern without using numbers.  The second sentence should use the percents to illustrate the pattern.  So you might write something like this.  "There is very little difference between Evangelical Lutherans and Mainline Lutherans in their belief in God.  For example, 100.0% of Evangelical Lutherans believe in God and 98.3% of Mainline Lutherans also believe in God."

What about Chi Square?  Chi Square is a test of significance that tests the null hypothesis that the two variables are unrelated to each other.  In statistical speak, we would say that the null hypothesis is that the variables are statistically independent.  Chi Square tests this null hypothesis and tells you whether you should reject or not reject it.  If you can reject it, then you have evidence that the two variables are related to each other.  If you can't reject it, then you don't have any evidence of such a relationship.

To interpret Chi Square in your output, look at the first row for "Pearson Chi Square" and the column for "Asymptotic Significance."  In your output, it should read ".013".  This is the probability that you would be wrong if you rejected the null hypothesis. This tells you that it's unlikely that this is a chance relationship.  There probably is some relationship between these two variables.  Our rule is to reject the null hypothesis when the significance value is < .05.  In other words, when the probability of being wrong is less than five out of one hundred.  Even though the percent difference is very small (i.e., 100.0% – 98.3% or 1.7%), it's still statistically significant.  In other words, it's probably not a chance difference.  This is due to the large number of cases we have to compare.  There were 513 Evangelical Lutherans and 718 Mainline Lutherans in our table.  With such large numbers of cases, even small differences can be statistically significant.

A measure of association is a statistic that measures the strength of the relationship.   The Chi Square test doesn't tell you anything about the strength of the relationship.  You need a measure of association to do that.  There are many different measures of association.  For our measure of association, we used Kendall's tau-b.  Tau-b can be used when both of your variables are ordinal.  Ordinal means that the categories have an inherent order to them.  In other words, they are ordered from high to low or from low to high.  In our example, tau-b is .084 which means that there is only a very weak relationship between the two variables.  Just because our Chi Square is significant doesn't mean that it is a strong relationship.  Measures of association are most useful for comparing the strength of the relationship in two or more tables.

Part VII – Now It's Your Turn

Decide which two religions you want to compare and select out the respondents in these two religions.  Check to make sure that you did this correctly by running a frequency distribution for the variable you used to select out the two religions.  Run the appropriate crosstabs to see if the two religions differ in terms of their beliefs.  Use the variables for which you created frequency distributions in part 5 (i.e., RBL1, RBL2, RBL4, RBL5, RBL6, and RBL7.  This will give you six separate tables.  Be sure to get the column percents, Chi Square, and Kendall's tau-b for all three crosstabs.

Write one paragraph describing your findings for each of these six tables.  Follow the approach I described above by writing two sentences.  The first sentence should describe the pattern without using numbers.  The second sentence should use the percents to illustrate the pattern.  Be sure to use the column percents, Chi Square, and Kendall's tau-b in your answer. 

 


 

[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.

[3] There is a slight discrepancy here.  When you add 263 and 4,006 you actually get 4,269 and not 4,270.  You're off by one.  That's due to the way the data are weighted and is nothing to worry about.

[4] Exercise_1CR discusses what we mean by these different religious traditions and gives you links to articles providing more information.