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Posted 08-26-2018 11:04 PM
(1273 views)

Hi all,

I have a confusion selecting right statistical method to analyse my data (please see the attached excel file as an example).

I have used 4 different extraction method (EM_1 to EM_4) to release protein from bacteria. But I have used 2 methods to determine the amount of protein release from these 4 different extraction techniques. I am interested in comparing the efficiency of 4 extraction methods as well as comparing the amount of protein obtained in two different conditions (that is in cell solution and cell-free solution). Basically, my independent variables/treatments are 4 extraction methods and my outcome (dependent variable) is amount of protein release from each extraction method. However, I am also interested in showing amount of protein in two conditions (that is protein amount in cell solution and cell-free solution). So, these two conditions , though are outcome (dependent variables) seems like treatment, because I want to compare and say protein is higher /lower in one of these conditions.

My question;

1. Is this one way ANOVA ?

2. Is this 2 way anova (Completely randomized design)

3. Is this 2 way ANOVA (completely randomized block design , two conditions of protein amount as a block)

Please suggest me what type of analysis is suitable for these data set ?

Thank you very much !

3 REPLIES 3

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I would say that your treatment structure is a two-way factorial: extraction method (4 levels) and condition (2 levels). But you have not told us anything about your design structure so we have no way of knowing whether the design is a CRD, or a RCBD, or a split-plot, etc.

If you do an internet search on "experiment treatment structure design structure", you'll find several helpful links.

I hope this helps.

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Thank you so much for your reply.

My design is RCBD. My two conditions are block and each block contains all

treatments that is extraction methods (EM_1 to EM_4). I want to make two

conditions (protein in solution and protein in cell-free solution) as a

block. Since, these two conditions are the sources of variations that are

not related to the treatments.

I tried to right code for this analysis but could not succeed. I have only

done one way anova using SAS earlier, and now I am having difficulties

analyzing data. would you please help me writing a code for the data

attached.

*data* enzyme;

input ext_methods;

do block = *1* to *? (could not figured out what should i write here)*

input protein ;

output;

end;

cards;

could not figured out how I place data here.

*;*

*proc* *glm*;

class block ? ;

model protein=block ? ;

*run*; *quit*

My design is RCBD. My two conditions are block and each block contains all

treatments that is extraction methods (EM_1 to EM_4). I want to make two

conditions (protein in solution and protein in cell-free solution) as a

block. Since, these two conditions are the sources of variations that are

not related to the treatments.

I tried to right code for this analysis but could not succeed. I have only

done one way anova using SAS earlier, and now I am having difficulties

analyzing data. would you please help me writing a code for the data

attached.

*data* enzyme;

input ext_methods;

do block = *1* to *? (could not figured out what should i write here)*

input protein ;

output;

end;

cards;

could not figured out how I place data here.

*;*

*proc* *glm*;

class block ? ;

model protein=block ? ;

*run*; *quit*

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I regret that I am not able to figure out your design structure from the additional information that you've provided.

I doubt that two blocks are defined by the two conditions. Blocks are typically considered to be random effects factors which determine the variance of the response and do not affect the mean of the response. I believe you are thinking of condition as a fixed effects factor; if condition is a fixed effects factor, then it cannot also be a random effects factor. So you'll want to think more about your design structure.

Blocks are clusters of experimental units: units within a particular block are assumed to be "similar", and units between blocks are assumed to be "different". You may have clustering in your experimental design, or you may not.

Design issues can be complicated, so if there is someone at your institution who could help, I suggest talking with them. There is, of course, a lot of books, papers, etc. on the topic of design.

Or you could try again here, with a lot more detail about how you ran the experiment.

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