WEBVTT 00:00:00.000 --> 00:00:01.455 align:middle line:90% [MUSIC PLAYING] 00:00:01.455 --> 00:00:16.020 align:middle line:90% 00:00:16.020 --> 00:00:17.910 align:middle line:84% Hello and welcome to a quick tutorial 00:00:17.910 --> 00:00:22.740 align:middle line:84% on how to calculate a multiple linear regression using JASP. 00:00:22.740 --> 00:00:24.450 align:middle line:84% Now, for our purposes here, we are 00:00:24.450 --> 00:00:26.430 align:middle line:84% going to use the exact same example 00:00:26.430 --> 00:00:27.900 align:middle line:84% for the multiple linear aggression 00:00:27.900 --> 00:00:30.210 align:middle line:84% that we used in Quantitative Research 00:00:30.210 --> 00:00:31.950 align:middle line:84% Methods for Communication, A Hands-On 00:00:31.950 --> 00:00:34.110 align:middle line:90% Approach, The Fourth Edition. 00:00:34.110 --> 00:00:36.720 align:middle line:84% In that case, we wanted to look at the combined 00:00:36.720 --> 00:00:41.130 align:middle line:84% linear relationship among communication apprehension, 00:00:41.130 --> 00:00:43.770 align:middle line:84% assertiveness, responsiveness, and willingness 00:00:43.770 --> 00:00:46.680 align:middle line:84% to communicate with the belief that everyone 00:00:46.680 --> 00:00:49.650 align:middle line:84% should be required to take public speaking in college. 00:00:49.650 --> 00:00:51.647 align:middle line:84% So let's go ahead and look at this. 00:00:51.647 --> 00:00:54.230 align:middle line:84% So to calculate this, I'm going to come up here to Regression, 00:00:54.230 --> 00:00:58.710 align:middle line:84% and I'm going to scroll down to Linear Regression. 00:00:58.710 --> 00:01:00.510 align:middle line:84% Then, I want to find the variables 00:01:00.510 --> 00:01:01.650 align:middle line:90% that I am looking for. 00:01:01.650 --> 00:01:03.150 align:middle line:84% So let's start off with what we call 00:01:03.150 --> 00:01:05.280 align:middle line:84% the covariates in this case, which 00:01:05.280 --> 00:01:06.720 align:middle line:84% are going to be the Communication 00:01:06.720 --> 00:01:11.480 align:middle line:84% Apprehension, Assertiveness, Responsiveness, 00:01:11.480 --> 00:01:13.620 align:middle line:84% and the last one is going to be that Willingness 00:01:13.620 --> 00:01:14.645 align:middle line:90% to Communicate. 00:01:14.645 --> 00:01:16.020 align:middle line:84% And we're going to be correlating 00:01:16.020 --> 00:01:18.840 align:middle line:84% this with that belief that public speaking should be 00:01:18.840 --> 00:01:20.475 align:middle line:90% taken by everyone in college. 00:01:20.475 --> 00:01:23.100 align:middle line:84% So I'm going to go ahead and put that in the Dependent Variable 00:01:23.100 --> 00:01:24.660 align:middle line:90% box. 00:01:24.660 --> 00:01:26.160 align:middle line:84% And immediately, all the information 00:01:26.160 --> 00:01:28.920 align:middle line:84% that we need for R-squared has been populated. 00:01:28.920 --> 00:01:32.560 align:middle line:84% So our R-value, as you can see here, is 0.31. 00:01:32.560 --> 00:01:38.400 align:middle line:84% R-squared is 0.096, so roughly 9.6% of the variance 00:01:38.400 --> 00:01:41.660 align:middle line:84% can be accounted for by the model of big communication 00:01:41.660 --> 00:01:44.160 align:middle line:84% apprehension, assertiveness, responsiveness, and willingness 00:01:44.160 --> 00:01:45.510 align:middle line:90% to communicate. 00:01:45.510 --> 00:01:47.940 align:middle line:84% The next one is the ANOVA summary table. 00:01:47.940 --> 00:01:52.500 align:middle line:84% We know our degrees of freedom are 4 and 582. 00:01:52.500 --> 00:01:55.650 align:middle line:90% The F-test is 15.52. 00:01:55.650 --> 00:01:57.750 align:middle line:84% And we can see that our probability 00:01:57.750 --> 00:02:03.690 align:middle line:84% value or our significance level is P is less than 0.001. 00:02:03.690 --> 00:02:07.990 align:middle line:84% So that gives us our basic, overall regression model. 00:02:07.990 --> 00:02:10.710 align:middle line:84% So we know that the regression is statistically significant. 00:02:10.710 --> 00:02:15.120 align:middle line:84% So now let's look and see which of the four covariates 00:02:15.120 --> 00:02:17.680 align:middle line:84% are accounting for unique variance. 00:02:17.680 --> 00:02:19.360 align:middle line:84% So the very first one, to find this out, 00:02:19.360 --> 00:02:21.020 align:middle line:84% you can come over here to the P column. 00:02:21.020 --> 00:02:23.020 align:middle line:84% And the first one is Communication Apprehension, 00:02:23.020 --> 00:02:26.370 align:middle line:84% and you'll notice that it is less than 0.001. 00:02:26.370 --> 00:02:28.950 align:middle line:84% So this row is statistically significant. 00:02:28.950 --> 00:02:33.060 align:middle line:84% And that t-value is negative 6.90. 00:02:33.060 --> 00:02:37.470 align:middle line:84% And the beta weight is a negative 0.310. 00:02:37.470 --> 00:02:41.130 align:middle line:84% The standardized weight or the beta weight-- 00:02:41.130 --> 00:02:43.516 align:middle line:84% what that basically lets us know is the magnitude. 00:02:43.516 --> 00:02:46.140 align:middle line:84% And you can also think of it, in many ways, like a correlation. 00:02:46.140 --> 00:02:49.230 align:middle line:84% So because this is negative, we can definitely 00:02:49.230 --> 00:02:52.410 align:middle line:84% know that a person's level of communication apprehension 00:02:52.410 --> 00:02:55.587 align:middle line:84% is negatively-related, in this model, to their belief 00:02:55.587 --> 00:02:57.420 align:middle line:84% that all students should be required to take 00:02:57.420 --> 00:02:59.070 align:middle line:90% public speaking in college. 00:02:59.070 --> 00:03:01.140 align:middle line:84% So let's see if any of the other ones are. 00:03:01.140 --> 00:03:03.960 align:middle line:84% Well, Assertiveness, the P-value is 0.15, 00:03:03.960 --> 00:03:06.240 align:middle line:90% so that's greater than 0.05. 00:03:06.240 --> 00:03:09.240 align:middle line:90% Responsiveness is at 0.318. 00:03:09.240 --> 00:03:11.370 align:middle line:90% Again, greater than 0.05. 00:03:11.370 --> 00:03:15.090 align:middle line:84% And Willingness to Communicate is that 0.563. 00:03:15.090 --> 00:03:18.070 align:middle line:84% So again, that's not statistically significant. 00:03:18.070 --> 00:03:19.920 align:middle line:84% So the only variable that accounts 00:03:19.920 --> 00:03:22.560 align:middle line:84% for unique variance in this model 00:03:22.560 --> 00:03:24.900 align:middle line:90% is Communication Apprehension. 00:03:24.900 --> 00:03:28.710 align:middle line:84% And that is how you can run a multiple linear regression 00:03:28.710 --> 00:03:30.450 align:middle line:90% using JASP. 00:03:30.450 --> 00:03:32.600 align:middle line:90% [MUSIC PLAYING] 00:03:32.600 --> 00:03:44.582 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