1 / 32

Implementation of Statistical Methods using SPSS Sourish Saha PhD student Department of Statistics University of F

6/9/2012. SPSS: STA 3024 - Sourish Saha . 2. TOPICS . Manipulating Data Recoding, Subsetting Descriptive StatisticsComparing Means One-Sample T Test, Independent-Samples T Test, Paired-Samples T Test One-Way ANOVA, Multiple Comparison, CorrelationsSimple

evelyn
Download Presentation

Implementation of Statistical Methods using SPSS Sourish Saha PhD student Department of Statistics University of F

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. 6/10/2012 SPSS: STA 3024 - Sourish Saha 1 Implementation of Statistical Methods using SPSS Sourish Saha PhD student Department of Statistics University of Florida sourish@ufl.edu

    2. 6/10/2012 SPSS: STA 3024 - Sourish Saha 2 TOPICS Manipulating Data Recoding, Subsetting Descriptive Statistics Comparing Means One-Sample T Test, Independent-Samples T Test, Paired-Samples T Test One-Way ANOVA, Multiple Comparison, Correlations Simple & Multiple Regression Analysis Comparison of Several Groups Two-way ANOVA Chi-Square as a Test of Homogeneity Kruskal-Wallis Test Logistic Regression

    3. 6/10/2012 SPSS: STA 3024 - Sourish Saha 3 To Recode the Values of a Variable into a New Variable Transform -> Recode -> Into Different Variables Select the variables you want to recode. Enter an output (new) variable name and click Change. Click Old and New Values and specify how to recode values.

    4. 6/10/2012 SPSS: STA 3024 - Sourish Saha 4 To Select Subsets of Cases Based on a Conditional Expression Data Select Cases. Select If condition is satisfied. Click If. Enter the conditional expression.

    5. 6/10/2012 SPSS: STA 3024 - Sourish Saha 5 Exploring the data in SPSS Analyze   Descriptive Statistics     Descriptives Descriptives provides basic descriptive statistics: n, mean, standard deviation, min and max.

    6. 6/10/2012 SPSS: STA 3024 - Sourish Saha 6 Exploring the data in SPSS Analyze   Descriptive Statistics     Explore Explore provides more descriptive statistics, including the variance, skewness, kurtosis, the median, percentiles and other descriptive statistics and information. Plots Boxplots, stem-and-leaf plots, histograms, normality plots. Reasons for using the Explore procedure include data screening, outlier identification, description, assumption checking.

    7. 6/10/2012 SPSS: STA 3024 - Sourish Saha 7 Exploring the data in SPSS Analyze   Descriptive Statistics     Frequencies Frequencies produces a frequency distribution table. Statistics and plots. Frequency counts, percentages, cumulative percentages, quartiles, user-specified percentiles, bar charts, pie charts, and histograms and more…

    8. 6/10/2012 SPSS: STA 3024 - Sourish Saha 8 Exploring the data in SPSS Analyze   Descriptive Statistics     Crosstabs Crosstabs with 2 variables creates a two-way table or crosstabulation. With statistics button one can choose among many statistics, including the chi-square value along with its p-value. The Crosstabs procedure offers tests of independence and measures of association. One can obtain estimates of the relative risk of an event.

    9. 6/10/2012 SPSS: STA 3024 - Sourish Saha 9 Exploring the data in SPSS Analyze   Descriptive Statistics     Ratio Statistics The Ratio Statistics procedure provides a comprehensive list of summary statistics for describing the ratio between two scale variables.

    10. 6/10/2012 SPSS: STA 3024 - Sourish Saha 10 Means Analyze   Compare Means     Means The Means procedure calculates subgroup means and related univariate statistics for dependent variables within categories of one or more independent variables. The Means procedure is useful for both description and analysis of scale variables. A variety of statistics is available to characterize the central tendency and dispersion of your test variables.

    11. 6/10/2012 SPSS: STA 3024 - Sourish Saha 11 One-Sample T Test Analyze   Compare Means     One Sample t-test The One-Sample T Test procedure tests the difference between a sample mean and a known or hypothesized value. Allows you to specify the level of confidence for the difference Produces a table of descriptive statistics for each test variable

    12. 6/10/2012 SPSS: STA 3024 - Sourish Saha 12 Independent-Samples T Test Analyze   Compare Means     Independent Samples T-test The Independent-Samples T Test procedure compares means for two groups of cases. Ideally, for this test, the subjects should be randomly assigned to two groups, so that any difference in response is due to the treatment (or lack of treatment) and not to other factors. Also displayed are: Descriptive statistics for each test variable A test of variance equality

    13. 6/10/2012 SPSS: STA 3024 - Sourish Saha 13 Paired-Samples T Test Analyze   Compare Means     Paired-Samples T-test The Paired-Samples T Test procedure compares the means of two variables for a single group. It computes the differences between values of the two variables for each case and tests whether the average differs from 0.

    14. 6/10/2012 SPSS: STA 3024 - Sourish Saha 14 One-Way ANOVA Let be independent random samples from m normal populations with the ith population having parameters Assuming equal variances, we want to test the null hypothesis against the alternative that any two of the population means are unequal. ANOVA involves partitioning the total variation in the combined sample into two parts. One part explains the variation between the samples while the second part explains the variation within each sample (SST=SSG + SSE).

    15. 6/10/2012 SPSS: STA 3024 - Sourish Saha 15 One-Way ANOVA

    16. 6/10/2012 SPSS: STA 3024 - Sourish Saha 16 One-Way ANOVA For each group: number of cases, mean, standard deviation, standard error of the mean, minimum, maximum, and 95% confidence interval for the mean. Levene’s test for homogeneity of variance, analysis-of-variance table and robust tests of the equality of means for each dependent variable, user-specified a priori contrasts, and post hoc range tests and multiple comparisons: Bonferroni, Tukey’s honestly significant difference, Scheffé, and least-significant difference.

    17. 6/10/2012 SPSS: STA 3024 - Sourish Saha 17 Multiple Comparison tests Tests suitable for the simultaneous testing of several hypotheses concerning the equality of three or more population means. When samples have been taken from several populations, as a preliminary to the more general question of whether the populations differ, there is the simpler question of whether they have different means. If our null hypothesis is rejected then we wish to know where the differences lie, like for example using Tukey’s test (HSD).

    18. 6/10/2012 SPSS: STA 3024 - Sourish Saha 18 Multiple Comparison tests With m populations, If null is rejected then we wish to know where the differences lie. There are pairs of populations that could be compared.

    19. 6/10/2012 SPSS: STA 3024 - Sourish Saha 19 Bivariate Correlations The Bivariate Correlations procedure computes Pearson’s correlation coefficient (r), Spearman’s rho, and Kendall’s tau-b with their significance levels. Correlations measure how variables or rank orders are related. Before calculating a correlation coefficient, one should screen the data for outliers (which can cause misleading results) and evidence of a linear relationship. Pearson’s correlation coefficient is a measure of linear association. Two variables can be perfectly related, but if the relationship is not linear, Pearson’s correlation coefficient is not an appropriate statistic for measuring their association.

    20. 6/10/2012 SPSS: STA 3024 - Sourish Saha 20 Rank Correlation Coefficient Rank correlation is a method of finding the degree of association between two variables. The calculation for the rank correlation coefficient the same as that for the Pearson correlation coefficient, but is calculated using the ranks of the observations and not their numerical values. This method is useful when the data are not available in numerical form but information is sufficient to rank the data.

    21. 6/10/2012 SPSS: STA 3024 - Sourish Saha 21

    22. 6/10/2012 SPSS: STA 3024 - Sourish Saha 22 Recode & Compute Create new variable Transform -> Compute Give the name of the Target variable In the Numeric Expression box choose the Function of your choice

    23. 6/10/2012 SPSS: STA 3024 - Sourish Saha 23

    24. 6/10/2012 SPSS: STA 3024 - Sourish Saha 24

    25. 6/10/2012 SPSS: STA 3024 - Sourish Saha 25

    26. 6/10/2012 SPSS: STA 3024 - Sourish Saha 26

    27. 6/10/2012 SPSS: STA 3024 - Sourish Saha 27 Simple Linear Regression The simple linear regression is aimed at finding the "best-fit" values of two parameters in the following regression equation:     "the y-intercept of the regression line“ "the slope of the regression line" A popular method for finding the "best-fit" values is the Least Squares Regression method.

    28. 6/10/2012 SPSS: STA 3024 - Sourish Saha 28 Multiple Regression Multiple (linear) regression is a regression technique aimed at finding a linear relationship between the dependent variable and multiple independent variables. The multiple regression model is as follows: Multiple regression finds the set of parameters that provides the best fit between the model and the given data .

    29. 6/10/2012 SPSS: STA 3024 - Sourish Saha 29

    30. 6/10/2012 SPSS: STA 3024 - Sourish Saha 30 Kruskal - Wallis Test The Kruskal-Wallis test is a nonparametric test for finding if three or more independent samples come from populations having the same distribution. It is a nonparametric version of ANOVA.

    31. 6/10/2012 SPSS: STA 3024 - Sourish Saha 31

    32. 6/10/2012 SPSS: STA 3024 - Sourish Saha 32 Logistic Regression Useful for situations in which we want to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. Similar to a linear regression model BUT it is suited to models where the dependent variable is dichotomous. Logistic regression coefficients can be used to estimate odds ratios for each of the independent variables in the model.

    33. 6/10/2012 SPSS: STA 3024 - Sourish Saha 33 Logistic Regression To perform logistic regression, go to: Analyze Choose Regression Then click on Binary Logistic

More Related