Related Experiment Video
Updated: Jul 12, 2026
07:37
Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
The Hissom closure in Oklahoma: errors and interpretation problems in Conroy et al. (2003)
Kevin K Walsh1, Theodore A Kastner
1Developmental Disabilities Health Alliance, Inc., 1527 Forest Grove Rd., Vineland, NJ 08360-1865, USA. kwalsh@ddha.com
Mental Retardation
|September 15, 2006
Summary
A review of Conroy et al. (2003) found serious data integrity issues. The study on consumer outcomes after the Hissom Center closure had compromised findings due to data collection and analysis problems.
Area of Science:
- Disability Studies
- Research Methodology
- Intellectual Disability Research
Background:
- The Hissom Center in Oklahoma provided services for individuals with intellectual disabilities.
- Closure of institutions like the Hissom Center necessitates understanding consumer outcomes.
- Previous analyses of Hissom Center closure data exist.
Purpose of the Study:
- To critically review the methodology and findings of Conroy et al. (2003).
- To assess the validity of conclusions regarding consumer outcomes post-Hissom Center closure.
- To identify data integrity issues in the published research.
Main Methods:
- Replication of analyses reported by Conroy et al. (2003).
- Examination of data collection, management, and transcription processes.
- Comparison of original findings with replicated results.
Main Results:
- Significant discrepancies were found between reported and replicated analyses.
- The sample of 254 subjects was misconstrued and did not account for all relevant individuals.
- Data collection problems and analytical errors were identified, compromising study integrity.
Conclusions:
- The findings and conclusions of Conroy et al. (2003) are seriously compromised.
- Inappropriate interpretations and misleading conclusions stem from data integrity issues.
- The research does not support its stated conclusions due to fundamental flaws.
Related Concept Videos
01:14Errors In Hypothesis Tests
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
01:12Types of Errors: Detection and Minimization
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
01:15Systematic Error: Methodological and Sampling Errors
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
01:16Contaminants and Errors
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
01:19Errors and Mistakes in Surveying
Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of attention,...