CORRELATIONS        /VARIABLES=sem1perc sem2perc sem3perc sem4perc                         /PRINT=TWOTAIL NOSIG   /MISSING...
sem1perc            sem2perc         sem3perc          sem4perc                                                           ...
Missing Value Handling       Definition of Missing              User defined missing values are treated as                ...
Correlations                                                 NotesOutput Created                                          ...
**sem1perc         Pearson Correlation                             1          .197          -.690            .110         ...
Cases Used                        Statistics for each analysis are based on the                                           ...
document they may overwrite changes made by others or your >changes may be overwritten by others. >File opened C:UsersKCT ...
Case Processing Summary                                                                                     Cases         ...
Crosstabs                                                 NotesOutput Created                                             ...
Cases                                       Valid                         Missing                        Total            ...
NotesOutput Created                                                                            22-Mar-2012 15:00:59Comment...
sem3perc   Between Groups     60.060   2     30.030   .243   .786           Within Groups    2713.940   22   123.361      ...
Upcoming SlideShare
Loading in...5
×

Correlations

759

Published on

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
759
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
14
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Correlations

  1. 1. CORRELATIONS /VARIABLES=sem1perc sem2perc sem3perc sem4perc /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE.Correlations NotesOutput Created 15-Mar-2012 14:20:20CommentsInput Data C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Para mes.sav Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 25Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics for each pair of variables are based on all the cases with valid data for that pair.Syntax CORRELATIONS /VARIABLES=sem1perc sem2perc sem3perc sem4perc /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE.Resources Processor Time 0:00:00.015 Elapsed Time 0:00:00.015[DataSet1] C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Parames.sav Correlations
  2. 2. sem1perc sem2perc sem3perc sem4perc **sem1perc Pearson Correlation 1 .197 -.690 .110 Sig. (2-tailed) .346 .000 .600 N 25 25 25 25 **sem2perc Pearson Correlation .197 1 .525 .028 Sig. (2-tailed) .346 .007 .893 N 25 25 25 25 ** **sem3perc Pearson Correlation -.690 .525 1 -.241 Sig. (2-tailed) .000 .007 .246 N 25 25 25 25sem4perc Pearson Correlation .110 .028 -.241 1 Sig. (2-tailed) .600 .893 .246 N 25 25 25 25**. Correlation is significant at the 0.01 level (2-tailed).T-TEST GROUPS=gender(1 2) /MISSING=ANALYSIS /VARIABLES=mathscor /CRITERIA=CI(.95).T-Test NotesOutput Created 15-Mar-2012 14:47:00CommentsInput Data C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Para mes.sav Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 25
  3. 3. Missing Value Handling Definition of Missing User defined missing values are treated as missing. Cases Used Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis.Syntax T-TEST GROUPS=gender(1 2) /MISSING=ANALYSIS /VARIABLES=mathscor /CRITERIA=CI(.95).Resources Processor Time 0:00:00.000 Elapsed Time 0:00:00.009[DataSet1] C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Parames.sav Group Statistics gender N Mean Std. Deviation Std. Error Meanmathscor Male 11 89.55 6.817 2.055 Female 14 82.50 6.937 1.854 Independent Samples Test Levenes Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Difference Std. Error F Sig. t df Sig. (2-tailed) Mean Difference Difference Lower Uppermathscor Equal variances assumed .309 .584 2.540 23 .018 7.045 2.774 1.307 12.784 Equal variances not 2.545 21.793 .019 7.045 2.768 1.302 12.789 assumedCORRELATIONS /VARIABLES=sem1perc sem2perc sem3perc sem4perc /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE.
  4. 4. Correlations NotesOutput Created 15-Mar-2012 14:20:20CommentsInput Data C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Para mes.sav Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 25Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics for each pair of variables are based on all the cases with valid data for that pair.Syntax CORRELATIONS /VARIABLES=sem1perc sem2perc sem3perc sem4perc /PRINT=TWOTAIL NOSIG /MISSING=PAIRWISE.Resources Processor Time 0:00:00.015 Elapsed Time 0:00:00.015[DataSet1] C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Parames.sav Correlations sem1perc sem2perc sem3perc sem4perc
  5. 5. **sem1perc Pearson Correlation 1 .197 -.690 .110 Sig. (2-tailed) .346 .000 .600 N 25 25 25 25 **sem2perc Pearson Correlation .197 1 .525 .028 Sig. (2-tailed) .346 .007 .893 N 25 25 25 25 ** **sem3perc Pearson Correlation -.690 .525 1 -.241 Sig. (2-tailed) .000 .007 .246 N 25 25 25 25sem4perc Pearson Correlation .110 .028 -.241 1 Sig. (2-tailed) .600 .893 .246 N 25 25 25 25**. Correlation is significant at the 0.01 level (2-tailed).T-TEST GROUPS=gender(1 2) /MISSING=ANALYSIS /VARIABLES=mathscor /CRITERIA=CI(.95).T-Test NotesOutput Created 15-Mar-2012 14:47:00CommentsInput Data C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Para mes.sav Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 25Missing Value Handling Definition of Missing User defined missing values are treated as missing.
  6. 6. Cases Used Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis.Syntax T-TEST GROUPS=gender(1 2) /MISSING=ANALYSIS /VARIABLES=mathscor /CRITERIA=CI(.95).Resources Processor Time 0:00:00.000 Elapsed Time 0:00:00.009[DataSet1] C:UsersKCT BS 7AppDataLocalTempRar$DI02.624Parames.sav Group Statistics gender N Mean Std. Deviation Std. Error Meanmathscor Male 11 89.55 6.817 2.055 Female 14 82.50 6.937 1.854 Independent Samples Test Levenes Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Difference Std. Error F Sig. t df Sig. (2-tailed) Mean Difference Difference Lower Uppermathscor Equal variances assumed .309 .584 2.540 23 .018 7.045 2.774 1.307 12.784 Equal variances not 2.545 21.793 .019 7.045 2.768 1.302 12.789 assumedGET FILE=C:UsersKCT BS 7Downloadsdata.sav. >Warning # 67. Command name: GET FILE >The document is already in use byanother user or process. If you make >changes to the document they may overwrite changes made by others or your >changes may beoverwritten by others. >File opened C:UsersKCT BS 7Downloadsdata.sav GET FILE=C:UsersKCT BS 7Downloadsdata.sav.>Warning # 67. Command name: GET FILE >The document is already in use by another user or process. If you make >changes to the
  7. 7. document they may overwrite changes made by others or your >changes may be overwritten by others. >File opened C:UsersKCT BS7Downloadsdata.sav DATASET ACTIVATE DataSet1. DATASET CLOSE DataSet2. DATASET ACTIVATE DataSet1. DATASET CLOSE DataSet3. SAVEOUTFILE=C:UsersKCT BS 7Downloadsdata.sav /COMPRESSED. RECODE matscor (0 thru 300=1) (301 thru Highest=2) INTO newmat.VARIABLE LABELS newmat New MAT score is categorised into two. EXECUTE. CROSSTABS /TABLES=gender BY newmat /FORMAT=AVALUETABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.Crosstabs NotesOutput Created 22-Mar-2012 14:51:08CommentsInput Data C:UsersKCT BS 7Downloadsdata.sav Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 25Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics for each table are based on all the cases with valid data in the specified range(s) for all variables in each table.Syntax CROSSTABS /TABLES=gender BY newmat /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.Resources Processor Time 0:00:00.000 Elapsed Time 0:00:00.035 Dimensions Requested 2 Cells Available 174762[DataSet1] C:UsersKCT BS 7Downloadsdata.sav
  8. 8. Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percentgender * New MAT score is 25 100.0% 0 .0% 25 100.0%categorised into two gender * New MAT score is categorised into two CrosstabulationCount New MAT score is categorised into two 1.00 2.00 Totalgender Male 6 5 11 Female 3 11 14Total 9 16 25 Chi-Square Tests Asymp. Sig. (2- Exact Sig. (2- Exact Sig. (1- Value df sided) sided) sided) aPearson Chi-Square 2.932 1 .087 bContinuity Correction 1.671 1 .196Likelihood Ratio 2.964 1 .085Fishers Exact Test .115 .098Linear-by-Linear Association 2.815 1 .093N of Valid Cases 25a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 3.96.b. Computed only for a 2x2 tableCROSSTABS /TABLES=ugdegree BY backgrnd /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.
  9. 9. Crosstabs NotesOutput Created 22-Mar-2012 14:56:06CommentsInput Data C:UsersKCT BS 7Downloadsdata.sav Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 25Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics for each table are based on all the cases with valid data in the specified range(s) for all variables in each table.Syntax CROSSTABS /TABLES=ugdegree BY backgrnd /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.Resources Processor Time 0:00:00.000 Elapsed Time 0:00:00.000 Dimensions Requested 2 Cells Available 174762[DataSet1] C:UsersKCT BS 7Downloadsdata.sav Case Processing Summary
  10. 10. Cases Valid Missing Total N Percent N Percent N Percentugdegree * backgrnd 25 100.0% 0 .0% 25 100.0% ugdegree * backgrnd CrosstabulationCount backgrnd Arts &Science Commerce Professional Totalugdegree BBM &B.Com 3 2 0 5 B.Sc 6 1 0 7 B.A 3 1 0 4 B.E & B.TECH 0 2 6 8 Others 0 0 1 1Total 12 6 7 25 Chi-Square Tests Asymp. Sig. (2- Value df sided) aPearson Chi-Square 20.848 8 .008Likelihood Ratio 26.594 8 .001N of Valid Cases 25a. 15 cells (100.0%) have expected count less than 5. The minimumexpected count is .24.ONEWAY sem1perc sem2perc sem3perc sem4perc BY backgrnd /MISSING ANALYSIS.Oneway
  11. 11. NotesOutput Created 22-Mar-2012 15:00:59CommentsInput Data C:UsersKCT BS 7Downloadsdata.sav Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 25Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics for each analysis are based on cases with no missing data for any variable in the analysis.Syntax ONEWAY sem1perc sem2perc sem3perc sem4perc BY backgrnd /MISSING ANALYSIS.Resources Processor Time 0:00:00.016 Elapsed Time 0:00:00.009[DataSet1] C:UsersKCT BS 7Downloadsdata.sav ANOVA Sum of Squares df Mean Square F Sig.sem1perc Between Groups 63.393 2 31.696 .113 .894 Within Groups 6186.607 22 281.209 Total 6250.000 24sem2perc Between Groups 41.393 2 20.696 .534 .594 Within Groups 852.607 22 38.755 Total 894.000 24
  12. 12. sem3perc Between Groups 60.060 2 30.030 .243 .786 Within Groups 2713.940 22 123.361 Total 2774.000 24sem4perc Between Groups 46.893 2 23.446 .310 .736 Within Groups 1663.107 22 75.596 Total 1710.000 24
  1. ¿Le ha llamado la atención una diapositiva en particular?

    Recortar diapositivas es una manera útil de recopilar información importante para consultarla más tarde.

×