Determining Sample Size in Logistic Regression with G-Power
Year 2019,
Volume: 2 Issue: 1, 16 - 22, 01.01.2019
Aysel Yenipınar
,
Şeyma Koç
,
Demet Çanga
,
Fahrettin Kaya
Abstract
There
are several methods used to determine the sample size. Investigator; because of
the insufficient precious resources such as time, labor, money, tools and
equipment, it works by pulling the sample with a suitable sampling method from
the population it is examining. According to the statistics obtained from the
sample, he will make comments about the population and make decisions. The
correctness of the decisions made is closely related to the size of the sample.
For this reason, the problem of determining sample size is one of the first and
important problems of an investigator. A small sample of information causes
loss of information and misjudgments. A very large sample is contrary to the
purpose of sampling and resources are wasted. The calculation of the sample
size can now be done very easily via free programs.
References
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- Whittemore AS. 1981. Sample size for logistic regression with small response probabilities. JASA, 76: 27-32.
DETERMINING SAMPLE SIZE IN LOGISTIC REGRESSION WITH G-POWER
Year 2019,
Volume: 2 Issue: 1, 16 - 22, 01.01.2019
Aysel Yenipınar
,
Şeyma Koç
,
Demet Çanga
,
Fahrettin Kaya
References
- Buse A. 1982. The likelihood ratio, wald and lagran ge multiplier tests: an expository note. the american statistician, 36(3): 1.
- Demidenko E. 2007. Sample size determination for logistic regression revisited, Statist Med, 26: 3385-3397.
- Erdfelder E, Faul F, Buchner A. 1996. Gpower: A general power analysis. Behavior Research Methods, Instruments, & Computer, 28: 1-11.
- Faul F, Erdfelder E, Buchner A, Lang AG. 2009. Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Resarch Methods, 41: 1149-1160.
- Herrera AN, Gomez J. 2008. Influence of equal or unequal comparison group sample sizes on the detection of differential item functioning using the Mantel–Haenszel and logistic regression techniques. Quality & Quantity, 42: 739–755.
- Hsieh FY, Bloch DA, Larsen MD. 1998. A simple method of sample size calculation for linear and logistic regression. Statis Med, 17: 1623-1634.
- Lyles RH, Lin HM, Williamson JM. 2007. A practial approach to computing power for generalized linear models with nominal, count, or ordinal responses. Statis Med, 26: 1632-1648.
- Nemes S, Jonasson JM, Genel A, Steineck G. 2009. Bias in odds ratios by logistic regression modelling and sample size, BMC Medical Research Methodology, 9(56): 1-5.
- Moineddin R, Matheson FI, Glazier RH. 2007. A simulation study of sample size for multilevel logistic regression models, BMC Medical Research Methodology, 4(34): 1-10.
- Şahin M. 1999. Lojistik Regresyon ve Biyolojik Alanlarda Kullanımı. Yüksek Lisans Tezi. Kahramanmaraş Sütçü İmam Üniversitesi Fen Bilimleri Enstitüsü Zootekni Ana Bilim Dalı. Kahramanmaraş.
- Whittemore AS. 1981. Sample size for logistic regression with small response probabilities. JASA, 76: 27-32.