Instructions
Literature review:
This includes several sub-topics that have to be explained. Use APA. Therefore you need the paper information to know what to do and the R code). Experience with missing data and R are needed.
Validity of ANOVA:
There is another approach for assessing the validity of ANOVAs. We usually interpret the F-test by looking at the significance level of the test. This significance level is determined by the p-value, which reflects the probability of finding a less likely result under the null hypothesis - i.e. when there is no effect. As opposed to the F-test itself, its corresponding p-value behaves consistently under the null hypothesis. More specifically, for any ANOVA based on any given data set under the null hypothesis, the corresponding p-value has a uniform distribution, an expected value of .50 and requires the p-value to be confidence valid (a p-value of .05 should only occur 5 percent of the time).
Explain how to examine the following about the p-value:
- Has an expected value of .50 (theory + see the code to tell how I evaluated this point in my research; almost at the end of the code)
- Should be confidence valid (Neyman, 1934) in the sense that a p-value of .05 should only occur 5 percent of the time (theory and how did I test it in my research)
Interpretation of the results: use the script on R (some of the results are included)
- Expected value of the p-values: text with expected values and table + interpretation results
- Proportion E(p<=.05): interpretation result + table
- Proportion correct (i.e. how many p-values less than .05 are the same as the population)
-Summary of the results
The main question is to discover which statistics (D1, D2, D3..) is valid to use when working with missing data of different missingness (1%, 25%, 50%, 75%)
Those results (output) can be obtained by using the R script. I added some in the info document.
Explain simulation:
- Description of the structure of the simulation study (code r script) and reasons of the stepd (basically the multiple imputation steps and then testing the validity steps)
- Description of the analysis strategy and methods used
Discussion and conclusion:
- Which statistic is valid to use when working with missing data, and on which proportion of missingness + why
- Discussion points
- Implications of the results for theory formation
- Limitations of the current research
Other questions that I have:
- Why compare intercept only model with a model with predictors in the simulation
- How can I test the reliability?
Request:
- Add information when needed (see the research in this document)
The script:
library(mice)
library(miceadds)
library(magrittr)
library(dplyr)
library(purrr)
library(mvtnorm)
library(ggplot2)
#install.packages("ggplot2")
set.seed(123)
# Simulation parameters
nsim = 1000
rho = 0 # correlation set to zero to conform to null hypothesis (no effect)
# Generate data function
make.data <- function(n, correlation){
data <- rmvnorm(n = n, mean = c(0, 0),
sigma = matrix(c(1, correlation, correlation, 1),
nrow = 2, ncol = 2))
colnames(data) <- c("y", "x")
data %>% as_tibble() %>% return()
}
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