바로가기메뉴

본문 바로가기 주메뉴 바로가기

Korean Journal of Psychology: General

  • KOREAN
  • P-ISSN1229-067X
  • E-ISSN2734-1127
  • KCI

ISSN : 1229-067X

Article Contents

Prev
e-Submission

Vol.34 No.3

Citation Share

An introduction to non-linear data fitting using the Microsoft Excel

Korean Journal of Psychology: General / Korean Journal of Psychology: General, (P)1229-067X; (E)2734-1127
2015, v.34 no.3, pp.741-767
https://doi.org/10.22257/kjp.2015.09.34.3.741

(2015). An introduction to non-linear data fitting using the Microsoft Excel. Korean Journal of Psychology: General, 34(3), 741-767, https://doi.org/10.22257/kjp.2015.09.34.3.741

Abstract

Psychologists working on psychometric data often struggle with the process of data fitting which requires advanced knowledge about programming and mathematics. Although some commercial softwares reduce investigators effort to perform the process, still most of them are far from easy tools that a beginner tries them without hesitation. In contrast, the Microsoft Excel provides intuitive ways to fit data and calculate the parameters. In this paper, the processes of data fittings are demonstrated step by step using the Excel solver. Specifically, in fitting non-linear data, the least squares estimation and maximum likelihood estimation are introduced and the processes are compared to understand the difference. Finally, it was discussed how to statistically test the parameters between groups that were obtained from the data fitting.

keywords
data fitting, least squares estimation, maximum likelihood estimation, Psychometric functions, Excel solver, 자료 적합, 최소제곱추정, 최대우도추정, 심리측정함수, Excel solver

Reference

1.

Afifi, A., May, S., & Clark, V. A. (2012). Practical multivariate analysis. Taylor & Francis Group:Florida.

2.

Brown, A. M. (2001). A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet. Computer Methods in Biomedicine, 65, 191-200.

3.

Brown, A. M. (2006). A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel. Computer Methods and Programs in Biomedicine, 82, 51-57.

4.

Cunningham, D. E., & Wallraven, C. (2012). Experimental Design: From user studies to Psychophysics. Taylor & Francis Group:Florida.

5.

Dodson, C. S., Prinzmetal, W., & Shimamura, A. P. (1998). Using Excel to estimate parameters from observed data: An example from source memory data. Behavior Research Methods, Instruments, & Computers, 30(3), 517-526.

6.

Dunn, J. C. (2010). How to fit models of recognition memory data using maximum likelihood. International Journal of Psychological Research, 3(1), 140-149.

7.

John, E. G. (1998). Simplified curve fitting using spreadsheet add-ins. International Journal of Engineering Education, 14, 375-380.

8.

Kemmer, G., & Keller, S. (2010). Nonlinear least-squares data fitting in Excel spreadsheets. Nature Protocol, 5, 267-281.

9.

Kingdom, F. A. A., & Prins, N. (2010). Psychophysics: A practical introduction. Academic Press: London, UK.

10.

Lambert, R. J. W., Mytilinaios, I., Maitland, L., & Brown, A. M. (2012). Monte Carlo simulation of parameter confidence intervals for non-linear regression analysis of biological data using Microsoft Excel. Computer Methods and Programs in Biomedicine, 107, 155-163.

11.

Lasdon, L. S., Waren, A. D., Jain, A., & Ratner, M. (1978). Design and testing of a generalized reduced gradient code for nonlinear programming. ACM Transactions on Mathematical Software, 4(1), 34-50.

12.

Myung, I. J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47, 90-100.

13.

Stelmach, L. B., & Herdman, C. M. (1991). Directed attention and perception of temporal order. Journal of Experimental Psychology: Human Perception and Performance, 17(2), 539-550.

14.

Wichmann, F., & Hill, J. N. (2001). The psychometric function: Ⅰ. Fitting, sampling, and goodness fit. Perception & Psychophysics, 63(8), 1293-1313.

Korean Journal of Psychology: General

e-Submission OA Policy