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Mplus Discussion
Multilevel Data/Complex Sample
Tom Munk
posted on Thursday, November 17, 2005 - 12:05 pm
I've tried (and failed) to replicate MPLUS R-square values.
For example, in a two-level model with no predictors, the variances were 898(within) and 372(between). By adding a set of predictors at each level, I obtain variances of 844(w) and 99(b). These are reductions of 6%(w) and 73.4%(b). I would expect MPLUS's R-sq values to closely match these, but they don't. They are 22.5%(w) and 17.6%(b).
How are the MPLUS values calculated? Can they be interpreted as the fraction of the (within- or between-) variance explained by the predictors?
Linda K. Muthen
posted on Thursday, November 17, 2005 - 2:24 pm
R-square is variance explained divided by total variance. I would need to see what you are basing your numbers on the see what you are doing. Please send your input, data, output, and license number to
Huang Xiaorui
posted on Thursday, October 19, 2006 - 7:45 am
I am doing a twolevel model analysis, I can get R-square for each regression. But I can't get the whole model's R-square. How can it be displayed in Mplus? Thanks.
Within Level
Variable R-Square
Variable R-Square
Between Level
Linda K. Muthen
posted on Thursday, October 19, 2006 - 9:59 am
There is not an R-square for the full model.
liesbeth mercken
posted on Tuesday, September 18, 2007 - 1:28 am
If you have the following model
and you have to add X4 to the model in the following way:
is it possible that R-square for Y2 decreases after you entered X4 in the model? And is this the case because you added a corelation with Y2?
thank you,
Linda K. Muthen
posted on Tuesday, September 18, 2007 - 4:34 am
I'm not sure what might happen in this case. In regression, the means, variances, and covariaces of the covariates are not model paremters. You should not mention x1, x2, x3, and x4 in the MODEL command except on the right-hand side of ON. Also, why do you have y1 ON x2 and not y2 ON x4?
liesbeth mercken
posted on Tuesday, September 18, 2007 - 7:14 am
Hi Linda,
Thank you for your reaction.
Y2 on X2 is theoretically and empirically supported. The association between Y2 and X4 is completely new.
X4 is a new concept that is measured at the exact same time as Y2, so I can't use an ON statement as I then conclude causality in a specific direction.
But... I actually tried what you suggested Y2 ON X4,
but to be sure also X4 ON Y2.
Both models give significant and good results (also both interpretable)...
So I though it was best to choose for the safe road and keep it in the model as a correlation. Would you suggest to choose a direction?
thank you
Bengt O. Muthen
posted on Thursday, September 20, 2007 - 10:57 am
When you added x4 to the model you let it correlate only with y2 and y1, not x1-x3. This could cause a misspecified model with too high chi-square test of fit, in which case the estimates should not be interpreted. Perhaps you want to regress x4 on x1-x3. Also, you might have a misfitting model due to y1 not having direct influence from x1-x3.
In any case, even if you have well-fitting models, with these left-out effects I would say that the R-square behavior is not predictable.
Marko Neumann
posted on Friday, February 15, 2008 - 3:57 am
To my knowledge, the basis for the R� computation are the variance components of level 1 and level 2 resulting from the empty model. However, the R� calculated by Mplus does not seem to be identical to the R� values that result from using the empty model. Mplus seems rather to refer to the estimated within- and between level parts of the (co)variance given in the sample statistics output of the specified model which differ considerably from the variance components of the empty model, depending on the specified individual level predictors.
The dependent variable is school achievement. The empty model provides a within variance of 413 and a between variance of 775, and thus an intra class correlation of .652 (which is quite high, but normal in the tracked German secondary school system). Inclusion of level 1 predictor variables (e. g., prior knowledge, SES) results in an increased within variance(918) and a decreased between level variance (52) in the samp stats output. The residual variances in the specified model were 185 for the within and 52 (that means equal to the between variance part of the samp stats output) for the between level. R� for within was .80 (resulting from (918-185)/918= .798). On the contrary, computation of R� on the basis of the variance components of the empty model (like in HLM) would result in (413-185)/413= .55.
End of Part I
Marko Neumann
posted on Friday, February 15, 2008 - 3:59 am
Part II
Including level 2 predictor variables in the next step (e. g., school track, mean achievement) results in the following variance components: within = 825 and between = 72; the residual variances were: within = 183 and between = 27. In Mplus within R� was .78 (resulting from (825-183)/825= .778) and between R� was .63 (resulting from (72-27)/72= .625).
Computation of R� on basis of the variance components of the empty model would result in (413-183)/413= .56 for within and (775-27)/ 775= .97 for between.
To sum up, I have three main questions: Why do the variance components at level 2 in the samp stats output decrease after inclusion of predictor variables? Why do they differ so much from the variance components in the empty model? How do I have to interpret them? I would be very grateful if you could answer my question.
Linda K. Muthen
posted on Friday, February 15, 2008 - 9:30 am
The only reason that I can think of for sample statistics to differ is that the sample size has changed. If this is not the reason, please send your inputs, data, outputs, and license number to
Marko Neumann
posted on Monday, February 18, 2008 - 3:02 am
To my knowledge, the basis for the R� computation are the variance components of level 1 and level 2 resulting from the empty model. However, the R� calculated by Mplus does not seem to be identical to the R� values that result from using the empty model. Mplus seems rather to refer to the estimated within- and between level parts of the (co)variance given in the sample statistics output of the specified model which can differ considerably from the variance components of the empty model, depending on the specified individual level predictors.
I have three main questions: Why do the variance components at level 2 in the samp stats output decrease after inclusion of predictor variables? Why do they sometimes differ so much from the variance components in the empty model? How do I have to interpret them? I would be very grateful if you could answer my question.
Bengt O. Muthen
posted on Monday, February 18, 2008 - 9:13 am
Mplus computes R-square as the ratio of estimated explained variance in the numerator and estimated total variance in the denominator, where this is done for each level separately.
R-square on level 2 refers to proportion variance explained in random intercepts. With different level 1 predictors, the intercept definition changes (value of y when all x's = 0), so the level 2 R-square can therefore change.
posted on Sunday, March 02, 2008 - 11:32 pm
in one of the web courses Bengt mentioned that user can have good overall model fit (incl. GFI) but rather bad R-square for each of their latent variables. Could you please give me any further hints? Under which circumstances might that occur? Thanks a lot for your help.-Stephen
Linda K. Muthen
posted on Monday, March 03, 2008 - 7:34 am
R-square is not a test of model fit. It describes the variance of the dependent variable explained by a set of covariates. A model can fit the data well even when the set of covariates does not explain the variance in the dependent variable.
posted on Monday, March 03, 2008 - 3:08 pm
Dear Linda,
thanks for the response. But R-square for latent variables is not 1-residual variance?
posted on Monday, March 03, 2008 - 3:12 pm
sorry, didn't see the output with the standardized residual variances. I am o.k. now.
Thanks for your help. Best, Stephen
Darla Kendzor
posted on Tuesday, May 12, 2009 - 2:31 pm
I have submitted a manuscript for publication that describes a model which includes 4 continuous latent variables (IV/mediators) and 3 dichotomous observed outcome variables (DVs). A reviewer has asked me to include the percent of the variance accounted for by the IVs/mediators in the DVs. Mplus provides r-square values for each observed and latent variable, and I am wondering if mplus also provides an r-square for the whole model (i.e., the percent of variance in each of the outcomes [DVs] accounted for by ALL of the IVs/mediators)? If not, would it be meaningful to square the standardized total direct and indirect effect coefficient to obtain an r-square value this way? If neither of these ideas are possible, do you have any suggestions about how I might answer the reviewer's concern?
Linda K. Muthen
posted on Wednesday, May 13, 2009 - 9:52 am
I would give the R-squares for each regression. A model R-square does not make sense because the aim of the model is not to maximize variance explained but to reproduce variances and covariances. R-square is not a model fit statistic.
Lois Downey
posted on Tuesday, October 20, 2009 - 5:19 pm
Since Mplus doesn't provide a confidence interval around r-squared for clustered regression models, I've been computing the confidence intervals manually, using the point estimate and estimated standard error provided in the output. However, I'm invariably getting a lower bound that is negative. This seems counter-intuitive, given that r-squared cannot be less than zero. Are the negative values likely a result of rounding error, given that Mplus provides estimates rounded to 3 digits? (I'm multiplying the estimated standard error by 1.96 and then subtracting and adding the result to the point estimate.)
Linda K. Muthen
posted on Wednesday, October 21, 2009 - 9:47 am
This can happen because there are no restrictions put on the confidence intervals in their computation. I don't think it is a rounding error.
Alden Gross
posted on Monday, May 03, 2010 - 12:41 pm
Dear Drs. Muthen,
I'd like to report R^2 values from growth curve models for latent growth factors (intercept and slope). I understand MPLUS calculates R-square values as the (variance explained by the model) divided by (total variance) of an outcome.
I have two questions. First, what, other than stated regression paths, contribute to a latent outcome's R^2? Second, when I add demographic variables to explain the outcome, why does MPLUS' given R^2 decrease?
Here are some excerpts:
if sf |
i1 s1 |
i2 s2 |
if ON i1 i2; sf ON i1 i2;
I would expect R^2 for "if" to be a sum of squared standardized regression coefficients for "if" regressed on i1 and i2. For instance, if the standardized B for if on i1=0.375 and i2=0.574, I think R^2 =0.375^2+0.574^2= 47%. However, MPLUS's given R^2 = 76%, which is correctly (1-residual/total) = (1-0.146/0.607) = 0.759.
When I then add covariates in another model (e.g., if ON age sex education;), the MPLUS-given R^2 decreases.
Thanks so much!
Linda K. Muthen
posted on Tuesday, May 04, 2010 - 9:52 am
You have forgotten the covariance. You need to add:
Brondeel Ruben
posted on Saturday, December 04, 2010 - 6:39 am
I fitted a logistic regression model with complex data. I was asked how much variance is explained by the model. So I stated 'standardized' in the output command.
1. What kind of R-square is this? Is it an adjusted version of the R-square, something like a Nagelkerke's R-square?
2. Is it meaningful in this context?
3. How does it handle the complex data structure? Or is that irrelevant for the computation of the R-square (in this case, but also for the case of a continuous dependent)
Linda K. Muthen
posted on Sunday, December 05, 2010 - 11:12 am
The R-square is the variance explained in the latent response variable underlying the categorical variable. See the following book for further information:
Long, S. (1997). Regression models for categorical and limited
dependent variables. Thousand Oaks: Sage.
R-square is not affected by non-independence of observations. R-square is usually not used for logistic regression.
posted on Tuesday, May 10, 2011 - 7:52 am
I am able to replicate the r square produced by Mplus in latent variable SEM models. I am attempting to extend this to SEM models containing a latent interaction calculated with XWITH.
I use the formula provided by Mooijaart & Satorra (2009) to calculate % variance explained by the main effects and the interaction term and also include 2*cov*b1*b2 and the term that multiplies the b of the interaction term by the variance and covariances of the latent variables and sum all of these terms.
However, by this method, the variance explained in the model with xwith included is much lower than the variance explained by the model with only main effects (16% versus 35%). I feel that what is missing is the covariance of the main effects with the interaction term, which are not provided by Mplus. Is there a way to get these covariances from the program, calculate them, or force them to be 0?
Thank you.
Bengt O. Muthen
posted on Tuesday, May 10, 2011 - 10:46 am
You say
"and the term that multiplies the b of the interaction term by the variance and covariances of the latent variables"
But, that term should be b*b*(V(f1)*V(f2)+[cov(f1,f2)]**2).
Then you say
"I feel that what is missing is the covariance of the main effects with the interaction term"
There are no such terms missing because all third-order moments are zero due to the normality assumption of the factors.
posted on Wednesday, May 11, 2011 - 9:19 am
Thank you, Dr. Muthen. This is very helpful. Just to be clear, then, the main effects and interaction in a model with XWITH are completely uncorrelated with each other and can be considered independent?
Linda K. Muthen
posted on Wednesday, May 11, 2011 - 9:21 am
Hans Leto
posted on Thursday, May 31, 2012 - 1:56 pm
Dr. Muthen.
Which would be the formula to calculate R-squared for a three-way interaction.
For instance:
V(f4) = b1*2 V(f1)+ b2*2 V(f2)+ b3*2 V(f3) + 2*f1*f2 Cov(f1,f2) + b4**2 V(f1xf2xf3) +
I am not sure how to specify the Cov when I have three factors.
Thank you.
Linda K. Muthen
posted on Thursday, May 31, 2012 - 3:38 pm
R-square is 1 minus the standardized residual variance of the dependent variable.
Hans Leto
posted on Friday, June 01, 2012 - 2:52 am
But the standardized residual variance is not provided in the output with TYPE=RANDOM? How can I request or calculate it?
Linda K. Muthen
posted on Friday, June 01, 2012 - 9:55 am
See the following FAQ on the website:
Latent variable interactions
caroline masquillier
posted on Tuesday, July 17, 2012 - 5:26 am
Dear Drs. Muthen,
What kind of R-square do we get in Mplus output for a logistic regression? Is this an adjusted version of the R-square, something like a Nagelkerke's R-square?
Thank you very much
Linda K. Muthen
posted on Tuesday, July 17, 2012 - 11:01 am
The R-square is for the latent response variable. It is described in the Snijders and Bosker book Multilevel Analysis.
May Yang
posted on Wednesday, April 17, 2013 - 9:26 am
I believe this is along the same lines as Tom Munk's posting on 11/17/05. I am confused on how R2 is calculated in a multi-level model. So R2= variance explained/ total variance. When I run a null model (without any predictors), I get a within variance=2.234. 2.234 to me is the total variance. When level 1 predictors are added, I get variance estimate of 0.147. I would assume that R2 is then 0.147/2.234 = 0.066 but the output report R2 (obtained using standardized option) = 0.086. What am I missing here? Thank you.
Bengt O. Muthen
posted on Wednesday, April 17, 2013 - 12:23 pm
On each of the 2 levels, R-square is explained variance divided by total variance (on that level). Yes, 2.234 is the total variance on level 1, but when adding a predictors the variance parameter that gets estimated is the residual variance.
If this does not explain things, please send output to support.
Elina Dale
posted on Thursday, October 10, 2013 - 4:16 pm
Dear Dr. Muthen,
As you wrote, the R-square is the variance explained in the latent response variable underlying the categorical variable. Is it proportion?
In MPlus CFA output, the R-Square estimate is provided next to observed factor indicators. So, if we see an R-Sq of 0.400 next to y1 (factor 1 indicator), does it mean that 40% of variance in factor 1 is explained by this categorical variable (y1)? But then there are 3 other indicators that also have a similar R-Sq estimate and so, when you add them up they are >100%.
Also, what do residual variance values (Column 6 in R-Square table) mean in this case?
I am giving an example of the output below, so that you understand what res variance and R-Sq values I am referring to.
Observed variable (Column 1)
Estimate (Column 2) S.E. (Column 3)
Est./S.E. (Column 4)
Two-Tailed P-value (Column 5)
Residual Variance (Column 6)
Thank you!
Linda K. Muthen
posted on Friday, October 11, 2013 - 6:10 am
The R-square for y1 means that 40% of the variance of y1 is explained by the factor. Factor indicators are dependent variables and factors are independent variables in the factor model.
Residual variances are not model parameters for categorical variables. The values given under residual variance are computed after model estimation as remainders.
Johnson Song
posted on Thursday, March 06, 2014 - 3:18 pm
Dear Dr. Muthen,
If the statistically significance test of between-level R^2 indicates that the R^2 is not statistically significant at (R^2=.07,p>.05), does it mean that the portion of the variances of this latent variable explained by the predictors is not different from zero even though the regression coefficients actually is statistically significant (p=.017)?
Whether should I still report the significant regression coefficients?
Thank you so much for your advice!
Bengt O. Muthen
posted on Friday, March 07, 2014 - 4:39 pm
Q1. Yes. But note that the test of R-2 may not work as well as the test of the regression coefficient because the sampling distribution of R-2 may not be as close to normal.
Q2. I would do that. In general I don't report R-2 significance.
Lance Rappaport
posted on Wednesday, December 03, 2014 - 11:16 am
I understand that the stdyx option is not available when using random slopes in a multilevel context. I understand that r-square at level 1 cannot be estimated as it varies as a function of the grouping variable. My question is why an r-squared value cannot be computed for a level 2 variable?
For example, I have random slopes at level 1, which predict a level 2 endogenous variable. How could I compute an r-square for this endogenous variable? R-square for this variable should not vary as a function of anything included in the model.
Bengt O. Muthen
posted on Wednesday, December 03, 2014 - 4:30 pm
The R-2 for level-2 is well defined as you say. You can express it using Model Constraint with Model parameter labels.
Lance Rappaport
posted on Wednesday, December 03, 2014 - 5:07 pm
Thank you very much, especially for such a prompt response. Would it be okay if I asked how to use model parameter labels to capture the standardized residual variance?
Bengt O. Muthen
posted on Thursday, December 04, 2014 - 11:40 am
The general approach is shown in UG ex 5.20.
posted on Tuesday, January 13, 2015 - 9:53 am
Dear Drs. Muthen,
I�d like te replicate my R2 on the between level in a multilevel model.
In the null model the variance between is 7.699. In the Random Intercept Model the variance between is 0.619 (0.291 standardized).
When I calculate (7.699-0.619)/7.699 it is: R2= 0.920.
Mplus is giving me 0.709 (standardizes solution).
a) Is 0.709 the standardized version of 0.920? Or is my calculation wrong?
b) the R2 of 0.709 is not significant, is that plausible, although I have sicnificant (and not significant) L2 predictors?
Thank you very much in advance.
Bengt O. Muthen
posted on Tuesday, January 13, 2015 - 2:04 pm
What's the difference in the Mplus input specification of your null model and random intercept model?
posted on Wednesday, January 14, 2015 - 12:37 am
Dear Dr. Muthen,
thank you very much for your quick reply.
May I send you my output(s), please? It is a quite complex model...
Maybe too complex...
Linda K. Muthen
posted on Wednesday, January 14, 2015 - 6:20 am
You can send the outputs and your license number to
as long as your support contract is current.
Christine Kemp
posted on Thursday, April 30, 2015 - 2:20 pm
I am running a continuous-time survival analysis using the cox regression model (based on example 6.21). I included Output: stdyx and can see my standardized coefficients but the R-Square line is coming up blank. Do you know why this would be?
Bengt O. Muthen
posted on Thursday, April 30, 2015 - 6:22 pm
What residual variance would the R-square use?
Eric Thibodeau
posted on Friday, May 29, 2015 - 6:51 am
I'm running a very simple multiple regression model with one continuous outcome and 5 predictors. Three of the five predictors are considered covariates and not target predictors. I'm interested in the added value of the target predictors. My idea was to run two models, the first with just the three covariates predicting the outcome, and the second model with the two target predictors added. I would look at the change in R-squared. I know there are ways to test whether the R-squared increase was statistically significant, via an F-test of change. Is there any way to test that in Mplus? I've been told that the chi-square difference p-value would be equivalent. If I were to compare models using the chi-square difference test, would I set the parameters of the target predictors in my nested model to zero, and then free them up in the second model? Thanks!
Bengt O. Muthen
posted on Friday, May 29, 2015 - 8:09 am
No such F-test in Mplus. I would go about it the way you describe in your second to last sentence.
Dirk Pelt
posted on Wednesday, October 14, 2015 - 1:16 am
Dear Bengt and Linda,
I have read this whole thread and it appears that most questions relate to the fact that there appear to be two ways of calculating R
(please correct me if I'm wrong):
1. One based on comparison with a null model including a random intercept only. The formula is: (var of null model - var of model with predictors)/var of null model. This is the 'standard' way in multilevel models as described in Kreft and De Leeuw (1998), Snijders and Bosker etc. This formula can be applied to each level. This is all based on unstandardized coefficients/variables.
2. One that Mplus reports, which is 1-standardized residual or simply the sum of all standardized beta coefficients squared. Again separately for each level.
Which is the correct one to use? I believe that in the multilevel literature, standardizing of coefficients is always treated as something problematic. Thank you!
Bengt O. Muthen
posted on Wednesday, October 14, 2015 - 2:54 pm
For each level Mplus uses the standard R-square formula:
(1) (Variance explained by covariates)/(Total DV variance)
That happens to be the same as 1 - stand'd res var.
I think the formula you mention is the same as (1) because the var of null model is the total DV variance and when you say "var of model with predictors" you may be referring to the residual variance in the model with predictors. The difference is then what I call "variance explained by covariates".
li zhou
posted on Thursday, December 10, 2015 - 10:37 am
Dear Dr. Muthen,
I conducted a quite simple model as follows ('pl' is the DV):
bf by bf_know bf_info bf_fami bf_expe;
bl by bl_abse bl_sale;
si by si_price si_qual si_serv;
bf_know bf_expe with bf_info bf_fami;
bl on bf;
pl on bl;
pl on bf;
pl on si;
pl on age gender gi edu shopper
The 'STANDARDIZED MODEL RESULTS' were quite okay.
However, the results of 'R-SQUARE' were as follows:
Obser-Var Estimate S.E. Est./S.E. Two-Tailed P-Value
My questions are (regarding the 'R-SQUARE' results):
1)Does the estimate next to the 'PL' mean that only 6.6% variance of PL were explained by the whole model?
2)Though the other model fit indexes (CFI/TLI, SRMR, RMSEA) were quite good, as the model does not explain much of the variance of the DV 'PL', can I still report the significant coefficients?
3)If yes, how can I explain the small value of the R-SQUARE for 'PL'?
Bengt O. Muthen
posted on Thursday, December 10, 2015 - 6:18 pm
Please send output to Support along with your license number.
Rick Borst
posted on Wednesday, November 02, 2016 - 3:36 am
Dear Professors Muthen,
I analyzed a model with and without a latent interaction term (as a matter of fact it is one moderator which has an effect on two IV's). The outputs show that the R-square is higher without the interaction term than with the interaction term. Although, the interactions are significant. Is it possible to have significant interactions term but a lower r-square or is there something wrong with my input? If it is possible: does that mean that my model with interactions should be discarded despite the significant interactions?
Bengt O. Muthen
posted on Wednesday, November 02, 2016 - 5:26 pm
Q1. I think so because all parameter estimates change.
Q2. No, I would go by significance, not R-2.
Andre Maharaj
posted on Friday, December 16, 2016 - 3:34 pm
Hello there,
I have a similar question, and am not sure I am understanding all the comments.
I have a two level model that was run with two count dependent variables (nb).
I would like to get a (pseudo?) r-square for the amount of variance explained for each regression;
1. Is this possible? (the stdyx output reports estimates of 1 with two-tailed p values of 999.000 for the within level).
2. Is what I am looking for actually reported on the between level? (The estimates there are 0.136 for dv1 an 0.520 for dv2.
Thanks to everyone in advance.
Bengt O. Muthen
posted on Friday, December 16, 2016 - 5:47 pm
1-2. Level-1 r-square for count DVs is not defined because count regression does not have a residual variance. For Level 2 you work with continuous random effect variables so there the usual R-square for linear regression is used.
Andre Maharaj
posted on Friday, December 16, 2016 - 5:59 pm
Thanks for the quick response!
So the L2 explained variance is only for the L2 predictor then, correct?
Do you know of any other way to calculate an effect size for the level 1 regressions?
If not, how appropriate / inappropriate might it be to run the standard regression model and report a "standard" pseudo r-squared?
Any advice would be greatly appreciated!
Thanks again.
Andre Maharaj
posted on Saturday, December 17, 2016 - 12:32 pm
Also, and probably more importantly, I have 6 groups dummy coded in the regressions, and I would like to report an effect size for the groups with a significant difference relative to the reference group (run multiple times to change the reference groups).
Any thoughts on how I might do this would be appreciated.
Andre Maharaj
posted on Saturday, December 17, 2016 - 12:41 pm
Or should I just interpret the exponentiated coefficient as the effect size?
(Sorry for the multiple posts; couldn't find a way to edit the previous ones)
Bengt O. Muthen
posted on Monday, December 19, 2016 - 5:59 pm
Dec 16, 5:59 post:
Q1. Yes
Q2. No
Q3. Don't think so - I wouldn't focus on R-square for models without a residual - but check the books by Long or Hilbe.
Bengt O. Muthen
posted on Monday, December 19, 2016 - 6:01 pm
Just interpret the exponentiated coefficients. You can also express the model-estimated means for the count variable and/or consider the corresponding probability of having the count e.g. = 0. See, e.g., our new book.
Andre Maharaj
posted on Tuesday, December 20, 2016 - 9:40 pm
Thanks you kindly!
Hoda Vaziri
posted on Tuesday, March 21, 2017 - 12:02 pm
How can I calculate pseudo r-squared for a three-level model?
My output says standardized values are only available with ALGORITHM=INTEGRATION, but when I include this, I receive an error saying the integration algorithm is not allowed with threelevel models.
Bengt O. Muthen
posted on Tuesday, March 21, 2017 - 5:58 pm
Q1: Ask on multilevelnet.
Regarding your output, we have to see it to be able to say - send to Support along with your license number.
John G
posted on Sunday, June 11, 2017 - 10:27 pm
I've created a multilevel mediation model in Mplus 8 using instructions from Preacher, Zyphur, Zhang (2010). I've been asked by a reviewer to supply an R-square for the outcome variable. I've read through several of these discussion threads, and it seems that I need to request standardized outputs. However, when I add the "STANDARDIZED;" request, I receive the following error message:
"STANDARDIZED (STD, STDY, STDYX) options are available only for TYPE=TWOLEVEL RANDOM with ESTIMATOR=BAYES. Request for STANDARDIZED (STD, STDY, STDYX) is ignored."
Do you have any instructions to overcome this hurdle? Thanks in advance!
Bengt O. Muthen
posted on Monday, June 12, 2017 - 5:59 pm
It sounds like your model has a random slope. This situation has not be covered in terms of standardization or R-square. Bayes in Version 8 handles it.
John G
posted on Tuesday, June 13, 2017 - 5:19 pm
Thanks for the advice.
I wonder if the Bayes estimator does not work with this particular MSEM analysis. When I add "estimator is bayes" to the analysis specification, I receive the following error:
*** ERROR in MODEL command
Unrestricted x-variables for analysis with TYPE=TWOLEVEL and ESTIMATOR=BAYES
must be specified as either a WITHIN or BETWEEN variable. The following variable
cannot exist on both levels: IV1
The model ran fine with the analysis command "type is twolevel random." Do you have any further suggestions?
Bengt O. Muthen
posted on Tuesday, June 13, 2017 - 6:31 pm
We need to see the full output - please send to Support along with your license number.
Jo�o Maroco
posted on Friday, June 30, 2017 - 8:06 am
Can I add the Within level R^2 and the between level R^2 and get an estimate of the overall variability in my dependent variable that is explained by predictors at level 1 plus predictors at level 2 in an HLM model?
Bengt O. Muthen
posted on Friday, June 30, 2017 - 5:05 pm
You can get the total proportion variance explained but it is not the sum of the R-2s. Perhaps TECH4 is useful here.
Christian Marx
posted on Tuesday, October 24, 2017 - 12:50 pm
Dear Professors Muthen,
how does Mplus calculate R-Square in a latent regression model with dummies and continous variables as IV and a latent variable as DV?
I fail to reproduce R-Square when summing all squared stdyx coefficients which might be due to the the dummies. So, how do I have to calculate the partial r-square of the dummies?
Bengt O. Muthen
posted on Tuesday, October 24, 2017 - 1:02 pm
R-square is computed just like in regular regression. If the predictors are correlated it isn't enough to sum up squared regression coefficients because there are covariances too.
Christian Marx
posted on Tuesday, October 24, 2017 - 1:14 pm
Thanks a lot for your answer!
Does Mplus offer partial R-Square for each predictor?
Bengt O. Muthen
posted on Tuesday, October 24, 2017 - 1:39 pm
Stefan Kamin
posted on Tuesday, November 14, 2017 - 2:20 am
I am estimating a path model with Bayes (continuous and categorical DVs, non-informative priors). I would be interested to know how the r-squared is calculated in the Bayes framework (both for continuous and categorical dependent variables). I believe the calculation is different from traditional ols regression as I was not able to reconstruct the r-square by hand. I would appreciate some advice.
Bengt O. Muthen
posted on Wednesday, November 15, 2017 - 10:41 am
Bayes does it the usual way. For a categorical DV the residual variance is 1 due to using probit regression.
Jo�o Maroco
posted on Wednesday, June 20, 2018 - 1:57 am
Dear Bengt and Linda,
I read all the posts in this thread, but still didn't find any question close to the one I have.
I am running a 2-level HLM random slopes model. I got these results:
Number of missing data patterns 1
Number of clusters 68
Average cluster size 23.853
Variable Correlation
My Question is:
I have no significant predictors (I am using 29 predictors to explain level 2 variance, and no level 1 predictors), but still i have a R^2 = 0.999 non-significant...):
Within Level
Between Level
Observed Two-Tailed Rate of
Variable Estimate S.E. Est./S.E. P-Value Missing
So I am interpreting this as a problem of over-fitting. I have 29 predictors, for 68 clusters with an average cluster size of 23.853. Thus none of the 29 predictors are significant although R^2~1...
Is this interpretation correct?
Is there a rule of thumb relating the number of predictors per number of clusters/average cluster size that I can use?
Thanks for any input on this.
Warm regards,
Jo�o Mar�co
Bengt O. Muthen
posted on Wednesday, June 20, 2018 - 11:52 am
Send your full output to Support along with your license number.
Lena Keller
posted on Monday, September 10, 2018 - 6:12 am
Dear Bengt and Linda,
I'm fitting a (manifest) simple linear regression (1 predictor, 1 DV) vs. a (manifest) quadratic regression in a multi-group model with TYPE=COMPLEX and weights using MLR. Based on classic textbook knowledge, I would expect that more complex models explain more variance than simpler models. Counterintuitively, in some cases R� is larger in the linear model than in the quadratic model. When I calculate these models with an OLS approach, the quadratic model always explains more variance than the linear model.
I wonder if the reason for this counterintuitive finding could be that OLS vs. MLR estimation fits a different discrepancy function to fit model parameters?
Thanks in advance!
Bengt O. Muthen
posted on Monday, September 10, 2018 - 2:47 pm
Perhaps your multiple-group analysis uses equality constraints across groups in which case all bets are off.
Lena Keller
posted on Wednesday, September 12, 2018 - 11:54 pm
Thanks for your answer!
There are no equality constraints across groups, and we freely estimate the parameters in the linear and quadratic models, so this cannot be the reason.
Is there any another explanation for this phenomenon?
Bengt O. Muthen
posted on Friday, September 14, 2018 - 1:38 pm
Please send 2 outputs showing this phenomenon to Support along with your license number.
Jesus Garcia
posted on Friday, March 22, 2019 - 6:07 am
Hi Drs Muthen, I have a question about R2, Why my model show me R-square for each observed variable but not for the whole factor? is something wrong? e.g
Observed VARIABLE Estimate S.E Est/S.E Pvaleu
Bengt O. Muthen
posted on Friday, March 22, 2019 - 4:39 pm
R-square is given for factors as well; below the observed variable R-squares. If you don't see it, send your output to Support along with your license number.
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Health Considerations
Thousands of people have been affected by the recent nasty flu bug. So how can you make sure you take more care of yourself to stop it knocking you for six?! We can't live in a bubble, but there are ways you can take care of yourself throughout the year to ensure your body and your immune system is in tip top shape when the next flu season comes around.
Gina tells Paul how she unfortunately spent a lot of the Christmas holiday period sick in bed. So this year she has committed to making sure she looks after herself all year long!
For more information on flu have a look at the NHS website

subset: commoncrawl


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Permissive mode vs. Enforcing mode
There will be times when you have run into difficulty and need to determine whether your problem stems from SELinux or not. For just this eventuality, SELinux includes the capability of setting its mode from enforcing to permissive and back again. Enforcing mode is just what it sounds like, a mode that allows SELinux to enforce policy access decisions. This is the standard operating mode of SELinux. Permissive mode, on the other hand, is a mode designed for development and troubleshooting. It will still check the security policy to see whether an attempted operation should be allowed, and log denials to the system logs, but it will not actually deny any operation.
To change into permissive mode, be sure you are logged in to the sysadm_r role (see my previous article for details). Issuing a setenforce 0 command will put the system into permissive mode, while a setenforce 1 command will return you to enforcing mode. To determine the current SELinux mode, use the getenforce command.
If you want to completely disable SELinux, you can pass selinux=0 to the kernel command line at startup, but this is not advisable since it disables SELinux entirely and any new files will not be labeled with the correct file context, forcing you to relabel when you re-enable SELinux. It's better to use permissive mode, and you can set your system to always start up in permissive mode by editing your /etc/selinux/config file.
File Context Labeling
SELinux file types are attched to each file on your SELinux system using extended file attributes. The use of these attributes is integral and required by SELinux, and has some system administration ramifications you should be aware of.
When formatting a new filesystem for use with SELinux, you must use a filesystem that supports these extended attributes. The ext2 and ext3 filesystems support extended attributes, and the xfs filesystem also is known to work, but reiserfs does not currently include extended attribute support.
When backing up files on an SELinux system, you need to use a backup method that is aware of and backs up these extended attributes. For example, the standard tar command will not back them up, so you need to use star as a substitute. star is an extension of the tar command, so you shouldn't run into serious problems here, but this could have ramifications with any backup scripts you may have written that call the tar command.
A common cause of SELinux problems is caused by mislabeled files. If you run into strange errors or see files that are mislabeled, the best, most reliable way of fixing them is to issue a touch /.autorelabel command followed by a reboot. This will trigger a relabel upon startup of the system, before files are opened and services are started. The restorecon command can also be used to restore files to their proper context, but it won't change the running context of processes that were launched by a mislabeled binary, so you may still run into problems.
The chcon command can be used to change the context of a file, but if the file has a default context set in the policy it will be reset to that default if the entire filesystem is relabeled. chcon is most useful for testing new file contexts before making a change permanent in the policy, if your system depends on contexts set using chcon you may run into trouble if you ever need to perform a global relabeling.
Finally, it is important to be aware of the differences between copying and moving files using the cp or mv commands. When moving a file using mv, the destination file will retain its original context. When copying a file using cp, the file will inherit a new context based on the destination directory it was copied to. This is an important distinction that can result in trouble if it is overlooked.
Policy Booleans
Policy booleans are sections of policy that can be switched on or off, providing a basic level of policy configurability at runtime without requiring the recompilation of the entire security policy. For example, you might be running a webmail application on your server that requires the webserver process to be able to connect to your mail server ports and read mail files out of user's home directories. Rather than adding those permissions to the security policy where they would reduce security for those not running webmail, a policy developer would create a boolean that the local administrator could enable only if it is required. This helps maintain a high level of security and follows the principle of least privilege.
To view a list of the policy booleans in your running policy and their current states, use the sestatus command. This command will list your current enforcing mode and the enforcing mode from the /etc/selinux/config file among other information, and a list of all policy booleans and whether they are active or inactive.
You can view the current status of a single boolean by using the command getsebool and passing it the name of the boolean you want to view the state of. Booleans are set using the setsebool command, and passing it the name of the boolean you want to set followed by a 1 or 0 to set the boolean active or inactive respectively.
Some sample booleans from the EnGarde Secure Linux SELinux policy are httpd_webmail and user_ping. The httpd_webmail boolean is used for the exact situation used as an example above, while the user_ping boolean determines whether or not regular users are able to send ping packets over the network. Booleans can be as simple as a single allow statement, or can enable or disable large swathes of the policy depending on their purpose.
Our SELinux journey is almost done. Next time, we'll discuss policy development basics and see how we can troubleshoot policy denials and write new SELinux policy or modify existing policy to allow our SELinux system to get its jobs done while maintaining a high level of security. Until then, farewell and remember to stay secure.
Pax Dickinson has over ten years of experience in systems administration and software development on a wide variety of hardware and software platforms. He is currently employed by Guardian Digital as a systems programmer where he develops and implements security solutions using EnGarde Secure Linux. His experience includes UNIX and Windows systems engineering and support at Prudential Insurance, Guardian Life Insurance, Philips Electronics and a wide variety of small business consulting roles.


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Woopra 1.4 Released
Woopra Client 1.4 has been released to the public after a few months of beta testing. This new version includes a number of new features and enhancements. For instance, Woopra users now have the ability to embed a chat widget onto their site that enables inbound chatting whereas before, it (more…)


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Ag Equipment Intelligence (AEI), a monthly newsletter for the farm equipment industry, recently interviewed Ann Duignan, machinery analyst for JP Morgan, on machinery-buying trends in the U.S. Duignan analyzed USDA Census data from the most recent study conducted in 2007 and shared the following findings with AEI:
- There were 4.4 million tractors in use on U.S farms in 2007, down from 5% in 2002. Of the 4.4 million, 1.1 million, or 25%, of these units were over 100 hp. Comparatively, 29% were less than 40 hp and 46% were 40 to 99 hp.
- Of the 1.1 million 100-hp-plus tractors in use, 12% were produced in the 2002 to 2007 time frame, Duignan says. This percentage indicates that the vast majority of tractors in use in 2007 were more than five years old.
- In 2007, there were about 2.2 million farms, and an average of about 2 tractors per farm. Within the total farms, 986,000 were crop farms and these farms owned 2.4 million tractors.
- In 2007, 964,000 livestock producers owned 2 million tractors, 82% of which were less than 100 hp. Crop farms, in comparison, use higher-horsepower units, and 31% of their tractors were over 100 hp.
- Between 2007 and 2010, total new tractor sales in the U.S. were 503,000 units, Duignan estimates. She says this indicates that only 11% of the total fleet has been replaced since 2007.
- Between 2008 and 2010, U.S. sales of over-100-hp units were 76,000 cumulatively, and between 2002 and 2010, sales of these tractors were 181,000, Duignan says. "This implies that only 16% of the fleet has been replaced between 2002 and 2010."
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Measurement Period: 2007-2013
HP2020 Baseline: 1.2 (1.20000004768372)
HP2020 Baseline Year: 2007
HP2020 Target: 1.1
learn more about the color range
FOR SINGLE DATA YEARS: Death rates are calculated based on the resident population of the United States for the data year involved. For census years (e.g. 2010), population counts enumerated as of April 1 are used. For all other years, populations estimates as of July 1 are used. Postcensal population estimates are used in rate calculations for years after a census year and match the data year vintage (e.g. July 1, 2011 resident population estimates from Vintage 2011 are used as the denominator for 2011 rates). Intercensal population estimates are used in rate calculations for the years between censuses (e.g. 1991-1999, 2001-2009). Race-specific population estimates for 1991 and later use bridged-race categories.
FOR MULTIPLE DATA YEARS: Death rates are calculated based on the sum of the resident populations for each of the data years involved (e.g. the denominator of a rate for 2008-2010 combined is the sum of the population estimates for 2008, 2009, and 2010). For census years (e.g. 2010), population counts enumerated as of April 1 are used. For all other years, populations estimates as of July 1 are used. Postcensal population estimates are used in rate calculations for years after a census year and match the data year vintage (e.g. July 1, 2011 resident population estimates from Vintage 2011 are used as the denominator for 2011 rates). Intercensal population estimates are used in rate calculations for the years between censuses (e.g. 1991-1999, 2001-2009). Race-specific population estimates for 1991 and later use bridged-race categories.
This Indicator uses Age-Adjustment Groups:
Starting in the 2000 decennial census, the U.S Census Bureau has used the 1997 Office of Management and Budget (OMB) revised standards for the classification of Federal data on race and ethnicity. Thus, race data on the 2000 and 2010 census are not comparable with race data from data systems that continue to collect data using the 1977 OMB standards. The 1977 standards specified four single-race categories: American Indian or Alaska Native, Asian or Pacific Islander, black, and white. The 1997 standards required that Federal data collection programs allow respondents to select one or more race categories when responding to a query on their racial identity. This provision means that there are potentially 31 race groups, depending on whether an individual selects one, two, three, four, or all five of the race categories. For comparability, the National Center for Health Statistics (NCHS), in collaboration with the U.S. Census Bureau, developed methodology to bridge the race groups in Census 2000 and 2010 to the four single-race categories specified under the 1977 standards. Even though Federal programs were to fully implement the revised standards by January 1, 2003, the transition from the 1977 to the 1997 OMB standards has been uneven. Federal systems which rely on information obtained from vital records through state-based programs, such as the National Vital Statistics System, have not yet been able to fully implement the 1997 standards. For example, the U.S. standard birth and death certificates were revised in 2003 to include the 1997 OMB standards. However, as of 2011, 41 states, New York City, and the District of Columbia had adopted the 2003 U.S. standard birth certificate, and 36 states, New York City, and the District of Columbia had adopted the 2003 U.S. standard death certificate.
MethodologyThe bridging methodology was developed using information from the 1997-2000 National Health Interview Survey (NHIS). The NHIS provides a unique opportunity to investigate multiple-race groups because, since 1982, it has allowed respondents to choose more than one race but has also asked respondents reporting multiple races to choose a primary race. The bridging methodology developed by NCHS involved the application of regression models relating person-level and county-level covariates to the selection of a particular primary race by the multiple-race respondents. Bridging proportions derived from these models were applied by the U.S. Census Bureau to the Census 2000 Modified Race Data Summary file. This application resulted in bridged counts of the April 1, 2000 and April 1, 2010 resident single-race populations for the four racial groups specified in the 1977 OMB standards.
Vital statistics mortality data are a fundamental source of demographic, geographic, and cause-of-death information. This is one of the few sources of comparable health-related data for small geographic areas over an extended time period. The data are used to present characteristics of those dying in the United States, to determine life expectancy, and to compare mortality trends with those in other countries.
MethodologyThe National Vital Statistics System Mortality component (NVSS-M) obtains information on deaths from the registration offices of each of the 50 states, New York City, the District of Columbia, Puerto Rico, the U.S. Virgin Islands, Guam, American Samoa, and Northern Mariana Islands. By law, registration of deaths is the responsibility of the funeral director. The funeral director obtains demographic data from an informant. The physician in attendance at the death is required to certify the cause of death. When death is from other than natural causes, a coroner or medical examiner may be required to examine the body and certify cause. State death certificates are modeled on a U.S. Standard Certificate that is revised periodically. States provide the National Center for Health Statistics (NCHS) with death records in electronic format.
Postcensal population estimates are estimates made for the years following a census, before the next census has been taken. National postcensal population estimates are derived annually by updating the resident population enumerated in the decennial census using a components of population change approach. Each annual series includes estimates for the current data year and revised estimates for the earlier years in the decade. The U.S. Census Bureau also produces postcensal estimates of the resident population for each state and county by using a component of population change method at the county level. An additional component of population change, net internal migration, is involved. The state population estimates are produced by summing all county populations within each state. The Census Bureau has annually produced a postcensal series of estimates of the July 1 resident population of the United States based on Census 2000 by applying the components of change methodology to the Modified Race Data Summary file. So that the race data for 2000-based postcensal estimates will be comparable with race data on vital records, the Census Bureau has applied the National Health Interview Survey (NHIS) bridging methodology to each 31-race-group postcensal series of population estimates to obtain bridged-race postcensal estimates (estimates for the four single-race categories: American Indian or Alaska Native, Asian or Pacific Islander, black, and white).Vital rates for 2000 were calculated using the bridged-race April 1, 2000, census counts, and vital rates for 2001 and beyond were calculated using bridged-race estimates of the July 1 population from the corresponding postcensal vintage. Intercensal population estimates are estimates made for the years between two censuses and are produced once the decennial census at the end of the decade has been completed. They replace the postcensal estimates that were produced prior to the completion of the census at the end of the decade. Intercensal estimates are more accurate than postcensal estimates because they are based on both the census at the beginning and the census at the end of the decade and thus correct for the error of closure (the difference between the estimated population at the end of the decade and the census count for that date).
MethodologyThe following formula is used to derive the estimates for a given year from those for the previous year, starting with the decennial census enumerated resident population as the base: Resident population+ Births to U.S. resident women - Deaths to U.S. residents+ Net international migration. The postcensal estimates are consistent with official decennial census figures and do not reflect estimated decennial census under-enumeration. Estimates for the earlier years in a given series are revised to reflect changes in the components of change data sets (for example, births to U.S. resident women from a preliminary natality file are replaced with counts from a final natality file). To help users keep track of which postcensal estimate is being used, each annual series is referred to as a vintage and the last year in the series is used to name the series. For example, the Vintage 2001 postcensal series has estimates for July 1, 2000, and July 1, 2001, and the Vintage 2002 postcensal series has revised estimates for July 1, 2000, and July 1, 2001, as well as estimates for July 1, 2002. The estimates for July 1, 2000, and for July 1, 2001, from the Vintage 2001 and Vintage 2002 postcensal series, differ. Vital rates that were calculated using postcensal population estimates are routinely revised when intercensal estimates become available.
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What's copying your music really worth to you?
Quite a lot, it seems
How much would your iPhone be worth to you if the only music it could play had been bought on the device itself, from Apple? If your answer is "a lot less" or "not very much", then you're not alone. New empirical research has attempted to measure how much we value the ability to copy our music across formats and devices – and it's a significant sum.
Oliver & Ohlbaum Associates asked over 2,200 people about their music habits. Over a quarter use their digital music collections as a temporary cache for their CDs and most acquire less than 20 per cent of their digital music online. A conjoint analysis [PDF] then attempted to put a price on the perceived value of the functionality.
Many consumers wouldn't touch an MP3 player, even a basic one, if it couldn't play CDs. The value of playing your CDs ranges from £21 (44 per cent) of a basic MP3 player to £80 (32 per cent) of a top-end player costing around £250. For smartphones it ranges from £6.67 at the low end to £23.60. And CD shifting is worth £33.50 of a £499 fondleslab.
Almost three-quarters of respondents valued their digital music collections as the most important commercial data of any kind in their possession – ahead of games, books, TV and movies.
The research couldn't be more timely. By shifting your CDs into an online storage service like Apple's iCloud, or to a portable music player in the UK, you are technically committing copyright infringement. There's no longer any argument – if there ever was – about blowing away this anachronism. "We all agree there should be format-shifting and that the law is stupid," the Musicians Union chief John Smith said at Westminster recently. But the question is: how can this be changed?
Under EU law, member states can lift the anachronism by making an exemption for "format shifting", so long as some compensation (amount unspecified) is paid back to musicians – and most countries do this. Twenty-four member states offer compensation for implementing a format-shifting exemption; the UK is one of six hold-outs. And while the amounts are miniscule – 2 cents per capita in Romania for musicians, for example – they add up. The French composers' society sees 7 per cent of its revenue come from a levy.
Tech corps not on activists' radar
But this doesn't seem fair to everybody. Predictably, pirates and digital rights activists fume about "large corporations" making huge profits at the expense of individual artists and creators. But when they do, they rarely have an Apple or a Samsung in mind; prior to its recent dividend payout, Apple had amassed $100bn in cash reserves, and unlike Microsoft with its Zune player, hadn't voluntarily offered any compensation.
Pirates aren't alone, however. The UK's Intellectual Property Office (IPO) recently recommended that the exemption be lifted without compensation. It didn't attempt to quantify the economic harm, and (the IPO being the IPO) even offered some trademark surreal reasoning to support it. It argued that Britain could have invented the iPod – a view parroted by the anti-creative industry quango Consumer Focus.
The Oliver & Ohlbaum Associates research, commissioned by umbrella group UK Music, is a useful reality check – and quite a contrast to the "lobbynomics" the IPO produced to scupper No 10's Hargreaves Review.
It's worth remembering that there hasn't been a copyright dispute in history that hasn't been settled, at the end of the day, by a few pennies changing hands. So how much should it be, then. A pound a device? A fiver? ®
Microsoft's private Zune deal with Universal Music shows why, if we're going to have a levy, it should be an above-board one, not a back-room deal with a large record company. What are the odds that a dollar found its way into any artist's royalty statement?


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Tuesday, April 26, 2011
The making of a midwife
I have recently finished reading Patricia Harman's memoir (pictured here), Arms wide open: a midwife's journey. I have enjoyed the journey.
As I progressed through the book I welcomed insight into the way Patsy, an idealistic hippy wild child in the early 1970s, learnt about life and in that learning, she found midwifery.
I welcomed insight into the realities of the American counter-culture, war resisters, commune life, living without what most of their peers would call the basic necessities of life.
I welcomed the honesty of statements by Patsy, now a grandmother with a nice home and a day job, no longer attending births, such as "You'd think by my age I'd have everything figured out, but I don't have a clue and I'm more confused than when I was thirty." I concur.
I found to my surprise that Patsy's midwifery journey reminded me in many ways of my own. I was at the same time, learning about life, and discovering my midwife identity in a sort of mirror image journey.
Here's what I mean by a mirror image journey.
Patsy and I must be about the same age, and we gave birth to our babies at about the same time. I was living in Michigan in those formative years, the 1970s, in a little brick house with a basement, surrounded by tall oak trees that shed mountains of brown leaves each 'Fall'. I raked leaves in autumn, shovelled snow in the winter, planted spring gardens, and enjoyed home grown veges in the summer.
While Patsy learnt how to stay warm and well in an isolated primative log cabin, I, who had spent most of my life in the sub-tropics in Queensland, learnt how to live with central heating, and cook in a kitchen that had green carpet on the floor.
While Patsy and her companions had dropped out of education, I had already graduated as a midwife in my home country. Noel, my husband was a graduate student at Michigan State, working on the fascinating and previously unnoticed protective effect of colostrum in the newborn calf. I was absorbing scientific literature and knowledge as fast as I could, broadening my understanding of reproduction, and particularly the needs of mammalian newborns.
Like Patsy, I attended the local Lamaze birth preparation classes and learnt psychoprophylaxis and Lamaze breathing. Unlike Patsy, I did not discover homebirthing. I gave birth to my first three children in the local hospital, was moved in second stage to the delivery/operating room, positioned with legs in stirrups and hands held to boards by big pieces of velcro. I was told to "take a deep breath and push push push!"
While Patsy raised her children in a loosely knit 'family' of a commune, I was away from all my family, became a full-time mother, and was satisfied with that role. Apart from the help offered by a few neighbours and friends from our Church, I needed to be emotionally and physically self-sufficient.
While Patsy developed a sort of faith in the forces of nature, I continued in the Christian faith in which I had been nurtured.
My awakening in midwifery came later, in the early 1990s, when I thought that my four children no longer needed a parent to be at home for them all the time.
I was able to move without difficulty into homebirth, even though I had not given birth at home myself. The knowledge that stood by me had been instilled in my mind over the years of my own childbearing, building on the foundation that I had learnt in my student days at the Royal Women's Hospital in Carlton. The years of breastfeeding had given me insight into mother-baby bonding and nurture. The years of parenting had given me an understanding of what it means to promote health, and work in harmony with natural processes. The years of part time shift work, usually nights, in hospital maternity wards, had taught me that I wanted to be 'with woman' - that the 'one night stands' I was having in the hospitals were not optimal in any way.
Like a butterfly emerging from its quietness in the crysalis, I had metamorphosed, and came out of that space ready for action.
Enough from me for today. Your comments are, as always, welcome.
ps Arms wide open is Hardcover, or eBook, 324 pages. Publisher: Beacon Press. ISBN: 978-0807001387
4 comments:
Joy I am so glad you enjoyed the book! What a great write up!
- 12:37 am
Thank you, for the beautiful review and for being my far-away friend. It was great to see how our paths crossed and diverged.
Peace, Patricia (Patsy) Harman,
author of The Blue Cotton Gown
and Arms Wide Open: A Midwife's Journey.
- 1:33 am
- Carolyn Hastie said...
I heard about this book for the first time a couple of days ago. Now I read your write up and am inspired to buy the book. I see that Patsy has commented on your review and I agree, it is a beautiful review. Your comments about your own journey are delightful to read Joy. I remember visiting you in the 90's as you were embarking upon your private midwifery career and I thought how fortunate the women were to have a beautiful midwife like you to support them. Working with women and their families in the midwifery way is such a privilege and such a delight. Thanks for this opportunity to discover more about you and Patsy's book, love, Carolyn
- 8:20 pm
- Anonymous said...
your blog post is awesome. Thanks for sharing, would love to read your book.
- 5:47 am