6.2 Logistic regression with a categorical variable in R.
6.1 Regression modeling with categorical covariates.6 More topics on Multiple Imputation and Regression Modelling.5.2.6 Analysis of Variance (ANOVA) pooling.5.2.2 Pooling Means and Standard Deviations in R.5.2.1 Pooling Means and Standard deviations in SPSS.5 Data analysis after Multiple Imputation.IV Part IV: Data Analysis After Multiple Imputation.4.14 Number of Imputed datasets and iterations.4.13 Imputation of categorical variables.4.12.1 Predictive Mean Matching, how does it work?.4.12 Predictive Mean Matching or Regression imputation.4.10 Guidelines for the Imputation model.4.4 The output of Multiple imputation in SPSS.4.1 Multivariate imputation by chained equations (MICE).3.4.2 Bayesian Stochastic regression imputation in R.3.4.1 Bayesian Stochastic regression imputation in SPSS.3.4 Bayesian Stochastic regression imputation.3.3.4 Stochastic regression imputation in R.2.8.2 Compare and test group comparisons.2.7.2 Compare and test group comparisons.II Part II: Basic Missing Data Handling.1.15 Useful Missing data Packages and links.1.6.4 Indexing Vectors, Matrices, Lists and Data frames.1.6.3 Vectors, matrices, lists and data frames.Here are my questions.ġ.Does it make sense if I only impute for experimental group? (Because missing data are mostly from this group)Ģ.Would it be more accurate if I just delete those who only participate once and keep those who participated at least two times then do estimated-maximization imputation?ģ. So far, I have checked Little’s MCAR test and separated variance t test (missing value analysis in SPSS) and both results show non-significant ( can I say my data is missing at random?). I have relatively high dropout rate in T2 (30% of students dropout and never return to my study and most of them are from experimental group). The research design is longitudinal (T1, T2, T3) with control group and experimental group. I am investigating students’ achievement goals change before and after the intervention (with questionnaire). Hello Jeremy, I am in the middle of data analysis and wonder if you could give some suggestions regarding imputation. I will be very happy to hear any further explanations. Hope the above footnote will be enough for my reviewers. Indeed, there is a discussion on this in these papers, but I had problems with applying the corrections discribed there. Discussion on the application of possible corrections of the degrees of freedom for pooled estimates in small and large samples can be found in Barnard and Rubin (1999) or Van Ginkel (2010)". No corrections were applied in case of these analyses. As I couldn't find a solution to that, I made such footnote in my thesis: "The degrees of freedom in paired-samples t-tests are higher (or in further cases lower) than expected, because the results are pooled from 5 imputed datasets. I would expect df=55252, and I get df=2742536455) or in other cases much lower than expacted. Thanks so much for addressing my question that time and sorry for not doing the same! Yes, what I am worried about is the fact that some of the df's are very high (e.g. Hello again Jeremy, In February I asked you about the problem with extreamly high degrees of freedom. Remember: any variables you include in a model together are producing "partial estimates", which means they are giving you estimates of there effects, controlling for ALL OTHER VARIABLES IN THE MODEL. UNLESS Q1 was a categorical variable (a.k.a.
You will know if your data is setup properly if it will produce mi pooled estimates in other types of analysis, then the problem is just that SPSS isn't supporting the pooled estimates for that analysis.Īs for your question about including a control variable, you just need to add the additional covariate to the model as a predictor along with X1, X2, and X3.įor example, if I wanted to control for variable "Q1", then I would just have the following: If it won't produce pooled estimates at that point, then SPSS just doesn't support pooled estimates for that analysis (perhaps turn to R). For a model to run on mi datasets: 1) your version of SPSS needs to support MI and 2) you must have your data split by imputation. First, your mi question: there is no modification to the syntax that will allow it to run via mi.