What Is A Moderator In Psychology?

Moderating variables – A moderator influences the level, direction, or presence of a relationship between variables. It shows you for whom, when, or under what circumstances a relationship will hold. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

Categorical variables such as ethnicity, race, religion, favorite colors, health status, or stimulus type,
Quantitative variables such as age, weight, height, income, or visual stimulus size.

Example: Moderator variables In a study on work experience and salary, you hypothesize that:

years of work experience predicts salary, when controlling for relevant variables,
gender identity moderates the relationship between work experience and salary.

This means that the relationship between years of experience and salary would differ between men, women, and those who do not identify as men or women. To test this statistically, you perform a multiple regression analysis for the data on work experience and salary, with gender identity added in the model. You compare the statistical significance of the model with and without gender identity included to determine whether it moderates the relationship between work experience and salary.

Why should you include mediators and moderators in a study? Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

Contents

1 What is an example of a moderator variable?
- 1.1 What is moderating role in psychology?
- 1.2 What are moderator variables in psychology?
  - 1.2.1 What is the difference between a moderator and a mediator?
2 What is an example of a moderator in psychology?
3 What is an example of moderation?
- - 3.0.1 Can a mediator be a moderator?
- 3.1 Is anxiety a mediator or moderator?
4 What makes a moderator a moderator?
5 What role is higher than moderator?
6 What are the three types of moderators?
7 What are the two types of moderators?
8 What is moderating behavior?
9 Can a moderator be continuous?

What is an example of a moderator variable?

A moderating variable is a type of variable that affects the relationship between a dependent variable and an independent variable, When performing regression analysis, we’re often interested in understanding how changes in an independent variable affect a dependent variable. We suspect that more hours spent exercising is associated with a lower resting heart rate. However, this relationship could be affected by a moderating variable such as gender, It’s possible that each extra hour of exercise causes resting heart rate to drop more for men compared to women. Another example of a moderating variable could be age, It’s likely that each extra hour of exercise causes resting heart rate to drop more for younger people compared to older people.

What is moderating role in psychology?

What is the difference between moderation and mediation? What is the difference between moderation and mediation? Quantitative Results Statistical Analysis Moderation and mediation are two very different ideas, so it is a little unfortunate that they not only have such similar names, but also tend to accompany the same citation: Baron and Kenny (1986).

While there are a few similarities between the two analyses that are important to understand, here we will focus on the differences between moderation and mediation.
To begin, we do need to understand that both analyses have to do with better understanding the relationship between an independent and dependent variable.

In this regard, both mediation and moderation have to do with checking on how a third variable fits into that relationship. For the purposes of understanding these two concepts, this is where the similarities end. Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.

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Moderation is a way to check whether that third variable influences the strength or direction of the relationship between an independent and dependent variable. An easy way to remember this is that the moderator variable might change the strength of a relationship from strong to moderate, to nothing at all.

It is almost like a turn dial on the relationship; as you change values of the moderator, a statistical relationship that you observed before might dissolve away.
For example, if you expected that the length of time studying related to the grades on a calculus test, you would probably be right.
Let’s say there is a strong relationship between time spent studying and grades.

However, that relationship may not hold true across the board; something like grade level might be a possible moderator. If you switch the value of this moderator from college student to elementary school student, that relationship is not likely to hold up.

No amount of studying is likely to help a second grader an A on a calculus exam, but for a college student, study time will matter a great deal. Mediation is a little more straightforward in its naming convention. A mediator mediates the relationship between the independent and dependent variables – explaining the reason for such a relationship to exist.

Another way to think about a mediator variable is that it carries an effect. In a perfect mediation, an independent variable leads to some kind of change to the mediator variable, which then leads to a change in the dependent variable. However, in practice, the relationships between the independent variable, mediator, and dependent variable are not tested for causality, just a correlational relationship.

The purpose of mediation analysis is to see if the influence of the mediator is stronger than the direct influence of the independent variable. An obvious real-life mediator is temperature on a stove. Water will not start to boil until you have turned on your stove, but it is not the stove knob that causes the water to boil, it is the heat that results from turning that knob.

To test something like this, we could check to see how tightly correlated the knob being turned is to the water’s state (i.e., is it boiling?). For the first few minutes there would be no effect, so we can treat that as a weak correlation. Compared to the relationship between the temperature of your stove top and the state of the water, we can see that it is actually the temperature of the stove (the mediator) that is causing the water to boil, not just the action of turning a knob (the independent variable).

What are moderator variables in psychology?

The term moderating variable refers to a variable that can strengthen, diminish, negate, or otherwise alter the association between independent and dependent variables. Moderating variables can also change the direction of this relationship. A moderating variable can either be categorical (e.g., race) or continuous (e.g., weight), and is used exclusively in quantitative, rather than qualitative, research.

Moderating variables are useful because they help explain the links between the independent and dependent variables.
Also sometimes referred to as simply moderators, these moderating variables provide additional information regarding the association between two variables in quantitative research by explaining what features can make that association stronger, weaker, or even disappear.

For example, in experimental studies, X (independent variable) causes Y (dependent variable). A third variable, M,

What is the difference between a moderator and a mediator?

In statistics, mediators and moderators help you understand the relationship between two variables. This article discusses the differences between mediator vs. moderator 1, frequently asked questions (FAQs) you should know, and a few mediator vs. moderator examples.

Compare correlations and causal relationships between two variables Emphasize the relationship between variables Judge the external validity of your research

A mediator (mediating variable) explains the process in which two variables relate. In contrast, a moderator (moderating variable) affects the direction and strength of this relationship. Mediator vs. moderator differ because of the following reasons:

The mediator shows the connection between two variables. For instance, sleep quality (independent variable) affects the quality of your work (dependent variable) through alertness. The moderator may be acting upon two variables, changing the strength and direction of that relationship. For instance, mental health status can moderate the relationship between sleep and work quality. The relationship is stronger for people without mental health conditions than for their counterparts.

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What is an example of a moderator in psychology?

Categorical variables such as ethnicity, race, religion, favorite colors, health status, or stimulus type,
Quantitative variables such as age, weight, height, income, or visual stimulus size.

Example: Moderator variables In a study on work experience and salary, you hypothesize that:

years of work experience predicts salary, when controlling for relevant variables,
gender identity moderates the relationship between work experience and salary.

Why should you include mediators and moderators in a study? Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

You might be interested: What Is An Illusion In Psychology?

How do you know which variable is moderator?

Moderating Variables – Ok, so we understand the fundamentals of control variables and how to deal with them. But what if a variable presents the possibility of affecting a relationship between an independent and a dependent variable instead of affecting the value of another variable? These are a special variable referred to as a moderating or moderator variable.

Instead of the arrow in your model pointing to another variable, the arrow coming from a moderating variable points to another arrow – in other words, the effect is on the relationship instead of on the variable itself. Moderating variables can be confused with control variables because they are often the same variable.

Age, gender, culture, socio-economic status, personality, marital status, education level, ethnicity, etc. can be either control variables or moderating variables. You decide which is which by asking yourself whether the effect of the variable in question will be directly on another variable in your model or on a relationship in your model.

If it is the former, then it is a control variable.
If the latter, it is a moderator.
Once you make the decision, you then model the variable appropriately and test it just like any other relationship in your model.
Remember, every arrow in your model is a hypothesis.
So, control variables are measured but their effect is not hypothesized.

Moderator variables are measured, and their effect is hypothesized. Time for an example. Suppose we are interested in a person’s perceived level of stress in their life on their resting heart rate. We hypothesize that subjects who perceive high levels of stress in their life will have a higher resting heart rate than those who perceive relatively low levels of stress.

Time to think about control and moderating variables.
What else could affect a person’s resting heart rate? The amount of exercise they get, their age, gender, race, and maybe many others.
So, are these candidates control variables or moderators? One could argue them either way.
You could consider age as a control variable, measure it, and statistically remove it from the regression.

You could do the same with gender. The problem with doing this is that anyone at any age and gender can have perceived stress in their life. So, it seems more reasonable that age or gender could alter the effect of stress on resting heart rate rather than directly affect a person’s resting heart rate.

Therefore, we make the decision to model age and gender as moderating variables and hypothesize their effect on the relationship between perceived stress and resting heart rate. We then proceed to test them like any other hypothesis in our model. Moderating variables are important because they often serve to help us see beyond what is happening between two variables by seeing why it is happening or why it isn’t happening.

They provide insight into a relationship we could not otherwise achieve without their inclusion in the model. This explanation of the difference between control variables and moderating variables is admittedly not very deep but it is intended to provide a basic explanation of each and how to tell them apart.

Once you understand the fundamental differences, the nuances associated with modeling and testing them become less onerous and easier to deal with. So, for the sake of simplicity, think of moderating variables as catalysts which serve to make a relationship stronger or weaker. Control variables are variables that you are not particularly interested in, but they can have a material effect on your dependent variable and you wish to eliminate their effects from the effect of your independent variable of interest on your dependent variable.

Mediator and Moderator Variables Explained

It’s always easier when you understand. About the Author: George M. Marakas is a professor of information systems and the director of the Office of Doctoral Programs for FIU Business. He received his PhD in information systems from FIU. His teaching expertise includes digital transformation, systems analysis and design, technology-assisted decision making, electronic commerce, management of I.S.

What are the main roles of a moderator?

A moderator facilitates, reviews, and guides a discussion or debate and related interactions to ensure all shared content is appropriate and follows community rules. You can find moderators in a variety of industries and contexts online or at events.

What is a moderator in research?

A moderator is a variable that modifies the form or strength of the relation between an independent and a dependent variable.

What is an example of moderation?

Other forms: moderations Eating foods in moderation is a key to maintaining a healthy diet. That means you have a little bit of everything, rather than gorging, say, on steak and ice cream. The word moderation describes a middle ground often in either behavior or political opinions.

noun quality of being moderate and avoiding extremes noun the trait of avoiding excesses noun the action of lessening in severity or intensity “the object being control or moderation of economic depressions” synonyms: mitigation noun a change for the better

DISCLAIMER: These example sentences appear in various news sources and books to reflect the usage of the word ‘moderation’, Views expressed in the examples do not represent the opinion of Vocabulary.com or its editors. Send us feedback EDITOR’S CHOICE

Can a mediator be a moderator?

It is possible that the same variable may serve as both a mediator and a moderator. For instance, study time might serve both roles. First, as a mediator, the new curriculum might lead to higher performance be- cause it causes students to study more.

Is anxiety a mediator or moderator?

Table 2 – Predictors for depression using anxiety as the mediator.


Predictor	Step 1	Step 2
	B	B	95% CI
Stress	.04 ***	.03 ***	(.02,.04)
Self-esteem	−.04 ***	−.03 **	(−.05, −.01)
Anxiety		.05 **	(.02,.08)
R 2	.47	.49
F	86.61 ***	63.72 ***
Δ R 2		.03
Δ F		10.06 **

A Sobel test was conducted to test the mediating criteria and to assess whether indirect effects were significant or not. The result showed that the complete pathway from stress (independent variable) to anxiety (mediator) to depression (dependent variable) was significant ( z = 2.89, p = .003). Mediation model showing that the effect of stress and self-esteem (independent variables) on depression (outcome) is mediated by anxiety (mediator). Changes in Beta weights when the mediator is present are highlighted in red. For the second aim, regression analyses were performed in order to test if stress mediated the effect of anxiety, self-esteem, and affect on depression. The first regression showed that anxiety ( B = .07, 95% CI, β = .37, t = 4.57, p <.001), self-esteem ( B = −.02, 95% CI, β = −.18, t = −2.23, p = .03), and positive affect ( B = −.03, 95% CI, β = −.27, t = −4.35, p <.001) predicted depression independently of each other. Negative affect did not predict depression ( p = 0.74) and was therefore removed from further analysis. The second regression investigated if anxiety, self-esteem and positive affect uniquely predicted the mediator (i.e., stress). Stress was positively associated to anxiety ( B = 1.01, 95% CI, β = .46, t = 7.35, p <.001), negatively associated to self-esteem ( B = −.30, 95% CI, β = −.19, t = −2.90, p = .004), and a negatively associated to positive affect ( B = −.33, 95% CI, β = −.27, t = −5.02, p <.001). A hierarchical regression analysis using depression as the outcome and anxiety, self-esteem, and positive affect as the predictors in the first step, and stress as the predictor in the second step, allowed the examination of whether anxiety, self-esteem and positive affect predicted depression and if this association would weaken when stress (i.e., the mediator) was present. In the first step of the regression anxiety ( B = .07, 95% CI, β = .38, t = 5.31, p = .02), self-esteem ( B = −.03, 95% CI, β = −.18, t = −2.41, p = .02), and positive affect ( B = −.03, 95% CI, β = −.27, t = −4.36, p <.001) significantly explained depression. When stress (i.e., the mediator) was controlled for, predictability was reduced somewhat but was still significant for anxiety ( B = .05, 95% CI, β = .05, t = 4.29, p <.001) and for positive affect ( B = −.02, 95% CI, β = −.20, t = −3.16, p = .002), whereas self-esteem did not reach significance ( p < = .08). In the second step, the mediator (i.e., stress) predicted depression even when anxiety, self-esteem, and positive affect were controlled for ( B = .02, 95% CI, β = .25, t = 3.07, p = .002). Stress improved the prediction of depression over-and-above the independent variables (i.e., anxiety, self-esteem and positive affect) (Δ R 2 = .02, F (1, 197) = 9.40, p = .002). See Table 3 for the details.

What makes a moderator a moderator?

5. Clarity: Clear and concise communication – A powerful moderator communicates clearly and concisely, using straightforward language without being confrontational or biased. Their job is to ensure the speakers and audience members understand each other and get along, creating a positive and productive atmosphere.

At a panel discussion on diversity and inclusion, for example, a moderator could use Pigeonhole Live’s to display the most commonly used words during the discussion.
This would help highlight any problematic language or ideas and allow the moderator to steer the conversation in a more positive and inclusive direction.

The moderator could also use moderation tools to ensure that any inappropriate comments or questions are not included in the discussion, creating a safe and respectful environment for all participants.

What role is higher than moderator?

Who is a Moderator on Facebook? – A moderator is next in line (or below) to the administrator (in terms of hierarchy) in a Facebook group. It is the admin who chooses a moderator and delegates roles and responsibilities to him/her. He/she may look after group posts, member requests, group activity, and is responsible for the efficient functioning of the Facebook group.

What are the three types of moderators?

Types of Moderating Materials – There are several different types of moderating materials, and each have places where they are used more effectively. Typically-used moderator materials include heavy water, light water, and graphite, The relative properties of these materials are compared below. The moderators vary in terms of their moderating abilities, as well as in their costs.

What are the two types of moderators?

Quasi-moderators affect the true relationship between two variables and, at the same time, are independently associated to the dependent variable. Pure moderators are those that affect the true relationship between the dependent and independent variable but are not independently associated with the dependent variable.

What is moderating behavior?

INTERNAL MODERATORS OF HUMAN BEHAVIOR – Internal moderators of human behavior include variables such as intelligence, level and type of expertise, personality traits, emotions/affective factors, attitudes/expectations, and cultural values. These moderators are complex, they interact, and they can influence performance in multiple directions.

For example, individuals with a low level of expertise and aggressive personality characteristics will probably select different strategies than individuals with a high level of expertise and nonaggressive personalities.
However, the form of this relationship cannot currently be predicted.
With regard to emotions, fear may result in a tendency to interpret a situation as dangerous, while anger may result in an interpretation that others are hostile.

Furthermore, an individual with an aggressive personality who is fearful may behave differently than an aggressive individual who is self-confident.

Can a moderator be continuous?

This section contains the following topics: 1) Continuous moderator example; 2) Categorical moderator example; 3) Simple slope computations; 4) Display of results; and 5) References; 1. Continuous moderator example In the following continuous moderator example, I will be describing a set of data where depression, a continuous variable, is considered to be the dependent variable. Stress, a continuous variable, is considered to be the main effect. Social support coping, a continuous variable, is considered to be the moderating variable, i.e., the variable that affects the linear relationship between stress and depression. This example is considered to be a “continuous moderator” case because the moderating variable is continuous. Thus, if one was interested in determining whether social support moderates the influence of stress upon depression, one would choose “Continuous Data Entry” in the first menu. The next menu asks for one to enter labels for the graph and statistical information. Labels. The four labels are: 1) the descriptive label that goes at the top of the graph (e.g., Moderation of the Effect of Stress on Depression by Social Support); 2) the X axis label (e.g., Stress); 3) the Y axis label (e.g., Depression); and 4) the name of the moderating variable (e.g., Social support). Note that it is standard practice to display the main effect on the X axis, the dependent variable on the Y axis, and the moderating variable (in this case, social support) is represented by the three lines that are plotted on the graph. Statistical information. The rest of the menu requires that one input information taken from the regression analysis output. In particular, one must enter the unstandardized regression coefficient (B), the mean, and the standard deviation of both stress (the main effect) and social support (the moderating variable). In addition, the menu page requires the B for the interaction term and the constant. All of the Bs can be obtained from the multiple regression output generated by your software package, and the means and standard deviations may be computed in a simple descriptive statistics run on the same data. A commonly discussed issue concerns the means of the two continuous variables. Aiken and West (1991) and others recommend that the two continuous variables be centered for use in this type of analysis because the resulting graph will be centered on the mean for both the IV and the ModV. Doing so permits an easier interpretation of the pattern (sometimes). However, it is not essential to center your IV and ModV, but some users may wish to do so. If you choose to remove the mean from each variable, do so in the statistical package that your data reside in. Simply perform a compute statement in which the mean (e.g., 5.15) is subtracted from every value in a given column. Common practice is to subtract the mean from the IV as well as the ModV before one creates the interaction term by multiplying these two variables together. Note that the standard deviations of these two main effects are unchanged by centering. I could have stipulated in this menu that the means were 0.00 for the first two variables, but decided against this because some users may wish to create charts that would not require centered variables. Be careful that you enter the unstandardized regression coefficients (the Bs) instead of the betas. When you are transferring information from the statistical output to ModGraph, be careful that you note the order that the variables are listed in the output. For example, SPSS output lists the constant first instead of last. After you have entered all of these fields, hit the CALCULATE button and the computed means will be displayed in the 3 X 3 table at the bottom of the page. Hit SEE FIGURE, and the graph should be displayed. The graph is a,jpeg file which can be copied and pasted into your own documents if you wish. If you would like to examine the cell means, click on DATA ENTRY in the menu at the left of the page. This will take you back to the first page. The 3 X 3 matrix will contain the computed cell means that were used to create the figure. If you do not wish to use the,jpeg-based figure here, you can use these means to create a graph in Word or other program. Return to top 2. Categorical moderator example If one wishes to depict an interaction between a continuous variable and a two-level categorical variable (e.g., stress experienced by Chinese-American and European-American subjects as it affects depression), then one would choose the Categorical Data Entry option on the first menu. The menu is similar as for the continuous moderator, but there are a few differences. For example, one would type in the name of the group that receives a dummy code of 1, and similarly for the group that is determined to be the comparison group (it receives a value of 0). As with the continuous data menu, one is then required to enter Bs, means, and standard deviations where appropriate. One may wish to center the IV (which is continuous), but one would not center the ModV, of course, because it is a dummy-coded categorical variable. Several points need to be considered in this case as opposed to the continuous option. First, the categorical variable (whether gender, ethnic group, clinical diagnostic category, etc.) is considered to be the moderator. Second, the mean and standard deviation of the categorical variable are not requested because it is nonsensical in this case. However, you need to be clear as to the labeling of the comparison and the dummy-coded groups. Be sure that the coding of 0 and 1 are congruent with your dummy coded variable in the original dataset. In other words, you should have coded your categorical variable in your dataset as 0 or 1, and then multiplied it by the continuous IV. If you coded your categorical variable in your dataset as 1 and 2, then you have a problem. The ModV should be properly dummy-coded, i.e., at least one value should be zero. Third, your categorical ModV must have only two dummy-coded groups (e.g., depressed vs. personality disordered patients). The program, as currently constituted, cannot handle the three (or more) group case yet, but this improvement is countenanced and hopefully can be included in later versions. Return to top 3. Simple slope computations In interpreting the meaning of a figure, it is often important to know the values of the simple slopes, and to know whether these slopes differ significantly from zero. So, after the figure has been generated, go to the appropriate “Simple Slopes Computations” page (this can be found in the menu on the left hand side of the page). This page brings forward (in the upper right-hand corner) relevant information already entered in the data entry page, and asks for additional information to be supplied. After these critical items are entered, simply click on “CALCULATE” and simple slopes, standard errors, the degrees of freedom, t-values, and associated p-values are displayed. The uninitiated user may be puzzled by the task of providing these new items of information. Here are some helpful hints. First, when you run the regression analysis, request output to include the “covariance matrix”. Second, when this is obtained, search for the three critical items of statistical information. In statistical parlance, the matrix is an Sb matrix, where b refers to the number of coefficients examined in the regression model (3 in both the continuous and categorical examples). The matrix will look similar to this:

	b1	b2	b3
b1	-.467 (s11)	,076	,047 (s13)
b2	,076	-.022	-.001
b3	,047	-.001	-.891 (s33)

It is useful to know that “variance” refers to a cell in which the variable is arrayed against itself (e.g., b1 by b1), and “covariance” refers to a cell in which one variable is arrayed against another variable (e.g., b1 by b3). In this example, b1 refers to the regression coefficient for the main effect, b2 refers to the coefficient for the moderator, and b3 refers to the coefficient for the interaction term. s11 refers to the variance of the main effect (the number in the upper left hand corner, -.467); s33 refers to the variance of the interaction term (the number in the lower right hand corner, -.891); and s13 refers to the covariance between the main effect and the interaction term (the number in the upper right hand corner,,047). Thus, one should examine the variance-covariance table that is generated by your statistical program, and after making sure that the matrix is constructed in the way described above, select the three items and transfer them to the program. The number of subjects can be obtained by running a frequencies analysis on the data. A warning: be sure that your output conforms to this particular arrangement. SPSS will occasionally re-order the variables, particularly in complicated dummy-coded analyses. If it creates a differently ordered matrix, simply identify the two relevant variances and the one relevant covariance, and then input these into ModGraph. Another warning: SPSS and some other programs will output in the covariance matrix a value such as this:,000. The SPSS default seems to be three decimal points for variances and covariances, so very small numbers will be rounded up to,000. In actual fact,,000 is numerically the same as zero, and I have to warn you that entering,000 into ModGraph will cause a problem. The reason for this problem is that covariances cannot actually be a true zero value. So, if you enter,000 into ModGraph, it will treat this value as zero and will generate false outputs. Consequently, please do not enter,000 as a value. So what should one do? With SPSS, if you double-click on the matrix, or a value in the matrix, the program will open it up and give you the value to 12 or 15 decimal places. For example,,000 might become,000001756. In this case, just enter the value. Occasionally SPSS will give values in scientific notation, e.g., 1.7869E-6. When you obtain a value like this, you can convert it back to regular notation. The “E-6” tells you to move the decimal point to the left six places. Thus, 1.7869E-6 becomes,0000017869, and you should enter this value into ModGraph. Let’s assume that you do all of this correctly; then the outputted values at the bottom of the page will include the simple slopes and the associated t- and p-values. If the p-value is less than,05, then you can conclude that the slope of that particular line significantly varies from 0. Return to top 4) Display of Results In the continuous moderator example, the figure displays the IV main effect (i.e., stress) along the X-axis, and the moderating variable is depicted with three lines designated as high, medium, and low. The three levels of high, medium, and low (for both the continuous main effect as well as the continuous moderating variable) are computed using the mean as the medium value, one standard deviation above the mean as the high mean, and one standard deviation below the mean as the low mean (following Aiken & West, 1991). In the categorical data example, the figure displays the main effect (i.e., stress) along the X-axis, and the moderating variable is depicted with two lines designated as the two groups. The continuous data example should display symmetry among the three lines, but the categorical data example may not display symmetry between the two lines. Return to top 5) References For additional information and assistance, refer to these readings: Aiken, L.S., & West, S.G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage. Baron, R.M, & Kenny, D.A. (1986). The moderator – mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioural sciences (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum and Associates. Holmbeck, G.N. (1989). Masculinity, femininity, and multiple regression: Comment on Zeldow, Daugherty, and Clark’s “Masculinity, femininity, and psychosocial adjustment in medical students: A 2-year follow-up”. Journal of Personality Assessment, 53, 583-599. Holmbeck, G.N. (1997). Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures. Journal of Consulting and Clinical Psychology, 65, 599-610. Holmbeck, G.N. (2002). Post-hoc probing of significant moderational and mediational effects in studies of pediatric populations. Journal of Pediatric Psychology, 27, 87-96. Jose, P.E. (2013). Doing statistical mediation and moderation. New York: NY: Guilford Press. Return to top

Can gender be a moderating variable?

Abstract – Harter’s (1980) Intrinsic-Extrinsic Orientation scale was examined for evidence of empirical and construct validity. We hypothesized that subscales defining the motivational component of intrinsic motivation would be correlated with novelty, a collative motivational variable.

Partial support for the hypothesis was obtained for boys; correlations between novelty and Harter’s Curiosity subscale were,57,,64, and,58 for boys in the third, fifth, and combined grades, respectively, and correlations approached significance for Harter’s Challenge subscale. Not predicted were the correlations of,40,,68, and,46 obtained for girls in the third, fifth, and combined grades, respectively, between the Independent Judgment subscale (a cognitive-informational scale) and novelty.

Results indicated that gender operated as a moderator variable, with boys expressing collative motivation directly in an action-oriented form, and girls demonstrating it somewhat indirectly in a thought-oriented form.

What is an example of moderation?

What is an example of a moderating variable in a conceptual framework?

Conceptual framework: Moderating variables – Moderating variables influence the strength of the relationship between two variables in a conceptual framework. They are used to determine the external validity of the research conclusions based on their ability to strengthen, negate or otherwise affect the association between the independent vs.

Dependent variables.
Moderating variables are helpful in a conceptual framework because they illustrate the relationship between different variables in a research topic.
Example: Income levels can predict general happiness, although the relationship may be stronger for younger people than for older workers.

Age is the moderating variable in this conceptual framework.3 Moderators can be divided into categorical variables such as religion, blood group, or race and quantitative variables like height, age, and income. Example: In our study of drivers’ experience and accidents, we can introduce age as the moderating variable.

In this case, a driver’s age can influence the effect of years of experience on the number of accidents.
The researcher expects that ‘age’ moderates the effect of experience on road safety.
A conceptual framework also takes mediating variables into account.
They illustrate the impact of an independent variable on a dependent variable by showing how and why the effect occurs.

A variable is considered a mediator if:

It is caused by an independent variable. It affects the dependent variable. The statistical correlation between the dependent and the independent variable is more significant when it is considered than when it’s not.

Researchers use mediation analysis to test if a variable is a mediator using ANOVA and linear regression analysis. ANOVA (Analysis of Variance) tests the presence and strength of the statistical differences between the means calculated from several independent samples.

Example: ANOVA: Determines the effects of age, gender, and disposable income on average consumer spending per month.
Linear regression: Predicts the value of a dependent variable based on the value of the independent variable.
The main aims of linear regression in a conceptual framework are to test the effectiveness of a group of predictor values in predicting a result and identifying the significant predictors of the outcome.4 Example: An individual’s body weight has a linear relationship with their weight.

The researcher expects that as the height of the person increases, their weight increases. A set of observations can be plotted on a scatter plot to illustrate the strength of the correlation between the variables.

What are two examples of moderator in physics?

Hint: To continue the chain reaction in a nuclear reactor we have to control the speed of neutrons released from the fission reaction. Complete step by step answer: A moderator is a substance used to reduce the speed of neutrons. Therefore, it can sustain nuclear chain reaction in the reactor.

It can increase the probability of collision between neutrons and fuel rods.
Water, heavy water and graphite are the examples for moderators.
Therefore, the correct option is A.
Additional information: Moderators must have specifications for the purpose.
They are, Non-corrosiveness High melting point for solids and low melting point for liquids Chemical and radiation stability High thermal stability A nuclear reactor contains not only moderators but also fuel, control rods, coolant and containment.

Fuel: Uranium is the basic fuel used for the fission reaction. For this, pellets of uranium oxide are made into fuel rods and placed in the core of the reactor. Control rods: These are made up of neutron absorbing materials like cadmium or hafnium. These can control the rate of production of neutrons.

Thus, the chain reaction can be controlled.
Coolant: A fluid used to remove the excess heat from the system.
It helps to transfer the heat from the system to outside.
Containment: It is the structure to protect the system from outside intrusion and to protect outside from the effects of radiation.
Note: Absorption of neutrons are done by the control rods.

Moderators will try to reduce the speed of the neutrons. While neutrons get absorbed by control rods if the number of neutrons is more.

What is an example of a stress moderator variable?

Stress Moderator Variables Stress Moderator Variables are resources, skills, behaviors, and traits that can reduce the negative impacts of stress. For example, social support, an upbeat personality, meditation and regular physical exercise are examples of stress moderator variables that can help protect someone from the negative effects of a stressful life event such as loss of a job.

Sabrina Sarro