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Why is it Important?
Why is it Important?
A central research question in social science research focuses on the operationalization of complex constructs: Are indicators causing or being caused by the latent variable/construct measured by them?
Changes in the latent variable directly cause changes in the assigned indicators
Changes in the latent variable directly cause changes in the assigned indicators
Changes in the indicators directly cause changes in the latent constructs
Changes in the indicators directly cause changes in the latent constructs
Problems in Specification
Problems in Specification
It is important to determine whether a construct is reflective or formative. For a long time, people have been living the reflective world.
Reflective measurement is most commonly used, but in many cases a formative measurement would be appropriate.
Type I Error: When the construct is modelled reflectively, but it should be formative.
Type II Error: When the construct is modelled formatively, but it should be reflective.
Based on the research by Jarvis et al. (2003), 1/3 of the papers published were wrongly modelled.
Reflective construct is most commonly used, but in many cases a formative construct would be appropriate.
Reflective Construct
Reflective Construct
Construct: Diet
Construct: Diet
D1 = I eat healthy food
D2 = I don’t eat much junk food
D3 = I have a balance diet
Indicators must highly be correlated (Hulland, 1999)
Construct describes and defines the items
If factor loading is low, then the item can be deleted.
Characteristics of Reflective Model
Characteristics of Reflective Model
Direction of causality is from construct to indicators.
Indicators are expected to be highly correlated.
Construct describes and defines the items
When the construct score is high, all the answer to all indicators will be high.
When the construct score is low, all the answer to all indicators will be low.
The construct scores are reflected in the answers to the indicators.
Dropping an indicator from the measurement model does not alter the meaning of the construct.
Takes measurement error into account.
Similar to factor analysis; if factor loading is low, then the item can be deleted.
Changes in the latent variable directly cause changes in the assigned indicators
Changes in the latent variable directly cause changes in the assigned indicators
Formative Construct
Formative Construct
Construct: Health
Construct: Health
H1 = I have a balance diet
H2 = I exercise regularly
H3 = I get sufficient sleep every night
Indicators can have positive, negative, and no correlation (Hulland, 1999).
Items describe and define the construct.
If weight is low, then the item cannot be deleted.
Characteristics of Formative Model
Characteristics of Formative Model
Direction of causality is from indicators to construct.
The construct scores is influenced by the score of the indicators.
Indicators are not expected to be correlated.
When all indicators are high, the construct score will be high.
When all indicators are low, the construct score will be low.
Dropping an indicator may alter the meaning of the construct (No such thing as internal consistency reliability).
Based on multiple regression analysis (Need to take care of multicollinearity).
Changes in one or more of the indicators causes changes in the latent variable
Changes in one or more of the indicators causes changes in the latent variable
Satisfaction as a Formative & Reflective Construct
Satisfaction as a Formative & Reflective Construct
Source: Hair et al., (2014)
Guidelines for Choosing the Measurement Model Model
Guidelines for Choosing the Measurement Model Model
Reflective indicators are interchangeable. Deleting an item does not change the essential nature of the construct.
With formative indicators, omitting an indicator is omitting a part of the construct (Diamantopoulos & Winklhofer, 2001)
Guideline from Hair et al., (2014)
Guideline from Hair et al., (2014)
Source: Hair et al., (2014)
Further Reading
Further Reading
Chang, W., Franke, G. R., & Lee, N. (2016). Comparing reflective and formative measures: New insights from relevant simulations. Journal of Business Research, 69(8), 3177-3185. Click here.
Finn, A., & Wang, L. (2014). Formative vs. reflective measures: Facets of variation. Journal of Business Research, 67(1), 2821-2826. Click here.
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263-282. Click here.
Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2), 195-204. Click here.
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of consumer research, 30(2), 199-218. Click here.
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