The Depression Anxiety Stress Scales (DASS): Normative data and latent structure in a large non-clinical sample

The introduction of the Alternative Model of Personality Disorders (AMPD) in the fifth edition of the Diagnostic and Statistical Model of Mental Disorders (DSM-5, APA, 2013) represented a substantive change in how personality disorders (PDs) are diagnosed. One barrier to its adoption (among several) in clinical practice, however, is a lack of information as to what constitutes an elevated score on the 25 domains and facets that comprise Criterion B. Unique sets of facets can be configured to assess any one of six PDs retained in the AMPD; each of these facets can in turn be added to create a PD sum score. In the current study, using the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012), we report mean scores using this instrument that align with 1.0, 1.5, and 2.0 standard deviation elevations for each of these six PDs on the basis of Krueger and colleagues (2012) representative sample, and compare these to those obtained from a community and a clinical sample. These normative data may be useful to clinicians in determining whether a client has elevated scores on pathological personality domains, facets, or PDs.

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The Positive and Negative Affect Schedule (PANAS): Construct validity, measurement properties and normative data in a large non-clinical sample

British Journal of Clinical Psychology ◽

10.1348/0144665031752934 ◽

2004 ◽

Vol 43 (3) ◽

pp. 245-265 ◽

Cited By ~ 1254

Author(s):

John R. Crawford ◽

Julie D. Henry

Keyword(s):

Negative Affect ◽

Construct Validity ◽

Normative Data ◽

Clinical Sample ◽

Measurement Properties ◽

Positive And Negative Affect

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The Prospective and Retrospective Memory Questionnaire (PRMQ): Latent structure, normative data and discrepancy analysis for proxy-ratings

British Journal of Clinical Psychology ◽

10.1348/014466505x28748 ◽

2006 ◽

Vol 45 (1) ◽

pp. 83-104 ◽

Cited By ~ 64

Author(s):

John R. Crawford ◽

Julie D. Henry ◽

Aileen L. Ward ◽

John Blake

Keyword(s):

Normative Data ◽

Latent Structure ◽

Retrospective Memory ◽

Discrepancy Analysis ◽

Memory Questionnaire

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The SF-36 Physical and Mental Health Summary Measures: An Example of How to Interpret Scores

Journal of Health Services Research & Policy ◽

10.1177/135581969800300206 ◽

1998 ◽

Vol 3 (2) ◽

pp. 92-96 ◽

Cited By ~ 37

Author(s):

Crispin Jenkinson

Keyword(s):

Mental Health ◽

Normative Data ◽

Clinical Sample ◽

Community Sample ◽

Sleep Apnoea ◽

Postal Survey ◽

Physical And Mental Health ◽

Obstructive Sleep ◽

Sf 36 ◽

Mental Health Component Summary

Objectives: To provide normative data, in the form of percentile scores from a community sample, for the Physical Component and Mental Health Component Summary scores derived from the SF-36, and to provide an example of how to interpret scores on these measures, comparing normative results with data from a clinical sample. Method: Normative data were gained from a postal survey using a questionnaire, containing the SF-36 and a number of other items concerned with lifestyles and illness. The questionnaire was sent to 13 042 randomly selected subjects between the ages of 18 and 64 years, drawn from Family Health Services Authority computerised registers for four English counties. The clinical sample comprised 84 patients aged 18–64 years diagnosed with obstructive sleep apnoea (OSA) who were asked to take part in the study. The Physical Component Summary (PCS) score and Mental Health Component Summary (MCS) score gained from the SF-36 health status measure were the outcome measures. Results: The community survey achieved a response rate of 72% (n = 9332). All 84 patients in the age range 18–64 years approached to take part in the OSA study agreed to do so; complete data were available for 60 patients. Results indicated that, prior to treatment, 75% of OSA patients' scores on the PCS/MCS were less than the standardised mean score of 50 and fell in the lowest 34% of scores in the general population. However, after treatment, over 50% of patients scored above the standardised mean score on both the PCS and MCS and more closely mirrored the distribution of the normative sample. Conclusion: The data provided here should enable a more meaningful presentation of data than is generally provided in research papers presenting SF-36 summary scores.

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Taxometric Investigation of Depression: Evidence of Consistent Latent Structure Across Clinical and Community Samples

Australian & New Zealand Journal of Psychiatry ◽

10.1080/00048670701261194 ◽

2007 ◽

Vol 41 (5) ◽

pp. 403-410 ◽

Cited By ~ 14

Author(s):

Tim Slade

Keyword(s):

Anxiety Disorders ◽

Clinical Sample ◽

Community Sample ◽

Research Data ◽

Latent Structure ◽

Data Sets ◽

Sample Type ◽

Maximum Eigenvalue ◽

Taxometric Analysis ◽

Taxometric Analyses

Objective: Past taxometric studies of depression have yielded equivocal results. Diversity of sample type may provide one explanation for this. The aim of the present study was to examine the latent structure of depression across clinical and community samples using exactly the same taxometric procedures involving exactly the same indicators of depression. Method: Two taxometric procedures, MAXEIG (maximum eigenvalue) and MAMBAC (mean above minus mean below a cut), were carried out on a clinical sample of 960 outpatients with mood and anxiety disorders. Simulated categorical and dimensional data sets as well as other consistency tests aided in the interpretation of the research data. Results were compared to a prior taxometric analysis in a community sample. Results: The results of the current taxometric analyses were consistent with a dimensional latent structure and were compatible with the findings from identical analyses in a community sample. Conclusions: The findings of the current study highlight the importance of identifying factors that may contribute to, and explain, differences in the identified latent structure of depression.

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