Research Methods in Clinical Neuropsychology (PSB3ECN01)
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Summary of all Articles Research Methods in Clinical Neuropsychology
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Research Methods in Clinical Neuropsychology (PSB3ECN01)
Institution
Rijksuniversiteit Groningen (RuG)
A clear, structured summary of all the articles you need to learn for the course “Research Methods in Clinical Neuropsychology”. The main points of each article are clearly discussed. Graphs and images have been added for clarification.
Research Methods in Clinical Neuropsychology (PSB3ECN01)
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SAMENVATTING ARTIKELEN RESEARCH METHODS
WEEK 1: BASIC STUDY DESIGNS
ARTIKEL 1 H3 CHOOSING THE STUDY SUBJECTS: SPECIFICATION,
SAMPLING, RECRUITMENT (HULLEY ET AL.)
SUMMARY
1. Most clinical research is based, philosophically and practically, on the use of a
sample to represent a population.
2. The advantage of sampling is efficiency: It allows the investigator to draw
inferences about a large population by examining a subset at relatively small
cost in time and effort. The disadvantage is the sources of error it introduces: If
the sample is not sufficiently representative for the research question at hand the
findings may not generalize well to the target population, and if it is not large
enough the findings may not sufficiently minimize the role of chance.
3. In designing a sample, the investigator begins by conceptualizing the target
population with a specific set of inclusion criteria that establish demographic
and clinical characteristics of subjects well suited to the research question.
4. She then selects an appropriate accessible population that is geographically
and temporally convenient, and defines a parsimonious set of exclusion criteria
that eliminate subjects who are unethical or inappropriate to study.
5. The next step is to design an approach to sampling the population. A
convenience sample may be adequate, especially for initial studies of some
questions, and a consecutive sample is often a good choice. Simple random
sampling can be used to reduce the size of the sample if necessary, and other
probability sampling strategies (stratified and cluster) are useful in certain
situations.
6. Finally, the investigator must design and implement strategies for recruiting
a sample of subjects that is sufficiently representative of the target population
to control systematic sources of error, and large enough to control random
sources of error.
,ARTICLE 2: PLANNING THE MEASUREMENTS: PRECISION, ACCURACY, AND
VALIDITY
SUMMARY
1. Variables are either numerical or categorical. Numerical variables are
continuous (quantified on an infinite scale) or discrete (quantified on a finite
scale such as integers); categorical variables are nominal (unordered) or
ordinal (ordered), and those that have only two categories are termed
dichotomous.
2. Variables that contain more information provide greater power and/or allow
smaller sample sizes, according to the following hierarchy: continuous variables
> discrete numeric variables > ordinal variables > nominal and dichotomous
variables.
,3. The precision of a measurement (i.e., the reproducibility of replicate
measures) is another major determinant of power and sample size. Precision is
reduced by random error (chance) from three sources of variability: the
observer, the subject, and the instrument.
4. Strategies for increasing precision that should be part of every study are to
operationally define and standardize methods in an operations manual.
Other strategies that are often useful are training and certifying observers,
refining and automating the instruments, and repetition—using the mean of
repeated measurements.
, 5. The accuracy of a measurement is the degree to which it approximates a gold
standard. Accuracy is reduced by systematic error (bias) from the same three
sources: the observer, subject, and instrument.
6. The strategies for increasing accuracy include all those listed for precision
with the exception of repetition. In addition, accuracy is enhanced by
unobtrusive measures, by calibration, and (in comparisons between groups)
by blinding.
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