Measurement Models and Variable Relationships

 

  1. Measuring variables/constructs.

Measurement model

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  • Before addressing anything in the papers, please discuss the following general concepts. Please find and provide references.
    1. Measuring variables/constructs.

What do we mean by the overall concept of the measurement model? 

The overall concept of measurement model is used to define loadings of observed items (which are classified as measurements) on their anticipated latent variables (also referred to as constructs) (Weigl, 2008; O’Leary, 2004).

What is Indicator Reliability, how is it measured, and what is an acceptable criteria?

Indicator reliability can be defined as the indication of a variance contribution which often may be interpreted through the latent variable (Weigl, 2008; O’Leary, 2004). Ideally, indicator reliability is measured by ensuring that 50% of indicator’s variance is derived directly from latent variables. Based on the above statement, it is clear that the loadings have to be higher than 0.7. The criterion that is employed is that loadings that are found to be below 0.4; for instance, in PLS models, they should not be included in the final results.

What is Construct Internal Consistency Reliability, how is it measured, and what is an acceptable criteria?

Construct internal consistency reliability is used to expound the extent to which a construct is defined by its indicators (Weigl, 2008). The mentioned framework demands that different indicators, belonging to the same construct, have to demonstrate high correlation. It is measured by using Cronbach’s Alpha approach, and one investigates internal consistency exhibited by different items classified into one construct. The criterion is to attain a coefficient which can be equal or even above 0.7.

 What is the difference between the two and why are they important?

The main difference between indicator reliability and construct internal consistency reliability is that the former is concerned with whether selected indicators are free from errors and can be calculated using various formulas while the later use Cronbach’s Alpha (calculated using SPSS) to ensure that there is internal consistency. The two are important in ensuring that any given content is valid and reliable.

What do we mean by the overall concept of measurement model validity?

Measurement model validity explains the ability of any selected measurement model to yield accurate results depending on the latent constructs.

What is Convergent Validity, how is it measured, and what is an acceptable criteria?

Convergent validity can be defined as the level of validity attained when all items, included in a measurement model, are proved to be statistically significant (Awang, n. d.; Gefen, Straub, & Boudreau, 2000; Bhattacherjee, 2012). Ideally, it is measured by computing the Average Variance Extracted (AVE). Basically, the acceptable value of AVE ought to be or higher than 0.5. As a result, one has to drop low factor loading items given that they can lead to failed convergent validity.

What is Discriminant Validity, how is it measured, and what is an acceptable criteria?

Discriminant validity is defined as the validity that ascertains that a measurement model does not include redundant items (Awang, n. d.; Gefen et al., 2000; Bhattacherjee, 2012). Discrepancy measures, for instance, Modification Indices (MI) utilized in identifying redundancy in selected models. Normally, when MI figures are high, it suggests that there is a high level of redundancy (Awang, n. d.). Hence, a researcher is directed to delete any item and run tests until desirable outcomes are attained. The acceptable criterion is that the correlation, between selected exogenous constructs, ought not to be more than 0.85. Reasonably, if two exogenous constructs have a figure of more than 0.85, they are redundant or worse still; there is the existence of multicollinearity problem.

What is the difference between the convergent and discriminant validity and why are they important?

The difference between discriminant and convergent validity is their evaluation tools (Awang, n. d.). For example, convergent validity employs AVE and at the same time, discriminant validity uses MI. Indeed, both are important given that they ascertain that a measurement model is valid and insignificant items are not included for accurate results.

What is a 1st Order Construct and 2nd Order Construct?

Specifically, 1st order constructs have observable variables which are the indicators of constructs (Ping Jr., 2002). Studies suggest that the model has four main constructs which include confidence cooperative, decision making, affective commitment, and goal congruence. Besides this, 2nd order constructs “have unobservable constructs as their indicators” (Ping, 2002). Examples of 2nd order constructs are networking, risk-taking, and innovativeness.

What is the difference in terms of indicators?  I.e. Does a 2nd order construct have indicators?  If, so under what conditions? 

As highlighted in question nine, 1st order constructs have observable indicators while 2nd order constructs have unobservable indicators (Ping Jr., 2002). A formative relationship between constructs and indicators is witnessed in 1st order constructs. For one to observe indicators in second order construct, first order constructs have to be one-dimensional and exploratory factor analysis has to be conducted.

  1. Relationships between constructs via regression modeling, path modeling, or structural modeling.

What is Explained Variance?  What does it apply to i.e. what type of variable?  What is the Statistic used?  What is an acceptable level?

Explained variance is used to explain the measure “for the validity of formative constructs” and it “reflects whether the construct is sufficiently captured by its formative indicators” (Döscher, 2014). It is applied to assess the incompleteness of different indicators. In particular, its statistic is R2, and its acceptable level is 0.748 (Döscher, 2014).

What is Effect Size?  What does it apply to?  What are possible statistics used? What are acceptable levels?

Effect size (ES) refers to the quantitative comparison of two groups when the impact of treatment effect is assessed (University of Oxford, 2017). Specifically, ES is applied to evaluate the effectiveness of a particular intervention. When calculating ES there are different levels, for instance, 0.2 (small effect size), 0.5 (medium effect size), and 0.8 and above (large mean effective size) derived when Cohen formula is used (University of Oxford, 2017). The acceptable level is 0.8 and above given that it represents significant correlations between two groups.

For a regression model – what is statistic used measure the general relationship between two variables?  What are acceptable Levels?

In the regression model, the statistic used to measure the relationship between different variables is regression function. R2, calculated in regression analysis, assumes values that are between 0 and 1. Hence, the acceptable values have to be closer to 1 and not 0.

For a path model or structural model what is the statistics used to measure the relationship between two variables?  What are acceptable Levels?

In path model or structural model, the statistic used is to measure the relationship between variables is Absolute Fit Index and the acceptable level is 0.08.

What are some differences between the statistic used in regression model and the one in the path or structural model?

The difference between the statistic that is used in the regression model and the one path or structural model is how they describe the relationship between variables. For example, in regression, they illustrate the relationship that exists between a response variable and predictor variables (Henseler, Ringle, & Sinkovics, 2009). On the other hand, in path or structure model, a statistic used shows significance and goodness fit (Statistic Solutions, 2017).

  1. If the measurement model comes up with acceptable and the relationships between variables (variance, effect size, and regression/path statistic) are acceptable, what does this indicate about the hypothesis they are testing?

In the event that different measurement models used to validate the acceptable and prove the relationship between variables, then, the hypotheses can be considered to be valid as well as reliable.

 

 

 

  1. What are the major variables being studied, how many indicators are there, are they reflective or formative?

The first variables are as examples.

EXPLORING INTENTIONS TO USE VIRTUAL WORLDS FOR BUSINESS 
Name of Variable # of Indicators or Sub-dimensions Reflective or Formative
Perceived Ease of Use 3 Indicators Reflective
Perceived Usefulness 3 Indicators Reflective
Perceived Enjoyment 3 Indicators Formative
Computer Playfulness 3 Indicators Formative
Computer Self-Efficacy 4 Indicators Formative
Computer Anxiety 3 Indicators Formative
Behavioral Intention 3 Indicators Reflective
The impact of transformational leadership on employee creativity: the role of learning orientation
Transformation Leadership 4 Sub Dimensions Reflective
Employee Outcome 4indicators Reflective
Transformational Leadership 4indicators Reflective
Learning Orientation 4indicators Reflective
Intellectual Stimulation 1 Indicator Reflective
Inspirational Motivation 1 Indicator Reflective
Idealized Influence 1 Indicator Reflective
Individual Consideration 1 Indicator Reflective
Intellectual Stimulation 1 Indicator Reflective
Employee Creativity 2 Indicators Formative

 

 

EXPLORING INTENTIONS TO USE VIRTUAL WORLDS FOR BUSINESS  The impact of transformational leadership on employee creativity: the role of learning orientation
What Tool (software) was used to Test the Model.

SmartPLS Software

 

What Tool (software) was used to Test the Model.

Analysis of a Moment Structures (AMOS)

Reliability
How was Indicator Reliability tested and what statistics did they use?

Indicator reliability was tested through the assessment of composite reliability. The statistic used was above 0.70 for each variable.

How was Indicator Reliability tested and what statistics did they use?

Indicator reliability was tested through the assessment of composite reliability. The statistic used was 0.982.

 

How was construct internal consistency reliability tested and what statistics did they use?

It was tested using Cronbach’s Alpha. The statistics used was above 0.79.

 

How was construct internal consistency reliability tested and what statistics did they use?

It was tested using Cronbach’s Alpha. The statistics used was 0.792.

Validity
How was convergent validity tested and what statistics did they use?

Convergent validity was tested through the use of T-analysis. The statistics used was in the ranges of 0.80 (lower bound) to 0.90 (high bound).

How was convergent validity tested and what statistics did they use?

Convergent validity was tested through the use of Bentler-Bonnet Delta Coefficient. The statistics used was 0.961.

 

How was discriminant validity tested and what statistics did they use?

Discriminant validity was tested by arranging the variables in a diagonal matrix and comparing them with the square roots of Average Variance Extracted (AVE). The statistics used ranged between 0.11 (lower bound) and 0.95 (high bound).

 

How was discriminant validity tested and what statistics did they use?

Similar to convergent validity, discriminant validity was tested through the use of Bentler-Bonnet Delta Coefficient. The statistic that was used was 0.961.

 

Path or Structural Relationship
Was Explained Variance for appropriate variables addressed?  What was the statistic used?

Explained variance for appropriate variables was not addressed.

 

Was Explained Variance for appropriate variables addressed?  What was the statistic used?

The Explained variance for appropriate variables was addressed. The statistics used was 60%.

Was Effect Size between appropriate variables addressed?  What was the statistic used?

Effect size between appropriate variables was not addressed.

Was Effect Size between appropriate variables addressed?  What was the statistic used?

 

Effect size between appropriate variables was not addressed.

What statistic was used to articulate the relationship between the variables?

The statistic that was used to articulate the relationship between variables was path model analysis.

What statistic was used to articulate the relationship between the variables?

The statistic that was used to articulate the relationships between variables was path model analysis.

 

 

Which paper addressed more “measurement and or regression” statistics?

Exploring Intentions to use Virtual Worlds for Business, by Shen and Eder (2009), employed more measurement or regression statistics compared to the other source. Hence, the above-mentioned paper was rigorous in testing hypotheses from an inferential statistic perspective.

 

 

 

 

 

 

References

Awang, Z. (n. d.). Chapter 3: In A handbook on SEM. Research Gate. Retrieved from https://www.researchgate.net/file.PostFileLoader.html?id=560baab96225ff0fa18b4567&assetKey=AS%3A279314508599314%401443605176580

Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. Text Books Collections.

Döscher, K. (2014). Recovery Management in Business-to-Business Markets: Conceptual Dimensions, Relational Consequences and Financial Contributions. Springer Science & Business Media.

Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4(1), 7.

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009).  The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited.

Jyoti, J., & Dev, M. (2015). The impact of transformational leadership on employee creativity: the role of learning orientation. Journal of Asia Business Studies, 9(1), 78-98.

O’Leary, M. (2004). Measuring disaster preparedness: A practical guide to indicator development and application. iUniverse.

Ping Jr., R. (2002). Testing latent variable models with survey data. Retrieved from http://www.wright.edu/~robert.ping/lv/in_i.doc

Shen, J., & Eder, L. B. (2009). Exploring intentions to use virtual worlds for business. Journal of electronic commerce research, 10(2), 94.

Statistic Solutions (2017). Path analysis. Retrieved from http://www.statisticssolutions.com/factor-analysis-sem-path-analysis/

University of Oxford. (2017). What is an effect size? Retrieved from https://www.cebi.ox.ac.uk/for-practitioners/what-is-good-evidence/what-is-an-effect-size.html

Weigl, T. (2008). Strategy, structure and performance in a transition economy. Springer Fachmedien.

 

 

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