Binary Logistic Regression in SPSS, psychology homework help

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To prepare

  • Review the datasets provided.
  • Construct a research question based on one of those datasets.
  • Remember that your dependent variable must be dichotomous.
  • Think about how you might use the odds ratio in your analysis to simplify the interpretation of your results.

By Day 5

The Assignment

For the week 5 assignment, please use the GSS2014_student_8260 binary logistic regression.sav file. Use “Gun in Home” as the dependent measure and the other three variables as predictors. Be sure to discuss how you used the Political Party variable in your problem; i.e., are you treating it as a nominal, ordinal, or scale in SPSS? Evaluate the overall model and interpret all of the logits in the model. Finally, discuss whether the model may have real world implications.

Use SPSS to answer the research question you constructed. Then, compose a 1- to 2-paragraph analysis in APA format in which you answer the following questions:

  1. What is the null hypothesis for your question?
  2. What research design(s) would align with this question?
  3. What dependent variable was used and how is it measured?
  4. What independent variable is used and how is it measured?
  5. How well did the model fit?
  6. If you found significance, what is the strength of the effect?
  7. What is the answer to your research question?
  8. What are the possible implications of social change?

Be sure to include your data output with your analysis.

*Note: The transcript of a Binary logistic regression video is attached. If used, here is the proper reference:

Laureate Education (Producer). (2017d). Binary logistic regression [Video file]. Baltimore, MD: Author.

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Binary Logistic Regression: Week 5 Binary Logistic Regression: Week 5 Program Transcript MATTHEW JONES: This week, we're going to continue with logistic regression, except now we're going to add more predictor variables. That is, we're going to be building multiple logistic regression model. Last week, we looked at the effect of gender on fear of asking for help. This week, we're going to add one more variable-- age. That is, we're going to examine the effect of gender on fear of asking for help while controlling for age, and vice versa. Let's go to SPSS to see how we do this. To conduct our binary logistic regression, we're going to go up here to Analyze and Regression, just as you're familiar with in fitting ordinary least squares models, except now we're going to move over to Binary Logistic Regression. So our dependent variable is fear of asking for help. So remember, we have two variables in here about fear of asking for help. We have the original variable, fear of asking for help at time point 1-- and actually, there is a time point 2 and a time 3 as well-- but also, this fear of asking for help dichotomous variable. So remember, in binary logistic regression, the outcome variable is binary. So we move that over into our dependent variable. And then our covariates are going to be gender and student age. Now, you might remember from ordinary least squares regression, whenever you have a categorical variable-- and gender is a categorical variable here-- you have to dummy code that. It was already dummy coded in this data set as 0 and 1, but there's a handy little feature in SPSS that helps you with dummy coding just in case you have a variable that has more than two attributes to it, maybe something like political party affiliation or race or ethnicity. But we can go up here to Categorical. And we move gender into there. The interesting thing about the categorical covariate box in logistic regression is it lets you build reference categories. So it will go ahead and take care of much of that for you. So you just have to tell it whether you want to reference first or last as the reference category. So here by default, it's listed as last. So my variable of gender is coded as 0 for male and 1 is female. So it's going to say, all right, the reference category is the last. That is the highest value, 1. So 1 is higher than 0, and 1 is coded as female. So when I get a coefficient, that is always going to be compare to females. Click Continue. And one thing I do want to mention under Options is you have this HosmerLemeshow goodness of fit test. And this was a pretty popular test that's been used to assess how well your logistic regression model fits. Currently, Hosmer no longer recommends the use of this test, so I'm not going to go over it here. But © 2017 Laureate Education, Inc. 1 Binary Logistic Regression: Week 5 you might want to talk to your instructor if he or she has a preference about that test. So we're going to click OK, and that will give us our output. And we get quite a bit of output in the logistic regression model. Here we see we have some case processing summary. It's quite handy that we have no missing cases here. Again, this is a simulated data set. But a couple of things that I really want to pay attention to before I start interpreting output is the coding-- so first of all, my dependent variable coding. So I see here that my dependent variable yes-- so yes, I'm afraid of asking for help is coded as yes, and no is 0. So yes equals 1, so it's predicting fear of asking for help. Also, here's that categorical variable coding. So it gives you some parameter coding. So it's telling me male equals 1. So just as I mentioned in the categorical variable box, whenever we see that coefficient and we're interpreting the values for the gender variable, it's going to be for males, again, compared to females. So the first block we get here is the beginning block, block 0. This is sometimes called the null model or the intercept only model. So it gives me a little bit of information. Some people just go past it rather quickly. But depending on what you want to find out, you might want to spend a little bit more time with it. Let's just tell you quickly where some of these numbers come from. So you see this percentage correct here. So the percentage correct, 57. So that is taking 191 and dividing it by 335, which is a total sample. 191 plus 144 equals 335. So 57% had no fear, and 43% had fear. So with no other information-- so again, the null model, no other information-- the best strategy is to predict students will have no fear. Indeed, we can see just looking at raw counts, those counts are indeed higher. Here we have the variables in the equation, again, for the null model. This little exp(B) refers to the odds ratio. So it's predicting the odds of having fear. Sometimes this is interpreted. Sometimes it's not. It's just depends on what you are interested in here. The next piece of output gives us the variables not in the equation. So this is gender and age. These are the variables we're interested in. And already, we can see there's going to be some significance there. So let's go ahead and scroll down to block 1, which is going to be our full model. So I'm looking here at model summary. So in an ordinary least squares regression, you're probably used to interpreting the r square and the adjusted r square. You really don't have that in a logistic regression model. It gives you a © 2017 Laureate Education, Inc. 2 Binary Logistic Regression: Week 5 model summary, and SPSS defaults to this thing called negative 2 log likelihood. The Cox and Snell r square and the Nagelkerke r square. So I'm just going to focus on these two right here that say r square. Sometimes, they're technically not r square, and so we often refer to them as pseudo r square. So sometimes, you'll see that in the literature when you're reading some results, the pseudo r square. I find for my benefit, the easiest one to interpret is the Nagelkerke, and simply because that has a possible range of values from 0 to 1 where Cox and Snell goes from 0 to 0.75. So it's easier to interpret, I think, for most people 0.05 out of 100 rather than 0.03 out of 75. So I can treat this like my adjusted r square and ordinary least squares regression where I'm saying-- I'm turning this into a percentage and saying, OK, my predictor variables account for 5.1% of what I'm observing in my outcome variable. So nothing too exciting there. It's not a large pseudo r square. But nonetheless, there is something there. There is some predictive value. So now I'm moving down to my second classification table. Now, we saw the classification table above, but this is including our predictor variables. So now we see the percentage correct is a little higher, 61.2%. So we get a little bit more information. So here's the meat and potatoes, if you will, of a logistic regression analysis, the variables in the equation. So we have a beta, just like we would in an ordinary least squares regression. But that's interpreted in a rather different fashion. Because in logistic regression, you're talking about log odds. So it's not as easy to say as you would in ordinary least squares regression, for every one-unit increase in x, y is going to increase this much, because you have to talk in units of log odds, which the vast majority of people probably find quite confusing. So we tend to focus on the exponent of that, which is an odds ratio. And that's what we talked a little bit last week in our Excel template about. So here, when we're looking at gender, the first thing we explore is whether it's statistically significant or not. 0.03, it is below the conventional threshold of 0.05. So we can judge that statistically significant. So what we can say about men-- because remember, we're talking about men here. It's parameter coded as 1. And so females were originally coded as 1. But now we're looking at females as the reference category. So the odds of being fearful of asking for help are lower for men. And here's a really important part in how you interpret it-- compared to females. Because remember, it's that ratio. © 2017 Laureate Education, Inc. 3 Binary Logistic Regression: Week 5 So maybe if it's-- again, as we discussed last week, some of these, when you have a negative beta here, your exponent, your odds ratio is going to be that below that 1.0, 1.0 being all things are equal. Sometimes, it's easier to invert that. So we might want to take the reciprocal of that. We have 1 divided by 0.559 gives us 1.67. So we might want to interpret that in terms of the females. What this means is the odds of females being fearful of asking for help are 1.67 times higher compared to men controlling for age. Or we could just simply keep it and interpret it as the 0.599 for males. Again, it's up to you what makes most sense to you and your audience. You could always go back through the procedure and change the parameter coding in your categorical box and use males as a reference category. And you would get that 1.67 odds ratio in your output instead. So looking at our age, you see that is also statistically significant, 0.003, well below the 0.05 threshold. We have a positive beta here. Our odds ratio is really not that exciting. You see it's pretty close to 1, but nonetheless slightly higher than 1 and statistically significant. So what we can say about that is for every one-unit increase in age compared to the previous age-- so remember how we're interpreting ratios-- the odds of fear of asking for help increased by 1.038, controlling for gender. So again, remember, we were still doing a type of multiple regression where we're controlling for the combined effects of our independent variables. So here for the odds ratio for gender, again, if we're having some difficulty interpreting this odds ratio below 1, or we really wanted to interpret females compared to males, we could just simply go back and change our parameter coding. So we go back in to Analyze, Regression, Binary Logistic. And everything is still in there. So we just go back to Categorical. And what we do here is highlight this. So again, remember, by default, it said last, so a reference category last. This is the highest coded value or attribute for that variable. And coded in the data, 1 equals female, so it's saying, OK, female is going to be your reference. Maybe we want it to be male. So then we just simply go to first. Important step here is don't forget to hit Change. Because it will let you hit Continue, and it will actually never change it until you hit Change. So don't forget that. And it tells you right here up in categorical covariates. OK, now first is here in parentheses. Click Continue. So let's click OK. And now we should get an odds ratio that is 1 divided by 0.559. And Indeed, we do. There we had gender, 1.67. So maybe that might be a little easier to interpret. © 2017 Laureate Education, Inc. 4 Binary Logistic Regression: Week 5 Binary Logistic Regression: Week 5 Additional Content Attribution FOOTAGE: GettyLicense_160463144 simonkr/Creatas Video/Getty Images GettyLicense_626791754 AzmanL/Creatas Video/Getty Images GettyLicense_114759820 David Baumber/Vetta/Getty Images © 2017 Laureate Education, Inc. 5
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Explanation & Answer

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Analysis of the Binary Logistic Regression in SPSS
The null hypothesis of the research is that there is no statistical significance of gun in the home
concerning age, social, economic index and political party affiliation. Moreover, this an
experimental research type of research design which requires field data collection through
interviewing and filling of t...

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