Data Coding, Entry, and Checking in SPSS: Code Data for Data Entry

1. Guidelines for Data Coding Coding is the process of assigning numbers to the values or levels of each variable. Before starting the coding process, we want to present some broad suggestions or rules to keep in mind as you proceed. These suggestions are adapted from rules proposed in Newton and Rudestam’s (1999) useful

Data Coding, Entry, and Checking in SPSS – Problem 2.1: Check the Completed Questionnaires

Now examine the incomplete, unclear, or double answers in questionnaire. Stop and do this now, before proceeding. What issues did you see? The researcher needs to make rules about how to handle these problems and note them on the questionnaires or on a master “coding instructions” sheet so that the same rules are used

Data Coding, Entry, and Checking in SPSS – Problem 2.2: Define and Label the Variables

The next step is to create a data file into which you will enter the data. If you do not have the program open, you need to log on. When you see the startup window, click the Type in data button; then you should see a blank Data Editor that will look something like

Data Coding, Entry, and Checking in SPSS – Problem 2.3: Display Your Dictionary or Codebook

Now that you have defined and labeled your variables, you can print a codebook or dictionary of your variables. It is a very useful record of what you have done. Notice that the information in the codebook is essentially the same as that in the variable view  so you do not really have to

Data Coding, Entry, and Checking in SPSS – Problem 2.4: Enter Data

After defining and labeling the variables, your next task is to enter the data directly from the questionnaires or from a data entry form. Sometimes researchers transfer the data from the questionnaires to a data entry form (like Table 2.1) by hand before entering the data into SPSS. This may be helpful if the questionnaires

Data Coding, Entry, and Checking in SPSS – Problem 2.5: Run Descriptives and Check the Data

In order to get a better “feel” for the data and to check for other types of errors or problems on the questionnaires, we recommend that you run the statistics program called Descriptives. To compute basic descriptive statistics for all your subjects, you will need to do these steps: Select Analyze  → Descriptive Statistics

Importing data of other software in SPSS

In this post, we will show you how to import data in SPSS by two ways: Copying and pasting data, and directly Importing data files of other software (download datasets). 1. Copying and pasting In practice, the easiest way to get data into Stata is often overlooked: you can copy data from most applications

Exporting anh Copying SPSS Outputs to Word or Excel

There are 2 ways to export and copy SPSS outputs to Word or Excel. We will show you how to do it. Let’s now download and open bank.sav; and open it in SPSS. We’ll use it for walking you in this post. Right. So with our data open, let’s create some output by running

Measurement and Descriptive Statistics with SPSS: Frequency Distributions

Frequency distributions are critical to understanding our use of measurement terms. We begin this chapter with a discussion of frequency distributions and two examples. Frequency tables and distributions can be used whether the variable involved has ordered or unordered levels or values. In this section, we only consider variables with many ordered values. A

Measurement and Descriptive Statistics with SPSS: Levels of Measurement

Measurement is the assignment of numbers or symbols to the different characteristics (values) of variables according to rules. In order to understand your variables, it is important to know their level of measurement. Depending on the level of measurement of a variable, the data can mean different things. For example, the number 2 might

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Measurement and Descriptive Statistics with SPSS: Descriptive Statistics and Plots

Now we expand our discussion of frequency distributions to include frequency tables, which are constructed in very similar ways for all four types of measurement. A difference is that with nominal data, the order in which the categories are listed is arbitrary. In Fig. 3.3, the frequency table listed protestant, catholic, and then not religious. However, protestant could be put after or between catholic and not religious because the categories are not ordered. In ordinal and approximately normal data, the order cannot vary (e.g., medium always should be between low and

Measurement and Descriptive Statistics with SPSS: The Normal Curve

Figure 3.10 is an example of a normal curve. The frequency distributions of many of the variables used in the behavioral sciences are distributed approximately as a normal curve when N is large. Examples of such variables that approximately fit a normal curve are height, weight, intelligence, and many personality variables. Notice that for

Exploratory Data Analysis (EDA) with SPSS

1. What Is EDA? After the data are entered into this program, the first step to complete (before running any inferential statistics) is EDA, which involves computing various descriptive statistics and graphs. Exploratory data analysis is used to examine and get to know your data. Chapters 2, 3, and especially this chapter, focus on

Problem 4.1: Descriptive Statistics for the Ordinal and Scale Variables with SPSS

In this problem, we use all of the HSB variables that we initially labeled as ordinal or scale in the Variable View. With those types of variables, it is important to see if the means make sense (are they close to what you expected?), to examine the dispersion/spread of the data, and to check

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Problem 4.2: Boxplots for One Variable and for Multiple Variables with SPSS

In addition to numerical methods for understanding your data, there are several graphic methods. In Chapter 3, we demonstrated the use of histograms and also frequency polygons (line graphs) to roughly assess normality. The trouble is that visual inspection of histograms can be deceiving because some approximately normal distributions don’t look very much like

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Problem 4.3: Boxplots and Stem-and-Leaf Plots Split by a Dichotomous Variable with SPSS

Now let’s make a boxplot comparing males and females on math achievement. This is similar to what we did in Chapter 3, but here we request statistics and stem-and-leaf plots. 4.3. Create a boxplot for math achievement split by gender. Use these commands: Analyze → Descriptive Statistics → Explore  The Explore window (Fig. 4.5) will

Problem 4.4: Descriptives for Dichotomous Variables with SPSS

Now let’s explore the dichotomous variables. To do this, we do the Descriptives command for each of the dichotomous variables. Once again, we could have done Frequencies, with or without frequency tables, but we chose Descriptives. This time we select fewer statistics because the standard deviation, variance, and skewness values are not very meaningful

Problem 4.5: Frequency Tables for a Few Variables with SPSS

Displaying Frequency tables for variables can help you understand how many participants are in each level of a variable and how much missing data of various types you have. For nominal variables, most descriptive statistics are meaningless. Thus, having a frequency table is usually the best way to understand your nominal variables. We created

Data File Management and Descriptive Statistics with SPSS – Problem 5.1: Count Math Courses Taken

Sometimes you want to know how many items the participants have taken, bought, done, agreed with, and so forth. One time this happens is when the subject is asked to “check all that apply.” In Chapter 2, we could have counted how many aspects of the class assignments (reading, homework, and extra credit) the

Data File Management and Descriptive Statistics with SPSS – Problem 5.2: Recode and Relabel Mother’s and Father’s Education

Now we will Recode mother’s education and father’s education so that those with no postsecondary education (2s and 3s) have a value of 1, those with some postsecondary will have 2, and those with a bachelor’s degree or more will have a value of 3. It is usually not desirable to dichotomize (divide into