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Moderation - Science topic

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Hello everyone,
I would like to do a multiple regression with 4 independent variables and 1 dependent variable. Also i have a dichotomous moderator "gender" which is split in female = 1 and male = 2.
How do i test the moderator with SPSS to see if it is linear?
I have already checked the assupmptions of the multiple linear regression with the dependent variables and independent variable using partial regression plots, But how can i check the dichotomous moderator if it is linear?
Thanks in advance!
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Again, there is no way that a dichotomous variable could have a non-linear relationship with another variable. Therefore, no need or possibility to check for non-linearity.
You can see this by plotting the dichotomous variable against the DV in a scatter plot. The dichotomous variable has only two possible values on the x axis. The OLS regression line will go through the group means. You could not fit anything other than a straight line through the two means.
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Is it possible to conduct a moderation analysis when all variables (X, Y and W) are categorical (dichotomous)? I believe I have identified a moderation effect by splitting the data file based on my moderator (W) and observing different effects of the predictor (X) on the outcome (Y) but when I explore the interaction in a PROCESS moderation model (Model 1), the interaction term is not significant. Am I missing something? All variables have been dummy coded.
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I recommend that you use the free statistical program JAMOVI which allows the inclusion of multiple moderators and mediators.
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Greetings. Can I use the following formula to categorize as low, moderate, and high- Class Interval=(Max-Minimum)/no of class So for my case, I need 3 categories, so for example, Max=5, Min=1,
interval=(5-1)/3=1.33.
so 1-2.33= low, 2.34-3.67=moderate, 3.68-5= high....... Can I categorize it this way? If so, provide a reference.
Or what should be the proper way to categorize factor scores? Please suggest me with reference. I would be obliged.
Thanks
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I would say the best approach is to not categorize because you loose information, and any cut off values are arbitrary. So unless you absolutely have to trichotomize your scores I would recommend keeping the scores continuous.
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I am just wondering which theory best describes moderating role of price conciseness between purchase intentions and behavior?
Arif
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Hello Arif
I have research about this issue. And I recognize that we should consider how much the consumer charge for a product. I research it in
psychological context. I think the most helpful approach is Anchoring Theory. Maybe my experimental research could help you about it:
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I'm seeking for water mazes with different task difficulties, or criteria to rank them in terms of cognitive complexity (e.g. easy, moderate, hard).
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Thanks Professor Joy 🙏
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Hello to all,
I do not know how to make mediation and moderation with covariates in SPSS with a categorical IV and DV (both have two categories 0 and 1). I am doing a between-subject design.
Thanks in advance for our help;
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2 category DV means.you need logistic regression not ols. Setting up moderation and mediation is in
the first screenshot. The second attachment covers logistic regression. Best wishes David Booth
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I would like to invite you to participate in dissertation study on the Work From Home (WFH): The Moderating Role of Working from Home  on Employee’s Work Life Balance, Job Satisfaction and Job Stress.The objective of the study is to examine impact of working from home on employee’s work life balance, job satisfaction and job stress. The second objective is to find out how does working from home moderate the relationship of work life balance, job satisfaction and job stress.
The survey should not take more than 15 mins.
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Mario Muilwijk Can help me complete the survey? Thank you!
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I tried to test a moderation effect with Mplus and found that x had a significantly positive effect on y when the moderator was high and x had a significantly negative effect on y when the moderator was low, but the p-value of the difference between the direct effects at the two levels was 1. Is it reasonable? How should this result be interpreted? Thank you very much!
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In general no, it is 0.99 but in some cases yes ! .regards
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In Hayes model 58, the moderation variable is input to both the path of 1.independent variable->mediation variable and 2.mediation variable->dependent variable path. At this time, when the moderation effect in the path of 1 is rejected and only the moderation effect in the path of 2 is adopted, how to interpret the moderated-mediation effect?
Even in the above case, the effect difference of -1SD, Mean, and +1SD is presented. At this time, if the bootstrapping LLCI and ULCI values of all effects do not include 0, can this result be interpreted as having a moderated-mediation effect?
We look forward to hearing from our seniors.
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Hello Park,
So, you are saying that:
1. The IV * moderator interaction does not explain a non-zero part of the relationship between the IV and mediator; and
2. The mediator * moderator interaction does explain a non-zero part of the relationship between the mediator and the DV.
If so, why worry so much about the label; just explain what you and your team have discovered and articulate what it means in light of relevant theory and related research.
For reporting purposes, have a look at this previous post concerning the same model and the practice of interpreting sub-hypotheses that are reported by the software when the overall hypothesis (of zero moderation) is not significant:
Good luck with your work.
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Could anyone tell me which factors can moderate relationship between any related variables to employee/staff and organizational agility? Thank you very much!
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If you want to promote analysis with multiple moderators I recommend the free software JAMOVI.
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MGA (multigroup analysis) in AMOS: when analysing the moderator effect of a categorical variable, "apparently" there is difference between the beta values, but there is no significant chi square difference. What can we do in this case?
We’ve been working on an expanded TPB model by using AMOS for the Structural Equation Modeling. CFA, modelfit were good, and now in the structural model (with also good modelfit and logical path effects) we would like to analyse the moderator effect of generations (X and Y) as a categorical grouping variable.
How is it possible that, despite the "apparently" big difference between the beta values, there is no significant chi square difference in the unconstrained and the constrained structural weights models?
Concrete example (there are more in the analysis):
Health Consciousness → Behavior (direct effect): Beta of Y generation=0.133***; Beta of X generation=0.048 (no significance)
According to the AMOS processing, there is no significant chi square difference in the unconstrained and the constrained structural weights models. In this case is there a moderation?
There was a similar question earlier in RG, to which Holger Steinmetz replied:
Rohra, Diksha. (2017). Re: How to test moderation effect in AMOS?. Retrieved from: https://www.researchgate.net/post/How-to-test-moderation-effect-in-AMOS/5a223e6cf7b67e7aa879f906/citation/download.
Steinmetz, Holger. (2017). Re: How to test moderation effect in AMOS?. Retrieved from: https://www.researchgate.net/post/How-to-test-moderation-effect-in-AMOS/5a240baddc332dfee50ddd24/citation/download.
In his replied Dr. Holger Steinmetz mentioned that they observed this in one of their previous study, but it is still not entirely clear what happens in such cases. How should this be interpreted?
We would also need references about this.
Can anyone help us?
Thank you in advance!
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Dear Christian and Holger!
I'm László, Éva's colleague (she's out of office now). We are very grateful for your detailed answers! Your responses are very useful for us. We are working on the solution and the right interpretation. Thanks again!
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Hi,
I have a within-between participants design. The mediator and DV are measured twice (pre-post manipulation) and the moderator is a personality variable. Does anyone have an example of a Mplus code for moderated-mediation analysis for such a design?
Guy
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In my framework I have introduced a variable called technostress where the direct and mediating relationship is non-significant, however, the moderation has significant relationships. I have compiled supporting facts to explain. However, I'm seeking your advice is this normal and possible?
Direct
H1 Technostress -> PWB 0.258 Not Significant
Mediation
H2 Technostress -> PWB -> Retention 0.252 Not Significant
Moderation
H3 Techno*Resilience -> PWB 2.837 Significant
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In the explanation, you did not elaborate. What I suggest you have to calculate
as a)Total effect [IV-DV]
b) path a [IV-mediator]
c) path b [Mediator-DV]
"H2 Technostress -> PWB -> Retention 0.252 Not Significant" this is indirect effect not a mediation effect. I have attached a picture of the table for your explanation.
Run the analysis again and refer the following paper
Qalati, S. A., Ostic, D., Shuibin, G., & Mingyue, F. (2022). A mediated–moderated model for social media adoption and small and medium-sized enterprise performance in emerging countries. Managerial and Decision Economics, 43(3), 846-861. doi:https://doi.org/10.1002/mde.3422
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I have a question regarding moderation effect.
I am testing a model with one one IV (A) and one DV (B) and I want to test the moderating effect of M on this path.
Is it necessary to investiagte the reliability and validity for cross construct(B*M) ?
or I only have to investigate reliability and validity for A construct and B construct ?
Help from one of the PLS-Experts in this forum would be highly appreciated!
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  • No there is no need to include moderation in both analysis just report the other variables [refer to this paper 10.1002/mde.3422]
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The result obtained was significant and negative. Can I state that the moderating variable weakens the relationship between the independent and dependent variables? [PLS-SEM]
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You have to plot the interaction effect first and then interpret for example moderation of [variable name] which demonstrates that the link between [variable a - variable b] is stronger when [variable] is low as compared to when it is high
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I am just wondering which theory best describes moderating role of price conciseness between purchase intentions and behavior?
Arif
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I will propose to you the theory of planned behavior and the Elaboration likelihood model
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I was wondering how I can see in my results if the moderator just weaken a positive relationship between A and B or if the moderator is responsible for that the positive relationship between a and b becomes negative.
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Monika Schmitz Moderating effects have the potential to modify the sign of a connection. It affects both the strength and the direction of the connection. It can crystallize the influence of the inserted variable and also indicate if it causes the DV to fall or grow.
A moderating variable, often known as a moderator variable or simply M, modifies the magnitude or direction of an impact between two variables, x and y. In other words, it influences the connection between an independent variable, also known as a predictor variable, and a dependent variable, also known as a criterion variable.
A moderator variable, abbreviated M, is a third variable that influences the strength of the association between a dependent and independent variable. A moderator is a third variable in correlation.
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I ran a moderated mediation model and obtained the attached results. Please can anyone help with the interpretation because the result somehow confuses me?
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Arti Aasha Thank you, but the video demonstrated a different model path.
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Dear researchers,
Some feed ingredients contain high energy value and others may contain moderately low energy content. In such conditions is it conceivable to make a substitution?  If possible How?  Weight by weight or calorie by calorie? I require your valuable comment.
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Exchanges should be made on the basis of digestible or metabolizable energy content of the feedstuffs. Digestion, especially in young animals, is of utmost importantie for the utilisation of nutrients by the animals. The same applies for proteins or amino acids.
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Dear group members and experts,
I am working on a mediated moderation with 3 different moderators probing 3 paths of the mediated model (attached the image)
I couldn't find this model in Hayes's process list of models. Can somebody help me how to perform moderator analysis in this case?
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Thank you very much for your answers. It worked well for me.
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Can mediating or moderating variables become one of the explanations for why the r-squared result is low?
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David Morse Hello, David. Thank you so much for your answer
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I am running a Partial Least Squadre Path Modelling in R and I have a Moderating effect (explained by one scalar variable) between my exogenous x and my endogenous y.
The only variable that moderates the effect ranges from 0 to 5.
The path coefficient between x and y is 0.37.
The moderating effect given by the interaction between x and the variable (using product indicator approach) is -0,1. What does it mean?
Thank's for your attention.
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In moderation analysis, the R2 change is important. As such, you will first look at the R2 change from the main effect model. The R2 change value indicates that with the addition of the interaction term , the R2 has changed
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Usually, mediators and moderators are tested in quantitative studies. However, can we test them in a qualitative study such as a case study?
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The basic answer is that if you test anything, it would not be a qualitative study, but a mixed methods study. Testings as it is understood in quantitative analyses does not exist in quantitative studies. Potential mediators and moderators could be examined using qualitative methods. I hope this is helpful!
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To calculate the interaction effect of two variables, the literature recommends following the procedure recommended by Aiken and West (1991) and Jaccard, Turrisi, & Wan (1990) which consists of variables are grand mean centered prior to calculating the interactions. What steps should be followed to calculate the grand mean centered variables?
#multiple linear regression #interactioneffect #moderation
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Miguel, so how should it be calculated when one variable is continuous and the other ordinal?
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Suppose there are 200 buildings in an area. We define the construction age of each of the buildings as old, moderate and new. If I pick any building from the 200 buildings, what is the probability that we have selected a old, moderate or new age building? In this case, age is a random variable and we want to know which type of probability distribution is suited for this case.
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Binomial distribution. Each outcome either "is" or "is not" a particular level. The respective probabilities are simply: nnew/200 , nmoderate/200, and nold/200
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İf you have any reference about how i can report model 16 in process macro. Especially the interaction of two moderators and interpreting their different levels.
Thank you in advance!
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I'm using SmartPLS for the analysis of my master thesis. I found the effect of variable X on Y nonsignificant and still examined the moderation effect of M on that effect. A professor of mine told that it's not reasonable to examine moderation while the effect is nonsignificant.
I wonder If the effect of X on Y is positive for some individuals while negative for others because of the variable M then wouldn't that effect can be insignificant yet still examining the moderation can be logical?
I hope I managed to tell my problem. Can you please tell me which of these approach is true with some references.
Thank you in advance
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You are absolutely right. There could be a significant interaction (moderator) effect despite the fact that the main effect is zero. For example, a drug might have a positive (beneficial) effect for older people but a negative (detrimental) effect in younger people. Then, positive and negative effects may cancel out so that the overall (main) effect of the factor "drug" is zero. Yet the moderation (interaction) effect drug*age would be non-zero and important to know about.
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International students' acculturative stress scale (ASSIS) consists of 36 items and 7 subclasses.
The focus of the research is to determine the prevalence among the sample population. The scoring of the scale ranges from 36 to 180 with a Likert scale of 1 to 5 for each question and it is observed that the higher the score higher is the stress and vice versa.
Since these questionnaires do not have a common breakpoint for low and high-stress levels, can we rank low, moderate, and high levels using an interquartile range (IQR)?
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Thank you for your kind suggestion.
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How do I analyze the relative importance of moderator in one regression ? Any statistical limitations?
e.g. W and Z are both the moderator of the X-Y relationship. Both W and Z are continuous variables.
1. Could I know W is relative importance(stronger) than Z on positively moderating X-Y relationship?
2. If yes, how to conduct in spss or other software? Is it still plausible when one of moderators held no moderating effect on X-Y effect?
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I think there is not just a statistical aspect to this question. How do you define "importance". For example, imagine the effects of W and Z would be identical, as well as the effects of W*X and Z*X. Clearly, no statistical decision could be made, since all (conditional) effects would be the same. But if W and Z interventions for example, W could be much easier and cheaper to implement than Z, therefore, ceteris paribus, W would be more important than Z.
Similar with your question, I think you should intepret how much W and Z change the relation of X-Y, how much is it possible to change W and Z itself and hence Y (for example by calculating how much change in Y would be possible at all, if you would take extreme values of W and Z, to get an impression) and then think about W and Z itself, how important these variables are for your research. Therefore, I think you cannot answer this question only in statistical terms.
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Apologises I'm really confused and don't know how to do, appreciate any guidance you can give
Aim
1. To explore if anxiety is predicted by stress and treatment delay and whether this is moderated by Brief Cope strategies (Brief COPE) .
Design
1 continuous outcome variable – anxiety (let’s call this H)
2 continuous predictor variables (let’s call these D, S)
3 Continous Moderator - (lets call these BC - ef, bc pf and bc avoidant). These are inputted into SPSS as 3 separate variables as the questionnaire b-cope does NOT allow you to create a total score (by adding ef + pf + avoidant).
- D – delay
- S – Stress (measured by pss-10)
- BC pf - Brief Cope - 1 (consists of 4 questions with each questions represent a different factor)
- BC ef - Brief Cope – 2 (consists of 9 questions with each questions represent a different factor)
- BC avoidant - Brief Cope – 3 (consists of 4 questions which each questions represent a different factor)
To answer the aim I know i need to complete a hierarchial multiple regression but I don't know what to enter on what model or whether I need to do separate regressions and again what should be entered with what.
Q1. Can you please advise how my regression models would look as I can't work this out given my predictors, moderators and outcome variable listed below.
E.g. Model 1 ...
Model 2 ...
Q2. Do I need to look at interactions? If so which ones, how would this be put into SPSS ie in which models.
Possible Interaction examples ?
Stress x bc ef
Stress x bc pf
Stress x avoidant
Delay x bc ef
Delay x bc pf
Delay x avoidant
Q3. Do I need to run separate hierachial multiple regressions? If so can you please write how the model would look ie. Model 1 ..
Model 2...
To confirm I have completed only parametric tests. I have 1 group completing all predictor /moderators variables.
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It sounds like you would end up with a regression model in which you have a very large number of predictor (independent) variables as well as many interaction terms (since you have so many different COPE variables). My advice would be to first think about meaningful ways to reduce the number of COPE variables to be included in the model, for example, by aggregation (calculation of a summary COPE score), factor analysis, or simply selection of the theoretically most meaningful COPE variables. Otherwise you might run into various problems when entering all individual COPE items into the regression (e.g., potential collinearity, too many individual significance tests, large model with many predictors, loss of power to detect interaction and other effects).
Other than that, you could run a hierarchical regression model with only the main effects (predictors, no interaction terms) in the first model, then add the interaction terms in Model 2 to see if they add anything to the prediction of the outcome. But, once again, I would try to reduce the overall number of predictors first to avoid problems in the analysis.
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My main model is a moderated mediation (model 7). If I analyze my variables within a simple mediation model (model 4) I have a significant a path. If I then include the moderator and analyze model 7 I have no significant interaction of my IV and W and no significant a path. Is this a suppression effect? How should I interpret this?
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Could it be simply redundancy or a power problem? How do the parameters change? You included two additional predictors (W and X*W), so two more variables that explain variance in M, so that the X-->M path is attenuated. Besides that, you no longer have 1 "a" path but you have as many as you have groups in W or you have to pick points of W (e.g. 16th, 50th and 84th percentile) to estimate the "a" paths. Is neither of them significant?
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Hello,
I am writing my thesis and struggling with the interpretation of my regressions. My question is, am i allowed to conclude there is a moderation effect in (3), even when regression (1) (and (2)) are not significant. I am doubting because in that case the model only becomes significant because of the added variable and interaction variable.
Thanks!
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My first answer still stands, more indeps do not change anything :)
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To find a potential moderator for my studies in the area of finance?
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Yahaya!Thank you Sir
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Our IV has 4 quadrants which all are not significantly correlated with the DV but the hypothesis include moderation, backed by literature.
Can we still proceed to moderation analysis?
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Maryam Hussain as Rainer Duesing suggests, there is no need for your variables to be > 0.05 or <0.05 to be included in your model. As was suggested, it is better to have a rationale/theory driven question based on which you collect samples which fit to the hypothesis. Why would you bias your study to only showing p<0.05? Wasn't this the hypothesis?
Additionally, path analysis are more suited when there is a specific rationale behind the question. One can always find "the best" model making some statement on the correlation >0.6 and <0.05. But without a rationale and added meaning it is simply a correlation.
It is perfectly fine to keep your study exploratory (hypothesis generating) and make this clear from the start. This next study (hypothesis addressing) might bring stronger and more convincing evidence to the table as you formed a clear rationale before and gathered new data not and addressed the hypothesis less biased by any selection procedure.
I hope this helps in clarifying your study aims
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Hi all!
I've been looking into the effects of pre-order promotion design on pre-order sales of videogames.
My IV"s are binary, 1 moderator (AAApublisher, referring to firm size) is binary, and my second moderator (Sentiment, low is negative, high is positive) is continuous. Sentiment has some missing values. My DV is number of pre-order sales, which I've had to log-transform due to its distribution.
I've attached my conceptual model.
When I make a simple linear regression model with only my IV's, the results suggest that they've got decent explanatory power of the DV, but when I add my moderators, these main effects disappear. As I understand, these moderators (and not my IV's) are actually the cause of the changes in my DV.
Looking forward to hearing your thoughts on these results. Thanks!
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Sorry- hard for me to help without understanding what you are researching.
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I wonder why some gender-related studies compared the mean difference, but others used moderation analysis? Does moderation analysis really provide more accurate findings? How moderation analysis provides more accurate findings than mean difference? What is the pro and con of these two analyses by comparison?
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Analysis of mean differences and moderator analysis are not the same thing. For example, in a 2 X 2 design with a disordinal interaction, the overall mean difference between two groups may be zero because the sign of the mean difference can be opposite across the levels of the second factor. Therefore, it is important to study both main effects and interactions (moderation) in designs with multiple independent variables.
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In psychological research, I am interested in testing the possible moderating function of some variables - including gender - on the relation between X and Y.
I have collected data from 100 heterosexual couples - same data for the men and for the women. The couples are also identifiable.
I am not interested in the dyadic aspect of the associations between the variables. Can I just check for moderation among my 200 respondents using hierarchal regression or Process ignoring the fact that there is indeed a relationship between the men and the women? Or must I take this fact into consideration in some manner?
If at all possible I would like not to complicate matters with the APIM methodology (or some other dyadic analysis) which I am not fluent with but to use the statistical tools that I am familiar with (ANOVA, regression, Process).
Thanks!
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You should not ignore the fact that there are dependencies arising from the dyadic nature of your design. Treating the couples as if they were independent observations would very likely bias your statistical inference (tests of significance etc.)
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I am currently running a moderated moderated mediation (PROCESS Model 21) and have come across puzzling output. The Index of Moderated Mediation and the Indices of Conditional Moderated Mediation by W are coming out as .000 (see below). Any help with the interpretation of this output is extremely appreciated.
Index of moderated moderated mediation
Index BootSE BootLLCI BootULCI
.000 .000 .000 .000
Indices of conditional moderated mediation by W
PASSFEAR Index BootSE BootLLCI BootULCI
-7.103 .000 .000 .000 .000
.000 .000 .000 .000 .000
7.103 .000 .000 .000 .001
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Hi there, thanks for your response! I learned that I was not using enough decimal places in my output; after adjusting to 8 decimal places, my data output was no longer coming out as '.000' (I can see now that my CI contains zero).
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Let's say I have a 3-way interaction and I perform a hierarchical regression, testing:
- Model 1 with only control variables.
- Model 2 including X and Y as well.
- Model 3 including the two moderators W and Z.
- Model 4 including the interaction effects (XW; XZ; WZ; and XWZ).
The relationship between X and Y is significant in model 2, but when I include the moderator variables (W and Z) it becomes non-significant, while the moderator Z and the interation effects XZ and XWZ are significant.
My question: Is there something wrong? What could be the theoretical and/or methodological explanation for that influence dissapearing?
The only idea that came to me is that the influence of the moderator variable Z is so strong that the direct effect of X on Y becomes non-significant. But I am afraid I might be doing something wrong and the model is not valid.
Thank you in advance!
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I love the idea of centering the variables Abdi-Basid Adan .
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What does the peer review research say for how risky it is for people with hypermobility syndrome to be pregnant? Are they considered moderate or high risk ?
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There can be risk of increased complications in pregnancy. Postural tachycardia/palpitations, fibromyalgia, and postpartum hemorrhage can be seen and multidisciplinary evaluation would be needed including cardiac monitoring.
Please see this link:
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Hi All
I recently done a Systematic review and checked the quality of methodology of the included studies (almost 30 in number) for the effect of intervention by Down's and Black quality assessment tool. However, there were not a single study with high quality.
My main questions are:
1. Is it okay to continue SR without any meta analysis as there are not any high quality studies seen through Down's and Black quality assessment tool? If so, is there any article for the justification, will be greatly appreciated.
2. Is it possible to do the meta analysis of only moderate quality studies rather than including all other low quality studies in the same Systematic review?
Thanks, and regards
Preet
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Meta-analysis is a mathematical method of pooling the results of several or more studies; a meta-analysis may be based on a systematic review, but this is not always the case. A systematic review is a multistage process aimed at the identification of all reliable evidence regarding a specific clinical problem. Systematic reviews make it possible to objectively address particular issues according to the current state of clinical knowledge and therefore constitute a reliable basis for clinical decision-making. An appropriate systematic review should include: 1) a defined clinical question, 2) pre-specified inclusion and exclusion criteria, 3) complex search for medical evidence sources according to a search strategy, 4) critical evaluation of reliability of identified clinical trials, 5) qualitative or quantitative data synthesis and 6) evidence based conclusions. These simple criteria, formulated by Cook et al. more than 10 years ago, allow to differentiate between a reliable systematic review and a "quasi-systematic" one, as well as between a reliable meta-analysis based on a systematic review and a potentially misleading meta-analysis without a systematic review.
Review articles, systematic reviews and meta-analyses: which can be trusted? - PubMed (nih.gov)
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Dear fellow researchers,
I wish to do a moderated serial mediation in PROCESS. Hayes book (version 2013) shows a short overview of different models. However, moderated serial mediation models are not in it.
In de attachement you can view moderated serial mediation model 85. But I wish to do another model instead (see other figure). The difference between these 2 models are the paths that are moderated.
Could anyone help me find which model this is in PROCESS? Thank you!
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Thank you both for your help! Using process is indeed a matter of convenience. It has been a while since I have used SEM analysis using Mplus. Maybe this could be an alternative indeed. First I will try to get a hold of the newest version of the book by Hayes. Thank you a lot! Kind regards.
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Hi,
First time writer of a paper here. Following my research I have found that my moderator has an insignificant effect on the main relationship between the dependent and independent variable. However, the moderator variable appeared to have a very strong direct effect on the dependent (outcome) variable. I am currently trying to explain this insignificant effect in the discussion but am not sure what the reason of this insignificance is.
My question is: Does the strong direct effect of the moderator on the dependent variable mean that it is impossible for the moderator variable to have a moderating effect on the main relationship?
Again I am very new to all this so my knowledge is quite limited. Thanks in advance!
Bart
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Hello Bart,
A variable proposed as a moderator may, of course, have a significant direct effect on a DV, regardless of whether it actually interacts to a significant degree with some chosen IV.
Why might your scenario occur?
1. There genuinely is little or no interaction of the IV and the proposed moderator in the population.
2. Your sample size was inadequate to detect the presence of an interaction of the IV and proposed moderator.
3. Your sample was unrepresentative of the population.
4. One or more of your measures was of inadequate technical quality for representing the intended target variable.
5. It's just a type II error (the lack of significant interaction, that is).
Good luck with your work.
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Hi all,
At the moment I'm writing my bachelorthesis, so my knowledge of statistics is quite limited.
In my first hypothesis I am looking for a relationship between an independent variable ADHD (yes/no) and a continuous dependent variable 'inhibitory control' with repeated measures (2 conditions of a task) by using a mixed ANOVA (so between and within subject).
In my second hypothesis I stated that this relationship is moderated by a dichotomous variable 'alcohol use' (high/low).
I'm having a hard time trying to figure out how to add a moderator to this mixed ANOVA analysis.
Help is greatly appreciated!
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There seems so much wrong in your question in the first place. For an ANOVA, your DEPENDENT variable needs to be metric/continous and your INDEPENDENT variable to be nominal. So, I guess you mixed this up? Or do you mean, that your first IV is "ADHD" (between subjects, 2 levels) and your second IV "Inhibitory Control" (within subjects/repeated measures, 2 levels)? Your DV is then however you operationalized it (e.g. reaction time or an inhibitory control questionnaire etc).
If my assumption is correct, you already have a 2-factorial split plot ANOVA (or sometimes "mixed ANOVA", although this may be confusing since there are also "mixed-effect models", which are something different) with one within subjects (IC) and one between subjects (ADHD) factor. If "alcohol use" shall moderate this, you need to add a third factor to the model, so you'll have a 3-factorial split plot ANOVA with one within and two between subjects factors. But your hypothesis is quite vague "In my second hypothesis I stated that this relationship is moderated by a dichotomous variable 'alcohol use' (high/low).", since the relationship between ADHD and IC is already qualified by an interaction and not a simple correlation. How should alcohol moderate this relationship? Which groups x conditions combinations should be different for participants with low vs. high alcohol use?
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Hello everyone,
I have a little problem regarding interpreting my results of pairwise comparisons (one-way ANOVA) for my masters thesis.
Situations is as follows:
I have the following three variables:
1. perceived social status (PSS, dependent variable)
2. ECO (independent and binary variable: ecological good (ECO_02) vs. non ecological good (ECO_01))
3. KS (collective guilt; moderator variable, also binary: low collective guilt (KS_M = 0) vs high collective guilt (KS_M = 1))
I have the following two hypothesis:
1. The consumption of ecological good increases the PSS of a person more than the consumption of non ecological good.
2. The influence of an ecological good on the PSS is amplified by collective guilt.
I want to test both hypothesis.
With which table (seen in both pictures) is a testing of the hypothesis better? Or is a reformulation of the hypothesis needed?
Thank you very much!
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David Morse , "and affirms that there is an interaction of Eco and KS.": no, it does not. If you want to know if the data support an interaction (difference in Eco-effects between levels of KS), then it should be tested.
There is an important issue with the analysis and of interactions, particularly regarding their interpretation (e.g.if there is functional synergism or antagonism). Here it makes a huge difference it the effects are considered additive or multiplicative (and what the true relation of the effects is).
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Journals with a high/moderate impact factor are needed to incorporate EVs considering a publication and review time not to exceed 5-6 months.
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Have you found a suitable journal yet? If not, here are some additional ideas:
International Journal of Energy Research
International Journal of Electrical Power & Energy Systems
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I am conducting research on determinants of entry mode. Where equity vs. non-equity is my DV and I have a few IV's; family ownership (dummy), international experience, market competition and dynamism. Furthermore do I have a moderating variable which is host-country network (dummy variable). Is the interpretation of the interaction effect of Family Ownership x Host-Country Network on Entry Mode different than for the other variables? I haven't found any literature on the interaction effect between 3 categorical variables.
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Two questions. Have you done an interaction plot? the second one is are you using logistic regression? Best wishes David Booth
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We ran a study using a process model (it's a serial-parallel mediation model; Hayes' model 81). See attached image.
A is a dichotomous variable and the effect of A on E is actually serially mediated by the paths B-C and B-D. (this was our finding in Study 1).
We were asked to test a moderation relationship in a follow-up study, employing a categorical moderator. In the model presented, the moderator will affect the relationship between A (again a categorical variable) and B (continuous variable). In this case, in the follow-up study, can the moderation relationship be tested using simply the interaction in an ANOVA (A is categorical, the moderator is categorical and the DV would be B, which is continuous), leaving aside the rest of the variables of the PROCESS model? Is there something wrong with this approach?
Or should we instead, in the follow-up study, run the whole model again and test the moderation using a more complex PROCESS model (this time with the moderator included)?
Thank you very much for your help.
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Hello Luis,
To the extent that adding a moderator to help explain more of the variance in variable B (by adding M and A*M as predictors of B), it is possible that you'd get different answers as to the mediation amounts for the C and D variables (e.g., coefficient products for b->c * c=>e and b->d * d->e) may well differ from what you found in the first study. For this reason, I'd include the full model (moderation and mediation portions).
Good luck with your work.
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Hi community!
Currently finalising my Master thesis research. I manipulated my independent variable to have three conditions. Originally, I proposed a 3(3 conditions of independent v)x2 (dichotomous moderator)factorial design. However, the moderator records the environmental consciousness of participants and is actually continuous. I can split it up into high and low to turn it into the proposed factorial design but I do not know if I should.
I ran the analysis with the continuous moderator in SPSS with PROCESS and got significant (p<0.001) results. But I do not know what I would call my research design when I use the moderator as a continuous variable? Is it one factorial?
If I turn it into a dichotomous variable, is it then preferred to use a Manova instead of the PROCESS macro? Or both? And what is the argumentation behind either one or both?
Last question: I hypothesized the moderation of two variables. In case they are both dichotomous is it a 3x2x2 design or is it TWO 3x2 designs?
Thanks for your help!!!
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PROCESS has always allowed dichotomous independent variables and moderators. Because the mathematics is the same for dichotomous X, W, and/or Z as it is for continuous variables, just put your dichotomous variable(s) in the PROCESS command or menu for X, W, and/or Z. You should NOT use the multi categorical option with a dichotomous independent variable or moderators. A multi categorical variable has three or more categories. In earlier releases of version 3, using the multi categorical option with a dichotomous independent variable or moderator could, in some circumstances, produce incorrect output. As of version 3.3, PROCESS won't let you specify X, W, or Z as multi categorical when dichotomous.
Note that you should never put a multi categorical variable in as X, W, or Z without telling PROCESS that the variable is multi categorical using the multi categorical option. Doing so will generally produce nonsense output.
Moreover, check the template file for a suitable model type for your model. I do not see any model with 1 IV, 1 MOD, and 2 DVs. However, according to my understanding, you can use Model no. 1 twice for a different DV each time.
sa=i&url=https%3A%2F%2Fwww.sheffield.ac.uk%2Fpolopoly_fs%2F1.885172!%2Ffile%2F90_Moderation_Meditation.pdf&psig=AOvVaw0GfUn96lVPQF1oV2ByoKDz&ust=1653827367907000&source=images&cd=vfe&ved=0CAwQjRxqFwoTCJicr5uZgvgCFQAAAAAdAAAAABAT
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I have conducted a moderation analysis with two independent moderators. (Using Hayes Process model 2). I have attached the output for your reference.
I am looking for the impact that financial wellbeing and gender has the relationship between marital status and loneliness.
My variables are:
Y: Loneliness (continuous variable - low values indicating low loneliness)
X: Marital status (single or married- dummy coded to have single as the reference category)
W (mod1): Financial wellbeing (continuous- low values indicating low financial wellbeing)
Z (mod2): Gender (male or female- dummy coded to have male as the reference category)
As you will see, all the p-values show significance
I have to questions:
1. Is this selection of variables appropriate for the moderation analysis I am undertaking?
2. Can someone please help me interpret this output? When interpreting the conditional effect I am seeing all the values under 'effect' are negative but I am not sure how to interpret this in relation to the predictor (being single coded as 0).
Any guidance would be greatly appreciated!
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A moderator analysis is used to determine whether the relationship between two variables depends on (is moderated by) the value of a third variable. This relationship is commonly between:
1. A continuous dependent variable and continuous independent variable, which is modified by a dichotomous moderator variable;
2. A continuous dependent variable and continuous independent variable, which is modified by a polychromous moderator variable; or
3. A continuous dependent variable and continuous independent variable, which is modified by a continuous moderator variable. In this guide,
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I'm using SmartPLS to investigate a model. My question is if the moderator variable (Z) can be also used as predictor of variables (X1, X2) which their affects on another variable (Y) is moderated by Z? Or will it cause certain problems?
I'm adding a figure to be clearer.
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Yes
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How do I check the Endogenity in AMOS if i have one IV (Knowledge sharing) and one DV (Performance). And how do I check the robustness if I have Knowledge sharing as IV and Performance as DV and Gender as a moderator.
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there are various methods to do this in AMOS or SPSS
check this link:
Also this video explains the concept very well which might be helpful for you:
test whether coefficient on your regression is significant. If it is, conclude that X and error term are indeed correlated; there is endogeneity.
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I ran Process Model 59 with three parallel mediators for my Master Thesis. So I have read that in model 59, the indirect effect is a nonlinear function of the moderator, so no index of moderated mediation is provided.
What conclusions can I then draw from the results? Can anyone help me with the intepretation of the output? How do I know whether my moderated mediation model is signficant then?
Thank you for your help!
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did you find any material for your question? I am also looking and haven't found it yet. Thank you.
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I want to use a scale for my study but they have not defined a cut off in terms of ranges determining high, moderate or low. In such cases, how can one define the cut off for the sample they are taking, especially if the scale is new and not used that frequently? In studies it has been used, the cut off is not described in them either.
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Hello Neha,
1. Define the terms normatively. One common set of thresholds would be something like this: high = 1 SD or more above mean for your sample; moderate = within the range of mean up to (but not including) +/- 1SD from mean; and low = 1 SD or more below the mean.
2. Arbitrarily select thresholds to divide the batch into equal portions (like quartiles, quintiles, deciles, etc.).
3. Choose values that link to some established threshold, if these exist. For example, if income was the variable of interest, does it fall above or below what the government would classify as the "poverty line?"
4. Consult with domain experts and select consensus recommendations for what might comprise meaningful threshold(s) within the context of your research question(s).
Good luck with your work.
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Hi! I am doing a moderation analysis using model 1 in PROCESS by Hayes. I have a binary IV, and the output tells me my moderator is not significant. however, I would like to check if it is significant or not for each conditions of my IV.. But the output is not generating the conditional effects, I have tried everything and nothing works. What should I do?
thank you!
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Hello Francisca,
If the moderation (e.g., IV, "X," by W interaction, where W is the purported moderator) is nonsigificant, then that implies there are no simple/conditional effects that differ from one another.
If you wish to verify this, you could run the simple effects tests (IV differences at a specific level of W; and, for that matter, W differences at specific levels of the IV). These are the conditional tests.
In SPSS, the syntax to elicit simple effects tests for a two-way design would look like this:
unianova Y by X W
/ method = sstype(3)
/ intercept = include
/ emmeans = tables(X*W) compare (X) adj(bonferroni)
/ emmeans = tables(X*W) compare (W) adj(bonferroni)
/ print = descriptive
/ criteria = alpha(.05)
/ design = X W X*W .
Where: Y = DV; X = IV; W = moderator.
The two lines starting with "emmeans" are the commands for the simple effects/conditional tests.
Good luck with your work.
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Hello Community,
unfortunately, I have experienced trouble with estimating my model in IBM SPSS AMOS for my master thesis. My research design is a 2x2-Between-Subject-Design. As you can see in my model, the variables LUX and OKO are dummy variables resembling all for conditions. On top of that, I have two variables KS and PS moderating the effects of both dummies.
All rectangle variables are observed variables and all eclipse variables are latent.
It would be a tremendous help if someone can answer me the following questions:
  1. How do you calculate a model with a 2x2-Between-Subject-Design in general?
  2. How do I incorporate moderators? As Interactions?
Thank you all very much!
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You can estimate this model within the framework of path analysis / structural equation modeling. The moderated effects can be represented and tested by including interaction (product) terms as in moderated regression analysis (e.g., computing and including a product variable LUX*OKU to represent the potential interaction between the two conditions).
The interactions between the binary condition variables (LUX, OKU) and latent variables (e.g., KS*LUX) are more difficult to model if the latent variables are represented with multiple indicators (i.e., if there is a measurement model with multiple observed variables for each latent factor). In that situation, latent moderated structural equation modeling (LMS; Klein & Moosbrugger, 2000) or the so-called product-indicator approach may be used to model the latent-by-observed interactions (both would be complicated in your case though given the presence of multiple interaction effects in the model). To my knowledge, LMS is not currently available in AMOS (it is available in Mplus), but you could potentially use the product-indicator approach. See, e.g.,
Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65(4), 457–474. https://doi.org/10.1007/BF02296338
An easier way would be to represent the KS, PS, ZSM, and BSS variables by observed (sum or mean) scores (rather than by multiple indicators). In that case, you would not have to deal with the complications of modeling latent interaction effects. When all variables are observed variables in your data set, you can simply compute and include manifest product terms to model the interaction/moderator effects.
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My research question has its independent variable as WC and dependent variable as AQ. Now I am adding a interaction by introducing my moderator BF. The interaction term is WC*BF. However, I did a chi square test for WC and BF to see if there are independent, and the results showed they are signidicantly correlated with each other.
What should I do in this situation?
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Hello Yani,
The usual concerns in such settings surround: (a) confounding, especially in studies in which no direct manipulation of variables has taken place; and (b) biased estimators of regression coefficients (and artificially large standard errors). So-called instrumental variables might be helpful, but may not be identifiable or accessible for a given scenario. Larger N can help with the upward biased SE values, of course.
From a practical perspective:
Other than nonsensical textbook examples (e.g., foot size and intellectual functioning), it's difficult to find variables in real life that are completely uncorrelated. So, you have an IV and a moderator which are correlated. That does not prevent you from investigating whether the moderator value affects the IV-DV relationship in a meaningful way. Do be aware that, if confounding (with another or other variables) is the reason for the IV-M relationship, you should avoid, or at the very least, qualify any inferences about "cause."
Good luck with your work.
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I was tasked to use SPSS process for my thesis and check if participant generation (Gen X, Millenial, and Gen Z) has a moderating effect on the regression between the IV and DV. How could this work? Every video I’ve seen so far seems to use just dichotomous categorical variables such as gender. Should I dummy code each generation as 1,0 like other videos suggested or is there another way to use a non-dichotomous variable?
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I agree with both answers by Christian Geiser and Bruce Weaver , but if you really need/want to use PROCESS (which is not necessary as explained), there is a button "multicategorical" since at least version 3.5 (I did not check for even older versions), where you can tell PROCESS which variables (including moderators) are multicategorical and also which coding scheme should be used (e.g. indicator, effect...).
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Hi, I wanted to start a brainstorming session to develop a greenhouse design using easy to acquire material and the following goals
  • Use a temperature moderating climate battery design
  • Goal for the design to stand alone and operate off grid
  • Complete hydroponic growth process, use of only inert strata for any root support
  • Internal plant scaffolding design to maximize use of space and increase crop yield
  • Organic pest control. Develop a self-sustained entomological environment within the greenhouse both for pollination and pest control
I hope many of you will find this project interesting, feel free to share any ideas of prototypes that you feel is useful to achieve these goals.
I will help moderate this discussion, anyone else with subject matter experience can also help moderate the discussion
I look forward to hearing from everyone on this topic. Thank You
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So, the goal is to collect the best available ideas and/or practical experience out there to develop a best practice design that I intend to then build and study.
I found an online tool at Climate Battery Calculator - eco systems design, inc. (ecosystems-design.com) that I used
For a 100 x 50 ft x 10 ft high greenhouse with a 5 ft tall roof structure it gives us a total vol of roughly 53000 cuft.
Now the next question for me is how often does this air need to be recycled and that may give us an idea of the diameter of the piping and the power of the fans needed.
Next I am curious to know how deep the piping should be and how many linear feet of underground piping will be needed to maintain a temp differential of up to 25 deg F between the inside and the outside of the greenhouse both at the high and low end.
Your point about the need to anticipate practical problems is very valid, I would love to hear from anyone who has experience building one of these.
This should help us make a rough estimate of the possible cost to build the structure int he substrata.
This is a windy post but hopefully we can build on this to get into the details of building the climate battery
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I do have exogenous variables, control variables, and moderating variables in my study.
My question is: Shall I run the algorithm and include the control variables as well as the interaction effect of the moderation? Or the algorithm can only be run for the exogenous and moderating variables, and I should exclude the control and interaction effect?
Thanks in advance
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Suppose Variable-A and Variable -B combinedly influence Variable-C. What will be the writing technique for the result?
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If both the independent and moderating variable are interval, then describing the nature of their interaction can be complex. Here is a well-known expert's discussion of this kind of interaction:
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Hi!
I need some input regarding survey questions for moderated regression analysis for my
My subject is how influencermarketing(moderator) affects the causal relation between Electronic-Word(independent variable) and brand loyalty(bl)(dependent variable?)
Here is my first draft for questions:
For me to spread positive E-wom, It is important for the clothing brand to:
Pick an influencer with a trustworthy and credible message(drivers of bl)
Pick an influencer that you can relate to in terms of clothing preferences
Pick an influencer that regularly offers new exciting content(sticky environment)
Offer a beneficial influencer campaign/discount code
Pick an influencer that provides valuable, informative information about the brand
Promote an influencer that offers an entertaining experience
In general, Influencer marketing increases my positive associations with the brand
But I dont know how to categorize the variables for the analysis? Or do i need to redo all of my survey?
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Hello Beata,
before going to data, you have to come up with a theoretical model that optimally focuses on unitary/one-dimensional variables (IV, DV and moderator). "Optimally" means: of course you can focus on aggregate constructs or measured indexes that summarise a bunch of things but that makes a theoretical argumentation about of the effects (including moderation) a bit blurry.
But even if you have to stick to aggregate constructs (which is often the case in marketing) as these are often inherently aggregate, you have to base your decision on the construct itself and its essential facets. THEN you can measure these facets. Don't start with measures.
HTH
Holger
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I have a moderated mediation model with Joy as independent variable, Gender as moderator and Stress as mediator. So before I made the moderated mediation model analysis, I did a correlation analysis first and found that Gender is significantly related to Stress, and also Joy is related to Stress. Then I used the Model 7 in PROCESS v.4.0 doing the analysis. The result is that firstly, the relationship between Joy and Stress disappear, but at the same time Gender is not a moderator. Secondly, the relationship between Gender and Stress also disappear.
I am so confused with these changes. What can cause these differences ?
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Give more information and show the results? What sample size?
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I am conducting a moderated mediation analysis using Hayes' Process Macro Model 8. Although my mediation is significant, one of the interaction effects is not significant (indirect effect). I am having an insignificant index of moderated mediation also but at the same time, I am having significant conditional direct as well as an indirect effect of X on Y at all the three levels of the moderator. Where does this significant conditional direct and indirect effect come from?
How do I interpret the result?
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If all indirect effect, at all chosen values of your moderator, are "significant" and nearly the same, it just means that the moderator does not change the indirect effects enough to be significant. Therefore, you have no evidence of a moderating effect and hence the index of moderated mediation is not significant. But this has nothing to do with the indirect effect itself. Apparently, you have evidence for an indirect effect, but this just does not change with different values of your moderator. Nothing more, nothing less.
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Hello,
if you have two moderators in your design [1 IV and 1 DV], one interaction was significant and the other one was not. Is it necessary to interpret why this variable did not play a moderating role? despite the fact that the main effects were significant?
Thank you
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But keep in mind that absence of evidence is not evidence for absence! You may not interpret the non-significant result as evidence that there is no effect. This may not be concluded from significance testing. If you expected an effect, you should have an a priori idea how large the smalles effect of practical relevance should be. You can compare the empirical results, the effect size and confidence intervals, with your a priori idea. If they are the same (or even larger) then you have a power problem, although the effect was in the anticipated range, it did not turn our significant. If it is much smaller and if the empirical effect size is not compatible with your implied effect (by inspecting the confidence intervals) it may be just the case that the true effect is smaller than expected.
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In my research, I used 7-point Likert scale for measuring situation and agreement. They are used and coded respectively as below:
Situation:
Far less = 1
Moderately less = 2
Slightly less = 3
Almost the same = 4
Slightly more = 5
Moderately more = 6
Far more = 7
Agreement
Strongly disagree = 1
Disagree = 2
Slightly disagree = 3
Neutral = 4
Slightly agree = 5
Agree = 6
Strongly agree = 7
My problem is the questionnaires were distributed to two opposite groups (i.e. claimant and defendant).
When measuring the situation, I asked the respondents about a comparative degree that most accurately describes their positions.
For example, I asked the claimant if they had less/ almost the same/ more resource than the defendant and vice versa. If the claimant chooses less resource, which is equivalent to the defendant choosing more resource. Therefore, I tried to code the data as follows:
Far less/ more = extremely asymmetric= 1
Moderately less/ more = moderately asymmetric = 3
Slightly less/ more = slightly asymmetric = 5
Almost the same = symmetrical= 7
May I ask if it is appropriate for me to convert and interpret the data like the above? Are there other ways that can help me to better analyse the data?
Thanks
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Nan Cao Non-parametric tests for independence, such as Spearman's correlation or the chi-square test, should be used for ordinal data (individual Likert-scale items). Use parametric tests such as Pearson's r correlation or t-tests for interval data (overall Likert scale scores).
A Likert scale is made up of four or more Likert-type items that reflect related questions and are combined to form a single composite score/variable. Likert scale data may be examined as interval data, which means that the mean is the most accurate indicator of central tendency. To explain the scale, utilize means and standard deviations.
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Hello,
How to interpret a positive indirect effect, in a moderation analysis when path a and path b are negative (and significant)?. Also, the total and direct effects are positive.
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Good to follow, thank you, professors.
All the best,
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Good day,
For my bachelor degree project I have to do a 2 moderator design. I've already done the calculation and reported the results, however, I have yet to come up with an withstanding conclusion to the outputs of the statistical calculations.
There are 2 moderators influencing a a simple regression (basically the macro model 2 of PROCESS).
I have prepared a diagram with the names of the variables and the relationships. Note that the pseudonyms in brackets are meant for the shortened version of the names used in SPSS.
Basically, I used Haye's PROCESS 4.0 and SPSS to do the statistical work. I will post a pastebin link to the full outputs of the analyses. However, in the meantime, here is a short summary of my findings:
I did 3 separate tests. One with the full model, and 2 others where I "discarded" one of the two moderators to see the changes.
  • First test - Full model (2 moderators and a predictor on a criterion)
Nvr Hstr
Adzv------------------>Agr
17% of variance explained
Adzv, Nvr, (adzv x nvr) not significant
Hstr and Hstr x adzv significant ----> 1/2 interaction variables significant
  • Second test - Hstr is omitted
Nvr
Adzv------------------>Agr
16.45% of variance explained
Nvr and (adzv x nvr) not significant ----> 0/1 interaction variable significant
Though Adzv becomes significant in this scenario
  • Third test - Nvr is omitted
Hstr
Adzv------------------>Agr
11.97% of variance explained
Adzv not significant, but Hstr x Adzv is -----> 1/1 interaction variable significant
Now, Adzv and Agr have a .335 correlation (effect size 11%)
And Hstr and Adzv have a .237 correlation (effect size 5%)
Basically what I would like to ask is how to report the first test (is it statistically viable, having 1/2 interaction variables significant? Is it only partially viable?) and how to take in these changes that happen when one or the other moderators is omitted? From my very limited knowledge in statistics, I suspect there may be a suppressor variable at work here. Namely, Nvr might just not be a significant moderator, while Hstr might be suppressing Adzv, while also being a significant moderator for the model.
Thanks a lot in advance for all the good help! And I'm sorry for the shortened names of the variables, though they may not make sense in english, they are the shortened version of the romanian words used to describe them, hence why there may be confusion for "Adzv".
pastebin:
All the best!
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Hello Sabin,
The full model shows support for moderation due to the Hstr variable, but not for the Nvr variable. The two single-moderator models essentially show the same thing: Hstr (Histrionism) singly or jointly shows moderation, whereas Nvr Neuroticism) does not.
The fact that Adzv does (when combined with Nvr only) or doesn't show significance (when combined with Hstr) as an IV suggests that Hstr shares variance with Adzv.
The difference in R-squared (between Adzv & Hstr model vs. full model) would likely be statistically significant, given your sample size, suggesting that Nvr (and the second interaction term) does bring something to the table. As you propose, this could well be an example of a suppressor effect.
Good luck with your work.
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Let us suppose that we have an intervention. Can we study what mediators could affect the results of the intervention? Can we study what moderators could affect the results of the intervention? And why.
I had a discussion with colleagues about this issue, and when we did not agree I decided that other views could be helpful.
Thanks for your contribution.
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See my letter "Two Aha Moments, The Scientific Method and Repair of the World." At least there is mounting evidence that avoiding physical and psychological abuse pf children, authoritative parenting and promoting moral learning can result in peaceful societies and societal relations.
Best Regards,
Jerry
Gerald Katzman
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Ran into a ditch with my statistical package and got them uninstalled;
where can I get AMOS or Hayes Process Macro?
Can anyone link to any free software for moderation analysis?
Thank you.
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You may use PLS-SEM for mediation analysis. Kindly visit the link.