User:Rkang101/HCL

Lead section
The hypomania checklist (HCL) is a self report questionnaire created by Jules Angst in 2005 designed to detect hypomanic symptoms in patients with Major Depressive Disorder to aid clinicians in determining a Bipolar II or Bipolar Spectrum Disorder diagnosis. In the creation of the HCL, Angst and team used a population of adults ranging from 30-50 years of age, but since its creation it has been validated in adults ages 18 and older in both inpatient and outpatient settings, and has proven to have good psychometric properties for both reliability and validity. It is a 32 item, multiple choice assessment that takes approximately 20 minutes to complete. The HCL is used in both clinical and research settings and has helped clinicians more accurately assess symptoms of hypomania, decreasing rates of misdiagnosis for Bipolar II and Bipolar Spectrum Disorders.

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.

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 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
There are multiple version of the HCL, and the most recent iteration of the HCL is the HCL-32, a more robust and developed version of the HCL-20. The HCL was developed to fill the need for a clinical useful and timesaving assessment of hypomanic symptoms in an effort to aid in the diagnosis of bipolar II. Further, other measures were not sensitive to the episodic nature of hypomania, and, or, were only sensitive to mania and not hypomania. The HCL's main purpose was to screen for bipolar II and bipolar spectrum disorders in patients with major depressive disorder.

Impact
The HCL can be used in clinical and research settings. It has good psychometric properties, and is shown to have good sensitivity to hypomania in patients diagnosed with unipolar or bipolar major depressive patients.

Use in other populations
The HCL has been translated and validated in over 15 different 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

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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).

R/SPSS/SAS syntax
R code goes here

SPSS code goes here

SAS code goes here