I want to do a meta-analysis on how plant diversity affects soil carbon storage. For some studies, plant diversity was considered a categorical variable. The authors set plant diversity gradients and many replicates. These results have mean, replicates, and standard error. For other studies, plant diversity was considered a continuous variable. The authors investigated dozens of plots, which had different species richness and soil carbon storage, and no replicates and mean. The results were represented using a scatter diagram plus a regression line. I wonder how I should solve this problem. Should my research only include those studies with replicates, mean and standard error, discarding the latter?
Xiangdeng Du This is a very confused account of what you have. Categorical variables are nominal variables. You should not calculate mean and standard deviation from categorical variables. It is only possible to do this with interval level data. You claim to have gradients. This means that the data is not categorical, but ordinal data. I cannot help you until it is clear what sort of data you do have.
I know it is the custom with some researchers to treat ordinal variables in the same way that you calculate interval level data. but strictly speaking you should not do this. So they calculate mean and standard deviation, but such analysis is useless.
If you do have categorical data the inferential test to use is chi squared. if the data is ordinal then because you have two independent groups, the test to use is the Mann Whitney U test for independent groups. Hope this is helpful.
bear with me-- i am an editor and proofreader, so compelled to write this:
"conbine" the word is combine-- there is o such word in the english language; do use spell check
When conducting a meta-analysis with studies that have both categorical and continuous independent variables, such as in your case with plant diversity, you can approach the analysis in the following way:
For studies with categorical variables:
- Collect data on means, standard errors, and sample sizes for each level of plant diversity. This allows you to compute effect sizes (e.g., mean difference, standardized mean difference) for each comparison of interest.
For studies with continuous variables:
- Obtain the necessary statistics for each study, such as correlation coefficients or regression slopes, which represent the relationship between plant diversity and soil carbon storage.
It's important to note that the inclusion or exclusion of studies should be based on the research question, relevance, and quality of the studies rather than solely on the availability of certain statistical measures. The decision to include studies with different data formats should be guided by the research objectives and the comparability of the studies within each group.
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