1 / 14

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 –

gage-robles
Télécharger la présentation

QUANTITATIVE DATA ANALYSIS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related