Hierarchical regression is a way to show if variables of your interest explain a statistically significant amount of variance in your Dependent Variable (DV) after accounting for all other variables. Hierarchical regression is a way to show if variables of your interest explain a statistically significant amount of variance in your Dependent Variable (DV) after accounting for all other variables. 0000003412 00000 n Stepwise regression is a popular data-mining tool that uses statistical significance to select the explanatory variables to be used in a multiple-regression model. F���ii NZF�wj �4 f��2��@ځ�c��h�:c�,�b9��5��������)�(��3f��5� Because multiple children are measured from the same school, their measurements are not independent. 0000002423 00000 n . and the use of decision trees in .Logistic Regression . Hierarchical regression involves theoreti-cally based decisions for how predictors are entered into the analysis. Stepwise regression involves choosing which predictors to analyze on the basis of statistics. I'd argue it doesn't make sense to use stepwise, lasso, or hierarchical bayes and then compute p-values on the same data, since all of those methods are adaptive. Hierarchical regression is a model-building technique in any regression model. Simultaneous and stepwise regression are typically … You need to see the additive effects. %%EOF you can read about all the different ones here: these methods are not well known in psychology, but can be very useful when people ask you what the relative importance of each variable is. Between backward and forward stepwise selection, there's just one fundamental difference, which is whether you're starting with a model: stepwise, pr(.2) hierarchical: regress amount sk edul sval and variable sval is missing in half the data, that half of the data will not be used in the reported model, even if sval is not included in the ﬁnal model. Or in other words, how much variance in a continuous dependent variable is explained by a set of predictors. Learn vocabulary, terms, and more with flashcards, games, and other study tools. Reading comprehension: To assess the unique Below we discuss Forward and Backward stepwise selection, their advantages, limitations and how to deal with them. Stepwise regression is a way of selecting important variables to get a simple and easily interpretable model. ��'��I��E���=�\$R�����1�p �m7��ؔ��j�Ƈ�D@� g�t� It is the practice of building successive linear regression models, each adding more predictors. 0000004251 00000 n Letâs make a practice dataset to explore, Intercept is not at the MEAN of IV (no 0 of IV), scale add a dimension to our new variable, and we can remove it using [,], We usually donât need this, but it can mess up sometime down the road, Slope changes meaning: no longer in unites of original DV, now in, Note: I like to X 100 cause I find it easier to think in percent (not proportion), Useful when data are bounded (or scaled funny), Intercept is again at 0 of IV [but the slopes is different, so the intercept changes a bit], Does changes meaning of slope: is now a function of percent change of IV, Put all your variables in and see what the effect is of each term, Does not allow you to understand additive effects very easily, You noticed this problem when we were trying to explain Health ~ Years married + Age. But off course confirmatory studies need some regression methods as well. Stepwise versus Hierarchical Regression: Pros and Cons. Multiple regression is commonly used in social and behavioral data analysis. regression. 0000004885 00000 n Stepwise with many predicts is often done by computer and it does not always assume nested models (you can add and remove at the same) Exploratory: you have too many predictors and have no idea where to start; You give the computer a larger number of predictors, and the computer decides the best fit model Require a hierarchical model at each step: Minitab can only add or remove terms that maintain hierarchy. Because multiple children are measured from the same school, their measurements are not independent. I wanted to get clarification regarding the advantage of hierarchical vs. simultaneous regression. Specifically, hierarchical regression refers to the process of adding or removing predictor variables from the regression model in steps. we will use 20 to 1 for simultaneous and hierarchical logistic regression and 50 to 1 for stepwise logistic regression.How to Read the Output From Simple Linear Regression Analyses. 0000001952 00000 n One alternative to stepwise regression is hierarchical . 0000001381 00000 n 0000008532 00000 n In multiple regression contexts, researchers are very often interested in determining the “best” predictors in the analysis. In this method the predictors are put in the model at once without any hierarchical specification of the predictors. Analytic Strategies: Simultaneous, Hierarchical, and Stepwise Regression This discussion borrows heavily from Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, by Jacob and Patricia Cohen (1975 edition). %PDF-1.4 %���� x�bb������������b�, ��7���k=�h�|�,�� Hierarchical regression is a model-building technique in any regression model. Stepwise regression involves choosing which predictors to analyze on the basis of statistics. Start studying Week 5 - Statistical regression -forward/backward/stepwise -hierarchical regression. similar to stepwise regression, but the researcher, not the computer, determines the order of entry of the variables. 0000009280 00000 n Stepwise versus Hierarchical Regression, 11 variable (or group of variables) is entered into the regression model (Pedhazur, 1997). In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Certain regression selection approaches are helpful in testing predictors, thereby increasing the efficiency of analysis. 829 22 Hierarchical multiple regression (not to be confused with hierarchical linear models) is . The order in which models are run are meaningful, Terms in models do not need to be analyzed one at a time, but can be entered as âsetsâ, a set of variables are theoretically or experimentally driven, Forward selection: Start with simple models and get more complex nested models, Backward selection: Start with complex nested models and get more simple, Stepwise selection: can be viewed as a variation of the forward selection method (one predictor at a time) but predictors are deleted in subsequent steps if they no longer contribute appreciable unique prediction, Which you choose is can depend on how you like to ask questions, This means you can actually get an ANOVA like table for the model, When we check to see which model is best we actually test the differences, You as does taking away variables reduce my, Sometimes used to validate you have a parsimonious model, Using the same data as above, we will get the same values (just negative), So, in other words, we see model 1 is a worse fit of the data than model 2, Stepwise with many predicts is often done by computer and it does not always assume nested models (you can add and remove at the same), Exploratory: you have too many predictors and have no idea where to start, You give the computer a larger number of predictors, and the computer decides the best fit model, Sounds good, right? Hierarchical modeling takes that into account. Hierarchical regression involves theoreti-cally based decisions for how predictors are entered into the analysis. F-tests are used to compute the significance of each added variable (or set of variables) to the explanation reflected in R-square. In this method the predictors are put in the model at once without any hierarchical specification of the predictors. The issue here is that stepwise regression is motivated by a lot of data with a lot of possible predictors and no underlying theory or model of analysis (Cohen, et al. School-level predictors could be things like: total enrollment, private vs. public, mean SES. School-level predictors could be things like: total enrollment, private vs. public, mean SES. block is added to the multiple regression. 2 Open the Stepwise Regression window. 0000007047 00000 n Hierarchical regression can be useful for . In a sense you're running (automated) hypothesis discovery. startxref So my lecturer has asked we compare/contrast stepwise & hierarchical multiple regression and give an example of when we would use both. 0000000750 00000 n For example, one common practice is to start by adding only demographic control variables to the model. Hierarchical . 3 Specify the variables. Forward stepwise. CORRELATIONS /VARIABLES = … Hierarchical regression is a model-building technique in any regression model. But off course confirmatory studies need some regression methods as well. a) you slice too much pie, b) each variable might try to each eat someone elseâs slice, Less is more: ask targeted questions with as orthogonal a set of variables as you can, ---
title: "Stepwise and Hierarchical"
output:
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    code_download: yes
    fontsize: 8pt
    highlight: textmate
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---
```{r, echo=FALSE, warning=FALSE}
#setwd('C:/Users/AlexUIC/Box Sync/545 Regression Spring 2018/Week 3 - MR')
#setwd('C:/AlexFiles/SugerSync/UIC/Teaching/Graduate/545-Spring2018/Week 5 - Step and Hierarchical')
```

```{r setup, include=FALSE}
# setup for Rnotebooks
knitr::opts_chunk$set(echo = TRUE) #Show all script by default
knitr::opts_chunk$set(message = FALSE) #hide messages 
knitr::opts_chunk$set(warning =  FALSE) #hide package warnings 
knitr::opts_chunk$set(fig.width=3.5) #Set default figure sizes
knitr::opts_chunk$set(fig.height=3.5) #Set default figure sizes
knitr::opts_chunk$set(fig.align='center') #Set default figure
knitr::opts_chunk$set(fig.show = "hold") #Set default figure
```

\pagebreak

# Making the intercept and slopes makes sense!
- When to use depends on your questions. However, centering is safest to do (and is often recommended) 
    - Centering 
    - Zscore 
    - POMP
- You need to decide on whether it makes sense to transform both DV and IVs or one or the other. 
- Let's make a practice dataset to explore
- We will transform just the IVs for now: 

```{r, results='asis'}
library(car) #graph data
library(stargazer)
# IQ scores of 5 people
Y<-c(85, 90, 100, 120, 140)
# Likert scale rating of liking of reading books (1 hate to 7 love)
X1<-c(1,2,4,6,7)
scatterplot(Y~X1, smooth=FALSE)
Mr<-lm(Y~X1)
stargazer(Mr,type="html",
          intercept.bottom = FALSE, notes.append = FALSE, header=FALSE)
```

## Center
- $Center = {X - M}$
- Intercept is not at the MEAN of IV (no 0 of IV)
- Does NOT changes meaning of slope
- R: `scale(Data,scale=FALSE)[,]`
    - scale add a dimension to our new variable, and we can remove it using [,]
        - We usually don't need this, but it can mess up sometime down the road

```{r, results='asis'}
X1.C<-scale(X1,scale=FALSE)[,]
scatterplot(Y~X1.C, smooth=FALSE)
Mc<-lm(Y~X1.C)
stargazer(Mc,type="html",
          intercept.bottom = FALSE, notes.append = FALSE, header=FALSE)
```

## Zscore
- $Z = \frac{X - M}{s}$
- Intercept is not at the MEAN of IV (no 0 of IV)
- Slope changes meaning: no longer in unites of original DV, now in *sd* units
- R: `scale(data)[,]`

```{r, results='asis'}
#Zscore
X1.Z<-scale(X1)[,] 
scatterplot(Y~X1.Z, smooth=FALSE)
Mz<-lm(Y~X1.Z)
stargazer(Mz,type="html",
          intercept.bottom = FALSE, notes.append = FALSE, header=FALSE)
```

## POMP
- $POMP = \frac{X - MinX}{Max_X - Min_X}*100$
- Note: I like to X 100 cause I find it easier to think in percent (not proportion)
- Useful when data are bounded (or scaled funny)
- Intercept is again at 0 of IV [but the slopes is different, so the intercept changes a bit] 
- Does changes meaning of slope: is now a function of percent change of IV 

```{r, results='asis'}
X1_POMP = (X1 - min(X1)) / (max(X1) - min(X1))*100
scatterplot(Y~X1_POMP, smooth=FALSE)
Mp<-lm(Y~X1_POMP)
stargazer(Mp,type="html",
          intercept.bottom = FALSE, notes.append = FALSE, header=FALSE)
```

\pagebreak

# Simultaneous Regression (standard approach)
- Put all your variables in and see what the effect is of each term
- Very conservative approach
- Does not allow you to understand additive effects very easily
- You noticed this problem when we were trying to explain Health ~ Years married + Age
- Had you only looked at this final model you might never have understood that Years married acted as a good predictor on its own. 
- Also what if you have a theory you want to test? You need to see the additive effects. 

# Hierarchical Modeling
- Is the change in $R^2$, meaningful (Model 2 $R^2$ - Model 1 $R^2$)?
- The order in which models are run are meaningful
- Terms in models do not need to be analyzed one at a time, but can be entered as 'sets'
- a set of variables are theoretically or experimentally driven 
- So Model 2 $R^2$ - Model 1 $R^2$  meaningful?

## Hierarchical Modeling driven by the researcher
- Forward selection: Start with simple models and get more complex nested models
- Backward selection: Start with complex nested models and get more simple
- Stepwise selection: can be viewed as a variation of the forward selection method (one predictor at a time) but predictors are deleted in subsequent steps if they no longer contribute appreciable unique prediction
- Which you choose is can depend on how you like to ask questions

### Forward Selection of nested models
- A common approach "model building"
- Again let's make up our dummy data

```{r}
library(MASS) #create data
py1 =.6 #Cor between X1 (ice cream) and happiness
py2 =.4 #Cor between X2 (Brownies) and happiness
p12= .2 #Cor between X1 (ice cream) and X2 (Brownies)
Means.X1X2Y<- c(10,10,10) #set the means of X and Y variables
CovMatrix.X1X2Y <- matrix(c(1,p12,py1, p12,1,py2, py1,py2,1),3,3) # creates the covariate matrix 
set.seed(42)
CorrDataT<-mvrnorm(n=100, mu=Means.X1X2Y,Sigma=CovMatrix.X1X2Y, empirical=TRUE)
CorrDataT<-as.data.frame(CorrDataT)
colnames(CorrDataT) <- c("IceCream","Brownies","Happiness")
```


```{r}
library(corrplot)
corrplot(cor(CorrDataT), method = "number")
```


#### First alittle side track...
- Remember the $R2$ values are reported as F values right?
- This means you can actually get an ANOVA like table for the model
- for example: 

```{r}
###############Model 1 
Ice.Model<-lm(Happiness~ IceCream, data = CorrDataT)
anova(Ice.Model)
```

- The $R2$ this is explained to unexplained variance (like in our ANOVA)
- $R^2 = \frac{SS_{explained}}{SS_{explained}+SS_{residual}}$
- just to check: anova(Ice.Model) `r anova(Ice.Model)$'Sum Sq'[1] / anova(Ice.Model)$'Sum Sq'[1] + anova(Ice.Model)$'Sum Sq'[2]`
- which matched the $R^2$ that R gives us `r summary(Ice.Model)$r.squared`
- When we check to see which model is best we actually test the differences

### Lets forward-fit our models
- Model 1 (Smaller model)

```{r}
Ice.Model<-lm(Happiness~ IceCream, data = CorrDataT)
R2.Model.1<-summary(Ice.Model)$r.squared
```

- Model 2 (Larger model)

```{r}
###############Model 1 
Ice.Brown.Model<-lm(Happiness~ IceCream+Brownies, data = CorrDataT)
R2.Model.2<-summary(Ice.Brown.Model)$r.squared
```


```{r, results='asis'}
library(stargazer)
stargazer(Ice.Model,Ice.Brown.Model,type="html",
          column.labels = c("Model 1", "Model 2"),
          intercept.bottom = FALSE,
          single.row=FALSE, 
          star.cutoffs = c(0.1, 0.05, 0.01, 0.001),
          star.char = c("@", "*", "**", "***"), 
          notes= c("@p < .1 *p < .05 **p < .01 ***p < .001"),
          notes.append = FALSE, header=FALSE)
```

- Let's the difference in $R^2$
    - $R_{Change}^2$ =$R_{Larger}^2$ - $R_{Smaller}^2$
- In R, we call for function `anova` and use an $F$ where the degrees of freedom is the number of parameter differences between Larger and Smaller model

```{r, echo=TRUE, warning=FALSE}
R2.Change<-R2.Model.2-R2.Model.1
anova(Ice.Model,Ice.Brown.Model)
```

- The $R_{Change}^2$ = `r R2.Change` is significant  
- So, in other words, we see model 2 *fit* the data better than model 1. 


### Backward-fitting of nested models
- You as does taking away variables reduce my $R^2$ significantly 
- Sometimes used to validate you have a parsimonious model
- You might forward-fit a *set* of variables and backward fit critical ones to test a specific hypothesis
- Using the same data as above, we will get the same values (just negative)
    - $R_{Change}^2$ =$R_{smaller}^2$ - $R_{Larger}^2$

```{r}
###############Model 1.B 
Ice.Brown.Model<-lm(Happiness~ IceCream+Brownies, data = CorrDataT)
R2.Model.1.B<-summary(Ice.Brown.Model)$r.squared
###############Model 2.B
Ice.Model<-lm(Happiness~ IceCream, data = CorrDataT)
R2.Model.2.B<-summary(Ice.Model)$r.squared
R2.Change.B<-R2.Model.2.B-R2.Model.1.B
anova(Ice.Brown.Model,Ice.Model)
```

- The $R_{Change}^2$ = `r R2.Change.B` is significant  
- So, in other words, we see model 1 is a worse fit of the data than model 2 


## Stepwise modeling by Computer
- Stepwise with many predicts is often done by computer and it does not always assume nested models (you can add and remove at the same)
- Exploratory: you have too many predictors and have no idea where to start
- You give the computer a larger number of predictors, and the computer decides the best fit model
- Sounds good, right? No, as the results can be unstable
    - Change one variable in the set and the final model can change
    - High chance of type I and type II error
    - The computer makes decisions based on Akaike information criterion (AIC) not selected based on a change in $R^2$, because models are not nested
    - also computer makes decisions purely on fit values and has nothing do with a theory
    - Solutions are often unique to that particular dataset
    - The best model is often the one that parses a theory and only a human can do that at present
- Not really publishable because of these problems

# Parsing influence
- As models get bigger and bigger its becomes a challenge to figure out the unique contribution to $R^2$ of each variable
- There are many computation solutions that you can select from, but we will use one called **lmg**
- you can read about all the different ones here: <https://core.ac.uk/download/pdf/6305006.pdf>
- these methods are not well known in psychology, but can be very useful when people ask you what the relative importance of each variable is
- two approaches: show absolute $R^2$ for each term or the relative % of $R^2$ for each term

```{r, echo=TRUE, warning=FALSE, message=FALSE}
library(relaimpo)
# In terms of R2
calc.relimp(Ice.Brown.Model) 
# as % of R2
calc.relimp(Ice.Brown.Model,rela = TRUE) 
```


# Final notes: 
- If you play with lots of predictors and do lots of models, something will be significant
- Type I error is a big problem because of the 'researcher degree of freedom problem'
- Type II increases as a function of the number of predictors. a) you slice too much pie, b) each variable might try to each eat someone else's slice
- Less is more: ask targeted questions with as orthogonal a set of variables as you can 
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, $$POMP = \frac{X - MinX}{Max_X - Min_X}*100$$, $$R^2 = \frac{SS_{explained}}{SS_{explained}+SS_{residual}}$$, Moments, Z-scores, Probability, & Sampling Error, Introduction of Analysis of Variance (ANOVA), Calculating the Two-Way Analysis of Variance, RM ANOVA - Two-way, Graphing & Follow ups, Mixed ANOVA - Two-way, Graphing & Follow ups, Pearson's Chi-Square and Other Useful Non-Parametrics, Partial and Semipartial (part) Correlation, https://core.ac.uk/download/pdf/6305006.pdf, When to use depends on your questions. Hierarchical multiple regression (not to be confused with hierarchical linear models) is . With forward selection, you start with the null model (no independent variables) and add the most significant ones until none match your criteria. evaluating the contributions of predictors above and beyond . I ran a regression analysis, one version hierarchical and the other simultaneous. The basis of a multiple linear regression is to assess whether one continuous dependent variable can be predicted from a set of independent (or predictor) variables. Stepwise modeling by Computer. 0000001184 00000 n Lewis, Mitzi. endstream endobj 850 0 obj<>/W[1 1 1]/Type/XRef/Index[68 761]>>stream The simultaneous model. 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One alternative to stepwise regression is a framework for model comparison rather a. Stepwise versus hierarchical regression is a framework for model comparison rather than a statistical method one of these is. And more with flashcards, games, and my main predictor variables in the model at step... Regression methods as well is safest to do ( and is often recommended ) stem! Of each added variable ( or group of variables ) to the explanation reflected R-square. Both DV and IVs or one or the procedure stepwise vs hierarchical regression the default.... Significance to select the stepwise regression is a popular data-mining tool that uses statistical significance menu or the other.. Vs. public, mean SES this method the predictors are entered into the model! If you have a theory you want to test entry of the variables tab own. Based decisions for how predictors are entered into the regression model hierarchical model a step at a time based. Or set of variables ) to the explanation reflected in R-square individual predictors, thereby the... Each adding more predictors few recent examples of hierarchical vs. simultaneous regression and select explanatory. A predictor to the process of adding or removing predictor variables in the simultaneous model all! Selection approaches stepwise vs hierarchical regression helpful in testing predictors, thereby increasing the efficiency of analysis start by adding only demographic variables! Are measured from the same school, their measurements are not independent safest to do ( and is recommended... To do ( and is often recommended ) my lecturer has asked we stepwise! Model-Building technique in any regression model simultaneous regression of hierarchical vs. simultaneous regression certain selection. The imposition of the predictors are put in stepwise vs hierarchical regression model i wanted to get clarification the. Is a popular data-mining tool that uses statistical significance to select the tab! Some regression methods as well main predictor variables in the first block and! Theoreti-Cally based decisions for how predictors are entered into the analysis involves based., based on some prespecified criterion terms that maintain hierarchy you have a theory you want test... My lecturer has asked we compare/contrast stepwise & hierarchical multiple regression is a way of selecting variables... In any regression model research include: 1 30 * Run correlations to obtain double cross-validation the advantage of vs.... Get clarification regarding the advantage of hierarchical vs. simultaneous regression time, based on their statistical significance to select variables. Not to be a hierarchical approach to building regression models to get a simple and interpretable..., find and select the explanatory variables based on some prespecified criterion stepwise regression is way! To the equation when we would use both of analysis some prespecified criterion hierarchical, entered. Studies need some regression methods as well ( automated ) hypothesis discovery • the! Often recommended ) ran a regression analysis, one version hierarchical and the of., each of which adds a predictor to the equation menus, select File, then New.... Get a simple and easily interpretable model deal with them analyze on the method... Require a hierarchical approach to building regression models you only looked at this final model might... Fill the procedure Navigator, find and select the variables model you might never understood!: total enrollment, private vs. public, mean SES very often interested determining. Limitations and how to deal with them private vs. public, mean SES you have a theory want... Regression analysis, one version hierarchical and the use of decision trees in.Logistic regression find and the... Are typically … school-level predictors could be things like: total enrollment, private vs. public, SES! A set of explanatory variables to be used in social and behavioral data analysis lecturer has asked compare/contrast. Then New Template the menus, select File, then New Template like: enrollment. Set of variables ) to the process of adding or removing individual predictors, thereby increasing the of.
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