I am glad that Dr. Grande distinguished between moderator and mediator variables. The moderator variable represents strength and direction between the IV and DV. The mediator variable represents the relationship between the IV and DV.
I was having trouble trying to distinguish the difference between moderator and mediator variables. I performed a search and this video came up. This video solidified the understanding of these terms. Thank you!
Dr. Grande- When listening to Moderating and Mediator Variables, I thoroughly liked your statement, "How one variable can predict another variable." I liked how this video talked about different treatment modalities and how they would be researched. Many of these terms can be quite confusing, and to me seem overlapped. But, you are able to give a clear understanding on how these methods work and occur. When using Moderator and Mediator with IV, DV and how they are interchangeable or correlated provided me with information that I needed. Thank you again, for your thorough examples.
More variables! A moderator is the strength of strong or weak causality between variables, as the mediator is the relationship of why and how the two are related. I think the example diagrams were a great way to show the differences.
Thank you for the video Dr. Grande. I appreciate the explanation of what the moderator and mediator are and how they relate to the independent and dependent variables. I also appreciate learning the differences in the mediator and the compound variable, and having the visuals to get a better understanding of those terms through examples.
The examples provided about mediator variables and confound variables was extremely helpful to me. I haven't heard of these concepts in quite some time and these examples helped me to understand their differences. The examples made these concepts clear so I can understand what they mean when they appear in research.
I don't think I have ever heard of moderator, mediator, or confounding variables before. I was surprised that I was able to understand the basics after watching this short video. I think the charts and examples definitely helped me to take mental notes that I will remember without always looking at notes.
These terms were all new to me. I appreciate the examples however will need to review more for a complete understanding in order to proficiently identify these types of variables, especially distinguish between moderator and cofound variables so not to confuse one for the other.
I wasn't aware of these variables or their impact on the independent and dependent variables. The moderator would be responsible for change in strength or direction of the relationship but not explaining the relationship between the IV and DV. The mediator variable explains the relationship. The examples were helpful. I found the moderator explanation more helpful than the mediator explanation.
Dr. Grande, this video helped me understand my own research topic a bit better as it relates to independent variables, dependent variables, moderator, and mediator variables, and confound variables. Prior to this video, I was unaware what moderator, mediator, and confound variables were.
This video helped to break down the differences between moderator/mediator/confounding variables. The examples given made the differences clear. Very informative explanations of the different variables and causal relationships.
I was already familiar with independent and dependent variables, as I'm sure most people with an elementary knowledge of research are. However, I was not familiar with moderator and mediator variables. I now know that the moderator measures the strength whereas the mediator represents the relationship between the two variables. At first this information was overwhelming, but the examples provided helped me to better understand and differentiate between the two.
I was not aware of the various types of variables. The examples shown in this video clarified the moderator, mediator and confounding variables visually which helps me to retain the information. Also, it made it easy to take precise notes.
I must say that I never really was exposed to moderator variables. the examples shown were very clear and helped me understand these variables and what they do to the independent and dependent variables. Very good video.
I appreciate the distinction that independent variable and predictor variable, as well as the dependent variable and outcome variable are interchangeable when talking about a linear regression. I remember reading these in undergrad textbooks and wondering if they were similar. Clarification is great. The three examples of moderator variables concretely explained how a relationship could decrease, increase or change direction. I found this very helpful.
I had no idea what moderator and mediator variables were before watching this video. I liked how Dr. Grande took his time to explain it all. The examples really helped me understand this topic. It is hard for b to understand something without trying to apply it.
Dr. Grande's example about the moderating variables and treatment attendance rate served as a great example on how these variables directly affect how certain variables can weaken the relationship, strengthen it, and finally have the possibility to change the actual direction of the IV and DV interaction. I think the linear regression model served as a good way to explain the concept between moderators and mediators without making it overly confusing or complicated. The mediator variable reminded me of an old professor always saying 'correlation does not indicate causation'. It could simply be an extraneous variable not being considered in the study that could increase the strength of the IV on the DV.
Jessica Muzzin I also agree that the examples that Dr. Grande used to explain the relationships between mediator and moderator and how the related to the IV and DV were beneficial. They helped me visualize the concepts and also reminded me that correlation does not mean causation. It was interesting for me to see how these other variables could and are indicated in research studies. I am still a bit confused about confounding variables, I think it is due to the fact that they are so similar to mediator variables.
I remember being given an example in undergrad about correlation not equaling causation: as the number of ice cream sales increases, the number of drownings also increases. Obviously, the first does not cause the second, but instead both are affected by the third variable of the temperature rising. I realize now that I was being taught the concept of confound variables without actually knowing the term, which made Dr. Grande's explanation immediately make sense.
These are terms I have not previously encountered. Everything is interrelated and in research we must break everything down to isolate variables so that we can so causality. It is not an easy task! The examples here helped to see different relationships between different variables.
Thank you for the examples they help bring a better understanding for me. Mediator variables explain the relationship between I.V and D.V, whereas moderator variables show strength and direction but not cause. and confound variables is not caused by the I.V. Hopefully, more exposure to these concepts will help to solidify my understanding.
prior to watching this video, i did not have much experience doing research that discussed moderator variables. However, in some articles i have read, I noticed some discussion on potential mediator variables that could contribute to independent variables. At the time, i did not know there was a proper name for these contributing factors. This video was helpful with getting me to understand things i did not know about moderator variables as well.
I liked how the variables were explained. Moderator variables effect other variables as explained. Such as miles from an agency the treatment attendance rate tends to decrease. Then the mediator variable is the relationship between two variables. This was all explained in a way that was easy to grasp.
I was really happy to hear the examples of the moderator and confound variables! They made the terms much easier to understand! It was interesting to see, in the example for moderator variables, the different variables of the miles from the agency, the family support an the multiple jobs and how it affected the sessions by decreasing, increasing or changing the direction of attending those sessions. Seeing the example made it more understandable and less stressful and intimidating to think about!
This video was very informing on the different factors that play a role in independent and dependent variables. We never really touched upon the mediator and moderator in my undergraduate program. I never knew that the moderator variable relates to the strength of the dependent and independent relationships. Also, the mediator, which explains why, or how the relationship exists. The confounding variable relates to something that will have a negative effect on something else. The example given, that summer effects the number of studying hours has a direct impact on the studying. Not the other way around. This is what makes it a confound variable. Great video!
Agreed! In my undergraduate course we did not really touch on mediator and moderator variables. I found this to be very interesting to learn! I also loved his explanation of the difference between the mediator variables and confounding variables as I can see how they could get easily confused. I thought his explaintion made the differences clear.
I remember struggling to understand the difference between moderator and mediator variables in undergrad. I had multiple professors explain it to me and didn't understand the differences within each type of variable as well as the difference between the two. This video explained thoroughly these differences, and I now understand how moderator variable affect the strength of the relationship and mediator variables are a third variable to help to explain the original relationship. These two types of variables are very similar so I am glad for this extra understanding.
I don't actually remember my undergrad explaining mediator and moderator variables, but that's fine because this was a thorough, easy to understand primer of the concept.
Dr. Grande!! I am a subscriber who enjoys your analysis of different behaviors. I am currently in a research class and my professor posted your video for the purpose of learning about moderators. I am so excited to see my two worlds collide! I had no idea your older videos have this kind of content. So happy I found you on YT two years ago!
I like the examples used to explain both the mediating & moderator variables, as well as the distinction made between confounding & mediator variables. This video made them easy to understand, along with their relationships (or lack of) to the IV & DV.
This video was really my first exposure to these variables. My other research classes didn't discuss these at all so I really found this video to be informative and it definitely helped me understand the relationship between the variables and the impact they have on research.
Kimberly Dixon A lot of undergrad research classes neglect this subject, apparently. I know I'd read about the concepts before, but until this video I was shaky on their meanings.
Being able to visualize the examples is tremendously helpful. For example, when Dr. Grande defined confound variables explaining no relationship between the IV and the confounding variable, I was a little confused when I first saw Summer and Number of Vacation Hours as examples. After he explained that the # of vacation hours does not CAUSE summer (rather, the other way around), this is the determining factor between mediator and confound variables. Picturing the arrows will be helpful when I'm trying to recall the difference.
Dr. Grande, yes, we always want to know causality. IV predictor variable, DV outcome variable. Is a great way to remember these terms when conducting research.
This video served as a great resource in distinguishing between moderator and mediator variables. Prior to watching the video I was completely unaware that such variables existed. Causality is extremely important when noting cause and effect in an experiment. Moderator variables highlight the change or direction strength. The correlation between treatment attendance rate and socioeconomic status was enlightening; higher SES higher attendance rate, lower SES lower attendance rate. Identifying the moderator variables adds causality to such findings.
***** Yes! I agree this was a great video. There are several variables that can effect the outcome of a relationship. Specifically, the mediator and moderator.
Thank you for providing examples to explain moderator and mediator variables. The examples were very helpful and made the concepts easier to understand. Thank you.
This video was an eye opener to me in terms of cause and effect. It was interesting to see the relationship between independent and dependent variable and how moderators can change or strengthen the relationship and how mediators explain the relationship. I was also intrigued by the example for confound variable and now understand that though they are similar to mediators, the relationship between the IV and DV has no direct casualty on the confound variable. Though I can remember touching on these concepts in past classes, this video allowed me to understand the differences between the variable. I really appreciated the examples.
This whole video was all new to me. I am still taking the time to fully understand all the concepts. Like the research designs; the visual explanations were very helpful in getting to understand the terms and how they are applicable to research. This was particularly helpful with confound variables because they do seem very similar to mediator variables but the example of the number of vacation hours not causing summer is really stuck in my mind. So the independent variable in this case does not have a causal relationship with the confound variable and that causal relationship is the difference between mediator and confound variables. This was a very well explained set of concepts. Re-reading my notes it is hard to understand without thinking about the charts from the videos but when I do imagine the charts it makes sense.
Alicia Zahn Alicia, I agree the visual explanation was helpful. The concepts were explained well. I too took notes and will refer back to the visual charts from the video. Hats off to Dr. Grande for making research a little easier to understand or at least trying to help with visual aides and great examples.
This is where variables start to differ in meaning from math. However, the diagrams and the examples make it plain to me. Pointing out which is the predictor and the outcome was very useful.
This is my first experience with moderator and mediator variables and applications. In my prior courses this information was not taught, I was a little confused by the subject at first but the application examples provided a more thorough insight of how these all correlate
Great information! Kind of tedious, but Dr. Grande explained it in a way that I was able to grasp the concept of each variable. I found confound variables to be a little tricky. However, Dr. Grande's example of summer causing number of vacation hours negatively affecting number of study hours, as well as the summer negatively affecting study hours put it in perspective.
This was a great video that helped me distinguish between mediator and moderator variables. The example with private practice and treatment attendance rate is something that I will remember when working with these variables while designing and identifying future study designs.
The arrows that were represented really helped me understand exactly where both moderator and mediator variable fit into the other variables (independent and dependent). I also liked the plus and minus signs as well. Although moderator and mediator variables start with the same letter, they serve very different purposes.
I vaguely remember learning about these variables in the past, but found this video to be helpful in distinguishing among the types of variables discussed. It can be easy to confuse moderator and mediator variables and I felt the examples provided made it a bit easier to understand. I don't recall learning much about confound variables but the last example, and the way in which is was presented, was very useful.
Jacklin Skibicki reading the text about the differences of moderator and mediator variables made it very confusing. This video did help to understand the differences since Dr. Grande presented it so well.
This video nicely explained the difference between variables. The example of SE class affecting tx attendance was helpful. I understand how moderator variables either increase or decreases the DV or changes direction of the DV. Mediator variables explains the relationship between DV and IV. The example of how access to a vehicle may increase tx attendance because of one's SE status and ability to afford a vehicle to get to tx. There are a lot of terms to grasp but the examples are very helpful.
Michelle Robinson Michelle, I think I would have had a much harder time understanding the difference between moderator variables and mediator variables if it would not have been for his simple example on attending therapy. Perhaps it was the variables used, such as SES, access to a car, and the amount the patient works that made it easier to wrap my head around. I think it worked so well because it is a real life example that we can all probably relate to in some way or another.
The previous video mentioned it can be presumed that dependent variables are altered by the independent variable. I was surprised by the brief statement because this seems a little uncertain for research. Therefore, I appreciated that this video discussed mediator variables as ways of explaining the relationship between the independent and dependent variables.
The three examples provided in this video increased my understanding of mediator, confounding, and moderator variables. I also found it helpful that the words change in strength or direction were used for moderator and how or why was used for mediator. These description words easily help keep the two separate.
Bethany Elstrom My comment was very similar to yours! I, too, found the examples provided in the video to be very helpful and they increased my understanding of these variables. It would be easy to confuse moderator an mediator variables, but, by hearing these examples and remembering them, we will be able to keep them separate, just as you said! It is nice to have good examples such as these so we do not get confused.
I found this video to be wonderfully informative. I'd muddled with the concepts of moderator and mediator variables before, but this video clarified things nicely. I also did not know much about confound variables until this video.
I did not know that there were so many variables to consider when conducting research. I was able to understand the differences between some moderator, mediator, and confounding variables after viewing the visual charts with examples. They were very helpful. I will also remember that when considering if a variable is a confounding variable, I will check to see if the independent variable causes a change in the mediator variable.
I feel these variables are tricky to distinguish apart because they are similar, so you're right that it's a good idea to check causality to determine whether the variable is confounded or a mediator. You could also check to see if the variable is just impacting the strength of the relationship or if it is a possible explanation for the relationship in order to determine if it is a moderator or mediator variable.
This video was very helpful. Often times when we think of independent and dependent variables, we think of this on a much smaller scale. Examples such as the one where we may be private practice clinicians trying to see the connection between socio economic status and treatment attendance rate help us to gain a much better understanding of the way in which IV and DV are identified and the way in which they can contribute toward the outcome.
This video demonstrated the differences between moderator, mediator and confound variables in a very easy to understand manner. I appreciate the easy to follow examples given.
I completely agree. I am a visual learner for sure. If I can see a concept's application, I grasp it a lot better than if I just read/hear it. The graphics definitely helped.
This videos explained the two kinds of variables very clearly. It would be very helpful and interesting to know moderator variables in a causal relationship, as not only we know IV and DV's relationship, we also know the factors that strengthen or weaken or direct their relationship. As far as mediator variables, they explain why and how the relationship between IV and DV exists. However, we are not sure the true causes of the DV because mediator variable exists. We also have to be careful of confound variables.
I am a visual learner. Reading the text about the differences in mediator and moderator variables was very confusing. I liked the example of the motor vehicle to explain how these terms apply and describe the relationship between the IV and DV.
The visuals helped understand these terms and the examples as well. I still don't clearly understand the confound variables but the example helped. I was thinking about the families that serve in the community and i can say by experience that when a client finds multiple jobs the direction can certainly change and there are lower attendance rates.
Multiple jobs is something that I have never considered. This really made me think about the different kinds of people who not only participate in studies, but also go to counseling.
Love your videos. They've helped me understand many analyses that always confused me. One suggestion is to go through the slides a little faster. We can rewind if we need to hear your explanation
jstdun For the love of God, please don't go any faster! This is new to me and I appreciate any efforts in these videos for additional explanations. I think they are appropriately paced, but maybe not for somebody who is more familiar with it all :)
I believe it is important to note that in mediation example, the relationships between the predictor (socio economic status) and outcome (treatment attendance rate), predictor (SES) and mediator (Access to motor vehicle), and mediator (Access to motor vehicle) and outcome (treatment attendance rate) are all CORRELATIONS. If we want to convince the readers that these high correlations are likely to be causations, then we need to MANIPULATE predictor or mediator variables and see how mediator or outcomes change. This arrow "->" doesn't necessarily mean that A is causing B. It is CORRELATION! CORRELATION IS NOT CAUSATION :) Happy studying
So min_Luna Shin here u are talking about moderator not the mediator there is diffrence because moderator can be manipulated but mediator is like an extra explanation of the causation of the outcome .
Like covariates, I have never heard of moderator and mediator variables. These are very confusing yet interesting concepts in research. What confused me the most was, in the example of socioeconomic status and treatment attendance, was the difference between the miles from work and multiple jobs. As I understood it as the miles from agency increases the attendance rate decreases, and the more jobs the client has increases the attendance rate decreases. I have a difficult time understanding the difference between the two. I think a visual aid would have driven this point home. The mediator variable was much easier to understand and the example from the video did help my understanding of it.
The mediator and moderator variables were distinguished in this video. I understand the difference between the two now. Before this video, I have never heard of those concepts and I am looking forward to learning a lot more about them. Also,the diagrams were a good way to explain the difference. I am familiar with a confounding variable; however, when it was presented to me as an undergraduate, it was explained a lot simpler. It is more complex than I had imagined. This course makes me nervous.
I also had never heard of these two types of variables before. It will be interesting to see how these variables are used in research studies as well, now that we have been introduced to them. This course makes me nervous too because of all the concepts I thought I understood but are actually much more complex than I realized!
Thank you, Dr. Grande. Setting up my dissertation concept. The topics of mediating, moderating, and confounding variables didn't receive much treatment in my coursework. I have great concern over extraneous variables distorting my outcomes - needs its own consideration and treatment. Trying not to get too neurotic about this (!).
One of the most interesting things I found in this video was that moderator variables can actually change the direction of the relationship. I knew that they could change the strength of the relationship but find it very interesting that the direction of the relationship can be changed.
Mandy Moore Mandy, that stuck out to me, too. I was having a little trouble at first differentiating [+ miles from agency] and [+jobs] but had a light bulb moment when he explained the shift associated with a now higher socio-economic status.
I thought that this video did a good job in explaining the difference between moderator and mediator variables. The visuals really helped as well. I wasn't that knowledgable of the difference prior to watching this video so it really helped me to learn and understand.
Jillian Curtis I did not have a great understanding of moderator and mediator variables. After this video and the examples it is a lot clearer to me. I too notice how each series the videos build off one another. Each video you watch the previous video helps you gain a better understanding.
The visuals gave me a better understanding of moderators and mediators. The confounding variable influences both the IV and the DV, and although this variable is not accounted for in the experiment, I wonder if it can cause a negative outcome in the results?
I liked this video I think it was very helpful, distinguishing the difference between moderator and mediator variables seems pretty simple but I think I will need to watch the video again to really fully understand it.
This video content was tough for me to digest. However, I found that Dr. Grande's examples and visual charts were great at explaining moderator variables, mediator, and confound variables. I found it interesting that Summer was the cause of increased vacation hours and obviously not the reverse.
Alaina Van Gelder This was a very confusing video for me to understand as well. I had a difficultly understanding the diffrent types of moderator variables. However, his example for mediator variables helped me understand what they were.
Before this video I was not knowledgeable in what moderator or mediator variables are. The examples helped me to distinguish between the two and that moderator variables change the strength or direction between independent and dependent variables. The mediator variables explains the relationship between the independent and dependent variables along with how and why it exists. I think it is important to understand this and have that information available in order for change to occur. I would be interested in learning a little bit more on confound variables as I am not at all familiar with that term.
Candace Fernandez I agree. It is helpful to understand the difference between the two, and the way you have just presented the information basically sums it up! I would also like to discuss these concepts further, especially the confound variables, as I am not very familiar with them either.
The diagrams were helpful. Mediator and Moderator are more clear to me then they were before. The causal variable is still a little bit confusing but the arrows and explanation help.
Thank you for this information and effort. If I may make one suggestion please increase your audio output it is very difficult to hear even at full volume.
Thank you Dr. Todd. Very nice presentation :) I have one question where I see miles from agency and multiple jobs as same thing. when they increase the strength decrease. what is meant by change in direction? I could not see it yet although you did repeatedly explaining it . can you please add more elaboration if you don't mind ? :)
Is a mediator variable the same as an intervening variable? Both of their definitions sound quite the same. I'm interested in learning more about these variables. I thought that research only had independent, dependent, and control variable. This was helpful to learn more about variables in research.
What's the difference between access to a motor vehicle and miles from agency? Can you simply swap them and still make the same point? I think I'm still struggling.
I agree Remy, I didn't understand what the moderator and mediator had to do with IV and DV until he broke it down. I also liked that he explained that there were different names that refer to IV and DV so that we would recognize it if we saw it in research.
Moderator and mediator variables are new terms for me. If I am understanding them correctly, a moderator variable explains the change in strength and/or direction of a variable while a mediator variable explains the why behind the change. A mediator variable explains a connection between the IV and DV. So in a nutshell, these variables are to help understand the cause and effect between the IV and the DV.
Susan Wilder I think you have it right. I am still kind of confused to what a change in relationship and change of direction is. I know it is how the two variables relate to each other, but I would like another example because I am still confused. I think the two go hand in hand and can't have one without the other. Hopefully the explanations will click for me later on.
Susan Wilder I picked up on the same wording that you did for moderator and mediator. The change in strength or direction indicates moderator variables and the how or why indicates mediator variables. The diagrams that were used gave me a better understanding of the terms. I found it helpful to be able to see the terms in action per say in the diagrams.
Yes, these variables give more detailed information about the IV and DV. The examples of relationships, travel, and the agency gave me different perspectives for understanding the complex information. These videos are very valuable when it comes to understanding the literature for the research class.
The concept of moderator variables is one that is directly applicable to counselors. This is apparent by the example given by Dr. Grande about treatment attendance rate. This concept could even be one that could be used in treatment if attendance becomes an issue. I'm still not sure that I understand what "change in direction" actually is, so I might have to watch this video a few times or ask some of my classmates about this. My understanding is that because someone has more jobs, the SES becomes higher-- but this rise in SS does not increase attendance as predicted because of that moderator. Thus, having these jobs lowers the attendance and raises the SES, and that is the change in relationship?
Jessie Howard If I am not mistaken, a change in direction could be a change in whether it is a positive relationship or a negative relationship. So, let's say that someone who has a low SES gets a better job which enables them to get a reliable car. Having a reliable car would ensure they make it to their therapy appointments on time. As such, it would result in a change in direction of the relationship; it would become more positive.
I think demonstrating how the moderator and mediator variables can be analyzed in order to find results was helpful. I think in order for research to truly tell us something about human behavior, it is important to look at these variables which influence behavior.
If my independent variable is 'caregiver burden' and dependent variable is 'psychological distress,' the 'adaptive/maladaptive coping strategies' would be a moderator or a mediator variable?
What if the causality of a relationship between IV and a certain variable is unknown? How do I know whether the certain variable is a moderator or mediator then?
Could access to a motor vehicle actually also be a moderator to the relationship between the two other variables (i.e. the relationship between the IV and the DV will probably differ at varying levels of access to a motor vehicle).
I wanted to ask that can moderation analysis with PROCESS be possible with a binary dichotomous variable as the dependent variable? Please guide and help
Alright, this stuff was a bit difficult for me to understand. I get that the moderator variable moderates the strength of the relationship between the IV and DV. And then the mediator variable explains that relationship and (I think) seeks to help pinpoint the causal relationship? But once it got to confound variable I lost my understanding a bit. I understand that there's no relationship between the IV and the confound variable. But in that example in the video, I don't understand why there's a minus sign between the number of vacation hours and the number of hours studying? Is it just because the number of studying hours decreases when the number of vacation hours increases? I think listening to that part again would be helpful :P
Hi Jeff, I'd like to try to explain to you with my understanding of the video. You are understanding correctly. There is a minus sign between the # of vacation hours and the # of hours studying because # of hours studying decreases when # of vacation hours increases. As far as "summer time (season)" as a confound variable, it does not affect the relationship between #vacation and #studying, because #vacation does not increase summer time. Instead, summer time increases #vacation.
For reasons beyond me, this was very difficult for me to comprehend in regards to the name of the variable and how it interacts with the IV and DV. I will be revisiting this video I'm sure.
+Ashley Kilpatrick This was difficult for me as well. Some things seem to come easier than others that's for sure. :) It will be a video I will have to review as well.
I feel a little lost after this video. I understand some of the concepts, but not all of the concepts. The diagrams helped, but I am still a little mixed up with what changes with which variable. I will probably have to "re-watch" this video a few times. I liked how Dr. Grande explained how the variables may seem similar, but also, the reasons that distinguish them from one another.
Rachel Foster Initially, I was lost too! Especially with confound variables, but with Dr. Grande explaining CV along with his diagram made all the difference.
I felt a little lost too. I know I will have to re-watch this video and possible make flow charts of my own. I think the concepts are explained well but I think there is a lot to understand!
Rachel Foster I feel a little lost as well, but maybe if I watch it a few more times it will help. I think the diagrams were helpful and I will re-visit these.
I am glad that Dr. Grande distinguished between moderator and mediator variables. The moderator variable represents strength and direction between the IV and DV. The mediator variable represents the relationship between the IV and DV.
I am a visual learner so I really liked how this video incorporated various models to help explain moderator, mediator and confounding variables.
I agree. Topics that are complex are easier to comprehend with visuals and specific examples. I appreciate this teaching style.
I was having trouble trying to distinguish the difference between moderator and mediator variables. I performed a search and this video came up. This video solidified the understanding of these terms. Thank you!
Dr. Grande- When listening to Moderating and Mediator Variables, I thoroughly liked your statement, "How one variable can predict another variable." I liked how this video talked about different treatment modalities and how they would be researched. Many of these terms can be quite confusing, and to me seem overlapped. But, you are able to give a clear understanding on how these methods work and occur. When using Moderator and Mediator with IV, DV and how they are interchangeable or correlated provided me with information that I needed. Thank you again, for your thorough examples.
More variables! A moderator is the strength of strong or weak causality between variables, as the mediator is the relationship of why and how the two are related. I think the example diagrams were a great way to show the differences.
Thank you for the video Dr. Grande. I appreciate the explanation of what the moderator and mediator are and how they relate to the independent and dependent variables. I also appreciate learning the differences in the mediator and the compound variable, and having the visuals to get a better understanding of those terms through examples.
The examples provided about mediator variables and confound variables was extremely helpful to me. I haven't heard of these concepts in quite some time and these examples helped me to understand their differences. The examples made these concepts clear so I can understand what they mean when they appear in research.
I don't think I have ever heard of moderator, mediator, or confounding variables before. I was surprised that I was able to understand the basics after watching this short video. I think the charts and examples definitely helped me to take mental notes that I will remember without always looking at notes.
These terms were all new to me. I appreciate the examples however will need to review more for a complete understanding in order to proficiently identify these types of variables, especially distinguish between moderator and cofound variables so not to confuse one for the other.
I wasn't aware of these variables or their impact on the independent and dependent variables. The moderator would be responsible for change in strength or direction of the relationship but not explaining the relationship between the IV and DV. The mediator variable explains the relationship. The examples were helpful. I found the moderator explanation more helpful than the mediator explanation.
Dr. Grande, this video helped me understand my own research topic a bit better as it relates to independent variables, dependent variables, moderator, and mediator variables, and confound variables. Prior to this video, I was unaware what moderator, mediator, and confound variables were.
This video helped to break down the differences between moderator/mediator/confounding variables. The examples given made the differences clear. Very informative explanations of the different variables and causal relationships.
I was already familiar with independent and dependent variables, as I'm sure most people with an elementary knowledge of research are. However, I was not familiar with moderator and mediator variables. I now know that the moderator measures the strength whereas the mediator represents the relationship between the two variables. At first this information was overwhelming, but the examples provided helped me to better understand and differentiate between the two.
I was not aware of the various types of variables. The examples shown in this video clarified the moderator, mediator and confounding variables visually which helps me to retain the information. Also, it made it easy to take precise notes.
I must say that I never really was exposed to moderator variables. the examples shown were very clear and helped me understand these variables and what they do to the independent and dependent variables. Very good video.
I appreciate the distinction that independent variable and predictor variable, as well as the dependent variable and outcome variable are interchangeable when talking about a linear regression. I remember reading these in undergrad textbooks and wondering if they were similar. Clarification is great. The three examples of moderator variables concretely explained how a relationship could decrease, increase or change direction. I found this very helpful.
I had no idea what moderator and mediator variables were before watching this video. I liked how Dr. Grande took his time to explain it all. The examples really helped me understand this topic. It is hard for b to understand something without trying to apply it.
Dr. Grande's example about the moderating variables and treatment attendance rate served as a great example on how these variables directly affect how certain variables can weaken the relationship, strengthen it, and finally have the possibility to change the actual direction of the IV and DV interaction. I think the linear regression model served as a good way to explain the concept between moderators and mediators without making it overly confusing or complicated. The mediator variable reminded me of an old professor always saying 'correlation does not indicate causation'. It could simply be an extraneous variable not being considered in the study that could increase the strength of the IV on the DV.
Jessica Muzzin I also agree that the examples that Dr. Grande used to explain the relationships between mediator and moderator and how the related to the IV and DV were beneficial. They helped me visualize the concepts and also reminded me that correlation does not mean causation. It was interesting for me to see how these other variables could and are indicated in research studies. I am still a bit confused about confounding variables, I think it is due to the fact that they are so similar to mediator variables.
I remember being given an example in undergrad about correlation not equaling causation: as the number of ice cream sales increases, the number of drownings also increases. Obviously, the first does not cause the second, but instead both are affected by the third variable of the temperature rising. I realize now that I was being taught the concept of confound variables without actually knowing the term, which made Dr. Grande's explanation immediately make sense.
These are terms I have not previously encountered. Everything is interrelated and in research we must break everything down to isolate variables so that we can so causality. It is not an easy task! The examples here helped to see different relationships between different variables.
Thank you for the examples they help bring a better understanding for me. Mediator variables explain the relationship between I.V and D.V, whereas moderator variables show strength and direction but not cause. and confound variables is not caused by the I.V. Hopefully, more exposure to these concepts will help to solidify my understanding.
prior to watching this video, i did not have much experience doing research that discussed moderator variables. However, in some articles i have read, I noticed some discussion on potential mediator variables that could contribute to independent variables. At the time, i did not know there was a proper name for these contributing factors. This video was helpful with getting me to understand things i did not know about moderator variables as well.
I liked how the variables were explained. Moderator variables effect other variables as explained. Such as miles from an agency the treatment attendance rate tends to decrease. Then the mediator variable is the relationship between two variables. This was all explained in a way that was easy to grasp.
I was really happy to hear the examples of the moderator and confound variables! They made the terms much easier to understand! It was interesting to see, in the example for moderator variables, the different variables of the miles from the agency, the family support an the multiple jobs and how it affected the sessions by decreasing, increasing or changing the direction of attending those sessions. Seeing the example made it more understandable and less stressful and intimidating to think about!
This video was very informing on the different factors that play a role in independent and dependent variables. We never really touched upon the mediator and moderator in my undergraduate program. I never knew that the moderator variable relates to the strength of the dependent and independent relationships. Also, the mediator, which explains why, or how the relationship exists. The confounding variable relates to something that will have a negative effect on something else. The example given, that summer effects the number of studying hours has a direct impact on the studying. Not the other way around. This is what makes it a confound variable. Great video!
Agreed! In my undergraduate course we did not really touch on mediator and moderator variables. I found this to be very interesting to learn! I also loved his explanation of the difference between the mediator variables and confounding variables as I can see how they could get easily confused. I thought his explaintion made the differences clear.
I remember struggling to understand the difference between moderator and mediator variables in undergrad. I had multiple professors explain it to me and didn't understand the differences within each type of variable as well as the difference between the two. This video explained thoroughly these differences, and I now understand how moderator variable affect the strength of the relationship and mediator variables are a third variable to help to explain the original relationship. These two types of variables are very similar so I am glad for this extra understanding.
I don't actually remember my undergrad explaining mediator and moderator variables, but that's fine because this was a thorough, easy to understand primer of the concept.
Dr. Grande!! I am a subscriber who enjoys your analysis of different behaviors. I am currently in a research class and my professor posted your video for the purpose of learning about moderators. I am so excited to see my two worlds collide! I had no idea your older videos have this kind of content. So happy I found you on YT two years ago!
I like the examples used to explain both the mediating & moderator variables, as well as the distinction made between confounding & mediator variables. This video made them easy to understand, along with their relationships (or lack of) to the IV & DV.
This video was really my first exposure to these variables. My other research classes didn't discuss these at all so I really found this video to be informative and it definitely helped me understand the relationship between the variables and the impact they have on research.
Kimberly Dixon A lot of undergrad research classes neglect this subject, apparently. I know I'd read about the concepts before, but until this video I was shaky on their meanings.
Being able to visualize the examples is tremendously helpful. For example, when Dr. Grande defined confound variables explaining no relationship between the IV and the confounding variable, I was a little confused when I first saw Summer and Number of Vacation Hours as examples. After he explained that the # of vacation hours does not CAUSE summer (rather, the other way around), this is the determining factor between mediator and confound variables. Picturing the arrows will be helpful when I'm trying to recall the difference.
Dr. Grande, yes, we always want to know causality. IV predictor variable, DV outcome variable. Is a great way to remember these terms when conducting research.
Thank you for the video Dr. Grande. Such a clear explanation with example about moderator, mediator and confounding variables.
This video served as a great resource in distinguishing between moderator and mediator variables. Prior to watching the video I was completely unaware that such variables existed. Causality is extremely important when noting cause and effect in an experiment. Moderator variables highlight the change or direction strength. The correlation between treatment attendance rate and socioeconomic status was enlightening; higher SES higher attendance rate, lower SES lower attendance rate. Identifying the moderator variables adds causality to such findings.
***** Yes! I agree this was a great video. There are several variables that can effect the outcome of a relationship. Specifically, the mediator and moderator.
***** This video is a great resource. It really broadened my understanding of each variable and how they relate to relationship and causality.
Thank you for providing examples to explain moderator and mediator variables. The examples were very helpful and made the concepts easier to understand. Thank you.
Again, the use of examples in this video helped tremendously to understand the difference between the three variables.
This video was an eye opener to me in terms of cause and effect. It was interesting to see the relationship between independent and dependent variable and how moderators can change or strengthen the relationship and how mediators explain the relationship. I was also intrigued by the example for confound variable and now understand that though they are similar to mediators, the relationship between the IV and DV has no direct casualty on the confound variable. Though I can remember touching on these concepts in past classes, this video allowed me to understand the differences between the variable. I really appreciated the examples.
Ashlee Todd yes i have a better understanding of cause and effect
This whole video was all new to me. I am still taking the time to fully understand all the concepts. Like the research designs; the visual explanations were very helpful in getting to understand the terms and how they are applicable to research. This was particularly helpful with confound variables because they do seem very similar to mediator variables but the example of the number of vacation hours not causing summer is really stuck in my mind. So the independent variable in this case does not have a causal relationship with the confound variable and that causal relationship is the difference between mediator and confound variables. This was a very well explained set of concepts. Re-reading my notes it is hard to understand without thinking about the charts from the videos but when I do imagine the charts it makes sense.
Alicia Zahn Alicia, I agree the visual explanation was helpful. The concepts were explained well. I too took notes and will refer back to the visual charts from the video. Hats off to Dr. Grande for making research a little easier to understand or at least trying to help with visual aides and great examples.
This is where variables start to differ in meaning from math. However, the diagrams and the examples make it plain to me. Pointing out which is the predictor and the outcome was very useful.
This is my first experience with moderator and mediator variables and applications. In my prior courses this information was not taught, I was a little confused by the subject at first but the application examples provided a more thorough insight of how these all correlate
Great information! Kind of tedious, but Dr. Grande explained it in a way that I was able to grasp the concept of each variable. I found confound variables to be a little tricky. However, Dr. Grande's example of summer causing number of vacation hours negatively affecting number of study hours, as well as the summer negatively affecting study hours put it in perspective.
This was a great video that helped me distinguish between mediator and moderator variables. The example with private practice and treatment attendance rate is something that I will remember when working with these variables while designing and identifying future study designs.
The arrows that were represented really helped me understand exactly where both moderator and mediator variable fit into the other variables (independent and dependent). I also liked the plus and minus signs as well. Although moderator and mediator variables start with the same letter, they serve very different purposes.
I vaguely remember learning about these variables in the past, but found this video to be helpful in distinguishing among the types of variables discussed. It can be easy to confuse moderator and mediator variables and I felt the examples provided made it a bit easier to understand. I don't recall learning much about confound variables but the last example, and the way in which is was presented, was very useful.
Jacklin Skibicki I agree, it can be confusing to tell the difference, and I also thought that it was easier to understand after watching this video.
Jacklin Skibicki reading the text about the differences of moderator and mediator variables made it very confusing. This video did help to understand the differences since Dr. Grande presented it so well.
You know you're an OG if you remember Todd's statistics and research methods videos.
This video nicely explained the difference between variables. The example of SE class affecting tx attendance was helpful. I understand how moderator variables either increase or decreases the DV or changes direction of the DV. Mediator variables explains the relationship between DV and IV. The example of how access to a vehicle may increase tx attendance because of one's SE status and ability to afford a vehicle to get to tx. There are a lot of terms to grasp but the examples are very helpful.
Michelle Robinson Michelle, I think I would have had a much harder time understanding the difference between moderator variables and mediator variables if it would not have been for his simple example on attending therapy. Perhaps it was the variables used, such as SES, access to a car, and the amount the patient works that made it easier to wrap my head around. I think it worked so well because it is a real life example that we can all probably relate to in some way or another.
The previous video mentioned it can be presumed that dependent variables are altered by the independent variable. I was surprised by the brief statement because this seems a little uncertain for research. Therefore, I appreciated that this video discussed mediator variables as ways of explaining the relationship between the independent and dependent variables.
The three examples provided in this video increased my understanding of mediator, confounding, and moderator variables. I also found it helpful that the words change in strength or direction were used for moderator and how or why was used for mediator. These description words easily help keep the two separate.
I and it seems that many others found the videos clarified any confusion about the variable.
Bethany Elstrom My comment was very similar to yours! I, too, found the examples provided in the video to be very helpful and they increased my understanding of these variables. It would be easy to confuse moderator an mediator variables, but, by hearing these examples and remembering them, we will be able to keep them separate, just as you said! It is nice to have good examples such as these so we do not get confused.
I found this video to be wonderfully informative. I'd muddled with the concepts of moderator and mediator variables before, but this video clarified things nicely. I also did not know much about confound variables until this video.
I did not know that there were so many variables to consider when conducting research. I was able to understand the differences between some moderator, mediator, and confounding variables after viewing the visual charts with examples. They were very helpful. I will also remember that when considering if a variable is a confounding variable, I will check to see if the independent variable causes a change in the mediator variable.
Good point! I think that is key and something to remember- the relationship between the variable when identifying which kind it is.
I feel these variables are tricky to distinguish apart because they are similar, so you're right that it's a good idea to check causality to determine whether the variable is confounded or a mediator. You could also check to see if the variable is just impacting the strength of the relationship or if it is a possible explanation for the relationship in order to determine if it is a moderator or mediator variable.
This video was very helpful. Often times when we think of independent and dependent variables, we think of this on a much smaller scale. Examples such as the one where we may be private practice clinicians trying to see the connection between socio economic status and treatment attendance rate help us to gain a much better understanding of the way in which IV and DV are identified and the way in which they can contribute toward the outcome.
This video demonstrated the differences between moderator, mediator and confound variables in a very easy to understand manner. I appreciate the easy to follow examples given.
I agree with you Kara. For me the graphics made it much more easy to understand these examples, because they can be very abstract.
I completely agree. I am a visual learner for sure. If I can see a concept's application, I grasp it a lot better than if I just read/hear it. The graphics definitely helped.
I found the examples of mediator, moderator and confound variables very helpful in explaining how one variable can effect another variable.
This videos explained the two kinds of variables very clearly. It would be very helpful and interesting to know moderator variables in a causal relationship, as not only we know IV and DV's relationship, we also know the factors that strengthen or weaken or direct their relationship. As far as mediator variables, they explain why and how the relationship between IV and DV exists. However, we are not sure the true causes of the DV because mediator variable exists. We also have to be careful of confound variables.
I am a visual learner. Reading the text about the differences in mediator and moderator variables was very confusing. I liked the example of the motor vehicle to explain how these terms apply and describe the relationship between the IV and DV.
Jasmine Aviles Yes it was, so i ca say that this video helped me see exactly what they are and what they do
The visuals helped understand these terms and the examples as well. I still don't clearly understand the confound variables but the example helped. I was thinking about the families that serve in the community and i can say by experience that when a client finds multiple jobs the direction can certainly change and there are lower attendance rates.
Multiple jobs is something that I have never considered. This really made me think about the different kinds of people who not only participate in studies, but also go to counseling.
I thought the video explained very well the difference between mediator and moderator variables. I also found the examples and graphs very helpful.
Love your videos. They've helped me understand many analyses that always confused me. One suggestion is to go through the slides a little faster. We can rewind if we need to hear your explanation
jstdun For the love of God, please don't go any faster! This is new to me and I appreciate any efforts in these videos for additional explanations. I think they are appropriately paced, but maybe not for somebody who is more familiar with it all :)
Very helpful. I get it now because of the diagrams and the clear explanation. Thank you Dr. Grande.
I'm glad you found the video useful. Thanks for watching.
I believe it is important to note that in mediation example, the relationships between the predictor (socio economic status) and outcome (treatment attendance rate), predictor (SES) and mediator (Access to motor vehicle), and mediator (Access to motor vehicle) and outcome (treatment attendance rate) are all CORRELATIONS.
If we want to convince the readers that these high correlations are likely to be causations, then we need to MANIPULATE predictor or mediator variables and see how mediator or outcomes change.
This arrow "->" doesn't necessarily mean that A is causing B. It is CORRELATION!
CORRELATION IS NOT CAUSATION :)
Happy studying
So min_Luna Shin here u are talking about moderator not the mediator there is diffrence because moderator can be manipulated but mediator is like an extra explanation of the causation of the outcome .
Like covariates, I have never heard of moderator and mediator variables. These are very confusing yet interesting concepts in research. What confused me the most was, in the example of socioeconomic status and treatment attendance, was the difference between the miles from work and multiple jobs. As I understood it as the miles from agency increases the attendance rate decreases, and the more jobs the client has increases the attendance rate decreases. I have a difficult time understanding the difference between the two. I think a visual aid would have driven this point home. The mediator variable was much easier to understand and the example from the video did help my understanding of it.
The mediator and moderator variables were distinguished in this video. I understand the difference between the two now. Before this video, I have never heard of those concepts and I am looking forward to learning a lot more about them. Also,the diagrams were a good way to explain the difference. I am familiar with a confounding variable; however, when it was presented to me as an undergraduate, it was explained a lot simpler. It is more complex than I had imagined. This course makes me nervous.
I also had never heard of these two types of variables before. It will be interesting to see how these variables are used in research studies as well, now that we have been introduced to them. This course makes me nervous too because of all the concepts I thought I understood but are actually much more complex than I realized!
Thank you, Dr. Grande. Setting up my dissertation concept. The topics of mediating, moderating, and confounding variables didn't receive much treatment in my coursework. I have great concern over extraneous variables distorting my outcomes - needs its own consideration and treatment. Trying not to get too neurotic about this (!).
One of the most interesting things I found in this video was that moderator variables can actually change the direction of the relationship. I knew that they could change the strength of the relationship but find it very interesting that the direction of the relationship can be changed.
Mandy Moore Mandy, that stuck out to me, too. I was having a little trouble at first differentiating [+ miles from agency] and [+jobs] but had a light bulb moment when he explained the shift associated with a now higher socio-economic status.
I thought that this video did a good job in explaining the difference between moderator and mediator variables. The visuals really helped as well. I wasn't that knowledgable of the difference prior to watching this video so it really helped me to learn and understand.
Jillian Curtis I did not have a great understanding of moderator and mediator variables. After this video and the examples it is a lot clearer to me. I too notice how each series the videos build off one another. Each video you watch the previous video helps you gain a better understanding.
That actually helps me for my new job. Good Video, thank you 😊
The visuals gave me a better understanding of moderators and mediators. The confounding variable influences both the IV and the DV, and although this variable is not accounted for in the experiment, I wonder if it can cause a negative outcome in the results?
I liked this video I think it was very helpful, distinguishing the difference between moderator and mediator variables seems pretty simple but I think I will need to watch the video again to really fully understand it.
Agreed, I think that I will have to watch this video a few more time to comprehend it fully
This video content was tough for me to digest. However, I found that Dr. Grande's examples and visual charts were great at explaining moderator variables, mediator, and confound variables. I found it interesting that Summer was the cause of increased vacation hours and obviously not the reverse.
Alaina Van Gelder This was a very confusing video for me to understand as well. I had a difficultly understanding the diffrent types of moderator variables. However, his example for mediator variables helped me understand what they were.
This particular video was helpful in distinguishing the moderator and mediator variables in respect to my own research topic.
Before this video I was not knowledgeable in what moderator or mediator variables are. The examples helped me to distinguish between the two and that moderator variables change the strength or direction between independent and dependent variables. The mediator variables explains the relationship between the independent and dependent variables along with how and why it exists. I think it is important to understand this and have that information available in order for change to occur. I would be interested in learning a little bit more on confound variables as I am not at all familiar with that term.
Candace Fernandez I agree. It is helpful to understand the difference between the two, and the way you have just presented the information basically sums it up! I would also like to discuss these concepts further, especially the confound variables, as I am not very familiar with them either.
The diagrams were helpful. Mediator and Moderator are more clear to me then they were before. The causal variable is still a little bit confusing but the arrows and explanation help.
This helped clear up my understanding of a moderator and mediator variables.
Thank you for this information and effort. If I may make one suggestion please increase your audio output it is very difficult to hear even at full volume.
Love all your videos! Very clear explanations
I am glad you find the videos useful - thanks for watching.
Thanks Dr. Grande!!!!!! This helps me tons!!!!
Thank you Dr. Todd. Very nice presentation :)
I have one question where I see miles from agency and multiple jobs as same thing. when they increase the strength decrease. what is meant by change in direction? I could not see it yet although you did repeatedly explaining it . can you please add more elaboration if you don't mind ? :)
This video was helpful. I think I will have to watch it again to fully understand.
Is a mediator variable the same as an intervening variable? Both of their definitions sound quite the same. I'm interested in learning more about these variables. I thought that research only had independent, dependent, and control variable. This was helpful to learn more about variables in research.
What's the difference between access to a motor vehicle and miles from agency? Can you simply swap them and still make the same point? I think I'm still struggling.
Very helpful and insightful explanation... helped a lot. Thanks!
At first sight, this sounded baffling, but the explanation makes it an-easy-to-comprehend concept.
I agree Remy, I didn't understand what the moderator and mediator had to do with IV and DV until he broke it down. I also liked that he explained that there were different names that refer to IV and DV so that we would recognize it if we saw it in research.
Information was very helpful but I may need to process all of the factors. This was the first time I have been exposed to this information.
the examples of variables and visual diagrams were very helpful
Moderator and mediator variables are new terms for me. If I am understanding them correctly, a moderator variable explains the change in strength and/or direction of a variable while a mediator variable explains the why behind the change. A mediator variable explains a connection between the IV and DV. So in a nutshell, these variables are to help understand the cause and effect between the IV and the DV.
Susan Wilder I think you have it right. I am still kind of confused to what a change in relationship and change of direction is. I know it is how the two variables relate to each other, but I would like another example because I am still confused. I think the two go hand in hand and can't have one without the other. Hopefully the explanations will click for me later on.
Susan Wilder I picked up on the same wording that you did for moderator and mediator. The change in strength or direction indicates moderator variables and the how or why indicates mediator variables. The diagrams that were used gave me a better understanding of the terms. I found it helpful to be able to see the terms in action per say in the diagrams.
Yes, these variables give more detailed information about the IV and DV. The examples of relationships, travel, and the agency gave me different perspectives for understanding the complex information. These videos are very valuable when it comes to understanding the literature for the research class.
The concept of moderator variables is one that is directly applicable to counselors. This is apparent by the example given by Dr. Grande about treatment attendance rate. This concept could even be one that could be used in treatment if attendance becomes an issue. I'm still not sure that I understand what "change in direction" actually is, so I might have to watch this video a few times or ask some of my classmates about this. My understanding is that because someone has more jobs, the SES becomes higher-- but this rise in SS does not increase attendance as predicted because of that moderator. Thus, having these jobs lowers the attendance and raises the SES, and that is the change in relationship?
Jessie Howard If I am not mistaken, a change in direction could be a change in whether it is a positive relationship or a negative relationship. So, let's say that someone who has a low SES gets a better job which enables them to get a reliable car. Having a reliable car would ensure they make it to their therapy appointments on time. As such, it would result in a change in direction of the relationship; it would become more positive.
I think demonstrating how the moderator and mediator variables can be analyzed in order to find results was helpful. I think in order for research to truly tell us something about human behavior, it is important to look at these variables which influence behavior.
Yes, I agree. It is important to consider all types of variables when researching.
Great mediation video, I feel so relaxed that I'm going to sell all my weed, thank you, so much, let's be RUclips pals?
Can summer be considered as a moderator then? If the context supports a possibility of that kind of interaction?
Well explained thanks Dr Todd.
You're welcome, thanks for watching -
If my independent variable is 'caregiver burden' and dependent variable is 'psychological distress,' the 'adaptive/maladaptive coping strategies' would be a moderator or a mediator variable?
Thanks for the Video, it has iilustrated the relationship clearly
You're welcome!
What if the causality of a relationship between IV and a certain variable is unknown? How do I know whether the certain variable is a moderator or mediator then?
Shew! This was very informative. I grasp the concept I believe.
Could access to a motor vehicle actually also be a moderator to the relationship between the two other variables (i.e. the relationship between the IV and the DV will probably differ at varying levels of access to a motor vehicle).
hello sir, why the miles from agency, family support cannot be mediators?
I wanted to ask that can moderation analysis with PROCESS be possible with a binary dichotomous variable as the dependent variable? Please guide and help
Very helpful. thank you!
Alright, this stuff was a bit difficult for me to understand. I get that the moderator variable moderates the strength of the relationship between the IV and DV. And then the mediator variable explains that relationship and (I think) seeks to help pinpoint the causal relationship? But once it got to confound variable I lost my understanding a bit. I understand that there's no relationship between the IV and the confound variable. But in that example in the video, I don't understand why there's a minus sign between the number of vacation hours and the number of hours studying? Is it just because the number of studying hours decreases when the number of vacation hours increases? I think listening to that part again would be helpful :P
Hi Jeff, I'd like to try to explain to you with my understanding of the video. You are understanding correctly. There is a minus sign between the # of vacation hours and the # of hours studying because # of hours studying decreases when # of vacation hours increases. As far as "summer time (season)" as a confound variable, it does not affect the relationship between #vacation and #studying, because #vacation does not increase summer time. Instead, summer time increases #vacation.
For reasons beyond me, this was very difficult for me to comprehend in regards to the name of the variable and how it interacts with the IV and DV. I will be revisiting this video I'm sure.
+Ashley Kilpatrick This was difficult for me as well. Some things seem to come easier than others that's for sure. :) It will be a video I will have to review as well.
It is a good presentation
Thank you.
I feel a little lost after this video. I understand some of the concepts, but not all of the concepts. The diagrams helped, but I am still a little mixed up with what changes with which variable. I will probably have to "re-watch" this video a few times. I liked how Dr. Grande explained how the variables may seem similar, but also, the reasons that distinguish them from one another.
Rachel Foster Initially, I was lost too! Especially with confound variables, but with Dr. Grande explaining CV along with his diagram made all the difference.
I agree with you . i think that the term that was not clear for me was the confound variables.
I felt a little lost too. I know I will have to re-watch this video and possible make flow charts of my own. I think the concepts are explained well but I think there is a lot to understand!
Rachel Foster I feel a little lost as well, but maybe if I watch it a few more times it will help. I think the diagrams were helpful and I will re-visit these.