Evidence based assessment/Idiographic Progress Assessment
Idiographic Assessment of Progress
|Click here for instructions for lead section|
The lead section gives a quick summary of what the assessment is. Here are some pointers (please do not use bullet points when writing article):
Steps for evaluating reliability and validity
|Click here for instructions|
Instrument rubric table: Reliability
Note: Not all of the different types of reliability apply to the way that questionnaires are typically used. Internal consistency (whether all of the items measure the same construct) is not usually reported in studies of questionnaires; nor is inter-rater reliability (which would measure how similar peoples' responses were if the interviews were repeated again, or different raters listened to the same interview). Therefore, make adjustments as needed.
|Criterion||Rating (adequate, good, excellent, too good)||Explanation with Reference|
|Test-retest Reliability||Good||Intraclass correlation coefficients = 0.7-0.9|
|Click here for instrument reliability table|
Not all of the different types of reliability apply to the way that questionnaires are typically used. Internal consistency (whether all of the items measure the same construct) is not usually reported in studies of questionnaires; nor is inter-rater reliability (which would measure how similar peoples' responses were if the interviews were repeated again, or different raters listened to the same interview). Therefore, make adjustments as needed.
Reliability refers to whether the scores are reproducible. Unless otherwise specified, the reliability scores and values come from studies done with a United States population sample. Here is the rubric for evaluating the reliability of scores on a measure for the purpose of evidence based assessment.
Instrument rubric table: Validity
|Click here for instrument validity table|
Validity describes the evidence that an assessment tool measures what it was supposed to measure. There are many different ways of checking validity. For screening measures, diagnostic accuracy and w:discriminative validity are probably the most useful ways of looking at validity. Unless otherwise specified, the validity scores and values come from studies done with a United States population sample. Here is a rubric for describing validity of test scores in the context of evidence-based assessment.
Development and history
|Click here for instructions for development and history|
- What was the impact of this assessment? How did it affect assessment in psychiatry, psychology and health care professionals?
- What can the assessment be used for in clinical settings? Can it be used to measure symptoms longitudinally? Developmentally?
Use in other populations
- How widely has it been used? Has it been translated into different languages? Which languages?
Scoring instructions and syntax
We have syntax in three major languages: R, SPSS, and SAS. All variable names are the same across all three, and all match the CSV shell that we provide as well as the Qualtrics export.
Hand scoring and general instructions
|Click here for hand scoring and general administration instructions|
<Information about hand scoring and general instructions go here>
If there are any hand scoring and general administration instructions, it should go here.
CSV shell for sharing
|Click here for CSV shell|
Here is a shell data file that you could use in your own research. The variable names in the shell corresponds with the scoring code in the code for all three statistical programs.
Note that our CSV includes several demographic variables, which follow current conventions in most developmental and clinical psychology journals. You may want to modify them, depending on where you are working. Also pay attention to the possibility of "deductive identification" -- if we ask personal information in enough detail, then it may be possible to figure out the identity of a participant based on a combination of variables.
When different research projects and groups use the same variable names and syntax, it makes it easier to share the data and work together on integrative data analyses or "mega" analyses (which are different and better than meta-analysis in that they are combining the raw data, versus working with summary descriptive statistics).
|Click here for R code|
R code goes here
|Click here for SPSS code|
SPSS code goes here
|Click here for SAS code|
SAS code goes here
Here, it would be good to link to any related articles on Wikipedia. For instance:
|Click here for references|