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Quntative Data Analysis SPSS Exploring Assumptions

Quntative Data Analysis SPSS Exploring Assumptions. Overview. Assumptions……………Seriously..! Assumptions of parametric data Normal distribution Parametric test --- Nonparametric data = Wrong Conclusion Why? Test Selection Be a Critic Impress your seniors. Assumptions of parametric tests.

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Quntative Data Analysis SPSS Exploring Assumptions

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  1. Quntative Data Analysis SPSSExploring Assumptions

  2. Overview • Assumptions……………Seriously..! • Assumptions of parametric data • Normal distribution • Parametric test --- Nonparametric data = Wrong Conclusion • Why? • Test Selection • Be a Critic • Impress your seniors

  3. Assumptions of parametric tests • Four basic assumptions • Normally distribution • Different meaning in different context • Sampling distribution/error distribution • Homogeneity of variance • Same variance of data • Groups comparison (same variance of groups) • Correlational design (stable variance of a variable across all levels of other variable) • Interval data • Independence • Participants data independent of each other and uncorrelated errors (correlational desgin) • Between conditions non-independent b/w participants independent (Repeated Measure design)

  4. Normality • Frequency distribution • Values of skewness and kurtosis (Sig s = s/s.e • P–P plot (Analyze  Descriptives  P-P plot • cumulative probability of a variable against the cumulative probability of a particular distribution • Z-score of rank orders of data against their own z-scores • A diagonal distributed data  Normal distribution

  5. Analysis by groups

  6. Test of normal distribution • Kolmogorov–Smirnov test (K–S test) • Shapiro–Wilk test (more power than K-S) • Analyze descriptive statistics  explore • Normality Plots with tests • Non-significant (p > .05) = Normal Distribution • Reporting results: • D(df) = test-statistic, p > .05 • D = (Symbol for K-S), df = degree of freedom (sample size), test-statistic = K-S Statistic • Limitations • Large sample sizes  Always Significant

  7. SPSS window

  8. Homogeneity of variance • Equal variance • In groups data – at least one variable is categorical • All groups have equal variance • In correlation – both or all variables are continuous • A variable has equal variance for all levels of other

  9. Test of HV • Levene’s test • Analyze descriptive statistics  explore • Spread vs. level with Levene’s test • Non-significant (p > .05) = Equal Variance • Reporting results: • F(df1, df2) = 7.37, p < .01. • F = (Symbol for Levene’s test), df = degree of freedom (categories, sample size), test-statistic = F Statistic • Hartley’s Fmax (Variance ratio) • VR= largest group variance/the smallest • Smaller than the critical values

  10. Hartley’s FMax test

  11. Dealing with outliers • Remove the case • Transform the data • Change the score (a lesser evil) • The next highest score plus one • X = (z × s) + X = (mean + 3sd) • The mean plus two standard deviations

  12. Dealing with non-normality and unequal variances • Transforming data • Doesn’t change relationship b/w variables • Changes difference b/w variables • Choosing a transformation • trial and error • Levene’s test (Use Transformed option) • Types: • Log transformation (log(Xi)) • Square root transformation (√Xi) • Reciprocal transformation (1/Xi) • Reverse score transformations

  13. What Else • Evils of Transformation • Non-parametric tests • Robust methods • Trimmed mean • Bootstrap

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