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QUANTITATIVE DATA ANALYSIS

QUANTITATIVE DATA ANALYSIS. Professor Lisa High University of Windsor Faculty of Nursing. Levels of Measurement. Quantitative measures: level of measurements Types : Nominal – Ordinal – Interval – Ration -. Descriptive Stats - Univariate. Frequency distributions –

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QUANTITATIVE DATA ANALYSIS

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  1. QUANTITATIVE DATA ANALYSIS Professor Lisa High University of Windsor Faculty of Nursing

  2. Levels of Measurement • Quantitative measures: level of measurements Types: • Nominal – • Ordinal – • Interval – • Ration -

  3. Descriptive Stats - Univariate • Frequency distributions – • Central tendency – mode, median, medium • Variability

  4. Variability • Define: • RANGE • STANDARD DEVIATION

  5. BIVARIATE STATS - to describe relationships b/w two variables • CONTINGENCY TABLE • CROSS TABULATED • CORRELATION

  6. INFERENTIAL STATS - based on the laws of probability – provides a means for drawing conclusions about a pop’n – data from a sample • SAMPLING DISTRIBUTIONS • HYPOTHESIS TESTING • TYPE I AND TYPE II ERRORS

  7. Sampling Distributions • Based on assumption of random sampling from pop’n • This is widely violated • Sampling distribution of the mean – • Standard error of the mean -

  8. Hypothesis Testing • Define – provides objective criteria for deciding whether research hypotheses should be accepted as true or rejected as false • Hypotheses: (a) Researcher hypothesis – (b) Null hypothesis – - researchers use statistical tests to test hypotheses

  9. Type I and Type II Errors • Type I – rejecting the null hypothesis when it is true = false positive conclusion • Type II – acceptance of a false null hypothesis = a false negative conclusion • Level of Significance: controlling the degree of risk in making a Type I and Type II errors • Alpha – type I • Beta – type II – power analysis

  10. Tests of Statistical Significance • Test statistic: • Non-significant result:

  11. Parametric & Non-Parametric Tests • Parametric – • Non-Parametric – • Testing Hypothesis Testing Procedures: • Selecting an appropriate test statistic • Selecting the level of significance • Computing a test statistic • Determining degrees of freedom

  12. Bivariate Statistical Tests • t-tests • Analysis of variance • Chi-squared test • Correlation coefficients

  13. Mutivariate Statistical Analysis • Multiple regression • Analysis of covariance • Discriminant function analysis • Logistic regression • Factor analysis • MANOVA • Causal modeling

  14. Understanding & Evaluation • Table 15.14 – p. 377 – 2nd ed • Box 15.1 – p. 379 – 2nd ed • Box 15.2 – p. 380 – 2nd ed • Table 13.14 – p. 368 – 1st ed • Box 13.3 – p. 371 – 1st ed • Box 13.2 –p. 373 – 1st ed

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