FAQ: (GUI)

Common questions regarding use of the GUI are addressed in the following topics:

What kind of input files can I use?

What to do if variables are assigned incorrectly?

What output files can I get?

How do I create a same-level interaction?

How do I remove variables or change their centering options?

What graphs can I get?

Can graphs be modified?

How do I center variables?

How many interactions can be included in the model?

Explanation of error messages (GUI)

What kind of input files can I use?

Currently, only CSV files are supported. Here is an example of a Comma separated Value (*.csv file extension) file:

The Data page is used to read in data. Click on Select file to open an Open dialog box to select a data file.

By default, it is assumed that the data file is on the local hard drive disk (Local HDD). However, one may also use an URL, OneDrive of Google Drive to select the file from.

Once the file name, type and location has been specified, click Open to display the contents of the data file.

What to do if variables are assigned incorrectly?

Once variables are selected on the Data page, clicking the Update button prompts the program to automatically determine the appropriate level of the hierarchy each variable is associated with. Sometimes, however, problems can occur. To illustrate, consider the following example, based on the well-known HS&B data, in which students are nested with schools and we have information on the mean socio-economic status of each school represented by the variable MEANSES.

In this case, the program assigned the variable MEANSES to level-1. This is surely incorrect, as we know it to be a school rather than a student characteristic.

Inspection of the data in the first table for this variable shows that, instead of having the same value for MEANSES for all students within school with ID 1224 as would be expected of a school-level variable, the data for the second student (second record) shows a value of 0. This means that the values of this variable change within a school over students, and do not remain constant for all students within the school as a true level-2 variable should. This is most likely a data entry error and the best solution would be to clean the data and inspect it for similar problems with other variables.

However, the program allows the user to override program allocation without editing the data. If a user wishes to proceed regardless, the level-1 check box for MEANSES can be unchecked and the level-2 check box can be checked instead. Clicking the Update button again will retain this modification. In effect, the program respects the user’s opinion.

Should the user prefer the program’s allocation at a later stage, clicking Reallocate Levels will reset the level allocation to the initial automatic allocation performed by the program.

What output files can I get?

Results of the analysis are available from the Run page via links. Both HTML and standard txt file output are available. Moderation graphs, if requested, are also given here. In addition, all relevant files, from data to syntax to output, can be downloaded by clicking on the Download All link. This option is particularly useful should you want to rerun the analysis at a later date.

How do I create a same-level interaction?

It is not necessary to create same-level interactions prior to importing the data into the program. The program allows the user to create interaction terms on the fly. Same level interactions are specified during the model specification, using the Models page.

Consider the following random-intercept-only model modelling a student’s math achievement (MATHACH) as a function of the predictors MINORITY and SES.

Suspecting that there may be a significant interaction between these predictors, we wish to add a same level interaction term. To do so, we open the Level-1 Variables list and, holding the Control key down, select both variables.

Notice that, by default, these variables will be entered as Uncentered. In addition, the program allows us to add multiple variables in one of two ways:

As the setting minority*ses is exactly what we want, we click the radio button next to this option, and simply drag the selected variables into the level-1 equation

before releasing the mouse. Once the term has been dropped, the model becomes

The fixed effect in the equation represents the same-level interaction between the two level-1 variables MINORITY and SES.

It is also possible to add same-level interactions at a higher level. In the example below, an interaction term between the variables SIZE and SECTOR is being added to the first of the level-2 equations.

After dropping these into the model, the equation in question becomes

and is the fixed coefficient associated with the interaction term.

A single predictor may also be dragged on top of a predictor already in the model before releasing the mouse, creating an interaction term that way. When that is done, however, note that the predictor previously in the model is no longer present in the same form as before and if required, would have to be added back into the model.

In the model below, the predictors SIZE and SECTOR are already in the model:

Dragging the variable SIZE on top of SECTOR as shown below

creates a model with a two-way interaction size*sector, but there is no longer an individual coefficient for the variable SECTOR in the equation.

How do I remove variables or change their centering options?

A variable may also be removed from the model by simply clicking on the “x” next to the variable name in the equation. The centering of a variable may also be changed by moving the mouse over it to access the little pop-up menu below, on which an alternative form of centering may be selected.

What graphs can I get?

Currently, the program will provide graphs for moderation analyses only. To graphs may be obtained, a so-called simple slopes graph and a confidence interval graph. These will be familiar to users using the online tool at Interaction Effects in MLR, LCA, and HLM (quantpsy.org).

Simple slopes graph:

The first of the available graphs is a graph of the conditional regression line(s) describing the relationship between the outcome and the focal predictor as a function of the moderator. The graph will automatically show a line at each of three values of the focal variable: mean – 1 standard deviation, mean, and mean + 1 standard deviation. In other words, the value of the focal variable is held constant at three specific values. Values of the moderating variable are used to define the x-axis, and the graph is confined to the area (mean of moderator – 2 standard deviations, mean of moderator + 2 standard deviations).

Here is an example of a simple slopes graph:

The graph is produced as a *.png file with the name <syntax file name>_simple_slopes.png which can easily be inserted into a paper. A number of graph settings may also be modified by the user on the Graphing page within the program.

Confidence interval graph:

The second graph shows the regression line describing the relationship between the outcome and the focal predictor as a function of the moderator, along with a 95% confidence interval. It also shows the so-called region of significance, provided that the boundaries of this region falls within the scale set by the values of the moderator variable, which again defines the x-axis. The region between the lower and upper bound of the region of significance indicates the values of the moderator for which the slope of the regression of outcome on focal variable transitions from non-significance to significance. The graph produced by the program is saved to a *.png file with the name <syntax file name>_confidence_interval.png. An example of the confidence interval graph with regions of significance is shown below.

Can graphs be modified?

Graphs can be modified using the Graphing page. This page is only available for moderation analyses. When this page is first opened, all options are set to default values. By default, both a simple slope and confidence interval graph using these settings will be produced. To request only one type of graph, simply uncheck the check box next to Simple slopes or Confidence interval.

All options may be changed. For moderation model 1, there will only be one simple slopes graph and/or one confidence interval graph; for models 2 and 3 there will be three of each. In all cases, however, the following options apply:

How do I center variables?

Centering of variables is specified on the Models page and forms part of the variable selection process. Once an outcome variable is specified, all potential predictors at level-1 is accessed by clicking on the Level-1 variables list to the right of the window.

The user can either enter variables into the model individually or as a group. By default, predictors are entered uncentered. Alternatively, one can enter variable(s) as either group centered or grand centered at lower levels of the hierarchy, or as grand centered on the highest level of the model.

Consider the following level-1 model for a two-level model:

The intercept represents the expected outcome for subject from level-2 unit j who has a value of 0 on the predictor variable . This is the expected outcome if is used in its original, uncentered form.

If the predictor is used as a grand mean centered predictor, the model becomes

where represents the grand mean of all values, irrespective of the unit the value originates from.

If the predictor is used as a group mean centered predictor, the model becomes

where the group mean of all values from the j-th level-2 unit. There are as many group means as there are level-2 units.

In the table below, a small illustration of the numerical effect of group-mean and grand-mean centering is given for 2 hypothetical level-2 units.

Note that there is a marked difference between the raw data for the two units, yet after group-mean centering they are the same.

The images below show the notation used to indicate the three options.

Multiple variables may be selected simultaneously, and the choice of centering selected would apply to all the selected variables. The user has the option to apply the same three centering options to the selection made.

When multiple variables are selected simultaneously, another option is displayed in the Level-1 Variables field, offering the option to enter the selected variables as individual predictors or as an interaction term between the selected variables. The entry in the level-1 model will depend on the choice between the two options given below the list of variable names. In this case, between minority + ses and minority*ses.

If, for example, we want to add the predictors MINORITY and SES as individual group mean centered variables into the level-1 equation, the following selection is made:

After dragging the variables into the level-1 equation, the model becomes

with the two predictors added as independent, group centered predictors.

Should the interaction term be required, the corresponding selection will look like this

and the model will be updated to

showing the inclusion of the group centered interaction between the two variables MINORITY and SES. These images illustrate the difference between the option minority + ses and minority*ses.

How many interactions can be included in the model?

The maximum number of interactions allowed in the program is a 3-way interaction, in other words, an interaction of the form a*b*c. There are no limits on the number of individual 2-way or 3-way interactions.

In the level-1 model below, three predictors have been entered. 2-way Interactions between all possible pairs of the variables are also present in the model (for example minority*ses), along with a 3-way interaction (minority*female*ses). While it is possible to add more than 3 predictors simultaneously, selecting more than three variables at the same time will disable the option on the Level-1 Variables box that allows for creating an interaction effect.

Turning to higher-levels, the type of interaction that can be added to the model depends on the equation the selection is to be added to.

Consider the model

Suppose we would like to add an interaction term to the two level-2 equations. In the case of the first equation, that for a three-way interaction term of the form size*sector*disclim may be added

to obtain the equation

When we attempt to add a similar term to the second level-2 equation for , the program does not allow this. When we drag the interaction term into the equation, an

Why the difference in behavior? The answer lies in the fact that is the intercept equation, but is a slope equation.

If we substitute the into the level-1 equation, we obtain

However, if we could , we would get

and the last term, , would be a four-way interaction.

Although the same level-2 variables appear on the two level-2 equations, those on the equation for are already multiplied with the values of the level-1 predictor SES. This implies that for this equation, only 2-way interaction terms may be added so as not to exceed the program limit of maximum three terms a*b*c. If we had managed to add the three-way interaction to the second equation, we would in effect have added a 4-way interaction of the form a*b*c*d.

Apart from the 3-way interaction limit, there is no limit on the number of individual 2- or 3-way interaction terms that can be added to the model. In other words, a model with 10 2-way interactions and 4 3-way interactions would, theoretically be possible, if somewhat inadvisable in terms of estimation.

Explanation of GUI error messages

When reading in a syntax and data file for a previous analysis, a mismatch between syntax and data may occur, prompting the display of the message shown below. The program will tell you what data you selected, and what you should have selected to go with the selected syntax file.

Should you attempt to access the Models, Settings, Graphing or Run page without having first completed the Data page, the program will warn you about the omission:

This message will also appear if a data file has been opened, but no variable selection has been performed and/or the Update button was not clicked upon completion of selection.

If data have been specified, but no model has been set up via the Models page, the Settings page will be unavailable until the Models page has been completed.

When attempting to access the Graphing page for a non-moderation analysis, the program will remind you that graphing is only available for moderation analyses.