A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research
Q SPSS gives me an intraclass correlation coefficient (ICC) for "single measures" and A A measure is reliable if it produces the same results under a number of 1 (complete positive relationship) or -1 (complete negative relationship). Nov 16, It's called “One-Way Random” because 1) it makes no effort to disentangle . Click Statistics and check Intraclass correlation coefficient at the bottom. .. I was wondering if you might know about them in relation to estimate. Sep 15, There are a number of different intraclass correlations, and the classic You could label the columns Partner1 and Partner2, but there is no obvious way The same thing commonly happens when people are doing twin studies. We can therefore obtain a measure of the degree of relationship by asking.
Note also that this ICC is always non-negative, allowing it to be interpreted as the proportion of total variance that is "between groups. A number of different ICC statistics have been proposed, not all of which estimate the same population parameter.
There has been considerable debate about which ICC statistics are appropriate for a given use, since they may produce markedly different results for the same data. One key difference between the two statistics is that in the ICC, the data are centered and scaled using a pooled mean and standard deviation, whereas in the Pearson correlation, each variable is centered and scaled by its own mean and standard deviation.
This pooled scaling for the ICC makes sense because all measurements are of the same quantity albeit on units in different groups. For example, in a paired data set where each "pair" is a single measurement made for each of two units e. An important property of the Pearson correlation is that it is invariant to application of separate linear transformations to the two variables being compared.
This property does not make sense for the ICC, since there is no basis for deciding which transformation is applied to each value in a group.
However, if all the data in all groups are subjected to the same linear transformation, the ICC does not change. Use in assessing conformity among observers[ edit ] The ICC is used to assess the consistency, or conformity, of measurements made by multiple observers measuring the same quantity.
The restriction is straightforward: The questions are more complicated, and their answers are based upon how you identified your raters, and what you ultimately want to do with your reliability estimate. Here are the first two questions: Do you have consistent raters for all ratees? For example, do the exact same 8 raters make ratings on every ratee? Do you have a sample or population of raters?
If your answer to Question 1 is no, you need ICC 1. It is most useful with massively large coding tasks.
Intraclass Correlations (ICC) and Interrater Reliability in SPSS
For example, if you had ratings to make, you might assign your 10 research assistants to make ratings each — each research assistant makes ratings on 2 ratees you always have 2 ratings per casebut you counterbalance them so that a random two raters make ratings on each subject.
Or in other words, while a particular rater might rate Ratee 1 high and Ratee 2 low, it should all even out across many raters. If you have the same raters for each case, this is generally the model to go with. This means that the raters in your task are the only raters anyone would be interested in. This is uncommon in coding, because theoretically your research assistants are only a few of an unlimited number of people that could make these ratings.
For example, in our Facebook study, we want to know both.
Computing Intraclass Correlations (ICC) as Estimates of Interrater Reliability in SPSS
For example, consider Variable 1 with values 1, 2, 3 and Variable 2 with values 7, 8, 9. But if you are interested in determining the reliability for a single individual, you probably want to know how well that score will assess the real value.Intraclass Correlations
I can't explain why the formula above is expressed as a formula forrather than a formula for 2. But I am quite sure that Shrout and Fleiss are correct here. This issue arises frequently in the reliability literature. An easier way If you are using SPSS to analyze your data, there is an easier way to calculate this coefficient.
The advantage of this approach is that it also produces a confidence interval on our estimate. The procedure that we want is the Reliability procedure, which is an old procedure in SPSS that has not been rewritten at least in version 10 to use the more modern display of output.
However it can be invoked from the menu structure. That will produce the following dialog box. Notice that I have included the two variables Partner1 and Partner2. You next need to click on the Statistics box, which will give you Here I have selected the intraclass correlation coefficient, and then selected the One-Way Random model.
That is important--you don't want to take the default option.