# Specifying Measurement Model in SEMinR

### Specifying Measurement Model in SEMinR

This lecture on SEMinR Package will introduce How to Specify the Measurement Model in the SEMinR package.

## SEMinR

There are four steps to specify and estimate a structural equation model using SEMinR:

- Loading and cleaning the data
**Specifying the measurement models**- Specifying the structural model
- Estimating, bootstrapping, and summarizing the model

## Step 2: Specify the Measurement Model

- Path models are made up of two elements:
- The measurement models (also called outer models in PLS-SEM), which describe the relationships between the latent variables and their measures (i.e., their indicators), and
- The structural model (also called the inner model in PLS-SEM), which describes the relationships between the latent variables. We begin with describing how to specify the
**measurement models**. - Measurement model is assessed to establish the quality criteria (Reliability and Validity).
- Hypothesis tests involving the structural relationships among constructs will only be as reliable or valid as the construct measures.
- SEMinR uses the
**constructs()**function to specify the list of all construct measurement models. Within this list, various constructs can be defined using: **composite()**specifies the measurement of individual constructs.**interaction_term****()**specifies interaction terms.**higher_composite****()**specifies hierarchical component models (higher-order constructs; Sarstedt et al., 2019).- The
**constructs()**function compiles the list of constructs and their respective measurement model definitions. - We must supply it with any number of individual
**composite()**,**interaction_term****()**, or**higher_composite****()**constructs using their respective functions. - The
**composite()**function describes the measurement model of a single construct and takes the arguments shown in Table.

- SEMinR strives to make specification of measurement items shorter and cleaner using
**multi_items****()**, which creates a**vector**of multiple measurement items with similar names or**single_item****()**that describes a single measurement item. - A
*vector*is a sequence of data elements of the same basic type. Members in a*vector*are officially called components.*Vectors in R*are the same as the arrays in C language which are used to hold multiple data values of the same type. - For example, we can use
**composite()**for PLS path models to describe the reflectively measured Constructs

*composite(***“Put in Construct Name in Quotes”, ***multi_items**(***“Construct Code”, Starting ****Number:Ending**** Number***),**weights = **mode_A***)**;

*Collaborative Culture *construct with its indicator variables CC1, CC2, CC3, CC4, CC5, CC6:

- Explanations of mode A and mode B are discussed later. When no measurement weighting scheme is specified, the argument default is set to
**mode_A**.

`composite(“Collaborative Culture”, multi_items(“CC”, 1:6), weights = mode_A);`

- Similarly, if you have a single item construct, you can use
**composite()**to define the single-item measurement model as

**composite(“CUSA”, ****single_item****(“****cusa****”))**

- Using composite define your constructs in the mode, next, combine the measurement models within the
**constructs()**function, we can define the measurement model for the simple model like using constructs and composite**(see next slide)**.

**Note: If an error occurs, make sure you used the library(seminr) command in R to load the SEMinR package before executing the program code.**

The program code facilitates the specification of standard measurement models. However, the **constructs() **function also allows specifying more complex models, such as interaction terms (Memon et al., 2019) and higher-order constructs (Sarstedt et al., 2019). We will discuss the **interaction_term****() **function for specifying interactions in more detail later.

**Step 2 in Creating a Model **– Identify the variables in your study and Put them as Measurement Model.

```
#Step 2: Create measurement model
simple_mm <- constructs(
composite("Vision", multi_items("VIS", 1:4)),
composite("Development", multi_items("DEV", 1:7)),
composite("Rewards", multi_items("RW",1:4)),
composite("Collaborative Culture", multi_items("CC", 1:6)))
```

Here **simple_mm** is an object which stores the constructs in the study.

**<- **Can be considered as an equal sign that assigns the constructs to the object.

**constructs**** function holds the variables from the study, defined as ****composite ****(as discussed in the last slide) **

## Review of the Steps

Following is a brief review of the steps that have been discussed in SEMinR tutorials.

- Load the Library –
**library ()** - Load the Data –
**read.csv** - Review the Data –
**head()** - Specify the Measurement Model –
**constructs()**

## Reference

Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook.

The tutorials on SEMinR are based on the mentioned book. The book is open source and available for download under this link.

## Video Tutorial

## Additional SEMinR Tutorials

- An Introduction to R and R Studio
- An Introduction to SEMinR Package
- Create Project, Load, and Inspect the Data
- SEMinR Package: An Introduction to Evaluating Formative Measurement Model
- SEMinR Package: Analyzing Categorical Predictor Variables
- SEMinR Package: Bootstrapping PLS Model
- SEMinR Package: Evaluating Formative Measurement Model – Convergent Validity and Collinearity
- SEMinR Package: Evaluating Formative Measurement Model – Step 3- Indicator Weights
- SEMinR Package: Evaluating Formative Measurement Model – When to Delete Formative Indicators
- SEMinR Package: Evaluating Reflective Measurement Model
- SEMinR Package: Evaluating Structural Model
- SEMinR Package: Evaluating Structural Model – Step 4: Predictive Power (PLSPredict)
- SEMinR Package: Higher Order Analysis – REF-FOR
- SEMinR Package: Higher Order Analysis – REF-REF
- SEMinR Package: How to Solve Convergent and Discriminant Validity Problems
- SEMinR Package: Mediation Analysis
- SEMinR Package: Moderation Analysis
- SEMinR Package: PLS Estimation
- SEMinR Package: Print, Export and Plot Results
- SEMinR Package: Reflective Measurement Model Step 2: Consistency and Step 3: Convergent Validity
- SEMinR Package: Reflective Measurement Model Step 4: Discriminant Validity
- SEMinR Package: Single Item, SmartPLS Comparison and Summary of SEMinR
- SEMinR Package: Specifying the Structural Model