# Lecture 6 - Part II

26 views4 pages
27 Feb 2011
School
Department
Course
Which main variables will be cross-tabulated?
Uni-variate? Bi-variate? Multi-variate analysis?
Which measures are used to identify associations?
Example:
Frequency distribution:
-Age
-Education
-Occupation
-Number of children
-Duration of immigration
-Perceived health
-Car ownership
Cross-tabulation:
-Perceived health status with age, education, occupation, .....
Multi-variate perceived health as the dependent variable
Problems and Limitations
Problems - relating to logistical details
-E.g. availability of data, securing permission from ethics board, obtaining the
sample, etc...
Limitations - to structural problems relating to methodological aspects of the study
-E.g. Study design / sampling / measurement procedures.... used are not the best
but adoption was necessary for a number of reasons
Timeframe
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## Document Summary

Perceived health status with age, education, occupation, : multi-variate perceived health as the dependent variable. Problems and limitations: problems - relating to logistical details. E. g. availability of data, securing permission from ethics board, obtaining the sample, etc Limitations - to structural problems relating to methodological aspects of the study. Study design / sampling / measurement procedures used are not the best but adoption was necessary for a number of reasons. Independent variable: hypothesis, operationalisation, variable, dependent variable, theory, conceptual framework, models, diagrammatic models, simulation models, decision analysis models. ratio: parametric statistics, non-parametric statistics, analysis of nominal data. Frequency distributions www. notesolution. com: analysis of ordinal data. kendall"s tau: analysis of interval data. product moment correlation coefficients: reliability reproducibility, consistency, homogeneity, free from random error. Test-retest to examine reproducibility of the responses to the scale. Validity - internal validity, external validity: sensitivity proportion of actual cases who score as positive cases, specificity proportion of non-cases who test negative.

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