From Classroom to C-Suite: Generalizing Your Research Findings

Table of Links

Abstract and 1. Introduction

2. Experiment Definition

3. Experiment Design and Conduct

3.1 Latin Square Designs

3.2 Subjects, Tasks and Objects

3.3 Conduct

3.4 Measures

4. Data Analysis

4.1 Model Assumptions

4.2 Analysis of Variance (ANOVA)

4.3 Treatment Comparisons

4.4 Effect Size and Power Analysis

5. Experiment Limitations and 5.1 Threats to the Conclusion Validity

5.2 Threats to Internal Validity

5.3 Threats to Construct Validity

5.4 Threats to External Validity

6. Discussion and 6.1 Duration

6.2 Effort

7. Conclusions and Further Work, and References

5.4 Threats to External Validity

These threats concern with issues that may limit our ability to generalize the results of the experiment to other contexts, for example generalize it to industry practice. The use of students as subjects instead of practitioners might have exposed this validity. However, as pointed in [8] the use of students as subjects enable us to obtain preliminary evidence to confirm or refute hypotheses that can be tested later in industrial settings.

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:::info Authors:

(1) Omar S. Gómez, full time professor of Software Engineering at Mathematics Faculty of the Autonomous University of Yucatan (UADY);

(2) José L. Batún, full time professor of Statistics at Mathematics Faculty of the Autonomous University of Yucatan (UADY);

(3) Raúl A. Aguilar, Faculty of Mathematics, Autonomous University of Yucatan Merida, Yucatan 97119, Mexico.

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:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

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