# Evaluating Formative Measurement Model - Step 3: Indicator Weights

### SEMinR Lecture Series

This session compliments the step 3 of the formative measurement model assessment. The tutorial will guide on deciding when to delete a formative indicator.

## Formative Model Assessment

• When deciding whether to delete formative indicators based on statistical outcomes, researchers need to be cautious for the following reasons.
• First, formative indicator weights are a function of the number of indicators used to measure a construct. The greater the number of indicators, the lower their average weight.
• Formative measurement models are inherently limited in the number of indicator weights that can be statistically significant.
• Second, indicators should seldom be removed from formative measurement models since formative measurement requires the indicators to fully capture the entire domain of a construct, as defined by the researcher in the conceptualization stage.
• In contrast to reflective measurement models, formative indicators are not interchangeable, and removing even one indicator can therefore reduce the measurement modelâ€™s content validity (Bollen & Diamantopoulos, 2017).
• Important: Formative indicators with nonsignificant weights should not automatically be removed from the measurement model, since this step may compromise the content validity of the construct.

## Summary Formative Model Assessment

• After the statistical significance of the formative indicator weights has been assessed, the final step is to examine each indicatorâ€™s relevance. With regard to relevance, indicator weights are standardized to values between âˆ’1 and +1.
• Thus, indicator weights closer to +1 (or âˆ’1) indicate strong positive (or negative) relationships, and weights closer to 0 indicate relatively weak relationships.
• Table summarizes the rules of thumb for formative measurement model assessment.

## Complete Code

``````library(seminr)
# Create measurement model
simple_mm <- constructs(
composite("Vision", multi_items("VIS", 1:4), weights = mode_B),
composite("Development", multi_items("DEV", 1:7), weights = mode_B),
composite("Rewards", multi_items("RW",1:4), weights = mode_B),
composite("Collaborative Culture", multi_items("CC", 1:6)))
# Create structural model
simple_sm <- relationships(paths(from = c("Vision", "Development", "Rewards"), to = "Collaborative Culture"))
# Estimate the model
simple_model <- estimate_pls(data = datas, measurement_model = simple_mm, structural_model = simple_sm, missing = mean_replacement, missing_value = "-99")
# Summarize the model results
summary_simple <- summary(simple_model)
#Descriptive Stattistics Summary
summary_simple\$descriptives\$statistics
# Iterations to converge
summary_simple\$iterations
# Collinearity analysis
summary_simple\$validity\$vif_items
# Bootstrap the model on the PLS Estimated Model
boot_model <- bootstrap_model(
seminr_model = simple_model,
nboot = 1000, cores = parallel::detectCores(), seed = 123)
# Store the summary of the bootstrapped model
# alpha sets the specified level for significance, i.e. 0.05
summary_boot <- summary(boot_model, alpha = 0.05)
# Inspect the bootstrapping results for indicator weights
summary_boot\$bootstrapped_weights