SPSS is straight forward, and its English-like command language helps the user to go through the flow. SPSS introduces the following four programs that help researchers with their complex data analysis needs.

• Statistics Program: SPSS’s statistics program gives a large amount of basic statistical functionality; some include frequencies, cross-tabulation, bivariate statistics, etc.
• Modeler Program: Researchers are able to build and validate predictive models with the help of advanced statistical procedures.
• Text Analytics for Surveys Program: It gives robust feedback analysis. which in turn get a vision for the actual plan.
• Visualization Designer: Researchers found this visual designer data to create a wide variety of visuals like density charts and radial box plots.

Main contentsSee more from basic to advanced

Data Coding, Entry, and Checking in SPSS: Plan the Study, Pilot Test, and Collect Data

Plan the study. The research starts with identification of a research problem and research questions or hypotheses. It is also necessary to plan the research

SPSS Tutorials: Date-Time Variables in SPSS

This tutorial covers how SPSS treats Date-Time variables, and how to use the Date & Time Wizard to create and compute variables using dates and

SPSS Tutorials: Variable Types

A variable’s type determines if a variable numeric or character, quantitative or qualitative. It also dictates what type of statistical analysis methods are appropriate for

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

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

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,

Recoding Variables in IBM SPSS

This tutorial shows how to use Recode into Different Variables and DO IF syntax to change or merge the categories of string or numeric variables

Computing Variables in SPSS

This tutorial shows how to compute new variables in SPSS using formulas and built-in functions. The data file in example is available to download here.

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

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

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

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

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

Grouping or Splitting Data in SPSS

In SPSS, Split File is used to run statistical analyses on subsets of data without separating your data into two different files. When analyzing data, it

Sorting cases or variables in SPSS

Sorting a dataset rearranges the rows with respect to one or more variables. This tutorial discusses how to sort data using the drop-down menus in

How to Rank Cases in SPSS

In its simplest form, a rank transform converts a set of data values by ordering them from smallest to largest, and then assigning a rank

Weighting Cases in IBM SPSS

In SPSS, weighting cases allows you to assign "importance" or "weight" to the cases in your dataset. Some situations where this can be useful include: Your

Recoding String Variables (Automatic Recode) in SPSS

In SPSS, recoding categorical string variables to numeric codes and converting blank strings to missing values can be done automatically using Automatic Recode. The data

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

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

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

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.

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

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

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

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

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

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

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

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

Inferential Statistics in SPSS: General Design Classifications for Difference Questions

Many research questions focus on whether there is a significant difference between two or more groups or conditions. When a group comparison or difference question

Data File Management and Descriptive Statistics with SPSS: Saving the Updated HSB Data File

You should always save your data file if you entered new data or made any changes. If you forget to Save, you will have to

Data File Management and Descriptive Statistics with SPSS: Describing the Sample Demographics and Key Variables

Using the information that you computed in Chapters 4 and 5, we can now describe this sample of the High School and Beyond (hsb) data.

Data File Management and Descriptive Statistics with SPSS – Problem 5.5: Check for Errors and Normality for the New Variables

Run Descriptives in order to understand the new variables, check for errors, and see if they are distributed normally. Using Chapter 4 (Problem 4.1) as

Data File Management and Descriptive Statistics with SPSS – Problem 5.4: Compute Parents’ Revised Education With the Mean Function

We have decided to combine father’s and mother’s education scores because, as we will find out later, they are highly correlated. Thus, for some purposes,

Data File Management and Descriptive Statistics with SPSS – Problem 5.3: Recode and Compute Pleasure Scale Score

Now let’s Compute the average “pleasure from math” scale score (pleasure scale) from item02, item06 , item10, and item14 after reversing (Recoding) item06 and item10,

Problem 7.1: Chi-Square and Phi (or Cramer’s V) with SPSS

The statistics discussed in this first problem are designed to analyze two nominal or dichotomous variables. Remember, nominal variables are variables that have distinct unordered

Inferential Statistics in SPSS: Writing About Your Outputs

One of the goals of this book is to help you write a research report or thesis using the outputs. Thus, we have provided an

Inferential Statistics in SPSS: An Example of How to Select and Interpret Inferential Statistics

As a review of what you read in Chapter 1 and this chapter, we now provide an extended example based on the HSB data. We

Inferential Statistics in SPSS: Interpreting the Results of a Statistical Test

In the following chapters, we present information about how to check assumptions, do the commands, interpret the previous statistics, and write about them. For each

Inferential Statistics in SPSS: The General Linear Model

Whether or not there is a relationship between variables can be answered in two ways. For example, if each of two variables provides approximately normally

Inferential Statistics in SPSS: Selection of Inferential Statistics

How do you decide which of the many possible inferential statistics to use? Although this section may seem overwhelming at first because many statistical tests

Correlation and Regression with SPSS – Problem 8.2: Bivariate Pearson and Spearman Correlations

The Pearson product moment correlation is a bivariate parametric statistic used when both variables are approximately normally distributed (i.e., scale data). When you have ordinal

Correlation and Regression with SPSS – Problem 8.1: Scatterplots to Check Assumptions

A scatterplot is a plot or graph of two variables that shows how the score for an individual on one variable associates with his or

Problem 7.5: Cohen’s Kappa for Reliability With Nominal Data with SPSS

When we have two nominal variables with the same values (usually two raters’ observations or scores using the same codes), you can compute Cohen’s kappa

Problem 7.4: Cross-Tabulation and Eta with SPSS

There is an important associational statistic, eta, that is used when one variable is nominal and the other is approximately normal or scale. We will

Problem 7.3: Other Nonparametric Associational Statistics with SPSS

In addition to phi and Cramer’s V, there are several other nonparametric measures of association that we could have chosen in Fig. 7.2. They attempt,

Problem 7.2: Risk Ratios and Odds Ratios with SPSS

When you have two dichotomous variables and thus a 2 x 2 contingency table or cross-tabulation, you can compute risk ratios and odds ratios instead

Comparing Two Groups with SPSS – Problem 9.2: Independent Samples t Test

When investigating the difference between two unrelated or independent groups (in this case males and females) on an approximately normal dependent variable, it is appropriate

Comparing Two Groups with SPSS – Problem 9.1: One-Sample t Test

Sometimes you want to compare the mean of a sample with a hypothesized population mean to see if your sample is significantly different. For example,

Correlation and Regression with SPSS – Problem 8.6: Multiple Regression

The purpose of multiple regression is similar to bivariate regression, but with more predictor variables. Multiple regression attempts to predict a normal (i.e., scale) dependent

Correlation and Regression with SPSS – Problem 8.5: Bivariate or Simple Linear Regression

As stated earlier, the Pearson correlation is the best choice for a statistic when you are interested in the association of two variables that have

Correlation and Regression with SPSS – Problem 8.4: Internal Consistency Reliability With Cronbach’s Alpha

A very common measure of reliability in the research literature is Cronbach’s alpha. It usually is used to assess the internal consistency reliability of several

Correlation and Regression with SPSS – Problem 8.3: Correlation Matrix for Several Variables

If you have more than two ordinal or normally distributed variables that you want to correlate, the program will produce a matrix showing the correlation

Analysis of Variance with SPSS – Problem 10.2: Post Hoc Multiple Comparison Tests

Now we will introduce the concept of post hoc multiple comparisons, sometimes called follow­up tests. When you compare three or more group means, you know

Analysis of Variance with SPSS – Problem 10.1: One-Way (or Single Factor) ANOVA

In this problem, you will examine a statistical technique for comparing two or more independent groups on the dependent variable. The appropriate statistic, called One-Way

Comparing Two Groups with SPSS – Problem 9.6: Nonparametric Wilcoxon Test for Two Related

Let’s assume that education levels and visualization test scores are not normally distributed and/or other assumptions of the paired t test are violated. In fact,

Comparing Two Groups with SPSS – Problem 9.5: Using the Paired t Test to Check Reliability

In addition to comparing the means for two paired or matched samples, the paired t can be used in connection with checking reliability, especially test-retest

Comparing Two Groups with SPSS – Problem 9.4: Paired Samples t Test

In this problem, you will compare the average scores of each HSB student’s father’s and mother’s scores on the same measure, namely, their educational level.

Comparing Two Groups with SPSS – Problem 9.3: The Nonparametric Mann-Whitney U Test

What should you do if the t test assumptions are markedly violated (e.g., what if the dependent variable data are grossly skewed, otherwise non-normally distributed,

Several Measures of Reliability with SPSS – Problem 3.5: Intraclass Correlation Coefficients (ICC)

ICC performs a reliability analysis for two or usually more judges or observers who have rated the same somewhat subjective behavior. In our example, the

Several Measures of Reliability with SPSS – Problem 3.4: Test-Retest Reliability Using Correlation

Is there support for the test-retest reliability of the two visualization test scores? Let’s do a Pearson r for visualization test and visualization retest scores.

Several Measures of Reliability with SPSS – Problems 3.2 and 3.3: Cronbach’s Alpha for the Competence and Pleasure Scales

Again, is it reasonable to add the scores for these items together to form summated measures of the concepts of competence and pleasure? What is

Several Measures of Reliability with SPSS – Problem 3.1: Cronbach’s Alpha for the Motivation Scale

The motivation score is composed of six items that were rated on four-point Likert scales, from very atypical (1) to very typical (4). Do the

Analysis of Variance with SPSS – Problem 10.4: Two-Way (or Factorial) ANOVA

In previous problems, we compared two or more groups based on the levels of only one independent variable or factor using t tests (Chapter 9)

Analysis of Variance with SPSS – Problem 10.3: Nonparametric Kruskal-Wallis Test

What else can you do if the homogeneity of variance assumption is violated (or if your data are ordinal or highly skewed)? One answer is

Multiple Regression with SPSS – Problem 6.3: Hierarchical Multiple Linear Regression

In Problem 6.3, we will use the hierarchical approach, which is used when you want to enter the variables in a series of blocks or

Multiple Regression with SPSS – Problem 6.2: Simultaneous Regression Correcting Multicollinearity

In Problem 6.2, we will use the combined/average of the two variables, mother’s education and father’s education, and then recompute the multiple regression after omitting

Multiple Regression with SPSS – Problem 6.1: Using the Simultaneous Method to Compute Multiple Regression

To reiterate, the purpose of multiple regression is to predict an interval (or scale) dependent variable from a combination of several interval/scale and/or dichotomous independent/predictor

Problem 4.2: Principal Components Analysis on Achievement Variables with SPSS

Principal components analysis is most useful if one simply wants to reduce a relatively large number of variables to a smaller number of variables that

Problem 4.1: Factor Analysis on Math Attitude Variables with SPSS

In Problem 4.1, we perform a principal axis factor analysis on the math attitude variables. Factor analysis is more appropriate than PCA when one has

Several Measures of Reliability with SPSS – Problem 3.6: Cohen’s Kappa With Nominal Data

When we have two nominal categorical variables with the same values (usually two raters’ observations or scores using the same codes), we can compute Cohen’s

Problem 7.3: Moderation: Graphing the Slopes with SPSS

In Problem 7.3 we will graph the slopes using the data from the output from Problem 7.2. What do the slopes look like when previous

Running the Moderation analysis in SPSS using PROCESS

In the previous post, we will conduct a moderation analysis. Recall that the purpose of moderation is to investigate how the interaction between two or

Running the Mediation analysis in SPSS using PROCESS

The purpose of testing statistical mediation is to understand the effects of a presumed mediating variable on the relationship between a scale or dichotomous predictor

Mediation, Moderation, and Canonical Correlation

1. Mediation Mediation occurs when the observed relationship between two variables is due, at least in part, to a third variable. Statistically, mediation occurs when

Multiple Regression with SPSS – Problem 6.5: Backward Elimination Multiple Linear Regression

In Problem 6.5, we will use the backward approach, which is when all the variables are added into the model, then are eliminated one by

Multiple Regression with SPSS – Problem 6.4: Forward Multiple Linear Regression

In Problem 6.4, we will use the forward approach, which is when SPSS adds variables one at a time by assessing which variable has the

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• Installing SPSS Statistics (Mac)
• Installing SPSS Statistics (Windows)

A. Installing SPSS Statistics on Mac

1. Double click to extract SPSS_Statistics_xx_mac.dmg.

Once your SPSS Statistics file has been downloaded, double click to extract SPSS_Statistics_xx_mac.dmg.

2. Open SPSSStatistics.

NOTE:  The SPSSStatistics Drive will appear on your desktop or on your Finder window.

3. Double click SPSS_Statistics_Installer.

The rest of the installation process for the Mac is similar to the PC (Windows) installation.

Continue the installation process at: Open IBM SPSS Statistics file.

B. Installing SPSS Statistics (Windows)

4. Open IBM SPSS Statistics file.

Once your SPSS Statistics file has been downloaded find and after the installation has been prepared, a new window will appear.

Click Next.

5. Click I accept the terms in the license agreement.

The Software License Agreement will appear.

Click I accept the terms in the license agreement in order to install SPSS Statistics on your device.

Click Next.

6. Select  IBM SPSS Statistics Essentials for Python (optional)

By default, IBM SPSS Statistics – Essentials for Python will install. (optional)

If you would like to opt out of this add-on installation, click No, then click Next.

Otherwise, click Yes, then click Next.

Installing IBM SPSS Statistics Essentials for Python (optional)

7. Click I accept the terms in the license agreement.

Click I accept the terms in the license agreement.

Click Next.

8. Click I accept the terms in the license agreement

Click I accept the terms in the license agreement.

Click Next.

9. Choose your destination folder (optional)

Leave the default destination folder as is or choose a new destination folder path to install SPSS Statistics (optional).

Click Next.

10. Click Install to begin the installation.

11. The Installation process can take a few minutes.

12. Installation is complete.

Make sure the Start IBM SPSS Statistics xx Now box is checked.

Click Finish.

13. SPSS License Authorization Wizard.

When you run SPSS, the following screen will appear.

Click “Login with IBM ID” if you had an IBM account; then login with your username and passward.

Click “Lauch License Winzard” if you had a licence code.

Then you will see the screen shown below. Select “Authorised User licence” and “Next”.

Please copy and paste your Authorisation Code in the “Enter Code” box and click “Add” and “Next”. If you have more than one code, you can click Add and enumerate them one by one.

Your Authorization Code will be processed. Once your Authorization Code has finished processing, click Next.

You will receive a message that authorization has succeeded.

Licensing completed, click Finish.

Congratulations!  You have installed and authorized your SPSS software.

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In the calculation of SEM coefficients, AMOS uses the following methods:

• Maximum likelihood
• Unweighted least squares
• Generalized least squares
• Browne’s asymptotically distribution-free criterion
• Scale-free least squares

Amos is a powerful structural equation modeling (SEM) software helping support your research and theories by extending standard multivariate analysis methods, including regression, factor analysis, correlation and analysis of variance. Build attitudinal and behavioral models reflecting complex relationships more accurately than with standard multivariate statistics techniques using either an intuitive graphical or programmatic user interface. Amos is included in the Premium edition of SPSS Statistics (except in Campus Edition, where it is sold separately). You can also buy Amos as part of the Base, Standard and Professional editions of SPSS Statistics, or separately as a stand-alone application.

Main contentsSee more from basic to advanced

What Is Structural Equation Modeling?

Structural equation modeling (SEM) is a statistical methodology that takes a confirmatory (i.e., hypothesis-testing) approach to the analysis of a structural theory bearing on some

Introduction to IBM AMOS SPSS

AMOS is statistical software and it stands for analysis of a moment structures. AMOS is an added SPSS module, and is specially used for Structural Equation Modeling, path analysis, and confirmatory factor

Using the Amos Program

1. Key Concepts Building SEM models using Amos Graphics Building SEM models using Amos Tables View Corollary associated with single variable estimation Concept of model

The General Structural Equation Model

1. Symbol Notation Structural equation models are schematically portrayed using particular configurations of four geometric symbols—a circle (or ellipse), a square (or rectangle), a single-headed

Basic Concepts of Structural Equation Modeling

1. Latent versus Observed Variables In the behavioral sciences, researchers are often interested in studying theoretical constructs that cannot be observed directly. These abstract phe­nomena

Application 6: Testing Invariance of Latent Mean Structures (First-Order CFA Model) with AMOS

1. Key Concepts Distinguishing between observed and latent means Distinguishing between covariance and mean structures The moment matrix Critically important constraints regarding model identification and

Application 5: Testing Factorial Invariance of Scales from a Measurement Scale (First-Order CFA Model) with AMOS

1. Key Concepts Conceptual notion of factorial invariance (i.e., equivalence) Analysis of covariance (COVS) versus means and covariance (MaCs) structures Evidence of partial measurement invariance

Application 4: Testing the Validity of a Causal Structure with AMOS

1. Key Concepts The full structural equation model Issue of item parceling Addressing evidence of multicollinearity Parameter change statistic Issue of model parsimony and nonsignificant

Application 3: Testing the Factorial Validity of Scores from a Measurement Scale (Second-Order CFA Model) with AMOS

1. Key Concepts Model identification issue in higher-order models Determination of critical ratio differences Specification of equality constraints Likert scale scores analyzed as continuous versus

Application 2: Testing the Factorial Validity of Scores from a Measurement Scale (First-Order CFA Model) with AMOS

1. Key Concepts Assumption of multivariate normality The issue of multivariate outliers The issue of multivariate kurtosis Statistical strategies in addressing nonnormality SEM robust statistics

Application 1: Testing the Factorial Validity of a Theoretical Construct (First-Order CFA Model) with AMO

1. Key Concepts Hypothesized models conceptualized within a matrix format Error/uniqueness parameters Congeneric measures Working with model-refining tools in Amos Graphics Specification of data in

Procedural Steps in Structural Equation Modelling

Structural equation modelling includes six key steps. In addition to data collection, the steps are model specification, identification, estimation, evaluation and modification (Fig. 3.1). Model

Application 11: Addressing the Issues of Missing Data with AMOS

1. Key Concepts Unstructured versus structured missing data Basic patterns of missing data Ad hoc versus theory-based strategies for dealing with missing data Amos approach

Application 10: Use of Bootstrapping in Addressing Nonnormal Data with AMOS

1. Key Concepts SEM assumption of multivariate normality Concept of bootstrapping Benefits, limitations, and caveats related to bootstrapping Nonparametric (simple, naive) bootstrap Sample ML estimates

Application 9: Testing Change Over Time with AMOS: The Latent Growth Curve Model

1. Key Concepts Measuring change over three or more time points Intraindividual versus interindividual differences in change Factor intercept and slope as growth parameters Importance

Application 8: Testing Evidence of Construct Validity with AMOS: The Multitrait-Multimethod Model

1. Key Concepts The dual focus of construct validation procedures Convergent validity, discriminant validity, and method effects Reorientation of Amos Graphics models to fit page

Application 7: Testing Invariance of a Causal Structure (Full Structural Equation Model) with AMOS

1. Key Concepts Importance of cross-validation in SEM Approaches to cross-validation in SEM Testing invariance for a programmed set of measurement and struc­tural parameters based

Theory Should Lead Conceptualization for SEM Model

A problem you will see with many SEM studies is when a model is conceptualized apart from any theory. A theory should be a guide

A Confirmatory Approach to SEM

Testing a SEM model is said to take a confirmatory approach. Put another way, a conceptual model is determined a priori and then data is

Is SEM Causal Modeling?

You will often hear SEM referred to as a causal modeling approach. SEM does not determine causation between two variables. This is a misnomer that

Correlations Between Constructs in SEM Model

While directionality is calculated with a covariance analysis, the strength of the relationships is determined through a correlation analysis. A correlation between two concepts is

Basics of SEM Input: The Covariance Matrix

For the purposes of this book, I will strictly use a covariance-based approach to structural equation modeling.This method is the most robust for theory testing

What Is Structural Equation Modeling?

Structural equation modeling, or SEM, is a statistical method that examines the relation- ships among numerous variables in a simultaneous way. SEM is not considered

Data Screening for SEM Model

The first step before analyzing your SEM model is to examine your data to make sure there are no errors, outliers, or respondent misconduct. We

Greek Notation and SEM

Many SEM programs produce their results using Greek notations. They use Greek letters to represent different constructs and relationships. AMOS does not produce output using

How to Measure an Unobserved Construct in SEM Model

With an unobservable construct, we are trying to use indicators to measure the concept.With any unobservable analysis, you will rarely be able to say you

Independent vs. Dependent Latent Variables in SEM Model

Independent variables (also called exogenous variables) are the constructs that influence another variable. Dependent variables (called endogenous variables) are constructs influenced by independent variables. Figure

Understanding Diagram Symbols in SEM Model

SEM uses diagrams to denote relationships to be tested. It is important that you understand what these diagram symbols mean because AMOS is going to

Assumptions of SEM

With any statistical technique, assumptions are made. Here are a few of the assumptions with SEM that you need to be aware of going forward:

How Do I Calculate the Degrees of Freedom of SEM Model?

As stated earlier, AMOS will calculate your degrees of freedom, but if you have a problem or want to verify another researcher’s work, you need

Identification With SEM Models

Identification in regards to a SEM model deals with whether there is enough information to identify a solution (or in this instance, estimate a parameter).

Assessing Reliability for SEM Model

After you have screened your data on both the respondent and variable levels, the next step is to assess the reliability of your indicators to

How Do I Address Missing Data?

Before we address what to do with missing data, we need to understand why data goes miss- ing. Missing data is typically classified in three

How Do I Assess If I Have Missing Data?

We have already addressed how to find respondent abandonment, but finding missing data that takes place in a random manner can be more challenging. To

Screening for Impermissible Values in the Data

There are times when respondents simply key in a value wrong or list an invalid response to an inquiry.To test if an answer is outside

Tips to Using AMOS More Efficiently

Tip 1: Change the AMOS graphics page from portrait to landscape. In the AMOS graphic win- dow, the program will default with the page in

AMOS Functions Listed as Icons in Pinned Ribbon

This function allows you to draw an observable variable.You can drag the square in the graph- ics window to the size of the box you

Overview of the AMOS Graphics Window

When you open the AMOS graphics program, the software will display a window that looks like it has a white page in the middle of

Understanding the Validity of Measures for SEM Model

After screening your data and assessing if the measures are reliable, you need to examine the validity of your constructs and indicators. There are numerous

Sample Size: How Much Is Enough?

With covariance-based SEM, one of the major assumptions is that this technique requires a larger sample size than other statistical techniques. SEM relies on tests

What Do I Do if My SEM Model Is Under-Identified?

If your model is under-identified, you have two primary solutions to fix this problem. First, you can reduce the number of proposed parameter estimates. This

Model Fit and Fit Statistics of SEM Model

One of the advantages of SEM is that you can assess if your model is “fitting” the data or, specifically, the observed covariance matrix.The term

Setting the Metric in SEM Model

In SEM, each unobserved variable must be assigned a metric, which is a measurement range. This is done by constraining one of the factor loadings

Interpretation of Factor Loadings in CFA

The factor loadings in a CFA estimate the direct effects of unobservable constructs on their indica- tors.If an unstandardized factor loading is 2.0 for the

How Is a CFA Different From an EFA?

An exploratory factor analysis (EFA) is useful in data reduction of a large number of indicators and can be quite helpful in seeing if indicators

Introduction to Confirmatory Factor Analysis for SEM Model

Confirmatory factor analysis (CFA) is a statistical technique that analyzes how well your indi- cators measure your unobserved constructs and if your unobserved constructs are

Quick Reference to AMOS Functions

Shortcut Keys for AMOS Functions: F1 = Search Content Directory F2 = “Select one object at a time” button F3 = “Draw Observed Variable” button

What If I Have a Standardized Factor Loading Greater Than 1 in SEM Model?

A standardized factor loading greater than 1 is stating that you are explaining more than 100% of the variance in an indicator. In this instance,

How to Calculate Composite Reliability for SEM Model

I have discussed earlier that reliability of indicators can be explored by calculating Cronbach’s alpha, but this test has had its detractors. Yang and Green

If I Have a Weak Factor Loading in SEM Model, Should I Drop It?

If you have a factor loading that is near or below the .70 threshold, it does not mean you need to drop the indicator from

Assessing Convergent and Discriminant Validity of Your Measures in SEM Model

After initially establishing that each indicator is loading on its respective construct and the model has an acceptable fit to the data, we now need

CFA Example in AMOS

Let’s say I want to test a simple model that has three unobserved variables. The first vari- able is a concept called “Adaptive Behavior”, which

Modification Indices in SEM Model

Modification indices are part of the analysis that suggest model alterations to achieve a better fit to the data. Making changes via modification indices should

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Right, let’s now download and open bank.sav. We’ll use it for walking you through the main parts of the data editor.

SPSS Data View & Variable View

An SPSS data file always has two tabs in the left bottom corner:

• Data View is where we inspect our actual data and
• Variable View is where we see additional information about our data.

You can switch between Data View and Variable View by

• clicking the tabs in the left bottom corner;
• using the Ctrl + t shortkey;
• double-clicking a variable name in Data View;
• double-clicking an outline number in Variable View.

Let’s first take a close look at the main parts of the Data View tab. We’ll then proceed with variable view.

SPSS Data View

1. The data editor has tabs for switching between Data View and Variable View. For now, make sure you’re in Data View.
2. Columns of cells are called variables. Each variable has a unique name (“gender”) which is shown in the column header.
3. Rows of cells are called cases. Oftentimes, each respondent in a study is represented as a single case.
4. In SPSS, values refer to cell contents.
5. The status bar may give useful information on the data, for instance whether a WEIGHT, FILTER, SPLIT FILE or Unicode mode is in effect.

These are the main elements in Data View. Let’s now switch to Variable View.

SPSS Variable View

1. In the left bottom corner we find tabs for switching between Variable View and Data View. For now, select Variable View.
2. In Variable View, variables are shown as rows of cells.
3. The first column shows the variable name for each variable.
4. The fifth column may or may not contain a variable label. This describes the exact meaning of each variable.
5. The sixth column shows value labels: descriptions of the meaning of one, many or all values that a variable may contain.

In short, Variable View does not show the data itself but, rather, information about the data. This is sometimes called “metadata” or “the codebook”. In SPSS, however, it’s called the dictionary.

This is important to know because you may find commands like DISPLAY DICTIONARY or APPLY DICTIONARY in manuals. If you’re familiar with syntax, runningDISPLAY DICTIONARY.creates the output shown below: dictionary information as seen in variable view.

Dictionary information reported by running DISPLAY DICTIONARY.

Variable View – Value Labels

For some variables, it’s immediately clear what their values mean: a value of € 2500,- in a variable “gross monthly income” represents a gross monthly income of € 2500,-.
This is not always the case, however: answer categories for categorical variables are often represented by numbers -usually 1 through x. What these values represent is then stored in their value labels. Clicking the open value labels icon  for education_type displays all value labels for this variable.

Value labels for education_type.

These value labels tell us that a person with a value of 1 on education_type indicates somebody who studied “Law”. In a similar vein, “Economy” is represented by a value of 2, and so on.

Dictionary Information in Data View

Thus far, we explained that SPSS’ Data Editor always has 2 tabs:

• Data View in which we inspect our actual data values and
• Variable View in which we find information about our data –dictionary information.

Little known by many SPSS users is that we can see some dictionary information in Data View too. Let’s start off with value labels. Initially, we just see data values in Data View as shown below.

Now, if we click the value labels icon  we’ll see value labels instead of data values in data view.

So this allows you to look up what your data mean without having to switch between Data View and Variable View. Perhaps even more useful: place your mouse pointer on a variable name in Data View without clicking it. Now a yellow box with a lot of dictionary information pops up for a few seconds.

Starting from SPSS version 22, icons next to variable names tell us something about our variable types, formats and measurement levels -if correctly set, that is.

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Right. So with our data open, let’s create some output by running the syntax below; or see the video to running the descriptive analysis.

Running this opens an output viewer window as shown below.

As illustrated, the SPSS output viewer window always has 2 main panes:

• the output outline is mostly used for navigating through your output items and
• the actual output items -mostly tables and charts- are often exported to WORD or Excel for reporting.

In the output outline, you can also delete output items -SPSS often produces way more output than you ask for. Use the ctrl key to select multiple items. A faster way for deleting a selection of output items is OUTPUT MODIFY.
You can also collapse and reorder output items in the outline but I don’t find that too useful. So let’s turn to the actual output items. The most important ones are tables and charts so we’ll discuss those separately.

SPSS Output – Tables

We’ll usually want to make some adjustments to our output tables. One option for doing so is right-clicking the table and selecting Edit content > In Separate Window as shown below.

The pivot table editor window (shown below) allows us to adjust basically anything about our table.

That being said, we recommend youonly use the pivot table editor if everything else fails. One reason is that you can’t replicate and rerun whatever you do in the pivot table editor.

Variable and Value Labels in Output Tables

When I’m inspecting my data, I want to see variable names and labels in my output. The same goes for values and value labels because I want to know how my variables have been coded.

However, I want to see only labels in the final tables that I’ll report. One way for doing so is navigating to Edit > Options and selecting the Output tab.

Oddly, the options dialog has no Paste button. This is because it creates very messy syntax. A much better option for this than the crappy menu is just running the syntax below.

• TVARS is short for ”table variables”. It sets how variables are shown in tables: names, labels or both;
• TNUMBERS is short for “table numbers”. It sets how values are shown in tables: values, labels or both.

Running this syntax is a much better option than using the aforementioned menu. After doing so, all output tables we’ll run will show only variable and value labels.

SPSS Output – Charts

First off, you can adjust basically anything about charts in the chart editor window. You can open one by right clicking a chart as shown below.

This opens a chart editor window as shown below.

Right, so that’ll do for introducing SPSS output. I hope we clarified some basics.

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How to paste SPSS syntax?

Now let’s suppose I’d like to gain some insight into the percentages of male and female respondents. I could first navigate to Analyze > Descriptive statistics > Frequencies as shown below.

I’ll now (1) move gender into the variable box and perhaps (3) request a bar chart as well.

Now clicking Ok may seem the obvious thing to do. A much better idea, however, is to click the Paste button. Upon doing so, a new SPSS window opens which is known as the Syntax Editor. It’s recognized by the orange icon in its left top corner.

The Syntax Editor contains a FREQUENCIES command which holds the instructions we just gave SPSS in the Frequencies dialog. However, we don’t see the frequency distribution and bar chart we asked for. This is because we still need to run the command we just created.

How to run SPSS syntax?

The simplest way to run syntax is to select the command(s) you’d like to run and click the “run selection” icon  in your toolbar.

A faster way to run syntax is to use several shortkeys, especially

• F2 for selecting the command in which your mouse pointer is located;
• Ctrl + a for selecting all syntax;
• Ctrl + r for running all selected commands.

So let’s now run our pasted syntax. On doing so, a new window will open, containing our frequency table and barchart. This is an output window which we’ll discuss in our next tutorial.

How to get SPSS syntax?

The right way to do basically anything in SPSS -editing and analyzing data, creating tables and charts and more- is by running syntax. So how to get syntax? First off, using the Paste button from the menus adds syntax to your syntax window. If you don’t have a syntax window open yet, it’ll open one for you. Options for opening a syntax window are

• using the Paste button from the menus;
• drag and drop a syntax file into the Data Editor window (shown below);
• clicking the New Syntax  toolbar icon;
• Navigate to File > New > Syntax.

If you’ve a syntax window open, you still need the actual syntax. Options to get the syntax you need are

• use the Paste button from SPSS’ menu;
• copy-paste syntax from our tutorials, online forums and elsewhere;
• type the commands you need into the syntax window.

Now, typing syntax may seem like a crazy thing to do at this point. However,typing syntax is much easier than it seems because most of it can be dramatically simplified.

Writing simpler syntax

The syntax we just pasted from the menu was:

FREQUENCIES VARIABLES=gender
/BARCHART FREQ
/ORDER=ANALYSIS.

Now typing all that manually is a lot of work. However, we’ll get the exact same results if we run:

frequencies gender
/barchart.

Just typing and running this is much faster and easier than clicking through all menu options. So if you want to get real good -and real fast– with SPSS, start learning short syntax. This will take some practice but it will save you tons of time and effort in the longer run.

SPSS Syntax Files

We can now save all contents of our Syntax Editor as a syntax file by going to File > Save as… The resulting syntax file has the .sps (for “SPSS syntax”) file extension and is a plain text file. You can open, edit and save it with SPSS or any text editor such as Notepad++.

When saving syntax in newer SPSS versions, something like* Encoding: UTF-8.may be added. Just leave and ignore this, it’s not meant for you but, rather, some kind of “note to self” from SPSS.

Why even use SPSS syntax?

The single best SPSS practice is doing everything from syntax. Some reasons for this are

• you’ll always know exactly which steps you took in which order so you can prove that your results are correct;
• if you made some mistake -don’t we all sometimes?- you can correct it and rerun everything you did in just seconds;
• you’ll work way faster than from the menu and you never have to do things twice;
• some of the best SPSS tricks and time savers are available as syntax only.

So say you run 10 tables and charts from the menu. And then you realize you should have filtered out all respondents working in IT. Now you have to start all over again: remove the unwanted respondents and click your way through all the same menus and dialogs again…Sounds like a terrible idea. Doesn’t it?Unfortunately, I see students having to do days of SPSS work all over again on a daily basis. Not working from syntax really is the very worst SPSS practice.

So say I run those 10 tables and charts and I saved all syntax. Then I realize I should have filtered out all respondents working in IT. Ok. No problem. I’ll just addSELECT IF (jtype <> 3).to the top of my syntax and rerun all tables and charts in one go.

Thanks for reading!

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The research process begins with an issue or problem of interest to the researcher. This research problem is a statement that asks about the relationships between two or more variables.¹ Almost all research studies have more than two variables. Appendix B provides templates to help you phrase your research problem, and provides examples from the expanded high school and beyond (HSB) data set that is described in this chapter and used throughout the book.

The process of moving from a sense of curiosity, or a feeling that there is an unresolved problem to a clearly defined, researchable problem, can be a complex and long one. That part of the research process is beyond the scope of this book, but it is discussed in most books about research methods and books about completing a dissertation or thesis.

2. Variables

Key elements in a research problem are the variables. A variable is defined as a characteristic of the participants or situation in a given study that has different values. A variable must vary or have different values in the study. For example, gender can be a variable because it has two values, female or male. Age is a variable that can have a large number of values. Type of treatment/intervention (or type of curriculum) is a variable if there is more than one treatment or a treatment and a control group. The number of days to learn something or to recover from an ailment are common measures of the effect of a treatment and, thus, are also potential variables. Similarly, amount of mathematics knowledge can be a variable because it can vary from none to a lot.

However, even if a characteristic has the potential to be a variable, if it has only one value in a particular study, it is not a variable; it is a constant. Thus, ethnic group is not a variable if all participants in the study are European American. Gender is not a variable if all participants in a study are female.

In quantitative research, variables are defined operationally and are commonly divided into independent variables (active or attribute), dependent variables, and extraneous variables. Each of these topics is dealt with briefly in the following sections.

Operational Definitions of Variables

An operational definition describes or defines a variable in terms of the operations or techniques used to make it happen or measure it. When quantitative researchers describe the variables in their study, they specify what they mean by demonstrating how they measured the variable. ^[1] Demographic variables like age, gender, or ethnic group are usually measured simply by asking the participant to choose the appropriate category from a list.

Types of treatment (or curriculum) are usually operationally defined much more extensively by describing what was done during the treatment or new curriculum. Likewise, abstract concepts like mathematics knowledge, self-concept, or mathematics anxiety need to be defined operationally by spelling out in some detail how they were measured in a particular study. To do this, the investigator may provide sample questions, append the actual instrument, or provide a reference where more information can be found.

Independent Variables

There are two types of independent variables, active and attribute. It is important to distinguish between these types when we discuss the results of a study. As presented in more detail later, an active independent variable is a necessary but not sufficient condition to make cause and effect conclusions.

Active or manipulated independent variables. An active independent variable is a variable, such as a workshop, new curriculum, or other intervention, at least one level of which is given to a group of participants, within a specified period of time during the study.

For example, a researcher might investigate a new kind of therapy compared to the traditional treatment. A second example might be to study the effect of a new teaching method, such as cooperative learning, compared to independent learning. In these two examples, the variable of interest is something that is given to the participants. Thus, active independent variables are given to the participants in the study but are not necessarily given or manipulated by the experimenter. They may be given by a clinic, school, or someone other than the investigator, but from the participants’ point of view, the situation is manipulated. To be considered an active independent variable, the treatment should be given after the study is planned so that there could be a pretest. Other writers have similar but, perhaps, slightly different definitions of active independent variables. Randomized experimental and quasi-experimental studies have an active independent variable.

Attribute or measured independent variables. An independent variable that cannot be manipulated, yet is a major focus of the study, can be called an attribute independent variable. In other words, the values of the independent variable are preexisting attributes of the persons or their ongoing environment that are not systematically changed during the study. For example, level of parental education, socioeconomic status, gender, age, ethnic group, IQ, and self-esteem are attribute variables that could be used as attribute independent variables. Studies with only attribute independent variables are called nonexperimental studies.

Unlike authors of some research methods books, we do not restrict the term independent variable to those variables that are manipulated or active. We define an independent variable more broadly to include any predictors, antecedents, or presumed causes or influences under investigation in the study. Attributes of the participants as well as active independent variables fit within this definition. For the social sciences and education, attribute independent variables are especially important. Type of disability or level of disability may be the major focus of a study. Disability certainly qualifies as a variable because it can take on different values even though they are not given during the study. For example, cerebral palsy is different from Down syndrome, which is different from spina bifida, yet all are disabilities. Also, there are different levels of the same disability. People already have defining characteristics or attributes that place them into one of two or more categories. The different disabilities are characteristics of the participants before we begin our study. Thus, we might also be interested in studying how variables that are not given or manipulated during the study, even by other persons, schools, or clinics, predict various other variables that are of interest.

Other labels for the independent variable. SPSS uses a variety of terms, such as factor (Chapter 10) and grouping variable (Chapter 9), for the independent variables. In other cases, (Chapters 7 and 8) the program and statisticians do not make a distinction between the independent and dependent variable; they just label them variables. For example, technically there is no independent variable for a correlation or chi-square. Even for chi-square and correlation, we think it is sometimes conceptually useful to think of one variable as the predictor (independent variable) and the other as the outcome (dependent variable); however, it is important to realize that the statistical tests of correlation and chi-square treat both variables in the same way, rather than treating one as a predictor and one as an outcome variable, as is the case in regression.

Type of independent variable and inferences about cause and effect. When we analyze data from a research study, the statistical analysis does not differentiate whether the independent variable is an active independent variable or an attribute independent variable. However, even though most statistics books use the label independent variable for both active and attribute variables, there is a crucial difference in interpretation.

A major goal of scientific research is to be able to identify a causal relationship between two variables. For those in applied disciplines, the need to demonstrate that a given intervention or treatment causes a change in behavior or performance can be extremely important. Only the approaches that have an active independent variable (randomized experimental and, to a lesser extent, quasi-experimental) can provide data that allow one to infer that the independent variable caused the change or difference in the dependent variable.

In contrast, a significant difference between or among persons with different values of an attribute independent variable should not lead one to conclude that the attribute independent variable caused the dependent variable to change. Thus, this distinction between active and attribute independent variables is important because terms such as main effect and effect size used by the program and most statistics books might lead one to believe that if you find a significant difference, the independent variable caused the difference. These terms can be misleading when the independent variable is an attribute.

Although nonexperimental studies (those with attribute independent variables) are limited in what can be said about causation, they can lead to solid conclusions about the differences between groups and about associations between variables. Furthermore, if the focus of your research is on attribute independent variables, a nonexperimental study is the only available approach. For example, if you are interested in learning how boys and girls learn mathematical concepts, you are interested in the attribute independent variable of gender.

Values of the independent variable. SPSS uses the term values to describe the several options or categories of a variable. These values are not necessarily ordered, and several other terms, categories, levels, groups, or samples, are sometimes used interchangeably with the term values, especially in statistics books. Suppose that an investigator is performing a study to investigate the effect of a treatment. One group of participants is assigned to the treatment group. A second group does not receive the treatment. The study could be conceptualized as having one independent variable (treatment type), with two values or levels (treatment and no treatment). The independent variable in this example would be classified as an active independent variable. Now, suppose instead that the investigator was interested primarily in comparing two different treatments but decided to include a third no-treatment group as a control group in the study. The study would still be conceptualized as having one active independent variable (treatment type), but with three values or levels (the two treatment conditions and the control condition). This variable could be diagrammed as follows:

As an additional example, consider gender, which is an attribute independent variable with two values, male and female. It could be diagrammed as follows:

Note that in this program each variable is given a variable label; moreover, the values, which are often categories, have value labels (e.g., male and female). Each value or level is assigned a number used to compute statistics. It is especially important to know the value labels when the variable is nominal, that is, when the values of the variable are just names and thus are not ordered.

Dependent Variables

The dependent variable is assumed to measure or assess the effect of the independent variable. It is thought of as the presumed outcome or criterion. Dependent variables are often test scores, ratings on questionnaires, readings from instruments (e.g., electrocardiogram, galvanic skin response, etc.), or measures of physical performance. When we discuss measurement in Chapters 2 and 3, we are usually referring to the dependent variable. Dependent variables, like independent variables, must have at least two values; most of the dependent variables used in this book have many values, varying from low to high so they are not as easy to diagram as the independent variables shown earlier.

SPSS also uses a number of other terms for the dependent variable. Dependent list is used in cases where you can do the same statistic several times for a list of dependent variables (e.g., in Chapter 10 with one-way ANOVA). The term test variable is used in Chapter 9 for the dependent variable in a t test.

Extraneous Variables

These are variables (also called nuisance variables or, in some designs, covariates) that are not of interest in a particular study but could influence the dependent variable. Environmental factors (e.g., temperature or distractions), time of day, and characteristics of the experimenter, teacher, or therapist are some possible extraneous variables that need to be controlled. SPSS does not use the term extraneous variable. However, sometimes such variables are “controlled” using statistics that are available in this program.

^[1] To help you, we have identified the variable names, labels, and values using italics (e.g., gender and male) and have put in bold the terms used in the windows and outputs (e.g., Data Editor), and we use bold for other key terms when they are introduced, defined, or are important to understanding. Underlines are used to focus your attention on critical points or phrases that could be missed. Italics are also used, as is commonly the case, for emphasizing words and for the titles of books.

Source: Morgan George A, Leech Nancy L., Gloeckner Gene W., Barrett Karen C.

(2012), IBM SPSS for Introductory Statistics: Use and Interpretation, Routledge; 5th edition; download Datasets and Materials.

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We divide research questions into three broad types: difference, associational, and descriptive, as shown in the middle of Fig. 1.1. The figure also shows the general and specific purposes and the general types of statistics for each of these three types of research question. We think it is educationally useful to divide inferential statistics into two types corresponding to difference and associational hypotheses or questions.^[1] Difference inferential statistics (e.g., t test or analysis of variance) are used for approaches that test for differences between groups. Associational inferential statistics test for associations or relationships between variables and use, for example, correlation or multiple regression analysis. We utilize this contrast between difference and associational inferential statistics in Chapter 6 and later in this book.

Difference research questions. For these questions, we compare scores (on the dependent variable) of two or more different groups, each of which is composed of individuals with one of the values or levels on the independent variable. This type of question attempts to demonstrate that the groups are not the same on the dependent variable.

Associational research questions. Here we associate or relate two or more variables. This approach usually involves an attempt to see how two or more variables covary (e.g., if a person has higher values on one variable, he or she is also likely to have higher, or lower, values on another variable) or how one or more variables enable one to predict another variable.

Descriptive research questions. These are not answered with inferential statistics. They merely describe or summarize data for the sample actually studied, without trying to generalize to a larger population of individuals.

Fig. 1.1. Schematic diagram showing how the purpose and type of research question correspond to the general type of statistic used in a study.

Fig. 1.1 shows that both difference and associational questions or hypotheses explore the relationships between variables; however, they are conceptualized differently, as will be described shortly.^[2] Note that difference and associational questions differ in specific purpose and the kinds of statistics they use to answer the question.

Table 1.1 provides the general format and one example of a basic difference question, a basic associational question, and a basic descriptive question. Remember that research questions are similar to hypotheses, but they are stated in question format. We think it is advisable to use the question format for the descriptive approach or when one does not have a clear directional prediction. More details and examples are given in Appendix B. As implied by Fig. 1.1, it is acceptable to phrase any research question that involves two variables as whether or not there is a relationship between the variables (e.g., is there a relationship between gender and math achievement or is there a relationship between anxiety and GPA?). However, we think that phrasing the question as a difference or association is preferable because it helps one identify an appropriate statistic and interpret the result.

Complex Research Questions

Some research questions involve more than two variables at a time. We call such questions and the appropriate statistics complex. Some of these statistics are called multivariate in other texts, but there is not a consistent definition of multivariate in the literature. We provide examples of how to write certain complex research questions in Appendix B, and in Chapters 8 and 10, we introduce two complex statistics: multiple regression and factorial ANOVA. Complex statistics are discussed in more detail in our companion volume, Leech et al. (in press).

Table 1.1. Examples of Three Kinds of Basic Research Questions/Hypotheses

1. Basic Difference (group comparison) Questions

• Usually used for randomized experimental, quasi-experimental, and comparative approaches.
• For this type of question, the groups of individuals who share a level of an active independent variable (e.g., intervention group) or an attribute independent variable (e.g., male gender) are compared to individuals who share the other levels of that same independent variable (e.g., control group or female gender) to see if the groups differ with regard to the average scores on the dependent variable (e.g., aggression scores).
• Example: Do persons who experienced an emotion regulation intervention differ from those who did not experience that intervention with respect to their average aggression scores? In other words, will the average aggression score of the intervention group be significantly different from the average aggression score for the control group following the intervention?

2. Basic Associational (relational) Questions

• Used for the associational approach, in which the independent variable usually is continuous (i.e., has many ordered levels).
• For this type of question, the scores on the independent variable (e.g., anxiety) are associated with or related to the dependent variable scores (e.g., GPA).
• Example: Will students’ degree of anxiety be associated with their overall GPA? In other words, will knowing students’ level of anxiety tell us anything about their tendency to make higher versus lower grades? If there is a negative association (correlation) between anxiety scores and grade point average, those persons who have high levels of anxiety will tend to have low GPAs, those with low anxiety will tend to have high GPAs, and those in the middle on anxiety will tend to be in the middle on GPA.

3. Basic Descriptive Questions

• Used for the descriptive approach.
• For this type of question, scores on a single variable are described in terms of their central tendency, variability, or percentages in each category/level.
• Example: What percentage of students make a B or above? What is the average level of anxiety found in 9^th grade students? The average GPA was 2.73, or 30% had high anxiety.

^[1] We realize that all parametric inferential statistics are relational so this dichotomy of using one type of data analysis procedure to test for differences (when there are a few values or levels of the independent variables) and another type of data analysis procedure to test for associations (when there are continuous independent variables) is somewhat artificial. Both continuous and categorical independent variables can be used in a general linear model approach to data analysis. However, we think that the distinction is useful because most researchers utilize the dichotomy in selecting statistics for data analysis.

^[2]This similarity is in agreement with the statement by statisticians that all common parametric inferential statistics are relational. We use the term associational for the second type of research question rather than relational or correlational to distinguish it from the general purpose of both difference and associational questions/hypotheses, which is to study relationships. Also we want to distinguish between correlation, as a specific statistical technique, and the broader type of associational question and that group of statistics.

Source: Morgan George A, Leech Nancy L., Gloeckner Gene W., Barrett Karen C.

(2012), IBM SPSS for Introductory Statistics: Use and Interpretation, Routledge; 5th edition; download Datasets and Materials.

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For opening a Stata dataset, in the easiest way, just Double-click on a SPSS data file, which is a file whose extension is .sav. Note that The file extension may not be visible, depending on what options you have set in your operating system.

Also, you can firstly open SPSS, then select File > Open, or click on the Open button and navigate to the file you want.

In another way, you can open datasets recently used by selecting File > Recently used Data, and click on the dataset you want.

SPSS allows multiple datasets to be active at any one time in different data editor windows. That means we can use multiple datasets at once.

For saving a Stata dataset, there are 2 situations of saving a dataset, that has been changed; and saving an unnamed dataset or an old dataset under a new name. We can do it as following:

To save a dataset that has been changed, select File > Save; or just click on the Save button on SPSS menu. Note: once you overwrite a dataset, there is no way to recover your original dataset. With important datasets, you may want to either keep a backup copy of your original filename dataset, or save your changes to a dataset under a new name. This is no different from working with a word-processing document, except that recovering from an inadvertent save of a dataset is nearly impossible.

To save an unnamed dataset, or an old dataset under a new name: just select File > Save as….

Note: that Changes you have made to a dataset are not permanent until you save them. You work with a copy of the dataset in memory, not with the data file itself. This should not be surprising, because it is the way that you work with almost all applications on your computer.

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