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 also need to assess if you have any missing data. Once your data has been keyed into a data software program like Excel, SAS, or SPSS, the first thing

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 of an acceptable range, you need to go to your SPSS file, select the “Analyze” option at the top, and then select “Descriptive Statistics”. Next, you will select the

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 initially see if any data is miss- ing, let’s start in the SPSS data file. In SPSS, go to the “Analyze” option at the top, then select “Descriptive Statistics”,

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 ways: (1) missing completely at random, (2) missing at random, and (3) missing not at random. The first category, missing completely at random, is where the missing data is

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 predict the construct of interest.While having a single indicator for a construct might be easy, it does not provide us a lot of confidence in the validity of the

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). A model that is “under- identified” means that it contains more parameters to be estimated than there are ele- ments in the covariance matrix. For instance, let’s say we

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 to know how to calculate degrees of freedom.To determine your degrees of freedom, you can use this simple formula outlined by Rigdon (1994) for your measurement model: df =

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 means that you can delete a covariance or structural relationship. Second, you can add more exogenous (independent) variables. By adding more exogenous variables, you increase the number of observations

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 which are sensitive to sample size as well as to the magnitude of differences in covariance matrices. There are a litany of suggestions in regards to necessary sample size

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 validity tests that a researcher needs to be aware of to support the legitimacy of their findings. Before moving on, I want to initially introduce what validity means, and

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 the screen. This is your working area in AMOS.You need to try to keep your model within the confines of the white page because the software program can have

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 want. Example 3.1: This function allows you to draw an unobservable variable.You can drag the circle in the graphics window to the size of the circle you want. Note:

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 a portrait format. Most models take more space from left to right than from top to bottom. If you change the format of the page to landscape, you have

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 F4 = “Draw Unobserved Variable” button F5 = “Draw Path (single head)” button F6 = “Draw Covariance” button F7 = “Zoom in” button F8 = “Zoom out” button F9

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 uniquely different from one another. In a CFA, an unobservable construct is often referred to as a “fac- tor”. So, when I use the term “factor”, it represents an

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 are measuring more than one construct. EFAs are typically the first step in determining if an indicator is measuring a construct. In an EFA, the researcher is not denoting

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 direct effect of Customer Delight ® Delight1, then we expect a two-point difference in the indicator Delight1 given a difference of 1 point on the factor of Customer Delight.While

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 from the unobservable variable by assigning it a value of 1.0. The remaining loadings are then free to be estimated. The factor loading that is set to 1.0 is

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 “model fit” denotes that your specified model (estimated covariance matrix) is a close representation of the data (observed covari- ance matrix). A bad fit, on the other hand, indicates

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 be done very carefully and have justification. Blindly using the modification indices to achieve a better model fit can capitalize on chance and result in model adjustments that make