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Data Analysis Using SPSS

Data Analysis Using SPSS. SPSS-2. What is SPSS?. General Purpose Statistical Software Consists of three components Data Window - data entry and database (.sav) Output Window - all output from any SPSS session (.lst) Syntax Window - commands lines (.sps). SPSS-3. Data Entry & Preparation.

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Data Analysis Using SPSS

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  1. Data Analysis Using SPSS

  2. SPSS-2 What is SPSS? • General Purpose Statistical Software • Consists of three components • Data Window - data entry and database (.sav) • Output Window - all output from any SPSS session (.lst) • Syntax Window - commands lines (.sps)

  3. SPSS-3 Data Entry & Preparation • Data entry New or Recalled (SPSS or non-SPSS) • Data Definition • Data Manipulation and Variable Development

  4. SPSS-4 Data Definition • Purpose: Give meanings to the numbers for ease of reading the output • Involves • Data Format • Variable Name • Value Labels • Missing Values Command: Data Data Definition

  5. SPSS-5 Data Manipulation • Recoding • To give new values to old values (especially reversing negatively worded questions) • To form nominal variable from continuous data • Variable Development • To form new variables combinations of old ones or functions of old ones • Command: Transform  Recode/ Compute

  6. SPSS-6 Data Analysis - Descriptive • Purpose: • To describe each variable - What is the current level of the variable of interest? • Command • Frequency • Means, Minimum, Maximum, Standard Deviation, Quartiles, Standard Deviation • Analyze  Frequencies /Descriptives

  7. SPSS-7 Data Analysis - Descriptive • Frequencies for two or more nominal variables • Analyze  Summarize  Crosstabulation • Means of variables by subgroups defined by one or more nominal variables • Analyze  Compare Means  Means (Use of Levels)

  8. SPSS- 8 Parametric Test of Differences When • dependent continuous variable and we want to test differences across groups Command • Analyze  Compare Means  Independent t-test/ Paired t-test/ one-way ANOVA

  9. SPSS- 9 Non-Parametric Test of Differences When • dependent variable ordinal or normal assumption not met Command • Analyze  Non-parametric  2 Independent/ 2 related samples/ k independent samples/ k related samples

  10. SPSS- 10 Parametric Two-Way ANOVA When • continuous dependent variable and related groups Command • Analyze  General Linear Model  Simple • Note: Fixed Factor Effect

  11. SPSS- 11 Bivariate Relationship When • Covariation between two variables Correlation: • When both are continuous or ordinal Command Analyze  Correlate  Bivariate (with option for Spearman if both ordinal)

  12. SPSS- 14 Regression Analysis When • To establish relationship between one continuous dependent variable and a number of continuous independent variables Command Analyze  Regression  Linear (Use Statistics, Save options) Issues: • Assumptions of Regression - normality; constant variance, independence of independent variables; independence of error terms

  13. SPSS- 15 Regression Analysis Issues (cont.) • Outliers and Leverage Values • Choice of Selection Method of Independent Variables - Enter, Backward, Forward, Stepwise • Dummy Independent Variables Options • Residual Analysis; Influence Statistics, Collinearity Diagnostics, Normality Plots

  14. SPSS- 16 Regression Analysis Interpretation • Goodness of Model: R2, F-statistics, Adj. R2, Standard error • Strength of Influence of Independent Variables: beta and standardized beta

  15. SPSS- 17 Discriminant Analysis When • Dependent Variable is Nominal and the Purpose is to predict group membership on the basis of independent variables Command Analyze  Classify  Discriminant (Option: Classify by summary tables; Select - for holdout and analysis samples Issues • Similar to Regression

  16. SPSS- 18 Discriminant Analysis Interpretation • Goodness of Analysis: Hits Ratio - compared to maximum chance, proportional chance and Press Q. • Univariate Results: To establish the discriminating variables

  17. SPSS- 13 Factor Analysis When • To reduce the number of variables to underlying dimensions Command Analyze  Data Reduction  Factor (Option: rotation, save factor scores) Issues • Assumptions sufficient correlations between the variables (Bartlett test; anti-image, KMO test of sufficiency)

  18. SPSS- 12 Reliability Analysis When • Before forming composite index to a variable from a number of items Command Analyze  Scale  Reliability Analysis (with option for Descriptives item, scale, scale if item deleted) Interpretation • alpha value greater than 0.7 is good; more than 0.5 is acceptable; delete some items if necessary

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