The Hausman test: how to implement with Stata

The Durbin–Wu–Hausman test (also called Hausman specification test) is a statistical hypothesis test in econometrics named after James Durbin, De-Min Wu, and Jerry A. Hausman. The test evaluates the consistency of an estimator when compared to an alternative, less efficient estimator which is already known to be consistent. It helps one evaluate if a statistical model corresponds to the data. It is also applied in the context of linear

Lagrange Multiplier Test for Random Effects in Panel Data Analysis with Stata

In 1980, Breusch and Pagan developed a Lagrange multiplier test for random effects, so this test is also called Breusch-Pagan Lagrange Multiplier test. The test helps us choose between random-effects model regression and pooled OLS regression. In the following video, we will show you how to perform this test step by step on our

Basic Concepts and Tools for Programming in Stata

Some elementary concepts and tools, combined with the Stata capabilities described in earlier chapters, suffice to get started. 1. Do-files Do-files are text (ASCII) files, created by Stata’s Do-file Editor, a word processor, or any other text editor. They are typically saved with a .do extension. The file can contain any sequence of legitimate

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Sata Example Program

1. Example Program: multicat (Plot Many Categorical Variables) The preceding sections presented basic ideas and example short programs. In this section, we apply those ideas to a longer program that defines a new statistical procedure named multicat. Survey research produces datasets containing many categorical variables — sometimes 100 or more. Our 2010 General Social

Help File in Stata

Help files are an integral aspect of using Stata. For a user-written program such as multicat.ado, they become even more important because no documentation exists in the manuals. We can write a help file for multicat.ado by using Stata’s Do-file Editor to create a text file named multicat.sthlp. This help file should be saved

Monte Carlo Simulation by using Stata

Monte Carlo simulations generate and analyze many samples of artificial data, allowing researchers to investigate the long-run behavior of their statistical techniques. The simulate command makes designing a simulation straightforward so that it only requires a small amount of additional programming. This section gives two examples. To begin a simulation, we need to define

Matrix Sata Programming with Mata

Stata’s matrix programming language called Mata, is described in the two-volume Mata Matrix Programming manual. This rich topic lies beyond the introductory scope of Statistics with Stata. It seems fitting, however, to conclude the book with a brief look at Mata. Its programming tools open new paths for Stata’s development. Rather than undertaking the

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 Specification: The first step in SEM analysis is the model specification. It is performed prior to data collection and data modelling. This involves the devel- opment of a theoretical

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 a single procedure but rather a family of related statistical techniques. This family of analysis tech- niques examines the measurement properties of a variable along with the interrelationships between

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 and assessing the “struc- ture” of a specified model along with its relationships. Before we move forward, a discussion is warranted on concepts such as variance, covariance, and correlation

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 essentially derived from the covariance. With a covariance analysis, the calculated values do not have a definable range or limit. If you have two variables that are measured on

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 is used quite often with SEM. As stated earlier, SEM uses a covariance matrix as its input, so you are essentially looking at cor- relations between variables to determine

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 collected to test how well the model fits the data, thus trying to “confirm” the researcher’s hypotheses about how constructs influence one another. Joreskog (1993) outlines how SEM testing

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 to understanding a phenomenon of interest and ulti- mately help to explain why two variables are influencing each other. Sadly, it seems like many SEM models are conceptualized first

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: Multivariate Normal Distribution of the Indicators—there is an assumption that the data has a normal distribution. Dependent variables need to be continuous in SEM—while the independent variables do not

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 make you draw out your concep- tual model. One of the frustrating aspects of SEM is that there are often multiple terms that mean the exact same thing.You can

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 1.6 Source: Thakkar, J.J. (2020). “Procedural Steps in Structural Equation Modelling”. In: Structural Equation Modelling. Studies in Systems, Decision and Control, vol 285. Springer,

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 are capturing the “true” or actual score without some degree of error. Thus, an indicator is a function of the “true” score plus error: Construct Indicator =True or Actual

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 Greek notations, but you need to be familiar with them so you can understand the results from other SEM programs. What Do All These Greek Symbols Mean? Latent Constructs

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