Why we must adjust the p-value when performing multiple comparisons?
The rationalists have the following objections to that theory: 1) P-value adjustments are calculated based on how many tests are to be considered, and that number has been defined arbitrarily and variably; 2) P-value adjustments reduce the chance of making type I errors, but they increase the chance of making type II …
What is required for multiple regression?
Multiple linear regression requires at least two independent variables, which can be nominal, ordinal, or interval/ratio level variables. A rule of thumb for the sample size is that regression analysis requires at least 20 cases per independent variable in the analysis.
Do I need to adjust p-value?
A p-value adjustment is necessary when one performs multiple comparisons or multiple testing in a more general sense: performing multiple tests of significance where only one significant result will lead to the rejection of an overall hypothesis.
How do you change the p-value for multiple tests?
The simplest way to adjust your P values is to use the conservative Bonferroni correction method which multiplies the raw P values by the number of tests m (i.e. length of the vector P_values).
How many participants do I need for multiple regression?
For regression equations using six or more predictors, an absolute minimum of 10 participants per predictor variable is appropriate. However, if the circumstances allow, a researcher would have better power to detect a small effect size with approximately 30 participants per variable.
How to correct for multiple testing in logistic regression?
Everyone realized that the experiment-wide Type I error rate explodes well past the nominal .05 level as you increases the number of comparisons you tested. So various approaches, Bonferroni, Scheffe to name just two, to correcting for multiple testing and preserving the experiment-wide Type I error rate at 0.05 were developed.
When to adjust significance level for multiple testing?
In this post we will try to provide some tips on how a researcher could answer the fateful question: should I adjust or not the significance level for multiple testing? The problem of multiplicity is a problem that concerns almost all scientific research and has not got yet a complete and definitive answer.
Do you need to adjust p-values in a multiple regression?
PS: you may want to do a zero-inflated Poisson regression for the data you describe, instead of two separate regressions. Perneger, T.V. Cook, R.J. & Farewell, V.T. Rothman, K.J. No adjustments are needed for multiple comparisons. Marshall, J.R. Data dredging and noteworthiness.
How many independent variables are used in a multiple regression?
You have 2 outcome/dependent variables describing the demand (using the service yes/no, and the number of occasions). You have 10 predictor/independent variables that could theoretically explain the demand (e.g., age, sex, income, price, race, etc).