51"41A. It^ Unadjusted group means and standard deviations. Descriptives. Lower Bound
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1 .p. Mitirt 51"41A LOLA". PA) Oietn AAA1-) It^ vils( -1-tArt- 6 /t KA-A-A-- -c ve,e1431,la-,t( i_acvm,c5 Lefy "Ac-- The DV is what is being measured. t)j._ /AAA.. Om-- 14(1 At41"14 # of posts per participant per week Unadjusted group means and standard deviations. Descriptives Measures the accuracy with which a sample represents a population. A sample mean deviates from the actual mean of a population. N Mean Std. Deviation Std. Errpt95` )/ Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Twitter Facebook MySpace / Total / \ {/4 Number of participants for each IV. Standard deviation= square root of variance. Column contains estimates of Minimum and maximum range of input variables for each level of IV. population SD for each condition. ij-m" A R -5 4A, --I<L4Cet- k,las "As cvt s;crafti h ere 5oi.) rr.2-5 r- ika, ANA, ajtor- 12f/fru, A41.6 """ -Kc?Art kt) StrVv\ M eiesor-r,a,w1 ḵ---<jv r (C-tart
2 I The ANOVA for the DV I Type III Sum of Squares can explain how much variation there is in each model. You'll see that the total sum of squares is derived from within groups + between groups sum of squares. Using sum of squares to determine the dispersion of data points in the set, we can take these data points and fit them to a function to see how they were # of posts per participant r week Between Groups Within Groups Total generated. ANOVA Sum of Squares df Mean Square F Sig g v%) Degrees of freedom. Calculated by n - 1. Total derived from between groups df + within groups df. The df simply equates to the number of values in the analysis that are free to vary. Mean square is the estimate of the variance across groups. Can be calculated by the sum of squares / degrees of freedom. You can use this to estimate a population of variance. F statistic is calculated by between groups variance / within groups variance. If the F is greater than 1, there is a chance the variable is significant. In this example the F is very small (.61) and therefore has no chance of significance. The signif correlation. IV was L- AVER 1:7 / A/e) ecyttoorwr i6/ DV. If the significance level was less than.5, there would be significance. However, there is no significance here at the.941 level. -X- Tht' rs A:c.a.-72er e4,---s,4 4' 4 TA, c 6- er 5 TY./ L.:- F v. Aro S/ 4 iv/' e el of the case, the ith the 'It AP,c, c F 2 tz foze) 4,.1t /
3 Means table for the covariate. # of hours on intemet Unadjusted group means and standard deviations. OK Co td4,ckir W-4-fr 14-g--t-- Are' r ogicow.ci, 9,9qiptive V rster*fr.5 Measures the accuracy with which a sample represents a population. A sample mean deviates from the actual mean of a population. N Mean Std. Deistio Std. Err 95 / Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound I N I Twitter Facebook MySpace Total _ Number of participants for each IV. Minimum and maximum range of input variables for each level of IV.
4 ANOVA on the covariate. Type III Sum of Squares can explain how much variation there is in each model. You'll see that the total sum of squares is derived from within groups + between groups sum of squares. Using sum of squares to determine the dispersion of data points in the set, we can take these data points and fit them to a function to see how they were # of hours on Internet generated. ANOVA Sum of Squares df Mean Square F Sig. Between Groups ' Within Groups Total \ Degrees of freedom. Calculated by n 1. Total derived from between groups df + within groups df. The df simply equates to the number of values in the analysis that are free to vary. Mean square is the estimate of the variance across groups. Can be calculated by the sum of squares / degrees of freedom. You can use this to estimate a population of variance. F statistic is calculated by between groups variance / within groups variance. If the F is greater than 1, there is a chance the variable is significant. In this example the F is very large (11.312) and therefore has a chance of being significant. The signifi ce le of the correlation In s case, the correlated ith the DV. If the significance level was less than.5, there would be sigqicance. -riieltraccylc ilzwerer, there is significance here at the. level. -"rtik= 3 roc-- 'S 5/4/v/i_7(4v7 6-fief F(e/ e4)--_,1/. VP/12(.cY
5 Before conducting an ANCOVA, th omogeneity-of-regressiorissunnption should first be tested. The test evaluates the interaction between the covariate and the factor (IV) in the prediction of the dependent variable. A significant interaction between the covariate and the factor suggests that the differences on the DV among groups vary as a function of the covariate. I F telcks-e- 1+1$ 4-t_k_ 1,-tAkketnsLty 6/1-- CA/ d---far i5 S4 -ee/ ovt kli-cas 4 1"-t (ti Sources of variance are the specific effects referred to in research hypotheses. For ANCOVA, these are the IV (Social Media), Covariate (Internet hours), and the interaction for social x internet. I Covariate I Interaction The DV is what we are mearsurine. Source Tests of Between-Subjects Effects endent Variable: # of posts per participant per week Corrected Model Intercept Social Hours TVope I III Sum of Squares a The analysis for testing Homogeneity of Regression for ANCOVA design. df Mean Se/4 F Sig. LO (", SpciaL* Hours A Error %./ Total Corrected Total / a. R Squared = 7 (Adjusted R Squared/.811) Degrees of F ratio= mean square effect Sum of squared deviations freedom. divided by the error about the mean (SS). term. EX: Measure of variability /1.422=.815 Another term for variance. These are estimates of population variance. The source for this test is the Social*Hours interaction effect. The three regression lines formed by x = covariate (hours) and y = dependent variable (posts/week) are parallel to one another. In this case we fail to reject the null hypothesis, F(2, 21)=.815; therefore we have Homogeneity of Regression and are allowed to conduct
6 3.- Social Network: Twitter R2 Linear a.834 IV being measured. -.-)c C ; E 15.- R... * o. v O. II- st I i I I I I of hours on Internet i I Covariate I. Because the test for homogeneity was not significant, this means that the relationship between the number of posts per participant per week and number of hours on the interne IIS V, 3 he same for all 3 levels of the IV. This shows that we have ho m geneity of regression and an ANCOVA can be conducted. rr Is r#6:-- s4/7 c /9-i-L 4,ericfii,tea/
7 3.- Social Network: Facebook ft2 Linear =.9 IV being measured. 1) 4, 25.- I DV I L gg 2.-. O Cl. N tit Ct O I of hours on Internet Covariate
8 3.- Social Network: MySpace R2 Linear =.816 IV being measured. I DV I 25.- ao a. fo 2.-. U ra a. 1.- Q leo I 1 1 I i # of hours on Internet Covariate
9 The DV is what we are measuring. IV All 3 levels of the IV. Twitter Facebook MySpace Total -rhws L 7-/r OZSC.C- 64 /41&-'7.r/r, P D Ae.c) res s rip we Statistics c-srkie Dependent Variable: # of posts per participant per week Social Network td. Deviation N Number of participants for the Twitter cell. 1 5t level of IV. Number of participants for the Facebook cell. 2 nd level of IV. Number of participants for the MySpace cell. 3 rd level of IV. Mean values for each level of the IV followed by the grand mean for all participants. Standard Deviation= square root of variance. An index of variability. Column contains estimates of population SD for each specified condition. Total number of participants.
10 rt.).4 \t ft(vt,.) The DV is what we are measuring. 64 A4, Go 4.Cul4c-t3-44 " (Stir cf roc 1 itivwvk -6 tgiope r -kly 41,m) vciiagls - LA- if vv-esue. (-9?srk" Aws:)v A VaelaACA_ *VI Mean square is the estimate re-^a; SA-5 of the variance across groups. Can be calculated by the sum of squares / degrees of freedom. You can use this to estimate a 'JSAtIL. tiaaaas.- giaa oirbe. population of variance. Tests of Between-Subjects ffects Dependent Variable: # of posts per participant per week Nm 4:) 'W AS *- CA I aiktdc-- -Cr E ('' Kum, oe- Gtr) ACC-tek- Sources of variance. Covariate IV Error Term tit Source Corrected Model Intercept Hours Type III Sum of Squares a Social ,Error _, Total Corrected Total 7933., df Mean Square F Sig. Partial Eta CO ',- N- (NI a. R Squared =.835 (Adj sted R Squared =.814) Sum of squared deviations about the mean (SS). Measure of variability. Lam. Degrees of Freedom Q F ration=mean square effect divided by the error term. EX: /1.254= t--- Squared The Null Hypothesis states that there will not be a significant overall effect for type of social media when the covariate is partially out. Looking at the Effect of the Independent Variable (Social Network), you will see that we Reject the N F(2, 23)=25.18, Ate,5 ( wri,.(l...) co, A7k-X y5 emf A./2_ S/O-AgAe74.Z* p ii= *-"4- othesis, /6 Fre /e CSC-Pif f 4.45 Y
11 The DV is what we are measuring. Measures the accuracy with which a sample represents a population. A sample mean deviates from the actual mean of a population. IV Dependent Variables # of posts per pa ant per week Social Network Mean Std. Error 95% Confidence Interval Lower Bound Upper Bound All 3 levels of the IV. Twitter a Facebook a MySpace a a. Covarial4 appe in the model are evaluated at the following values: # of ho s on internet Covariate Adjusted means by way of the Covariate (Internet hours). The adjustment, or overall mean for the covariate. ANOVA is conducted using the estim
12 The DV is what we are measuring. Represents the mean difference, based on estimated means, between each level of the IV. For example, the mean difference between Twitter posts and Facebook posts is The mean difference between Twitter and MySpace posts is Measures the accuracy with which a sample represents a population. A sample mean deviates from the actual mean of a population. Pairwise omparisons Dependent Variable: # of posts per participant per week. (I) Social Network (J) Social Network Mean Difference (I-J) Std. Error Sig. b 95% Confidence Interval for Difference b Lower Bound Upper Bound Three levels of the IV. Facebook Twitter MySpace Twitter Facebook. MySpace MySpace Twitter * Facebook * Based on estimated marginal means *. The mean difference is significant at the.5 level. b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adju tments). This part of the table gives us the significance level for differences between the number of posts. The mean difference is significant at the.5 level. The test of within-subjects informed us that we have an overall significant difference in means, but we do not know where those differences occurred. This table (Pairwise) allows us to discover which specific means differed. We can see that there was significance difference between all levels of the IV. A "13 4 t c P4-/XS " we---g e.--- ttli P/eAfitfrer opri.,eitt r, q.; /
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