Survey and Measurement Development in R

Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.
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4 Hours13 Videos51 Exercises2,373 Learners
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Course Description

How can we measure something like “brand loyalty?” It’s an obvious concept of interest to marketers, but we can’t quite take a ruler to it. Instead, we can design and analyze a survey to indirectly measure such a so-called “latent construct.” In this course, you’ll learn how to design and analyze a marketing survey to describe and even predict customers’ behavior based on how they rate items on “a scale of 1 to 5.” You’ll wrangle survey data, conduct exploratory & confirmatory factor analyses, and conduct various survey diagnostics such as checking for reliability and validity.

  1. 1

    Preparing to analyze survey data

    Free
    In this chapter we will explore the use of surveys in marketing research and the importance of reliability and validity in measurement. We will begin the scale development process and perform exploratory data analysis on freshly-collected survey data.
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  2. 2

    Exploratory factor analysis & survey development

    Now that we have cleaned and summarized our survey results, we will look for hidden patterns in the data using exploratory factor analysis. These patterns form the basis of developing “factors” of an unobserved or latent variable. Of particular interest in this stage of survey development is internal reliability, or whether similar items in the survey produce similar scores.
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  3. 3

    Confirmatory factor analysis & construct validation

    Does the data as collected agree with prior beliefs about the latent variable of interest? In this chapter, we will use confirmatory factor analysis to formally test the hypothesis that our model fits our data. We can now answer the question of construct validity, or: “are we really measuring what we are claiming to measure?”
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  4. 4

    Criterion validity & replication

    In this chapter we will use our newly validated scale to test its relationship to demographic variables like age or spending habits. We will also confirm the reproducibility and replicability of the survey. Finally, we will explore the power of factor scores in modeling customer behavior.
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Collaborators
Chester Ismay
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George Mount

Data analytics educator
George works as an independent analyst and data analytics educator with the goal to help clients manage their data so they think more creatively. He holds a master’s degree in information systems with certificate of achievement in quantitative methods from Case Western Reserve University. He is also a technical expert and lead curriculum developer for Thinkful’s data analytics program. George blogs about data, innovation and career development at georgejmount.com.
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