Not all data are analyzable, especially the ones that were collected without a preplanned analysis.The %Deming_bootstrap SAS macros presented below computes the confidence intervals of the Deming regression slope and intercept estimates using the Bootstrap re-sampling method. I would suggest you to talk to someone who is experienced in this kind of research and get some practical input. This kind of repeated measurements require more sophisticated analysis beyond just ANOVA or linear regression. For instance, you might have observed the same subject today and yesterday.
Spss compute variable how to#
I am not too familiar about ecological/biological studies and how to deal with scenario like repeated observations. There will be a variable for the mother's time in seconds, another for the child's time in seconds. compare the seconds spent eating between a mother/child dyad, etc.) then it will be easier to input them as the so called "wide" form, with each pair occupying the same row. If you are planning to do paired t-test type of analysis (e.g. Generally, t-test, ANOVA type of test will require the data to be in a so called "long" form, which means there will be a second variable, a group, age, and sex. Without knowing the research questions and the design, it is hard to tell. In regards to layout for analysis as it will be grouped by month andĪge/sex dor further analysis would it be better to have a column forĮach groups line-by-line percentage or could I have it all in oneĬolumn deapite them being for different groups/months and then reapplyĪ grouping factor while doing the testing? Click the Option button and make sure the output will contain "Sum" by transferring the Sum into the right hand side. Put the percent variable into Dependent, and the group variable into Independent. You can use:Īnalyze > Compare Means > Means panel to get that. Now that each line has a percent, the group-specific total percentage of seconds will just be the group sum. Then you can compute the line-by-line percentage using compute: This will be the new total variable created at the end of the data set: If nothing is specified, then the total will be based on all the cases. If there are subgroup total you wish to compute, the put those subgroup indicator into the "Break Variables" panel. Then click the Function button below to change the function to "Sum" instead of "Mean." Your final screen should look like the picture below. Go to Data > Aggregate and put the second variable into the "Summaries of Variables" panel. But you can try to tinker with this suggestion and achieve your desired results. It's still somewhat unclear about what your denominator would be. If this is on the incorrect board still I will figure out how to get it moved. So I guess what I am asking is if there is some way to calculate the mean percentage of seconds while applying the required grouping factors?
I want to also do it by month as it is already well recognised that there is significant change in dietary composition month-month. To look at the difference most sources I look at, consider the proportion of time by age/sex in relation to each plant part. What I set out to determine is whether there is significant difference in ingestion of certain plant parts and nutrients by age/sex category. This is how the data is structured at present.Įach row is a recorded observation of a feeding event the date is in the first 3 columns, plant part, AgeSex category and group are all converted into numerical data. All the papers I have read compare mean percentages (of time) is there an easy way to calculate this or must i continue to slug away with excel? I simply want to compare proportion of time (seconds) spent eating certain plant parts by age/sex categories and months. I suspect it will have to do with creating a new variable but for the life of me can not figure it out. I am working with SPSS and have convinced myself there must be an easy way to do this without having to manually calculate it all in excel. I have seconds that I want to convert to their percentage value but by grouping variables.
I need to transform a fairly large data set in order to analyse a certain variable as a percentage.