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  • Power Analysis
  • Power calculation for comparing sample means from two paired samples
  • Power calculation for comparing sample means from two paired samples
  • Power Analysis

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  1. Statistics and Bioinformatics

Power Analysis

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Last updated 2 years ago

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Power Analysis

  • G*Power: Statistical Power Analyses for Windows and Mac

  • Power and Sample Size

  • Sample size estimation and statistical power analyses

  • PASS Documentation

  • NCSS PASS YouTube Channel

  • Tests for Two Means in a Repeated Measures Design

Power calculation for comparing sample means from two paired samples

  • G*Power: Statistical Power Analyses for Windows and Mac

  • Power and Sample Size

  • Sample size estimation and statistical power analyses

  • PASS Documentation

  • NCSS PASS YouTube Channel

  • Tests for Two Means in a Repeated Measures Design

Power calculation for comparing sample means from two paired samples

  • JAMOVI ile orneklem sayisi belirleme

Karsimiza cikan ikinci secenek "minimally-interesting effect size" (ilgilenilen en dusuk etki buyuklugu) bu analizin en onemli bolumunu olusturmaktadir. Arastirma oncesi orneklem belirlemek istiyorsaniz nasil bir etki buyuklugu beklediginizi belirlemeniz gerekir. Etki buyuklugu iki grup arasinda ne kadar buyuk bir fark oldugunun bir olcusudur (Cohen, 1992'ye bakmanizi oneririm). Beklenen etki buyuklugunu belirlemenin en kolay yolu literaturdeki benzer calismalardaki etki buyukluklerine bakmaktir.

Bagimsiz iki grubun karsilastirilamsinda kullanilan en populer etki buyuklugu olcusu "Cohen's d"dir. 1 birim d iki grup arasinda 1 standart sapma fark olduguna isaret eder. Cohen (1992) d=0.2 kucuk, d=0.5 orta, ve d=0.8 buyuk etki buyuklukleri olarak onermistir.

"power by effect size" tablosu ise eger "gercek" etki buyuklugu tahminimizden farkliysa ne olabilecegini aciklamaktadir.

Ornegin gercek etki buyuklugu "orta derece" degil de daha kucuk ise (ornegin 0.3'ten kucuk - tablonun ilk satiri), grup basina 86 gozlemimizin istatistiki gucu %50 den daha az olacak ve calismamiz buyuk ihtimalle bu etkiyi tespit edemeyecek ("likely miss").

buyuk etkiler soz konusu oldugunda daha kucuk orneklemler yeterli olabilmektedir

  • Selecting a sample size for studies with repeated measures

  • POWERLIB:SAS/IMLSoftware for Computing Power in Multivariate Linear Models

  • SPSS SamplePower

  • G*Power: Statistical Power Analyses for Windows and Mac

  • Power and Sample Size

  • Sample size estimation and statistical power analyses

  • PASS Documentation

  • NCSS PASS YouTube Channel

  • Tests for Two Means in a Repeated Measures Design

Power calculation for comparing sample means from two paired samples

  • G*Power: Statistical Power Analyses for Windows and Mac

  • Power and Sample Size

  • Sample size estimation and statistical power analyses

  • PASS Documentation

  • NCSS PASS YouTube Channel

  • Tests for Two Means in a Repeated Measures Design

Power calculation for comparing sample means from two paired samples

  • JAMOVI ile orneklem sayisi belirleme

Karsimiza cikan ikinci secenek "minimally-interesting effect size" (ilgilenilen en dusuk etki buyuklugu) bu analizin en onemli bolumunu olusturmaktadir. Arastirma oncesi orneklem belirlemek istiyorsaniz nasil bir etki buyuklugu beklediginizi belirlemeniz gerekir. Etki buyuklugu iki grup arasinda ne kadar buyuk bir fark oldugunun bir olcusudur (Cohen, 1992'ye bakmanizi oneririm). Beklenen etki buyuklugunu belirlemenin en kolay yolu literaturdeki benzer calismalardaki etki buyukluklerine bakmaktir.

Bagimsiz iki grubun karsilastirilamsinda kullanilan en populer etki buyuklugu olcusu "Cohen's d"dir. 1 birim d iki grup arasinda 1 standart sapma fark olduguna isaret eder. Cohen (1992) d=0.2 kucuk, d=0.5 orta, ve d=0.8 buyuk etki buyuklukleri olarak onermistir.

"power by effect size" tablosu ise eger "gercek" etki buyuklugu tahminimizden farkliysa ne olabilecegini aciklamaktadir.

Ornegin gercek etki buyuklugu "orta derece" degil de daha kucuk ise (ornegin 0.3'ten kucuk - tablonun ilk satiri), grup basina 86 gozlemimizin istatistiki gucu %50 den daha az olacak ve calismamiz buyuk ihtimalle bu etkiyi tespit edemeyecek ("likely miss").

buyuk etkiler soz konusu oldugunda daha kucuk orneklemler yeterli olabilmektedir

  • Selecting a sample size for studies with repeated measures

  • POWERLIB:SAS/IMLSoftware for Computing Power in Multivariate Linear Models

  • SPSS SamplePower

Power Analysis

  • G*Power: Statistical Power Analyses for Windows and Mac

  • Power and Sample Size

  • Sample size estimation and statistical power analyses

  • PASS Documentation

  • NCSS PASS YouTube Channel

  • Tests for Two Means in a Repeated Measures Design

Power calculation for comparing sample means from two paired samples

  • JAMOVI ile orneklem sayisi belirleme

Karsimiza cikan ikinci secenek "minimally-interesting effect size" (ilgilenilen en dusuk etki buyuklugu) bu analizin en onemli bolumunu olusturmaktadir. Arastirma oncesi orneklem belirlemek istiyorsaniz nasil bir etki buyuklugu beklediginizi belirlemeniz gerekir. Etki buyuklugu iki grup arasinda ne kadar buyuk bir fark oldugunun bir olcusudur (Cohen, 1992'ye bakmanizi oneririm). Beklenen etki buyuklugunu belirlemenin en kolay yolu literaturdeki benzer calismalardaki etki buyukluklerine bakmaktir.

Bagimsiz iki grubun karsilastirilamsinda kullanilan en populer etki buyuklugu olcusu "Cohen's d"dir. 1 birim d iki grup arasinda 1 standart sapma fark olduguna isaret eder. Cohen (1992) d=0.2 kucuk, d=0.5 orta, ve d=0.8 buyuk etki buyuklukleri olarak onermistir.

"power by effect size" tablosu ise eger "gercek" etki buyuklugu tahminimizden farkliysa ne olabilecegini aciklamaktadir.

Ornegin gercek etki buyuklugu "orta derece" degil de daha kucuk ise (ornegin 0.3'ten kucuk - tablonun ilk satiri), grup basina 86 gozlemimizin istatistiki gucu %50 den daha az olacak ve calismamiz buyuk ihtimalle bu etkiyi tespit edemeyecek ("likely miss").

buyuk etkiler soz konusu oldugunda daha kucuk orneklemler yeterli olabilmektedir

  • Selecting a sample size for studies with repeated measures

  • POWERLIB:SAS/IMLSoftware for Computing Power in Multivariate Linear Models

  • SPSS SamplePower

http://www.gpower.hhu.de/
https://www.youtube.com/watch?v=5ccl4nmtUpM
http://powerandsamplesize.com/
https://www.researchgate.net/publication/265399772_Sample_size_estimation_and_statistical_power_analyses
https://www.ncss.com/software/pass/pass-documentation/
https://www.youtube.com/channel/UCNcvYpuMaaaFaAanqkFXRbw/featured
https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/PASS/Tests_for_Two_Means_in_a_Repeated_Measures_Design.pdf
https://www.youtube.com/watch?v=RCox1fE8rQw
http://www.gpower.hhu.de/
https://www.youtube.com/watch?v=5ccl4nmtUpM
http://powerandsamplesize.com/
https://www.researchgate.net/publication/265399772_Sample_size_estimation_and_statistical_power_analyses
https://www.ncss.com/software/pass/pass-documentation/
https://www.youtube.com/channel/UCNcvYpuMaaaFaAanqkFXRbw/featured
https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/PASS/Tests_for_Two_Means_in_a_Repeated_Measures_Design.pdf
https://www.youtube.com/watch?v=RCox1fE8rQw
https://www.facebook.com/groups/sayisalarastirmayontemlerindesongelismeler/permalink/167090167301796/
http://www.bwgriffin.com/workshop/Sampling A Cohen tables.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734029/
https://glimmpse.samplesizeshop.org/
https://samplesizeshop.org/
https://homepage.divms.uiowa.edu/~rlenth/Power/
https://sites.google.com/site/optimaldesignsoftware/home
https://www.statsols.com/nquery
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228969/
https://github.com/SampleSizeShop/POWERLIB
https://www-01.ibm.com/marketing/iwm/iwmdocs/tnd/data/web/en_US/trialprograms/U741655I36057W80.html
http://www.gpower.hhu.de/
https://www.youtube.com/watch?v=5ccl4nmtUpM
http://powerandsamplesize.com/
https://www.researchgate.net/publication/265399772_Sample_size_estimation_and_statistical_power_analyses
https://www.ncss.com/software/pass/pass-documentation/
https://www.youtube.com/channel/UCNcvYpuMaaaFaAanqkFXRbw/featured
https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/PASS/Tests_for_Two_Means_in_a_Repeated_Measures_Design.pdf
https://www.youtube.com/watch?v=RCox1fE8rQw
http://www.gpower.hhu.de/
https://www.youtube.com/watch?v=5ccl4nmtUpM
http://powerandsamplesize.com/
https://www.researchgate.net/publication/265399772_Sample_size_estimation_and_statistical_power_analyses
https://www.ncss.com/software/pass/pass-documentation/
https://www.youtube.com/channel/UCNcvYpuMaaaFaAanqkFXRbw/featured
https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/PASS/Tests_for_Two_Means_in_a_Repeated_Measures_Design.pdf
https://www.youtube.com/watch?v=RCox1fE8rQw
https://www.facebook.com/groups/sayisalarastirmayontemlerindesongelismeler/permalink/167090167301796/
http://www.bwgriffin.com/workshop/Sampling A Cohen tables.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734029/
https://glimmpse.samplesizeshop.org/
https://samplesizeshop.org/
https://homepage.divms.uiowa.edu/~rlenth/Power/
https://sites.google.com/site/optimaldesignsoftware/home
https://www.statsols.com/nquery
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228969/
https://github.com/SampleSizeShop/POWERLIB
https://www-01.ibm.com/marketing/iwm/iwmdocs/tnd/data/web/en_US/trialprograms/U741655I36057W80.html
http://www.gpower.hhu.de/
https://www.youtube.com/watch?v=5ccl4nmtUpM
http://powerandsamplesize.com/
https://www.researchgate.net/publication/265399772_Sample_size_estimation_and_statistical_power_analyses
https://www.ncss.com/software/pass/pass-documentation/
https://www.youtube.com/channel/UCNcvYpuMaaaFaAanqkFXRbw/featured
https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/PASS/Tests_for_Two_Means_in_a_Repeated_Measures_Design.pdf
https://www.youtube.com/watch?v=RCox1fE8rQw
https://www.facebook.com/groups/sayisalarastirmayontemlerindesongelismeler/permalink/167090167301796/
http://www.bwgriffin.com/workshop/Sampling A Cohen tables.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734029/
https://glimmpse.samplesizeshop.org/
https://samplesizeshop.org/
https://homepage.divms.uiowa.edu/~rlenth/Power/
https://sites.google.com/site/optimaldesignsoftware/home
https://www.statsols.com/nquery
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228969/
https://github.com/SampleSizeShop/POWERLIB
https://www-01.ibm.com/marketing/iwm/iwmdocs/tnd/data/web/en_US/trialprograms/U741655I36057W80.html