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Abu-Bader Sample Size

Abu-Bader Sample Size App helps users to determine the minimum sample size required for the most utilized bivariate and advanced statistics (Pearson’s correlation, Student’s t-tests, ANOVA, ANCOVA, Chi-Square tests, and Multiple Regression Analysis) in order to achieve a statistical power of

Abu-Bader Sample Size App helps users to determine the minimum sample size required for the most utilized bivariate and advanced statistics (Pearson’s correlation, Student’s t-tests, ANOVA, ANCOVA, Chi-Square tests, and Multiple Regression Analysis) in order to achieve a statistical power of

Abu-Bader Sample Size

by yaniv yyassour
Abu-Bader Sample Size
Abu-Bader Sample Size
Abu-Bader Sample Size

What is it about?

Abu-Bader Sample Size App helps users to determine the minimum sample size required for the most utilized bivariate and advanced statistics (Pearson’s correlation, Student’s t-tests, ANOVA, ANCOVA, Chi-Square tests, and Multiple Regression Analysis) in order to achieve a statistical power of .80 given a number of requirements. The App describes the purpose of each test as well as each requirement used to determine the sample size

Abu-Bader Sample Size

App Details

Version
1.0
Rating
(1)
Size
0Mb
Genre
Education Reference
Last updated
January 11, 2015
Release date
January 11, 2015
More info

App Screenshots

Abu-Bader Sample Size screenshot-0
Abu-Bader Sample Size screenshot-1
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Abu-Bader Sample Size screenshot-4

App Store Description

Abu-Bader Sample Size App helps users to determine the minimum sample size required for the most utilized bivariate and advanced statistics (Pearson’s correlation, Student’s t-tests, ANOVA, ANCOVA, Chi-Square tests, and Multiple Regression Analysis) in order to achieve a statistical power of .80 given a number of requirements. The App describes the purpose of each test as well as each requirement used to determine the sample size
These requirements include level of significance (alpha = .001, .050, & .010) and effect size (small, medium, & large). Other requirements include one- vs. two-tailed hypothesis for Pearson’s correlation and Student’s t-tests, number of groups for one-way ANOVA and ANCOVA (groups = 3, 4, & 5), degrees of freedom for Chi-Square tests (df = 1, 2, & 3), and number of factors for Multiple Regression Analysis (factors = 2, 3, 4, & 5).

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