Quantitative methods

Approach phenomena through quantifiable evidence

Andy Weeger

Neu-Ulm University of Applied Sciences

August 28, 2023

Opening remarks

Overview

Methods that are based on the idea that theories can be proposed that can be falsified by comparing theory to carefully collected empirical data.

A focus on numbers, many cases and application of statistical analysis

Process

Overview

The process of quantitative research based on Recker (2021)

 

 

 

 

Operationalization

Measures and measurements based on Burton-Jones and Lee (2017)

 

 

 

Phases and outcomes

Phases and outcomes of the operationalization process

 

 

 

Reliability and validity

Meeting requirements for validity and reliability (Recker 2021, 93)

 

 

 

Examples

Construct Definition Indicators
Behavioral Intention Participant’s intentions
to use a particular system
in the future

- I intend to use the system in the next months

- I predict I would use the system in the next months

- I plan to use the system in the next months

Attitude towards Behavior An individual’s positive
or negative feelings about
performing the target behavior.

- Using the system is a bad/good idea

- Using the system is a foolish/wise idea

- I dislike/like the idea of using the system

- Using the system is unpleasant/ pleasant

Perceived Usefulness The degree to which a
person believes that using
a particular system would
enhance his or her job
performance.

- Using the system in my job would enable me to accomplish tasks more quickly

- Using the system would improve my job performance

- Using the system in my job would increase my productivity

- Using the system would enhance my effectiveness on the job

- Using the system would make it easier to do my job

- I would find the system useful in my job

Perceived Ease of Use The degree to which a person
believes that using a system
would be free of effort.

- Learning to operate the system would be easy for me

- I would find it easy to get the system to do what I want it to do

- My interaction with the system would be clear and understandable

- I would find the system to be flexible to interact with

- It would be easy for me to become skillful at using the system

- I would find the system easy to use

Table 1: Scales from TAM (Venkatesh et al. 2003)

Exercise

We want to find out whether blocking online games on work computers has a noticeable positive effect on work performance.

Key questions:

  • What are the constructs?
  • What are appropriate measures?
  • How do we design the study?
  • How can we demonstrate reliability, validity, and causality?

Measurement development

After having developed a conceptual definition of the construct, the first step is to identify and to develop potential items for the construct (Recker 2021).

Because developing and assessing measures and measurement is time-consuming and challenging, the first rule should always be to identify and re-use (wherepossible) measures and measurements that have already been developed and assessed.

Existing items—best case, you rely on established measures. Look for measurements reported in papers or use a measurement database such as TheoryOn or the Handbook of Management Scales.

New items—follow one of the guidelines published. You might start with Recker (2021)’s procedural model to create new measurement instruments for conceptually defined theory constructs.

Data collection

Process

The process of data collection

 

 

 

 

Biases

⚠️ systematic errors or distortions in the collection, analysis, interpretation, or reporting of data

Examples: Non-response bias, sampling bias, social desirability bias, common method bias

Reporting requirements

Sampling method,
resulting sample,
survey instrument and
evidence on validity and reliability

Data analysis

Overview

Data analysis can take the form of simple descriptive statistics or of more sophisticated statistical inferences such as

  • Univariate analysis: methods that analyze one variable (e.g., analysis of single-variable distributions)
  • Bivariate analysis: methods that analyze two variables (e.g., analysis of correlation)
  • Multivariate analysis: methods that simultaneously analyze multiple measurements on each individual or object under investigation (e.g., structural equation techniques such as LISREL or PLS)

PLS-SEM

Partial Least Squares Structural Equation Modeling (PLS-SEM) is a statistical technique used for analyzing relationships between variables in empirical research. It combines elements of both structural equation modeling (SEM) and regression analysis.

Complex relationships between constructs,
small sample sizes and
non-normal data

SmartPLS

SmartPLS (Ringle, Wende, and Becker 2022) is a popular software tool used for conducting PLS-SEM analyses.

  • It provides researchers with a user-friendly interface to specify, estimate, and evaluate complex models involving latent variables and observed indicators.
  • It is widely used in research fields such as business, management, marketing, information systems, and social sciences.
  • It offers an approachable platform for researchers who are new to PLS-SEM, as well as advanced functionalities for more experienced users.

Homework

Research recently published papers in your field that employ a quantitative method.

Try to understand the rational and approach, deduce important points for your research.

Q&A

References

Burton-Jones, Andrew, and Allen S Lee. 2017. “Thinking about Measures and Measurement in Positivist Research: A Proposal for Refocusing on Fundamentals.” Information Systems Research 28 (3): 451–67.
Hair, Joseph F, Jeffrey J Risher, Marko Sarstedt, and Christian M Ringle. 2019. “When to Use and How to Report the Results of PLS-SEM.” European Business Review 31 (1): 2–24.
Recker, Jan. 2021. “The Research Process.” Scientific Research in Information Systems: A Beginner’s Guide.
Ringle, Christian M., Sven Wende, and Jan-Michael Becker. 2022. “SmartPLS 4.” Computer Program. SmartPLS. https://www.smartpls.com/.
Straub, Detmar, Marie-Claude Boudreau, and David Gefen. 2004. “Validation Guidelines for IS Positivist Research.” Communications of the Association for Information Systems 13 (1): 24.
Venkatesh, Viswanath, Michael G Morris, Gordon B Davis, and Fred D Davis. 2003. “User Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly, 425–78.
Wright, Ryan T, Damon E Campbell, Jason Bennett Thatcher, and Nicholas Roberts. 2012. “Operationalizing Multidimensional Constructs in Structural Equation Modeling: Recommendations for IS Research.” Communications of the Association for Information Systems 30 (1): 23.