1 / 18

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.

josephinegarza
Télécharger la présentation

Data Analysis Using SPSS

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. 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

More Related