Design Resources Server |
|
||||
IASRI | |||||
Home |
<<Back Analysis Using SPSS Main
Procedure is: Start®
All Programs ®
SPSS for Windows® SPSS 15.0/ SPSS13.0/
SPSS10.0 (based on the version available on your machine) ®
Enter data in Data Editor ®
Analyze ® GLM ®
Univariate ®
Height ® [puts
Height under Dependent list: ] ®
Tree®
[put Tree under
Fixed Factor(s): ] Rep ®[put
Rep under Fixed Factor(s):] Continue ®
Model... [Opens Model
dialogue box] ®
Custom ® Build Term(s) ®
Main effects ®
[puts Rep, Tree under Model:
] ®
Continue ® OK. For performing the analysis, input the data in the following format. {Here call the Tree Specie Number as Tree and Replication as Rep.It may, however, be noted that one can retain the same name or can code in any other fashion}. Following
are the brief description of the steps along with screen
shots. ·
Open Data editor: Start® All Programs ®
SPSS for Windows®
SPSS 15.0/ SPSS13.0/ SPSS10.0 · Enter data in SPSS Data editor. There are two views in SPSS Data Editor. In variable view, one can define the name of variables and variable types string or numeric and data view gives the spreadsheet in which data pertaining to variables may be entered in respective columns.
· Once
the data entry is complete, Choose Analyze from the Menu
Bar. Now select Analyze®
General linear Model ® Univariate.
· This selection displays the following screen
· Select
Height and send it to the Dependent Variable box; Tree and
Rep may be selected for Fixed Factor(s) box. After
doing these the dialog box should be like this.
· Now
define model as per design adopted to analyze the data. A
Click on the model displays Univariate: Model dialog
box. Click on custom then change the Build Terms
as Main effects and send Tree(F) and Rep(F) to the Model
box. One gets the following screen
· Click Continue to return back to the Univariate dialog box. ·
Now select postHoc option to select the
desired multiple pairwise comparison procedure from the
following screen:
· Before
clicking OK one can check the options available there in the
Univariate by clicking the Options button and can
change there according to your requirements like
significance level, Descriptive Statistics
etc. · Now
for contrast analysis contrast should be defined in the
syntax editor. After defining model click continue click
paste syntax window will come automatically. Write
the following line. In the syntax editor. /Lmatrix
'1 2 3 4 10 vs 5 6 7 8 9' Tree 1 1 1 1 -1 -1 -1 -1 -1 1; /Lmatrix 'within group 1' Tree 1 -1 0 0 0 0 0 0 0 0; Tree 1 1 -2 0 0 0 0 0 0 0; Tree 1 1 1 -3 0 0 0 0 0 0; Tree 1 1 1 1 0 0 0 0 0 -4; /Lmatrix
'within group 2' Tree
0 0 0 0 1 -1 0 0 0 0;
Tree 0 0
0 0 1 1 -2 0 0 0;
Tree 0 0
0 0 1 1 1 -3 0 0;
Tree 0 0
0 0 1 1 1 1 -4
0; /Lmatrix '1 vs 2 3 4 5 6 7 8 9 10' Tree
9 -1 -1 -1 -1 -1 -1 -1 -1 -1; /Lmatrix '1 2 3 4 vs 9'
Tree 1 1 1 1 0 0 0 0 -4 0; /Lmatrix'1 2 3 4 vs 10' Tree 1 1 1 1 0 0 0 0 0 -4; /Lmatrix '5 6 7 8 vs 9' Tree 0 0 0 0 1 1 1 1 -4 0; /Lmatrix '5
6 7 8 vs 10' Tree 0 0 0 0 1 1 1 1 0 -4; The
following window will come.
To
answer all the questions from 1 to 5, the
following syntax may be used after creating the data file. UNIANOVA
Height BY
Tree Rep
/METHOD = SSTYPE(3)
/INTERCEPT = INCLUDE
/POSTHOC = Tree ( DUNCAN LSD )
/CRITERIA = ALPHA(.05) /Lmatrix
'1 2 3 4 10 vs 5 6 7 8 9' Tree 1 1 1 1 -1 -1 -1 -1 -1 1; /Lmatrix
'within group 1' Tree 1 -1 0 0 0 0 0 0 0 0;
Tree 1 1 -2 0 0 0 0 0 0 0;
Tree 1 1 1 -3 0 0 0 0 0 0;
Tree 1 1 1 1 0 0 0 0 0 -4; /Lmatrix
'within group 2' Tree 0 0 0 0 1 -1 0 0 0 0;
Tree 0 0
0 0 1 1 -2 0 0 0;
Tree 0 0
0 0 1 1 1 -3 0 0;
Tree 0 0
0 0 1 1 1 1 -4
0; /Lmatrix
'1 vs 2 3 4 5 6 7 8 9 10' Tree
9 -1 -1 -1 -1 -1 -1 -1 -1 -1; /Lmatrix
'1 2 3 4 vs 9' Tree 1 1 1 1 0 0 0 0 -4 0; /Lmatrix'1
2 3 4 vs 10' Tree 1 1 1 1 0 0 0 0 0 -4; /Lmatrix
'5 6 7 8 vs 9' Tree 0 0 0 0 1 1 1 1 -4 0; /Lmatrix
'5 6 7 8 vs 10' Tree 0 0 0 0 1 1 1 1 0 -4;
/DESIGN = Tree Rep .
Analysis Using SAS Analysis Using SPSS
Home Descriptive Statistics Tests of Significance Correlation and Regression Completely Randomised Design RCB Design Incomplete Block Design Resolvable Block Design Augmented Design Latin Square Design Factorial RCB Design Partially Confounded Design Factorial Experiment with Extra Treatments Split Plot Design Strip Plot Design Response Surface Design Cross Over Design Analysis of Covariance Diagnostics and Remedial Measures Principal Component Analysis Cluster Analysis Groups of Experiments Non-Linear Models
Copyright Disclaimer How to Quote this page Report Error Comments/suggestions |
||||
Descriptive Statistics | |||||
Tests of Significance | |||||
Correlation and Regression | |||||
Completely Randomised Design | |||||
RCB Design | |||||
Incomplete Block Design | |||||
Resolvable Block Design | |||||
Augmented Design | |||||
Latin Square Design | |||||
Factorial RCB Design | |||||
Partially Confounded Design | |||||
Factorial Experiment with Extra Treatments | |||||
Split Plot Design | |||||
Strip Plot Design | |||||
Response Surface Design | |||||
Cross Over Design | |||||
Analysis of Covariance | |||||
Diagnostics and Remedial Measures | |||||
Principal Component Analysis | |||||
Cluster Analysis | |||||
Groups of Experiments | |||||
Non-Linear Models | |||||
Contact Us | |||||
Other
Designed Experiments (Under Development) |
|||||
For
exposure on SAS, SPSS, Please see Module I of Electronic Book II: Advances in Data Analytical Techniques available at Design Resource Server (www.iasri.res.in/design) |
|||||