There are many effect size statistics for ANOVA and regression, and as you may have noticed, journal editors are now requiring you include one.
Unfortunately, the one your editor wants or is the one most appropriate to your research may not be the one your software makes available (SPSS, for example, reports Partial Eta Squared only, although it labels it Eta Squared in early versions).
Luckily, all the effect size measures are relatively easy to calculate from information in the ANOVA table on your output. Here are a few common ones: (more…)
If you’ve compared two textbooks on linear models, chances are, you’ve seen two different lists of assumptions.
I’ve spent a lot of time trying to get to the bottom of this, and I think it comes down to a few things.
1. There are four assumptions that are explicitly stated along with the model, and some authors stop there.
2. Some authors are writing for introductory classes, and rightfully so, don’t want to confuse students with too many abstract, and sometimes untestable, (more…)
Censored data are inherent in any analysis, like Event History or Survival Analysis, in which the outcome measures the Time to Event TTE. Censoring occurs when the event doesn’t occur for an observed individual during the time we observe them.
Despite the name, the event of “survival” could be any categorical event that you would like to describe the mean or median TTE. To take the censoring into account, though, you need to make sure your data are set up correctly.
Here is a simple example, for a data set that measures days after surgery until an (more…)
Time to event analyses (aka, Survival Analysis and Event History Analysis) are used often within medical, sales and epidemiological research. Some examples of time-to-event analysis are measuring the median time to death after being diagnosed with a heart condition, comparing male and female time to purchase after being given a coupon and estimating time to infection after exposure to a disease.
Survival time has two components that must be clearly defined: a beginning point and an endpoint that is reached either when the event occurs or when the follow-up time has ended.
One basic concept needed to understand time-to-event (TTE) analysis is censoring.
In simple TTE, you should have two types of observations:
1. The event occurred, and we are able to measure when it occurred OR
2. The event did NOT occur during the time we observed the individual, and we only know the total number of days in which it didn’t occur. (CENSORED).
Again you have two groups, one where the time-to-event is known exactly and one where it is not. The latter group is only known to have a certain amount of time where the event of interest did not occur. We don’t know if it would have occurred had we observed the individual longer. But knowing that it didn’t occur for so long tells us something about the risk of the envent for that person.
For example, let the time-to-event be a person’s age at onset of cancer. If you stop following someone after age 65, you may know that the person did NOT have cancer at age 65, but you do not have any information after that age.
You know that their age of getting cancer is greater than 65. But you do not know if they will never get cancer or if they’ll get it at age 66, only that they have a “survival” time greater than 65 years. They are censored because we did not gather information on that subject after age 65.
So one cause of censoring is merely that we can’t follow people forever. At some point you have to end your study, and not all people will have experienced the event.
But another common cause is that people are lost to follow-up during a study. This is called random censoring. It occurs when follow-up ends for reasons that are not under control of the investigator.
In survival analysis, censored observations contribute to the total number at risk up to the time that they ceased to be followed. One advantage here is that the length of time that an individual is followed does not have to be equal for everyone. All observations could have different amounts of follow-up time, and the analysis can take that into account.
Allison, P. D. (1995). Survival Analysis Using SAS. Cary, NC: SAS Institute Inc.
Hosmer, D. W. (2008). Applied Survival Analysis (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc.
Covariate is a tricky term in a different way than hierarchical or beta, which have completely different meanings in different contexts.
Covariate really has only one meaning, but it gets tricky because the meaning has different implications in different situations, and people use it in slightly different ways. And these different ways of using the term have BIG implications for what your model means.
The most precise definition is its use in Analysis of Covariance, a type of General Linear Model in which the independent variables of interest are categorical, but you also need to adjust for the effect of an observed, continuous variable–the covariate.
In this context, the covariate is always continuous, never the key independent variable, (more…)
One of the places that SPSS syntax excels at efficiency is when you’re creating new variables. This is especially true when you’re creating a LOT of new variables, but even one or two can be quicker if you write the syntax code instead of menus.
And just as importantly, you’ll have documentation for exactly how you created them. (You think you’ll remember now, but 75 new variables later, you’ll thank me).
So once you create a new variable, you should of course immediately assign a Variable Label, and if appropriate, Value Labels and Missing Data Codes using Syntax.
Another thing that helps keep your new variable clean and interpretable is to assign the format. The default format is F8.2, which indicates a numerical value
You could go into the Variable View screen and manually change the Width and Decimals columns, which indicate how many characters go before and after (for numeric variables) the decimal point.
But why do all that when you can just use a single command to define multiple variables?
The syntax command is FORMATS. Here is the command for some common formats:
FORMATS NumVar1 NumVar2 (F5.0)
/NumVar3 (F6.1)
/StringVar1 (A15).
You can see the FORMATS command is followed by the variable names, then the format in parentheses.
Numeric variables NumVar1 and Numvar2 will both get the same format: with 5 digits, and nothing after the decimal.
Numeric variable NumVar3 will have 6 digits total, with one after the decimal.
And string variable (i.e. its value contain letters) StringVar1 is 15 characters wide.
This will get you started, but you can get all the specifics in the FORMATS section of the Command Syntax Reference, which is included in the SPSS help.
[Note: Edited explanation of F6.1 to be 6 digits total, not 6 digits before the decimal).
