• Javier Miguelena added an answer:
    ANCOVA or repeated measures ANOVA?

    My experiments was mainly a split-plot design with water treatment as the main factor. Measurements were taken in 6 harvests from Dec 2012 to May 2013.  In Dec, all plots under irrigation; from Jan to March, half plots under irrigation and the other half withholding water; from Apr to May, all plots under irrigation. In each harvest, plants were defoliated  after other measurements were taken. I do not know whether I should do an ANCOVA with measurements in the first harvest as covariates, or do a repeated measures ANOVA?

    Javier Miguelena · The University of Arizona

    Interesting problem. REML means restricted maximum likelihood, it refers to a way of partitioning variation when you are including random effects. You absolutely need to include random effects in your model. The way I understand it, you are asking if there is an effect of changing the irrigation treatment. I would use a model that includes month (since measurements taken the same month are not fully independent due to cliamte) and plot (since plots might have different "personalities" that show up as you repeat the measurements) as random effects.

    The fixed effects should be treatment (continuous vs interrupted irrigation) and treatment time (before vs after irrigation change), as well as an interaction between the two. The interaction term tests the hypothesis that either of the treatments changes its response more than the other after the date when irrigation was interrupted. Your prediction, I think, is that the plots in the "interrupted irrigation" treatment will change more than the control.

  • Andrew Ekstrom added an answer:
    Any suggestion about using ANCOVA with repeated measures?

    My consulting adviser said that we can't use covariance method when there are more than 2 time points. But I'm not sure about it again!

    What's your idea about that?

    Andrew Ekstrom · University of Michigan

    Hey Roshanak,

    What do you consider the covariate and what do you consider factors/variables of interest?

    As a statistics student, I have taken stats classes from several different departments, psychology, education, stats, industrial engineering, Biostats, etc. What one person calls a covariate, another person calls a factor/variable. I was taught in my stats and IE classes that a covariate was a thing you want to test, that is not under your control. In my biostats and psychology classes, a covariate was a thing we wanted to test but was not of interest in our analysis. We included a covariate to remove some of the variability within the analysis.

    If I was analyzing your data in a stats class, I would use ANOVA with repeated measures. Depending upon what you call your variables, I would use a Fixed Effects, Mixed Effects of Random Effects model.   

  • Roshanak Soltani added an answer:
    When can I use ANCOVA?

    I know that we can consider pre-intervention amounts as covariate variable if we want to control initial differences. My question is: should these differences be statically significant or not? I mean, when there is a difference between pre-test scores, but it isn't statically significant, can I consider pre-test scores as covariate yet?

    Roshanak Soltani · University of Tabriz

     I have one more question.

    My consulting adviser said we can't use covariance method when there are more than 2 time points. But I'm not sure about that again!

    What's your idea about that?

  • Roshanak Soltani added an answer:
    What is the exact name of my test in spss?

    I have a question about the name of a specific test in spss.

    I have 4 separated groups (4 different interventions), and I measured the dependent variable over 4 time points. I considered my intervention groups as "between subjects factor" and the time points as "within subjects factor". In this case the suitable test would be "mixed ANOVA with repeated measures", right?

    Now if I have a covariate factor, what would be the NAME of the test? "mixed ANCOVA with repeated measures"? or what?

    Roshanak Soltani · University of Tabriz

    I have one more question.

    My consulting adviser said we can't use covariance method when there are more than 2 time points. But I'm not sure about that again!

    What's your idea about that?

  • Bruce Weaver added an answer:
    Can one use multiple logistic regression to estimate possible confounding effect?

    Multiple logistic regression to estimate possible confounder effect?
    We revealed that a protein A has significantly higher concentration in patients than in controls, but there might be a potential confounding variable B, which is also significantly different between controls and patients. I’d like to assess how important the effect of the variable B on concentration of protein A is. Is it OK when I compare simple logistic regression with the diagnosis (0=controls, 1=patients) being the dependent variable and the concentration of protein A being the independent variable with data from multiple logistic regression with variable B added among dependent variables?

    Bruce Weaver · Lakehead University Thunder Bay Campus

    Jurah, regarding your 3rd point, note that for ordinary least squares (OLS) models, it is the ~errors~ (not the outcome variable) that are assumed to be normally distributed.  For further discussion and commentary, click the link below.  HTH.

  • Emir Veledar added an answer:
    How can you compare two groups that are initially distinct in two moments of time?
    ANCOVA or difference between pre-and post? Some authors use ANCOVA with the variable measured in initial time as a covariate, and others employ the difference between pre-and post analyzed via t-test.
    Emir Veledar · Baptist Health South Florida
    Your question is formulated on a very vage way.
    I copy paste question from the top:
    "How can you compare two groups that are initially distinct in two moments of time? "
    So if we have two groups and they are initially distinct, it means that they are distinct at the baseline, so comparison on baseline is allready done.
    If they are "initially distinct in two moments of time" then it means that we compared them in 2 moments and we found them distinct.
    Can you re ask your question?
  • Simona Katholnig asked a question:
    What does it mean if a covariate turns the effect of an IV on DV from significant to insignificant in an ANCOVA?
    How should I interpret this? Does it mean it is a moderator? Or does it mean that there are just no effects of the IV (experimental manipulation)? Thanks!
  • Juan Mielgo-Ayuso asked a question:
    ANCOVA for repeated measures
    What is the difference between the two tables below? What significance does each table have?

    Tests of Between-Subjects Effects
    Measure: MEASURE_1
    Transformed Variable: Average
    Source Type III Sum of Squares df Mean Square F Sig.
    Intercept 400046,201 1 400046,201 1136,749 ,000
    Grupo 1791,273 1 1791,273 5,090 ,029
    Error 16892,227 48 351,921

    Tests of Within-Subjects Contrasts
    Measure: MEASURE_1
    Source factor1 Type III Sum of Squares df Mean Square F Sig.
    factor1 Linear 171,300 1 171,300 2,151 ,149
    factor1 * Grupo Linear 175,972 1 175,972 2,209 ,144
    Error(factor1) Linear 3823,351 48 79,653
  • John A. E. Anderson added an answer:
    Does anyone have suggestions for reporting a robust ANCOVA?
    I'm following the example in Andy Field's R book where he suggests that after failing the test for homogeneity of regression slopes, one might do a robust ANCOVA ala Wilcox 2005. I'm able to run the tests no problem, and interpreting them is also not an issue, but for output of the following nature (see below), does anyone know of a standard way to report this data?

    I think a way to start at least will be to report the standard ANCOVA up to the point where the interaction is significant and then say robust procedures were followed, how to report these though are a bit beyond me.

    ancova(covGrp1, dvGrp1, covGrp2, dvGrp2)
    [1] "NOTE: Confidence intervals are adjusted to control the probability"
    [1] "of at least one Type I error."
    [1] "But p-values are not"
    X n1 n2 DIF TEST se ci.low ci.hi p.value crit.val
    [1,] 10.30 20 12 -22.166667 2.7863062 7.955575 -47.42320 3.089867 0.0213100575 3.174696
    [2,] 11.30 28 17 -19.184343 2.7536447 6.966891 -39.98396 1.615273 0.0167914292 2.985495
    [3,] 12.45 32 23 -20.350000 3.9162704 5.196270 -35.02758 -5.672423 0.0008787346 2.824637
    [4,] 14.00 27 34 -8.314171 1.4638404 5.679698 -23.71193 7.083583 0.1524122220 2.711016
    [5,] 16.10 14 17 3.431818 0.3796813 9.038682 -22.28197 29.145604 0.7085490133 2.844860

    ancboot(covGrp1, dvGrp1, covGrp2, dvGrp2,tr = .2, nboot=2000)
    [1] "Note: confidence intervals are adjusted to control FWE"
    [1] "But p-values are not adjusted to control FWE"
    [1] "Taking bootstrap samples. Please wait."
    X n1 n2 DIF TEST ci.low ci.hi p.value
    [1,] 10.30 20 12 -22.166667 -2.7863062 -47.00379 2.670459 0.0355
    [2,] 11.30 28 17 -19.184343 -2.7536447 -40.93482 2.566135 0.0185
    [3,] 12.45 32 23 -20.350000 -3.9162704 -36.57264 -4.127360 0.0015
    [4,] 14.00 27 34 -8.314171 -1.4638404 -26.04606 9.417719 0.1525
    [5,] 16.10 14 17 3.431818 0.3796813 -24.78674 31.650380 0.6980
    John A. E. Anderson · University of Toronto
    I actually managed to get an answer from Andy Field about this...

    I'm posting his message below in case anyone else runs into this issue.

    Andy Field Hi John, the thing with the Robust stuff is that there simply isn't any real guidance because people don't use them much (yet). I guess for the ANCOVA methods in the R book, you could report the value of Dif and its CI and p for each of the design points. You'd probably need to explain a bit what the 'design points' actually mean as well. It's unchartered territory but my general principle is be clear about what you did, and you can;'t go wrong reporting confidence intervals:-)

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