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Question: Job performance under time pressure. Refer to the Academy of Management Journal (October 2015) study of how time pressure affects team job performance, Exercise 12.89 (p. 765). Recall that the researchers hypothesized a complete second-order model relating team performance (y) to perceived time pressure (x1), and whether or not the team had an effective leader (x2 = 1 if yes, 0 if no):

E(Y)=β0+β1x1+β2x22+β3x2+β4x1x2+β5x12x2

a) How would you determine whether the rate of increase of team performance with time pressure depends on effectiveness of the team leader?

b) For fixed time pressure, how would you determine whether the mean team performance differs for teams with effective and non-effective team leaders?

Short Answer

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Answer

a. The rate of increase of team performance with time pressure depends on effectiveness of the team leader will be determined by the interaction term. The interaction term explains the relation between the independent variables (here time pressure and effectiveness of the team leader) and how that relationship affects the dependent variable (team performance).

b. For given fixed time pressure, the changes in mean team performance for teams with effective and non-effective team leaders can be explained for effective team leader by and for non-effective team leader by .

Step by step solution

01

Interaction term

a. The rate of increase of team performance with time pressure depends on effectiveness of the team leader will be determined by the interaction term. The interaction term explains the relation between the independent variables (here time pressure and effectiveness of the team leader) and how that relationship affects the dependent variable (team performance).

b. For given fixed time pressure, the changes in mean team performance for teams with effective and non-effective team leaders can be explained for effective team leader by

.byE(y)=(β0+β3)+(β1+β4)x1+(β2+β5)x12andfornon-effectiveteamleaderbyE(y)=β0+β1x1+β2x12.

02

Interaction term

For given fixed time pressure, the changes in mean team performance for teams with effective and non-effective team leaders can be explained by putting x2 = 1 and 0 for effective and non-effective leader respectively.

Mathematically, for effective team leader

E(y)=β0+β1x1+β2x12+β3x2+β4x1x2+β5x12x2forx2=1E(y)=β0+β1x1+β2x12+β3(1)+β4x1(1)+β5x12(1)E(y)=(β0+β3)+(β1+β4)x1+(β2+β5)x12Andfornon-effectiveteamleaderE(y)=β0+β1x1+β2x12+β3x2+β4x1x2+β5x12x2forx2=0E(y)=β0+β1x1+β2x12+β3(0)+β4x1(0)+β5x12(0)E(y)=β0+β1x1+β2x12

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Most popular questions from this chapter

Question: The complete modelE(y)=β0+β1x1+β2x2+β3x3+β4x4+εwas fit to n = 20 data points, with SSE = 152.66. The reduced model,E(y)=β0+β1x1+β2x2+ε, was also fit, with

SSE = 160.44.

a. How many β parameters are in the complete model? The reduced model?

b. Specify the null and alternative hypotheses you would use to investigate whether the complete model contributes more information for the prediction of y than the reduced model.

c. Conduct the hypothesis test of part b. Use α = .05.

Personality traits and job performance. When attempting to predict job performance using personality traits, researchers typically assume that the relationship is linear. A study published in the Journal of Applied Psychology (Jan. 2011) investigated a curvilinear relationship between job task performance and a specific personality trait—conscientiousness. Using data collected for 602 employees of a large public organization, task performance was measured on a 30-point scale (where higher scores indicate better performance) and conscientiousness was measured on a scale of -3 to +3 (where higher scores indicate a higher level of conscientiousness).

a. The coefficient of correlation relating task performance score to conscientiousness score was reported as r = 0.18. Explain why the researchers should not use this statistic to investigate the curvilinear relationship between task performance and conscientiousness.

b. Give the equation of a curvilinear (quadratic) model relating task performance score (y) to conscientiousness score (x).

c. The researchers theorized that task performance increases as level of conscientiousness increases, but at a decreasing rate. Draw a sketch of this relationship.

d. If the theory in part c is supported, what is the expected sign ofβ2in the model, part b?

e. The researchers reportedβ^2=0.32with an associated p-value of less than 0.05. Use this information to test the researchers’ theory atα=0.05

Question: Determine which pairs of the following models are “nested” models. For each pair of nested models, identify the complete and reduced model.

a.E(y)=β0+β1x1+β2x2b.E(y)=β0+β1x1c.E(y)=β0+β1x1+β2x12d.E(y)=β0+β1x1+β2x2+β3x1x2e.E(y)=β0+β1x1+β2x2+β3x1x2+β4x21+β5x22


Goal congruence in top management teams. Do chief executive officers (CEOs) and their top managers always agree on the goals of the company? Goal importance congruence between CEOs and vice presidents (VPs) was studied in the Academy of Management Journal (Feb. 2008). The researchers used regression to model a VP’s attitude toward the goal of improving efficiency (y) as a function of the two quantitative independent variables level of CEO (x1)leadership and level of congruence between the CEO and the VP (x2). A complete second-order model in x1and x2was fit to data collected for n = 517 top management team members at U.S. credit unions.

a. Write the complete second-order model for E(y).

b. The coefficient of determination for the model, part a, was reported asR2=0.14. Interpret this value.

c. The estimate of theβ-value for the(x2)2term in the model was found to be negative. Interpret this result, practically.

d. A t-test on theβ-value for the interaction term in the model,x1x2, resulted in a p-value of 0.02. Practically interpret this result, usingα=0.05.

Suppose you have developed a regression model to explain the relationship between y and x1, x2, and x3. The ranges of the variables you observed were as follows: 10 ≤ y ≤ 100, 5 ≤ x1 ≤ 55, 0.5 ≤ x2 ≤ 1, and 1,000 ≤ x3 ≤ 2,000. Will the error of prediction be smaller when you use the least squares equation to predict y when x1 = 30, x2 = 0.6, and x3 = 1,300, or when x1 = 60, x2 = 0.4, and x3 = 900? Why?

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