missing data

  1. B

    general approach to handle missing data while doing simulation

    This is probably a naive question. I'm new to statistical simulation. So please be a little bit patient. I was just wondering, what are the general approaches to handle missing data fields while doing simulation? You can either add your own understandings or point me to some references or links...
  2. D

    Can iterative PCA be applied to grouped data?

    My data set consist of 156 individuals with fifteen variables. The variables consist of one body mass (dependent) variable and fourteen (independent) variables of different bird bone dimensions (of one type of bone). The 156 individuals can be divided over 30 bird species, where some groups of...
  3. I

    Missing value imputation on a non-normal distribution - EM vs Regression? Or else?

    Hi, Bit green when it comes to some of these methods, so please be gentle... but I'll try to provide as much info and concisely as possible. Any help sincerely appreciated. I have a non-normal dataset with missing at random data and am trying to determine which imputation method to use. I...
  4. A

    SPSS Statistics: Significant Differences between follow-up scores independent groups

    Hi, I have collected data on scores for a sample population before, 1 year after and 6 years after an intervention. All participants in the sample received the intervention. However, participants required the intervention for different reasons. I want to find out whether there is a...
  5. Z

    Missing data - seeking advice on approach!!

    INTRO:Planning list wise deletion of missing data from large data set (7772). Data is from online survey, participation self-select. Q's could be skipped (none mandatory). Repeated measures resulted from 'select all apply' answer option on several q's - each option became an individual variable...
  6. J

    Comparing missing and non-missing subjects for dependent variables

    Hello everyone. I am working with a data-set of a cohort study which has 3 different sets of paired data. For the third visit of the subjects, we have a lot of missing. So, I need to compare the basic characteristics (in the first and second visit) of the missing and non-missing subjects for my...
  7. B

    DIF Analyses with Missing Data

    Hi all, I'm currently analyzing items from a bank for K-12 education. I have a separate data set for each grade/subject combination (for example, 1st grade English is a set, 2nd grade English is a set, etc). Each data set contains 400-7000(!) items. I would love to able to run various DIF...
  8. E

    How to handle missing data?

    Hi, I just finished an online study in which participants filled out several questionnaires at baseline and after 6 weeks (posttest). There were two groups a control group and an intervention group. The intervention group received online treatment and control group was a waiting list...
  9. M

    Trend analysis / regression model with missing data

    Hi, I want to apply a Poisson GLM to count data, analyzing trends. However, there are missing counts (i.e. missing values in the outcome variable) in the dataframe. Is there any recommended method how to deal with this? I know there exists "multiple imputation methods" to fill the gaps in the...
  10. C

    Combining missing information of duplicate cases

    Hi I have a problem with a very large dataset (containing more than 20000 cases and about 350 variables). This dataset contains many duplicate cases of which in some rows some information is missing. What I need to do is to join information from duplicate rows with the same unique identifier...
  11. R

    How to handle missing values in lm.fit?

    Is there a way to use lm.fit without completely removing rows with missing data using R? Is there a way for lm.fit to leave out one piece of missing data but compare the rest? I've tried all of the na.actions and can't seem to find one that works. Any help is appreciated. Thanks!
  12. J

    Question Regarding Missing Data

    Hello everyone, I have several surveys completed with over 2000 participants. Some of the participants did not answer some of the questions (e.g. "Missing data"). We discussed using Median calculations to account for missing data. In addition to this, for data entry, we indicated a "6" for...
  13. A

    How to Handle Blank Fields in Multiple Linear Regression

    I am building a regression with 45 samples across 20+ independent variables. I am randomly selecting subsets of the variables and running many combinations of regressions to help avoid multicollinearity. However, my main issue is concerning missing data in my samples. Each of the 45 samples is...
  14. A

    Comparing categorical outcome variables in repeated measures design

    I am working on an observational prospective longitudinal study with a repeated measures design. The same categorical outcome is measured for five times, for all the participants, over a time period. So there are five outcome (dependent) variables and a few predictor variables. First of all, I...
  15. A

    Comparing proportion between categorical outcome variables, repeated measures design

    I am working on an observational prospective longitudinal study with a repeated measures design. The same categorical outcome is measured for five times, for all the participants, over a time period. So there are five outcome (dependent) variables and a few predictor variables. First of all, I...
  16. L

    Repeated measures ANOVA; missing data and multiple measures at each time point

    Hi TalkStats I have data from a project. The data is the blood pressure for each participant at day of "baseline" visit, and 3 time points after baseline visit. Index these baseline and post-baseline time points j=0,1,2,3. For the ith participant, at time point j, we have measurement...
  17. S

    Scale-wise testing for "missing completely at random"?

    Hi there! :) I want to impute my missing data by using maximal likelihood (ML) estimation in SPSS. I've read that concerning questionnaire data it's recommended to impute the missing data scale-wise, since the intercorrelations of the items of each scale lead to more realistic estimations of...
  18. K

    Dealing with missing data in Repeated-measures ANOVAs

    Afternoon all, I've been running a longitudinal follow-up study to assess outcomes following a psychological treatment for ME. As to be expected, there was a lot of drop-out by 12 months, the 5th time point of the follow-up. Usually I'd do a RM ANOVA to analyse this, but with the missing...
  19. H

    Random sampling from a database with missing values

    Hello everyone! I have a real life problem that I would like to have some help with. I'm somewhat familiar with statistics, but for some reason haven't come up with any good solutions to this. My problem is the following: I have a database of patients with varying amounts of variables...
  20. P

    Multiple Imputations in a Dataset

    Dear all, I'm a student working on a dataset based on a cross-sectional survey. The dataset includes around 200participants. The main analysis will involve a linear regression involving 3 predictors and an outcome continuous variable. Unfortunately, the data I have was collected over a long...