Hello. I want to perform an analysis of randomized block data in ANOVA, but can not find the proper command. My experiment design contains one categorical factor disturbance, which has 5 levels. Within each disturbance level, there are 4 replicates, makes a total of 20 pots. As the pots are placed in 4 different pools (each contains all the 5 disturbance treatments), the pool is treated as block. How can I customize this design in the GLM module in my Statista 10.0? Thanks a lot.
Hi, to customize your experiment design, you can go to Statistics | DOE module. DOE stands for Design of Experiment.
There are various designs available that you can choose and customize. You can click on the question mark on the top right corner of the dialog for more explanations of the design options listed.
To continue with my previous reply, if you have already collected your dataset, you can load the dataset to Statistica and open the GLM module (under "Statistica" | "Advanced models" tab) to analyze.
There are various methods listed like ANOVA, Analysis of covariance, and regression available for you to analyze your data.
I regret that we are not legally defended here to suggest what method you should use to analyze your data based on your experimental design. You can click on the question mark on top right corner of the GLM module dialog to get more explanations of those methods. It might help you make your decision on the analysis.
If you really need help on the analysis, we have a professional analyst team who can provide professional statistical analysis advises at reasonable charges. They are contactable at email address: DSG.Sales.Statistica.NAM.PSO@software.dell.com.
Hope it helps.
Thank you for your help. I have already collected my dataset, and wonder how to customize the analysis in ANOVA as it is not a factorial design. If you are not allowed to give me instruction for my experiment. Could you please tell me in detail how to perform the analysis with an example from the STATISTICA electronic manual?
Sometimes, experiments with an N of 1 are designed deliberately in order to reduce the SS error, yielding a more sensitive ANOVA design. Specifically, the observations in the design can be arranged in blocks, in a manner that allows computation of an unconfounded main effect estimate of the blocking factor. The error term is then reduced by the SS due to the blocking factor. (The term randomized blocks was first used by Fisher, 1926.)
Example. Suppose we want to test the yield of different varieties of wheat under three types of fertilizer. We have four different fields available for our research, and decide to treat them as an additional blocking factor in the design. The design could be summarized as follows:
In this example, we are actually not interested in the effect of the blocking variable itself, that is, any significant differences between fields are of no theoretical interest to us. However, by estimating the SS due to the blocking factor (Field), we may be able to reduce the error variance, allowing for more sensitive tests for the effect of Fertilizer, Variety, and the interaction between the two. Also note that in this type of design you also decide to ignore any interactions of the blocking variable with the variables of interest.
Setting up the data file. The data file for this experiment is set up in the same way as you would set up the file from a regular full factorial between-groups experiment. The file should contain three grouping variables (Fertilizer, Variety, and Field) with codes that uniquely identify to which cell in the design each case belongs. The fourth variable in the file would be the dependent variable (Yield).
Specifying and analyzing the design. In GLM, specify the DESIGN (either via the GLM Analysis Syntax Editor dialog or the GLM Analysis Wizard Between Design - Custom Between Design tab) to include only the effects of interest. For example, you could include in the model only the effects for Fertilizer, Variety, Field, and the interaction between Fertilizer and Variety. On the GLM Results - Quick tab, click the All effects button to review the ANOVA table for all effects.
"In GLM, specify the DESIGN (either via the GLM Analysis Syntax Editor dialog or the GLM Analysis Wizard Between Design - Custom Between Design tab) to include only the effects of interest. For example, you could include in the model only the effects for Fertilizer, Variety, Field, and the interaction between Fertilizer and Variety. On the GLM Results - Quick tab, click the All effects button to review the ANOVA table for all effects."
Please refer to this KB article on how to perform GLM analysis with covariates and interaction terms. You can refer to the "Method 2" in that KB article using "Analysis Wizard"-"Custom Between Design" to specify the covariate effects and interaction terms that you are interest to look at. In this situation, you would need to know what covariates and interactions that you keen to examine for your data.
Specifically, if I didn't consider the effect of fertilizer initially, and only fertilizer 1 was used, how should I perform this randomized block design? I tried to use the GLM Analysis Wizard Between Design - Custom Between Design tab, and if I include both of the two factors variety and field, the result was the same as that of main effects ANOVA, which did not match the design of this experiment.
Initially the model include the effects for Fertilizer, Variety, Field, and the interaction between Fertilizer and Variety.
Fertilizer + Variety + Field + Fertilizer * Variety.
If you donot consider Fertilizer and only fertilizer 1 is used, it means the Fertilizer and Fertilizer * Variety are removed from the model, and the model only include Variety + Field.
What do you mean by "the result was the same as that of main effects ANOVA, which did not match the design of this experiment. " ??
Do you have screenshot of your analysis result for investigation? Variety + Field