# Evidence-based assessment/Idiographic Progress Assessment

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# Idiographic Assessment of Progress
## Psychometrics
### Steps for evaluating reliability and validity
### 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 |
| Norms | | |
| Internal Consistency | Good | |
| Test-retest Reliability | Good | Intraclass correlation coefficients = 0.7-0.9 |
| Repeatability | | |
Click here for instrument reliability table :
Reliability
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.
Evaluation for norms and reliability for the XXX (table from Youngstrom et al., extending Hunsley & Mash, 2008; *indicates new construct or category)
CriterionRating (adequate, good, excellent, too good*)Explanation with references
NormsAdequateMultiple convenience samples and research studies, including both clinical and nonclinical samples[citation needed]
Internal consistency (Cronbach’s alpha, split half, etc.)Excellent; too good for some contextsAlphas routinely over .94 for both scales, suggesting that scales could be shortened for many uses[citation needed]
Interrater reliabilityNot applicableDesigned originally as a self-report scale; parent and youth report correlate about the same as cross-informant scores correlate in general
Test-retest reliability (stabilityGoodr = .73 over 15 weeks. Evaluated in initial studies, with data also show high stability in clinical trials[citation needed]
RepeatabilityNot publishedNo published studies formally checking repeatability :

### Instrument rubric table: Validity

Click here for instrument validity table :
Validity
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.
Evaluation of validity and utility for the XXX (table from Youngstrom et al., unpublished, extended from Hunsley & Mash, 2008; *indicates new construct or category)
CriterionRating (adequate, good, excellent, too good*)Explanation with references
Content validityExcellentCovers both DSM diagnostic symptoms and a range of associated features
Contruct validity (e.g., predictive, concurrent, convergent, and discriminant validity)ExcellentShows Convergent validity with other symptom scales, longitudinal prediction of development of mood disorders, criterion validity via metabolic markers and associations with family history of mood disorder. Factor structure complicated; the inclusion of “biphasic” or “mixed” mood items creates a lot of cross-loading
Discriminative validityExcellentMultiple studies show that GBI scores discriminate cases with unipolar and bipolar mood disorders from other clinical disorders effect sizes are among the largest of existing scales
Validity generalizationGoodUsed both as self-report and caregiver report; used in college student as well as outpatient and inpatient clinical samples; translated into multiple languages with good reliability
Treatment sensitivityGoodMultiple studies show sensitivity to treatment effects comparable to using interviews by trained raters, including placebo-controlled, masked assignment trials Short forms appear to retain sensitivity to treatment effects while substantially reducing burden
Clinical utilityGoodFree (public domain), strong psychometrics, extensive research base. Biggest concerns are length and reading level. Short forms have less research, but are appealing based on reduced burden and promising data :

## Impact

* 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

If there are any hand scoring and general administration instructions, it should go here.

### CSV shell for sharing

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).