Abstract
Regression analysis is one of the most frequently used tools in market research. In its simplest form, regression analysis allows market researchers to analyze relationships between one independent and one dependent variable. In marketing applications, the dependent variable is usually the outcome we care about (e.g., sales), while the independent variables are the instruments we have to achieve those outcomes with (e.g., advertising). Regression analysis can provide insights that few other techniques can. The key benefits of using regression analysis are that it can:
Learning Objectives
After reading this chapter, you should understand:
– What regression analysis is and what it can be used for.
– How to specify a regression analysis model.
– How to interpret basic regression analysis results.
– What the issues with, and assumptions of, regression analysis are.
– How to validate regression analysis results.
– How to conduct regression analysis in SPSS.
– How to interpret regression analysis output produced by SPSS.
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Notes
- 1.
Strictly speaking, the difference between predicted and observed y-values is \(\hat e\).
- 2.
This only applies to the standardized βs.
- 3.
Rules of thumb are almost never without drawbacks and caveats. For Green’s formula, these are that you need a larger sample size than he proposes if you expect small effects (thus a low expected such as 0.10 or smaller). In addition, if the variables are poorly measured, or if you want to use a stepwise method, you need a larger sample size. With “larger” we mean around three times the required sample size if the expected R2 is low, and about twice the required sample size in case of measurement errors or if stepwise methods are used.
- 4.
The tolerance is calculated using a completely separate regression analysis. In this regression analysis, the variable for which the tolerance is calculated is taken as a dependent variable and all other independent variables are entered as independents. The R2 that results from this model is deducted from 1, thus indicating how much is not explained by the regression model. If very little is not explained by the other variables, (multi) collinearity is a problem.
- 5.
For an application of the ACSI, see, for example, Ringle et al. (2010).
- 6.
We would like to thank Dr. D.I. Gilliland and AgriPro for making the data and case available.
References
Cohen J (1994) The Earth is round (P <.05). Am Psychol 49(912):997–1003
Field A (2009) Discovering statistics using SPSS, 3rd edn. Sage, London
Green SB (1991) How many subjects does it take to do a regression analysis? Multivariate Behav Res 26:499–510
Greene WH (2007) Econometric analysis, 6th edn. Prentice Hall, Upper Saddle River, NJ
Hill C, Griffiths W, Lim GC (2008) Principles of econometrics, 3rd edn. Wiley, Hoboken, NJ
Kelley K, Maxwell SE (2003) Sample size for multiple regression: obtaining regression coefficients that are accurate, not simply significant. Psychol Methods 8(3):305–321
Ringle CM, Sarstedt M, Mooi EA (2010) Response-based segmentation using FIMIX-PLS. Theoretical foundations and an application to ACSI data. Ann Inf Syst 8:19–49
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Mooi, E., Sarstedt, M. (2010). Regression Analysis. In: A Concise Guide to Market Research. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12541-6_7
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DOI: https://doi.org/10.1007/978-3-642-12541-6_7
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