This post describes how R can be used to create regression tables that combine multiple models or steps e. The approach presented here can be used to create tables within R Markdown documents or to create html tables that can be pasted into Word documents.

Using a regression approach to examine whether the predictors interacted, we followed the typical two-step procedure Cohen, Cohen, West, and Aiken, :. If adding the interaction terms results in a statistically significant improvement of the model, the next step ist to do follow-up analyses to visualize and better understand the interaction Cohen, Cohen, West, and Aiken, ; Spiller, Fitzsimons, Lynch, and McClelland, Although there are already easy-to-use packages to create nice tables for multiple regression models or blocks; e.

Of course, one could create individual tables for the different dependent variables in R and then combine these into a single table in Word. However, this kind of manual work is both error-prone and cannot be programmatically replicated as it happens outside the R environment. The data includes a lot of variables and two experimental conditions that are not needed for this analysis.

Reduce the data to the relevant variables and cases:. Fit an additive model for each dependent variable Step 1 and extract the relevant statistical information:. To be able to properly combine the information of the additive models with the interaction models at a later stage, it is necessary that the information from the additive model has the same number of rows as the longer interaction table and that the relevant information is located in the correct rows.

Moreover, the stats need to be saved as numeric variables to be able to compute R 2 -change later. Get the statistical information for the overall model, adjust the length of the object, make again sure that all stats columns are numeric, and combine the statistical information regarding the predictors and the overall model:.

The procedure for the interaction models is almost exactly the same. The only difference is that no extra rows need to be added:.

## Reporting Multiple Regressions in APA format – Part One

Use F tests to compare the additive and interaction model for each dependent variable and prepare the statistical information so it can be combined into a single table together with the statistical information of the additive and interaction models:. One thing that is missing is the extent to which R 2 changes when the interaction terms are added i.

Moreover, some formatting is required e. I used kableExtra to prepare the final table. The following code will display the table in the R Studio Viewer. The table can be copied and pasted into Wordwhere you can do some final formatting.

Tables can of course also be directly added to R Markdown documents:. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Using a regression approach to examine whether the predictors interacted, we followed the typical two-step procedure Cohen, Cohen, West, and Aiken, : Fit a model with the potentially interacting predictors Fit a second model that also includes the interaction terms of the predictors If adding the interaction terms results in a statistically significant improvement of the model, the next step ist to do follow-up analyses to visualize and better understand the interaction Cohen, Cohen, West, and Aiken, ; Spiller, Fitzsimons, Lynch, and McClelland, Benefits, overview, and preview The most important benefits of creating complex tables programmatically are: You can avoid mistakes that occur when copying and adapting R code e.

You can avoid mistakes that occur when transferring numbers from R to text editors e. In case you need to change something in your analyses e. You can quickly adapt the code to conduct similar analyses with other variables as was the case in this project, in which we examined similar processes in the context of health.

The analyses can be shared and replicated quickly and effortlessly. The code can directly be included in R Markdown documents to create papers directly from R. The table can be adapted to suit different requirements; for example, you can include other statistical information. The approach presented here includes the following steps: Preparation get the data ready Fit additive regression models direct effects and get the relevant statistics Fit multiplicative regression models direct and interactive effects and get the relevant statistics Compare the models Create the table The results will look more or less like this: Regression table with additive and interaction effects for multiple dependent variables 1.

Preparation Check if the necessary packages are available and install them if they are not: if! RData" The data includes a lot of variables and two experimental conditions that are not needed for this analysis.

Fit the interaction models The procedure for the interaction models is almost exactly the same. Mahwah, NJ: Lawrence Erlbaum. Spiller, S.

Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression.And so, after a much longer wait than intended, here is part two of my post on reporting multiple regressions. In part one I went over how to report the various assumptions that you need to check your data meets to make sure a multiple regression is the right test to carry out on your data.

In this part I am going to go over how to report the main findings of you analysis. The first thing to do when reporting results is to describe the test you carried out and why you did it. You need to make sure you mention the various variables included in your analysis. Something like this:. A multiple regression was conducted to see if intelligence level and extroversion level predicted the total value of sales made by sales persons per week.

Next you want to have a look at the various descriptive statistics you have. Now to be honest it is up to you where and how you report these. They can go in a table or in text and can be mentioned before or during your main analysis. How you do it generally depends on how many variables you have. One or two, just stick it in the text, more than that and you should make a table. Now you can just report the means and standard deviation values, as seen in the table below. However, if you really want your data to be complete you will need to include the bivariate correlation values, and that means running some extra tests.

Now I am not going to show you how to do that here, I may in a future post, as for now I want to focus on the main findings. I will say that if you do want to include these values then you need to run individual correlations on all your predictor variables against your dependent variable individually. Then you report the R value and the significance value for each one. Right, so once you have reported the various descriptive statistics the next thing you want to do is look and see if your results are statistically significant. This is the first thing you want to look for. If the significance value is less than. When it comes to reporting it you will want to include the F value and the relevant degrees of freedom. You need to report the degrees of freedom for both the regression and the residual error. Next you want to look and see how much of the variance in the results your analysis explains. For this you want to turn to the Model Summary table. The R Square value tells you how much of the variance in your analysis is explained by the various predictor variables. In this case it is. You also need to look at the Adjusted R Square value as well. This value takes into account the number of variables involved in your analysis.

If you add additional variables to the analysis the R Square value will tend to increase, however it will never decrease.

It is now standard practice to include this value when reporting your results. You should mention this when reporting your findings.

### Using R to create complex regression tables for Word and R Markdown

So is that it then?Statistics Solutions can assist with your quantitative analysis by assisting you to develop your methodology and results chapters. The services that we offer include:.

Data Analysis Plan. Write your data analysis plan; specify specific statistics to address the research questions, the assumptions of the statistics, and justify why they are the appropriate statistics; provide references. Explain your data analysis plan to you so you are comfortable and confident. Conduct descriptive statistics i. Please call to request a quote based on the specifics of your research, schedule using the calendar on t his page, or email Info StatisticsSolutions.

Call Us: Blog About Us. Typically, the only two values examined are the B and the p. However, all of them are useful to know. The first symbol is the unstandardized beta B. This value represents the slope of the line between the predictor variable and the dependent variable. So for Variable 1, this would mean that for every one unit increase in Variable 1, the dependent variable increases by 1. Also similarly, for Variable 3, for every one unit increase in Variable 3, the dependent variable decreases by 1.

The next symbol is the standard error for the unstandardized beta SE B. This value is similar to the standard deviation for a mean. The larger the number, the more spread out the points are from the regression line. The more spread out the numbers are, the less likely that significance will be found. This works very similarly to a correlation coefficient. It will range from 0 to 1 or 0 to -1, depending on the direction of the relationship.

The closer the value is to 1 or -1, the stronger the relationship. With this symbol, you can actually compare the variables to see which had the strongest relationship with the dependent variable, since all of them are on the 0 to 1 scale. In the table above, Variable 3 had the strongest relationship. The fourth symbol is the t test statistic t. This is the test statistic calculated for the individual predictor variable. This is used to calculate the p value. The last symbol is the probability level p.So this is going to be a very different post from anything I have put up before.

I am writing this because I have just spent the best part of two weeks trying to find the answer myself without much luck. Sure I came across the odd bit of advice here and there and was able to work a lot of it out, but so many of the websites on this topic leave out a bucket load of the information, making it difficult to know what they are actually going on about.

If you have no interest in statistics then I recommend you skip the rest of this post. Here is some that I pulled off the internet that will serve our purposes nicely. Here we have a list of sales people, along with their IQ level, their extroversion level and the total amount of money they made in sales this week. We want to see if IQ level and extroversion level can be used to predict the amount of money made in a week.

However, I will show you how to calculate the regression and all of the important assumptions that go along with it. We are going to use the Enter method for this data, so leave the Method dropdown list on its default setting. We now need to make sure that we also test for the various assumptions of a multiple regression to make sure our data is suitable for this type of analysis. There are seven main assumptions when it comes to multiple regressions and we will go through each of them in turn, as well as how to write them up in your results section.

Note: If your data fails any of these assumptions then you will need to investigate why and whether a multiple regression is really the best way to analyse it.

Information on how to do this is beyond the scope of this post. On the Linear Regression screen you will see a button labelled Save. Click this and then tick the Standardized check box under the Residuals heading. This will allow us to check for outliers. Click Continue and then click the Statistics button. Tick the box marked Collinearity diagnostics.

This, unsurprisingly, will give us information on whether the data meets the assumption of collinearity. Under the Residuals heading also tick the Durbin-Watson check box. This will allow us to check for independent errors. Click Continue and then click the Plots button. Then, under the Standardized Residual Plots heading, tick both the Histogram box and the Normal probability plot box. This will allow you to check for random normally distributed errors, homoscedasticity and linearity of data.

Click Continue. As the assumption of non-zero variances is tested on a different screen, I will leave explaining how to carry that out until we get to it. For now, click OK to run the tests. The first thing we need to check for is outliers. If we have any they will need to be dealt with before we can analyse the rest of the results.Kali ini kita akan main-main dengan yang namanya Regresi Stepwise. Hal ini sekaligus menjawab pertanyaan saudara kita Khalil Hamzah yang menanyakan tentang Regresi Stepwise.

OK kita mulai sekarang! Regresi Stepwise adalah salah satu metode untuk mendapatkan model terbaik dari sebuah analisis regresi. Adapun definisi lengkapnya dan prosedur metodenya bisa dibaca di buku Applied Regression Analysis Third Edition karangan Draper and Smith halaman Walaupun di software SPSS sendiri sudah tersedia tool untuk meregresikan langsung dengan metode Stepwise. Namun timbul pertanyaan, bagaimana prosedur keluarnya model dengan metode Stepwise tersebut?

Di sini saya akan coba aplikasikan prosedur yang dijelaskan di buku tersebut dengan memakai contoh. OK sebelum kita memulai silahkan anda unduh terlebih dahulu datanya di sini.

Sofware yang akan kita pakai adalah SPSS versi Stop prosedur. You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Main menu Skip to content. Langkah pertama : Hitung koefisien korelasi setiap variabel independen dengan variabel dependen. Langkah kedua : Koefisien korelasi x 4 dengan Y paling mendekati 1sehingga variabel x 4 adalah variabel pertama yang masuk ke dalam model.

Langkah ketiga : Estimasi model regresi dengan variabel independen x 4. Hitung korelasi parsial variabel independen tersisa, x1x2x 3 dengan Ysebagai variabel kontrol adalah variabel yang sudah masuk ke dalam model, x 4. Langkah kelima : Berdasarkan output SPSS di atas, x 1 mempunyai koefisien korelasi parsial paling mendekati 1sehingga x 1 masuk ke dalam model setelah x 4. Langkah keenam : Estimasi kembali model regresi, dengan memasukkan variabel x 1 setelah x 4. Hitung korelasi parsial variabel tersisa x 2 dan x 3 dengan Ysebagai variabel kontrol adalah x 4 dan x 1 Korelasi parsial x 2 dengan Y paling mendekati 1sehingga x 2 masuk ke dalam model, setelah x 4 dan x 1 Langkah kedelapan : Estimasi kembali model regresi dengan memasukkan variabel x 2 setelah x 4 dan x 1.

stepwise multiple regression example

Email required Address never made public. Name required. Post to Cancel. By continuing to use this website, you agree to their use. To find out more, including how to control cookies, see here: Cookie Policy.The following code creates a table of results for a series of regressions. The resulting table will look like the one shown below. The coefficients for each variable are listed, with their standard errors listed below them in parentheses.

As a first step, we need to define a format for the standard errors. Specifically, we need to tell SAS we want to set the number of decimal places displayed and put the value of the standard error in a set of parentheses " " and " ". The picture statement tells SAS we want to create a format for printing numbers. The final line instructs SAS that missing values should be shown as blank spaces. Next we use the output delivery system ODS to capture the results from a series of regression models.

The persist option allows ODS to collect output from more than one model statement; otherwise, the output would be collected only for the first model. Below the proc reg run, we stop collecting output with the ods output close statement. Finally, we print the contents of the new dataset containing our regression results. In the table statement, the variable option indicates that the rows should be defined by the values of the variable of that name.

That is, there should be one row for each value of variable.

## APA Styling – Tables

This takes care of defining what goes in the rows of our table. After the comma "," we define the columns of our table. The variable name model indicates that there should be one column in the table for each value of the variable model.The survey included some statements regarding job satisfaction, some of which are shown below. The main research question for today is which factors contribute most to overall job satisfaction?

The usual approach for answering this is predicting job satisfaction from these factors with multiple linear regression analysis. One of the best SPSS practices is making sure you've an idea of what's in your data before running any analyses on them.

Our analysis will use overall through q9 and their variable labels tell us what they mean. Now, if we look at these variables in data viewwe see they contain values 1 through So what do these values mean and -importantly- is this the same for all variables? A great way to find out is running the syntax below. Taking these findings together, we expect positive rather than negative correlations among all these variables.

We'll see in a minute that our data confirm this. Now let's see if the distributions for these variables make sense by running some histograms over them.

First and foremost, the distributions of all variables show values 1 through 10 and they look plausible. However, we have cases in total but our histograms show slightly lower sample sizes. This is due to missing values. For now, we mostly look at N, the number of valid values for each variable. We see two important things:. In the next dialog, we select all relevant variables and leave everything else as-is.

We then click P asteresulting in the syntax below. Note that all correlations are positive -like we expected. Most correlations -even small ones- are statistically significant with p-values close to 0. This means there's a zero probability of finding this sample correlation if the population correlation is zero.

Second, each correlation has been calculated on all cases with valid values on the 2 variables involved, which is why each correlation has a different N. The alternative, listwise exclusion of missing valueswould only use our cases that don't have missing values on any of the variables involved.

We usually check our assumptions before running an analysis. However, the regression assumptions are mostly evaluated by inspecting some charts that are created when running the analysis. Now that we're sure our data make perfect sense, we're ready for the actual regression analysis.

We'll generate the syntax by following the screenshots below. We'll explain why we choose Stepwise when discussing our output. By default, SPSS uses only our complete cases for regression. By choosing this option, our regression will use the correlation matrix we saw earlier and thus use more of our data.

In doing so, it iterates through the following steps:.

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