Questions related to Moderation
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!
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.
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,
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.
I am just wondering which theory best describes moderating role of price conciseness between purchase intentions and behavior?
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).
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;
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.
Here is the link to the survey : https://nottinghammy.asia.qualtrics.com/jfe/form/SV_5uUR1eV4gA4Rzeu
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!
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.
Could anyone tell me which factors can moderate relationship between any related variables to employee/staff and organizational agility? Thank you very much!
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!
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?
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?
H1 Technostress -> PWB 0.258 Not Significant
H2 Technostress -> PWB -> Retention 0.252 Not Significant
H3 Techno*Resilience -> PWB 2.837 Significant
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!
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]
I am just wondering which theory best describes moderating role of price conciseness between purchase intentions and behavior?
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.
I ran a moderated mediation model and obtained the attached results. Please can anyone help with the interpretation because the result somehow confuses me?
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.
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?
Can mediating or moderating variables become one of the explanations for why the r-squared result is low?
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.
Usually, mediators and moderators are tested in quantitative studies. However, can we test them in a qualitative study such as a case study?
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
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.
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!
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
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)?
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?
Apologises I'm really confused and don't know how to do, appreciate any guidance you can give
1. To explore if anxiety is predicted by stress and treatment delay and whether this is moderated by Brief Cope strategies (Brief COPE) .
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 ..
To confirm I have completed only parametric tests. I have 1 group completing all predictor /moderators variables.
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?
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.
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?
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!
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?
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).
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
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!
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 ?
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
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!
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!
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!
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!
Journals with a high/moderate impact factor are needed to incorporate EVs considering a publication and review time not to exceed 5-6 months.
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.
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.
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!!!
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!
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.
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!
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.
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?
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:
- How do you calculate a model with a 2x2-Between-Subject-Design in general?
- How do I incorporate moderators? As Interactions?
Thank you all very much!
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?
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?
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
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
Suppose Variable-A and Variable -B combinedly influence Variable-C. What will be the writing technique for the result?
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?
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 ?
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?
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?
In my research, I used 7-point Likert scale for measuring situation and agreement. They are used and coded respectively as below:
Far less = 1
Moderately less = 2
Slightly less = 3
Almost the same = 4
Slightly more = 5
Moderately more = 6
Far more = 7
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?
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)
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
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
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".
All the best!
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.
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?