This page shows an example regression analysis with footnotes explaining the output. These data hsb2 were collected on high schools students and are scores on various tests, including science, math, reading and social studies socst. The variable female is a dichotomous variable coded 1 if the student was female and 0 if male. In the syntax below, the get file command is used to load the data into SPSS. In quotes, you need to specify where the data file is located on your computer.
Remember that you need to use the. In the regression command, the statistic s subcommand must come before the dependent subcommand. You can shorten dependent to dep. You list the independent variables after the equals sign on the method subcommand. The statistics subcommand is not needed to run the regression, but on it we can specify options that we would like to have included in the output. Here, we have specified ciwhich is short for confidence intervals.
These are very useful for interpreting the output, as we will see. There are four tables given in the output. SPSS has provided some superscripts a, b, etc. Please note that SPSS sometimes includes footnotes as part of the output. We have left those intact and have started ours with the next letter of the alphabet. Model — SPSS allows you to specify multiple models in a single regression command.
This tells you the number of the model being reported. Variables Entered — SPSS allows you to enter variables into a regression in blocks, and it allows stepwise regression. Hence, you need to know which variables were entered into the current regression. If you did not block your independent variables or use stepwise regression, this column should list all of the independent variables that you specified.
Variables Removed — This column listed the variables that were removed from the current regression. Usually, this column will be empty unless you did a stepwise regression. If you did a stepwise regression, the entry in this column would tell you that. R — R is the square root of R-Squared and is the correlation between the observed and predicted values of dependent variable.
R-Square — R-Square is the proportion of variance in the dependent variable science which can be predicted from the independent variables math, femalesocst and read. This value indicates that Note that this is an overall measure of the strength of association, and does not reflect the extent to which any particular independent variable is associated with the dependent variable.
R-Square is also called the coefficient of determination. Adjusted R-square — As predictors are added to the model, each predictor will explain some of the variance in the dependent variable simply due to chance.
One could continue to add predictors to the model which would continue to improve the ability of the predictors to explain the dependent variable, although some of this increase in R-square would be simply due to chance variation in that particular sample.
The adjusted R-square attempts to yield a more honest value to estimate the R-squared for the population. The value of R-square was. Error of the Estimate — The standard error of the estimate, also called the root mean square error, is the standard deviation of the error term, and is the square root of the Mean Square Residual or Error.
This is the source of variance, Regression, Residual and Total. The Total variance is partitioned into the variance which can be explained by the independent variables Regression and the variance which is not explained by the independent variables Residual, sometimes called Error.
It only takes a minute to sign up. I want to see whether rumination mediates the relationship between co-rumination X and relationship satisfaction Yhowever I don't understand the indirect effect.
Moderated Mediation using PROCESS in SPSS, interpreting the output.
Path B is negative, and the indirect effect is negative whereas the direct effect is positive. How to interpret this?
And all are significantly different from zero. As you can see the direct effect and the indirect effect have two different sign. This is a typical case of inconsistent mediation MacKinnon, Fairchild, and Fritz This has been well addressed in other threads very much! The most important thing to notice is that, in case of suppression effect another name for inconsistent mediation ; the only requirement is the direct effect to be larger in magnitude than the indirect effect. This is clear in your output and you can claim inconsistent mediation.
So it means without using jargons "An increase in a decreases b which in turn increases c". You decide if this makes sense, as you know what each variable means and I don't.
According to my knowledge, path "a" says what is the expected change in your mediator variable, when your independent variable increases by 1 unit. So in your case, it would be that increase in co-rumination Xincreases rumination M.
Path "b" says what is the expected change in your dependent variable when mediator increases by 1 unit controlling for X.
So in your case, it would be that increase in rumination M decreases satisfaction Y. Looking at your output, it seems that the indirect effect is significant because the confidence interval does not include 0. This is, to me, a bit strange that you have a positive relationship between co-rumination and satisfaction but I know nothing about the theory. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered.
How to interpret this mediation analysis? Ask Question. Asked 1 year, 11 months ago. Active 11 months ago. Viewed 3k times.Are you trying to understand data from your research? Learn how and when to conduct mediation, moderation, and conditional indirect effects analyses? Or, perhaps, how to theorize and test your theoretical models? If so, this is the course for you! We will walk you through the steps of conducting multilevel analyses using a real dataset and provide articles and templates designed to facilitate your learning.
You'll leave with the tools you need to analyze and interpret the results of the datasets you collect as a researcher. By the end of this course, you will understand the differences between mediation and moderation and between moderated mediation and mediated moderation models conditional indirect effectsand the importance of multilevel analysis. Most important, you will be able to run mediation, moderation, conditional indirect effect and multilevel models and interpret the results. This course is supported by the BRAD Lab at the Darden School of Business, which studies organizational behavior, marketing, business ethics, judgment and decision-making, behavioral operations, and entrepreneurship, among other areas.
This course is giving a very detail and well structured insights into Data Analysis. Very useful to learn and develop analytical skills of available data. Welcome to the first week of our research methods course! We'll start with mediation analysis, following by parallel mediation, serial mediation, and moderation. Mediation is all about the mechanisms connecting the independent variable and dependent variable. Moderation refers to the circumstances under which the independent variable influences the dependent variable.
By the end of this week, you will know how, when, and where the independent variable influences the dependent variable and how to theorize and conduct analysis using SPSS.
Loupe Copy. Enroll for Free. From the lesson. What Is Mediation? Introduction to SPSS Running a Mediation Model Conducting a Parallel Mediation Analysis You will find links to the example dataset, and you are encouraged to replicate this example. Several additional questions about this example are provided at the end of this guide to promote your exploration of this analysis even further.
The example assumes you have already opened the data file in SPSS. However, what if you think the relationship between alcohol use and GPA is stronger for students who are younger than for students who are older i.
In this case, you could use a hierarchical linear regression. This example represents a hierarchical linear regression using a set of variables from a study conducted by Mandracchia and Smith in which data from adult male prisoners were used to explore the basic propositions of the interpersonal theory of suicide.
Specifically, after accounting for depression and hopelessness i. Refer to the corresponding Codebook for detailed information regarding these variables.
To produce a visual display of the dispersion of the data for each variable, you can create a histogram for each variable including the interaction terms, in which two variables are simply multiplied together in SPSS. To have SPSS create a series of histograms for these data which are also provided previously in the Student Guide under the section titled The Dataselect from the menu:. Click Continuethen OK. This will open another window which is your Output file that displays a series of Frequency Tables one for each variable you selectedfollowed by a series of Histograms one for each variable you selected.
These histograms give you a graphical depiction of the overall shape, ranges, central tendencies, and patterns of the data for each of the variables selected. To conduct the hierarchical linear regression analysis predicting suicide ideation as described in the Your Turn section of the Student Guide, select from the menu:.
To select variables for Block 2 of the analysis, click on the blue box that says Next in the top right corner above the Independent s box; note the change to Block 2 of 2.
To select variables to include in the Block 3 of the analysis, click on the blue box that says Next in the top right corner above the Independent s box; note the change to Block 3 of 3. Before running the analysis, click on the Statistics box in the top right corner of the Linear Regression box.
Select R squared change from the list on the right side of the Linear Regression: Statistics box. This will provide you with information about how much additional variance in the criterion variable i.
Click Continue to close out the Statistics box and then click OK at the bottom of the Linear Regression box to run the hierarchical linear regression analysis. The output that SPSS produces for the above-described hierarchical linear regression analysis includes several tables.
To interpret the findings of the analysis, however, you only need to focus on two of those tables. You can see the Model Summary table below Figure 6. Although data from each of the columns provide information about the analysis, the most critical information from this table appears in the following columns: R SquareR Square Changeand Sig. Block 1 i. When depression and hopelessness scores were added in Model 2, the value for R Square increased to.
This can be interpreted that the addition of depression and hopelessness scores contributes 8. To determine whether this is a statistically significant increase, look at the box on the far right side of the Model Summary table, titled Sig.
F Change. This table provides both Unstandardized Coefficients and Standardized Coefficientswhich are interpreted in the same manner as in other forms of regression analyses. Using the dataset provided, follow the same steps described above to see whether you can replicate the results for this hierarchical linear regression analysis.
How would you interpret these results given the new interaction term?
SPSS Inc. Other product and service names might be trademarks of IBM or other companies.Search everywhere only in this topic. Advanced Search. Classic List Threaded. Dear everybody, Currently I'm working on some organisational research.
Just to get a bit of a idea of what I'm research; I'm looking at impact of leadership on how enthusiastically work. I believe the relationship of the leader, and follower impact are important, as is the ambition of a worker. So I'm looking at: a. So I'm wondering whether I'm interpreting the output right.
I Interpret it as that: a. Next up are the "Conditional direct effect s of X on Y at values of the moderator s ". I interpret this as now I really don't know if this is right : a. And for the "Conditional indirect effect s of X on Y at values of the moderator s :", I interpret this as I don't really know whether this is right either : a. The moderated mediation, and they are all significant, and their effect increases as their values increase.
After this the line "Indirect effect of highest order product:" pops up, showing LMX as insignificant. Does anyone know what this means? Really sorry to bother you all with this load of SPSS output! I'm just really hoping there's a whizkid out here that can help me out!
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Values for dichotomous moderators are the two values of the moderator. Free forum by Nabble. Edit this page.Half of the group got the information that climate change caused the drought, and the other half of the group did not get information about the cause of the drought. In this blog we are purely focusing on the graphing part. If you would like to know more about the case, we would highly recommend you to read the book.
The dataset we are going to use is protest. Therefore, we open the disaster. Furthermore, we chose model number 1 simple moderation. Besides, we select the option to generate code for visualizing interactions. Finally, we also select the option that we want percentiles, and that we want to use the Johnson-Neyman technique to probe the interaction:.
Here you can see that there is a difference between those that were informed about climate change versus those that were not informed, but this effect changes depending on the level of skepticism regarding climate change.
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This results in the following graph, which shows you the conditional effect of Protest on Liking at values of the moderator Sexism, including the confidence intervals:. We hoped you found this blog informative, and please let us know your comments! If you would like to see more blogs with in depth explanation about how to interprete the results, please let us know. If you want to make graphs of other models: we have more templates available in our store!
Dawson, J. Interpreting interaction effects. DeCoster, J. Graphing moderated mediation. Hayes, A. New York: The Guildford Press. You must be logged in to post a comment. This site uses Akismet to reduce spam. Learn how your comment data is processed. Skip to content. Finally, we also select the option that we want percentiles, and that we want to use the Johnson-Neyman technique to probe the interaction: Model 1 — Graphing moderation The output file from this PROCESS macro will get you all the results you need.Moderated multiple regression using Hayes' Process Macro v3.3 with SPSS (July 2019)
This will result in the following graph: Here you can see that there is a difference between those that were informed about climate change versus those that were not informed, but this effect changes depending on the level of skepticism regarding climate change.Understanding moderation is one of those topics in statistics that is so much harder than it needs to be.
I have spoken with a number of researchers who are surprised to learn that moderation is just another term for interaction. In any case, both an interaction and moderation mean the same thing: the effect of one predictor on a response variable is different at different values of the second predictor. When we speak of moderation, we usually call the first predictor an independent variableand the second the moderator. Mathematically, there is no distinction. But it can help interpretation to think of them that way.
Moderation effects are difficult to interpret without a graph. It helps to see what is the effect of the independent value at different values of the moderator. If the independent variable is categorical, we measure its effect through mean differences, and those differences are easiest to see with plots of the means. Moderation says that those mean differences are not the same at every value of the moderator.
It can be hard to discern a pattern in how they differ without seeing it. For example, the mean difference may get larger as the moderator increases. Or it may flip signs. If the independent variable is continuous, we measure its effect through a slope of the regression line.
So you want to plot the predicted values of those regression lines. Moderation says that the slope of the regression line is different at every value of the moderator. Yes, that one regression equation really represents many different lines—one for every possible value of the moderator.
Once again, a positive slope may get larger or smaller as the moderator increases. Or it too can flip signs, going from a positive slope at low values of the moderator to a negative slope at high values.
If the moderator itself is continuous, you could potentially choose an infinite number of values at which to plot the effect of the independent variable. Luckily, plotting the effects of the independent variable at only a few values of the moderator are usually needed to see patterns. There are conventions to help you choose the best values of the moderator for plotting predicted values.
For example, one convention suggested by Cohen and Cohen and popularized by Aiken and West is to use three values of the moderator: the mean, the value one standard deviation above, and the value one standard deviation below the mean. The value one standard deviation below the mean can be beyond the range of the data.
In that case, using the minimum or some other small value of the moderator may be a better choice. Likewise, sometimes very specific values of the moderator are particularly meaningful. For example, in years of education, values of 12 and 16 generally indicate high school and college graduation.
If years of education was the moderator, plotting effects of the independent variable when education equaled 12 makes a lot of sense, even if the mean is But spending a little time thinking about a more appropriate value can make interpretation, and therefore communication to your audience, easier.
Tagged as: interpretingmoderation effects. Thank you very much for your advise. The page has been very helpful for my tests. Unfortunately I have a bit of a problem with my analysis as my test results are a complete mess especially the graphs. Could you tell me if it is even possible to interpret the moderation effect if my dependent variable and my moderator are measured on a 7-point Likert Scale and my independent variable is nominal?
Also my Levene test is significant. How to interpret the results of moderator? Hi, Thanks for sharing valuable information.