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Biomedical subjects

Susan L Jones

Publications and source records attributed to Susan L Jones.

4 recordsLinked to original sources
PubMed2003

The Shorter PROMIS Questionnaire: further validation of a tool for simultaneous assessment of multiple addictive behaviours.

There is both a theoretical and clinical need to develop a questionnaire that assesses a range of addictive behaviours. The Shorter PROMIS Questionnaire (SPQ) is a 16-scale self-report instrument assessing the use of nicotine, recreational drugs, prescription drugs, gambling, sex, caffeine, food bingeing, food starving, exercise, shopping, work, relationships dominant and submissive, and compulsive helping dominant and submissive. Clinical cut-off scores using the 90th percentile were derived from a normative group of 508 individuals. These cut-offs correctly identified 78-100% of cases within clinical criterion groups of specific disorders. The clinical sample also completed other validated scales assessing gambling, eating, alcohol, and drug use. Correlations were typically.7 with relevant SPQ scales. The SPQ food, drug, and alcohol scales were at least equivalent to validated comparison scales in the strength of their relationship to relevant clinical criterion groups. Internal consistency was high for all scales, and test-retest reliability was generally good. This clinically useful instrument provides a broad assessment of addictive problems, thereby benefiting both the treatment provider and the client.

Adolescent↗
PubMed2002

A comparison of imputation techniques for handling missing data.

Researchers are commonly faced with the problem of missing data. This article presents theoretical and empirical information for the selection and application of approaches for handling missing data on a single variable. An actual data set of 492 cases with no missing values was used to create a simulated yet realistic data set with missing at random (MAR) data. The authors compare and contrast five approaches (listwise deletion, mean substitution, simple regression, regression with an error term, and the expectation maximization [EM] algorithm) for dealing with missing data, and compare the effects of each method on descriptive statistics and correlation coefficients for the imputed data (n = 96) and the entire sample (n = 492) when imputed data are inculded. All methods had limitations, although our findings suggest that mean substitution was the least effective and that regression with an error term and the EM algorithm produced estimates closest to those of the original variables.

Algorithms↗