Assignment 3 requires that you complete descriptive statistics, correlation analysis, and multiple regression analysis just like in Assignment 2, with a new dataset.

This time I want to know some strategies that will yield greater individual performance. You will all eventually become managers charged with improving the performance of your employees. Classical management theory says that you should make certain that you are following the principles of management (PRI), have implemented the management process (PRO), and are reinforcing performance through an organizational behavior modification (OBM) program.

Let’s see if that’s the case. Follow these steps to determine if following the principles (PRI), administering the process (PRO), and reinforcing performance (OBM) leads to improved individual performance (IP).

1. Load the PRI_PRO_OBM_IP.sav data file into SPSS.
2. Generate the descriptive statistics table for the four variables (PRI, PRO, OBM, and IP) with the following command sequence: [analyze, descriptive statistics, descriptive, move all four variables to the variables box, options, click on kurtosis and skewness, continue, OK].
3. Copy the descriptive statistics table to your Word file and interpret the results of the skewness and kurtosis coefficients. I want to know if the variables are normally distributed. Remember:
   If the skewness and kurtosis values are between -2.00 and +2.00 you can conclude that the variables are normally distributed.
4. Generate the correlation matrix for the four variables (PRI, PRO, OBM, and IP) with the following command sequence: [analyze, correlate, bivariate, move all four variables to the variables box, OK ] . The correlation matrix contains the correlation coefficients for each pairing of the four variables and also provides significance level information.
5. Copy the correlation matrix to a Word document and interpret the six correlation coefficients by stating the type (Note A) and strength of each of the coefficients (Note B) and significance level of each of the coefficients (Note C). The following notes provide information necessary to make the interpretations.
   Note A: A correlation coefficient (R) measures the relationship between two variables. It can have values from -1 to +1. A coefficient of 0 means that the two variables are not related. A coefficient with a negative value means that the two variables are inversely related (as one goes up, the other goes down). A coefficient with a positive value means that the two variables are positively related (as one goes up, the other goes up).
   Note B: The closer that the coefficient ’ s value is to -1 or +1 the stronger the relationship between the variables. Absolute coefficients with values less than or equal to .25 are weak, between .26 and .50 are moderate , between .51 and .75 are strong, and between .76 and 1.00 are very strong.
   Note C: The significance level has to do both with the size of the sample and the relative strength of the coefficient. The larger the sample, the more likely a coefficient is significant. The larger the coefficient, the more likely it is to be significant. Note that SPSS tells you if the coefficient is significant at either the .01 level (**) or .05 (*). If a coefficient is not significantly different from zero, SPSS displays no asterisks.
6. Generate the regression results using the following command sequence: [analyze, regression, linear, move PRI, PRO, and OBM to the independent variables box and move I P to the dependent variables box, OK ] . This time you are conducting multiple linear regression analysis. This means that there are multiple independent variables. Note that four tables are generated. Only the model summary and coefficients tables are of interest.
7. Copy the model summary table to the Word document and interpret the R ² value according to the information provide below.
   The coefficient of determination (R ² ) can have values from 0 to +1 (the closer to +1, the stronger the relationship). It can be more easily interpreted, however. For example, if the R ² for the relationship between variables X and Y is .64, the interpretation would be "The variation is X expl ains 64% of the variation in Y." In this case there are three independent variables PRI, PRO, and OBM. So, your interpretation will go like this " PRI, PRO, and OBM combine to explain __% of the variation in I P."
8. Copy the coefficients table to the Word document. Formulate the regression equation from the unstandardized regression coefficients (in the B column) using the information provided below.
   The multiple linear regression formula takes this general form: Y = b0 + b 1* X 1 + b2*X2 + b3*X3 , where Y is the dependent variable, b0 is the i ntercept ; b 1, b2, and b3 are the unstandardized regression coefficients; and X 1, X2, and X3 are values of the independent variable. In this case, your formula takes this form: I P = b0 + b 1* PRI + b2* PRO + b3* OBM . Look in the B column of the coefficients table to find the values for b0 , b 1, b2, and b3 .
   Interpret the significance levels for the regression coefficients using the information provided below.
   Look to the far right in the coefficients table to the significance column. You are trying to determine if the regression coefficients (b ’s ) are significantly different from 0. If the computed significance level in the significance column is .01 or less, the regression coefficient is significantly different at the .01 level. If the computed significance level is greater than .01 but less than or equal to .05, the regression coefficient is significantly different at the .05 level. If the computed significance level is greater than .05, conclude that the regression coefficient is not significantly different from zero. Again, significance level has more to do with the sample size than with the absolute size of the regression coefficients.
9. Use the regression formula to predict IP if PRI is 1.0, PRO is 1.0 and OBM is 1.0. Now, predict IP if PRI is 7.0, PRO is 7.0, and OBM is 7.0. In other words, what’s the organization’s performance level if they have relatively low levels of PRI, PRO, and OBM versus relatively high levels?
10. What do you conclude from the results of the correlation and regression analyses of the relationship between IP and PRI, PRO, and OBM?

This is the most important step! Managers are constantly trying to identify strategies that improve the performance of their organizations. Are managers wise to implement PRI, PRO, and OBM for their organizations?

Price: $23.64

Solution: The downloadable solution consists of 8 pages, 1564 words.
Deliverable: Word Document