Correlations Notes Output Created 12-FEB-2024 23:01:50 Comments Input Data C:\Users\Administrator\Downloads\NCS-data set for assignments (8).sav Active Dataset DataSet1 Filter Weight Split File N of Rows in Working Data File 9282 Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics for each pair of variables are based on all the cases with valid data for that pair. Syntax CORRELATIONS /VARIABLES=AGE ABUSE /PRINT=TWOTAIL NOSIG FULL /MISSING=PAIRWISE. Resources Processor Time 00:00:00.05 Elapsed Time 00:00:00.05 Correlations Age Frequency of childhood abuse experiences Age Pearson Correlation 1 -.086** Sig. (2-tailed) <.001 N 9282 9153 Frequency of childhood abuse experiences Pearson Correlation -.086** 1 Sig. (2-tailed) <.001 N 9153 9153 ** Correlation is significant at the 0.01 level (2-tailed). GGraph Notes Output Created 12-FEB-2024 23:24:35 Comments Input Data C:\Users\Administrator\Downloads\NCS-data set for assignments (8).sav Active Dataset DataSet1 Filter Weight Split File N of Rows in Working Data File 9282 Syntax GGRAPH /GRAPHDATASET NAME="graphdataset" VARIABLES=ABUSE AGE MISSING=LISTWISE REPORTMISSING=NO /GRAPHSPEC SOURCE=INLINE /FITLINE TOTAL=NO SUBGROUP=NO. BEGIN GPL SOURCE: s=userSource(id("graphdataset")) DATA: ABUSE=col(source(s), name("ABUSE")) DATA: AGE=col(source(s), name("AGE")) GUIDE: axis(dim(1), label("Frequency of childhood abuse experiences")) GUIDE: axis(dim(2), label("Age")) GUIDE: text.title(label("Scatter Plot of Age by Frequency of childhood abuse experiences")) ELEMENT: point(position(ABUSE*AGE)) END GPL. Resources Processor Time 00:00:07.14 Elapsed Time 00:00:42.18 1. Compute the Pearson correlation, r, and a scatterplot of the two variables you used in the previous assignment. See pages 91-93 of the text for instructions on using SPSS to compute the statistics Assume you computed an r = +.50 for the variables, exercise and lifespan. 2. What is the magnitude of the correlation (high, medium, low)? High, or 'strong' (see page 87, Table 5.3) 3. What is the direction of the correlation (positive or negative)? Positive Note: the r value for a negative correlation would have a negative sign in front of it, e.g., -.50. 4. What prediction can you make based on the variables if their correlation, r = .50? 5. What prediction can you make based on the variables if their correlation, r = - .50? 9. Compute the partial correlation of the two variables controlling for the confounding variable