This procedure gives almost similar results to the Pipestat!!!
- In our design Accommodation had three levels (Coll, consist, mp) and the Participants were nested inside the accommodation (one Participant could participate in only one condition or a between-subject design)
- These both were crosses with Distance (5 levels)
- All of these were again crossed with repetition of distances.
- The trick is to consider the nested variables as a single unit when describing the model in Minitab
- So the model for this design would be
- condition participant(condition)
- distance distance*condition distance*participant(condition)
- repetition repetition*condition repetition*participant(condition) repetition*distance repetition*distance*condition repetition*distance*participant(condition)
- It is much better to do it in Pipestat as it figures out the model itself rather than us specifying it.
My original motivation to study the model was to do post-hoc test REGWQ etc. in Minitab but it looks like it does not provide that test.
- Arrange data in columns
- Analyze -> General Linear Model -> Univariate
- Select the Dependent Variable as the value based on which the ANOVA will be run or Grouping in the REGWQ will be done (Error)
- Select Fixed Factors as the independent variables of which the effects need to be found (Distance , Repetition for Anova) (only Distance for REGWQ)
- Select the Random Factor (Participant) [This one is important]
- Select the post-hoc tests you need.
- Click OK
|One dependent variable||Multiple dependent variables|
|Univariate F value||Multivariate F value (Wilks’ lambda)|
|Multiple independent variables (Multi-Factor ANOVA) is not MANOVA|
|based on a comparison of the Mean square error (Within-group variability) and the mean square effect (Between-group variability)||based on a comparison of the error variance/covariance matrix and the effect variance/covariance matrix|
|After getting a significant Multivariate effect, next step is to run Univariate test on each dependent variable to find which dependent variable was affected most and hence contributed in main multivariate effect.|
- getwd(): returns the current working directory
- setwd("C:/R/tables"): Sets the current directory
- source("src.R"): run the R source file
- y<-edit(y): opens up a notepad/spreadsheet with data from y
- data1<-edit(data.frame()): opens up a spreadsheet for data entry
- tbl<-read.table("table.dat", header = T, sep =""): reads table.dat with header and space as delimiter.
- dat.collim$Part <- factor(dat.collim$Part): It removes the unnecessary or empty levels for a variable.
Useful R Links:
- Graphical Attributes: http://www.statmethods.net/advgraphs/parameters.html
- Longer tail on the upper side means mean is higher than the median (median is shifted towards LQ) [a +vely skewed distribution]
- Longer tail on the lower side means mean is lower than the median (median is shifted towards UQ) [a -vely skewed distribution]
Importing .dat, .csv, xls file in Minitab
- File -> Open Worksheet [Choose .dat, .csv etc]
Removing empty cells in boxplot for between-subject design
- Double click the axis variable.
- Uncheck: “Include empty cells”.
- This shows only those ticks that has some data in it, which is very useful in case of between-subject design, as the participants are divided into different treatment groups.
Changing order of x-axis groups
Minitab picks a default ordering for x-axis groups (usually Alphabetical or Numeric ordering) If you want to change the order of group variables on x-axis (e.g. Low, Medium, High to Medium, Low, High):
- Select the column in the data window which represents the group in the graph.
- Right click and select Column->Value Order
- Select User-specified order
- Add the desired order, Click OK
- Any graph from now onward will follow that order
Changing color of x-axis groups
- The colors for groups can be selected in the dialog box at Tools->Options->Graphics-> Data View With Groups -> Symbol and Line Colors
- You can also change color for individual groups or sub-groups by double clicking on the bars and choosing appropriate attributes in the dialog box.
- Before double clicking, click just once to switch between different grouping variables.
- Assuming unpaired t-test.
- SEM bars are often about half or less smaller than 95% CI bars.
- SEM Bars
- When SEM bars overlap , There is surely no statistical significant difference (p>0.05).
- When SEM bars do not overlap, There may or may not be a statistical significant difference.
- When CI bars overlap, There may or may not be a statistical significant difference.
- When CI bars do not overlap, There is definitely a statistical significant difference (p<0.05).