Linear models fail to recognize the effect of clustering due to intraclass correlation accurately. However, under some scenarios force you to take into account that units are clustered into subgroups that at the same time are nested within larger groups. The typical example is the analysis of standardized tests, where students are grouped in schools, … Sigue leyendo Multilevel Modeling of Educational Data using R (Part 1)
Etiqueta: R
#PredictiveCOL – Forecasting Colombia’s peace plebiscite (final update)
For sure, this is the more exciting forecast I have ever done. On one hand, I am Colombian guy, and I really want to live in a peaceful country, and I do want a better place for raising my children. On the other hand, I am very serious when it comes to forecasting.Maybe you have … Sigue leyendo #PredictiveCOL – Forecasting Colombia’s peace plebiscite (final update)
Isolating confounding effects – Rankings and residuals
In a previous entry, we talked about the meaning and importance of isolating confounding variables. This entry is dedicated to the residuals and its relation to the variable of interest when controlling for some confounding factors.Let's think about education. This example is always a good illustration to understand this issue. Assume that the performance of … Sigue leyendo Isolating confounding effects – Rankings and residuals
I don’t care about that lost unit
Just assume that you have planned a survey along with the necessary sample size to obtain representativity. Let’s suppose the sample size is 100. However, as nonresponse is always present, unfortunately your effective sample size is 99. Consider the following figure. It shows two scatterplots, the one on the right (expected) has one more point that … Sigue leyendo I don’t care about that lost unit
IRT classic anchoring with R functions
The main goal of standardised tests is to produce scores that can be compared not only within subgroups of students (and subpopulations of interest) but between applications (in different times). In summary, researchers and methodologists must assure that all of the scores induced by the test are in the same scale in order to allow … Sigue leyendo IRT classic anchoring with R functions
IRT equating using R functions – The calibrated pool method
In the assessment of education it is very common to use Item Response Theory in order to produce measures of ability for the students that applied an standardised test. Moreover, if you want to gain comparability between applications you should know that it is not enough to use IRT models but you have to do … Sigue leyendo IRT equating using R functions – The calibrated pool method
Anchoring estimation or the perfect excuse to become "Bayesian"
Anchoring is an usual process when estimating abilities in test equating. This is about analyzing standardized tests, while maintaining a predefined scale. For example, assume that you have a set of 60 items in your test. However, two test forms (named Form A and Form B) are given to the students in two different times. … Sigue leyendo Anchoring estimation or the perfect excuse to become "Bayesian"
Parametric bootstrap
Assume we want to know the mean square error (MSE) of the sample median as a estimator of a population mean under normality. As you know, this is not a trivial problem. We may take advantage of the Bootstrap method and solve it by means of simulation. This way, for $b=1,\ldots, B$, we generate $X_{b1},\ldots, X_{bn} \sim … Sigue leyendo Parametric bootstrap
My talk in Quito – Sample size and causal effects
In this talk I introduce the concept of design-based estimation for the average treatment effect in public policy evaluation. In the final section of the talk I propose to use the samplesize4surveys R package in order to compute the sample size required to 1) properly quantify the effect of the program and 2) properly test … Sigue leyendo My talk in Quito – Sample size and causal effects
Item Response Theory Models in R and JAGS: simulation and bayesian estimation of 1PL models
In my job this kind of models are our daily bread. Item response theory models are used to asses the quality of education, and if you are serious about educational or psychological measurement, this kind of models are mandatory. First of all, IRT models are just that: stochastic models. There is nothing mysterious behind them, because … Sigue leyendo Item Response Theory Models in R and JAGS: simulation and bayesian estimation of 1PL models
For MAC users: weird messages when starting session in R
If you live in a Latin American country, and you have installed R in your MAC, maybe you have noticed that, after opening R, the following messages appear:During startup - Warning messages:1: Setting LC_CTYPE failed, using "C" 2: Setting LC_COLLATE failed, using "C" 3: Setting LC_TIME failed, using "C" 4: Setting LC_MESSAGES failed, using "C" … Sigue leyendo For MAC users: weird messages when starting session in R
Power of a Test and Sample Size in Complex Surveys
There are many approaches to computing sample size. In public policy evaluation, for example, one is usually tented to check if there is statistical evidence on the impact of a intervention over a population of interest. This vignette is devoted to explain the issues that you commonly find when computing sample sizes.You may have been … Sigue leyendo Power of a Test and Sample Size in Complex Surveys
Muestreo, encuestas y estadísticas oficiales 