Question: Job performance under time pressure. Time pressure is common at firms that must meet hard and fast deadlines. How do employees working in teams perform when they perceive time pressure? And, can this performance improve with a strong team leader? These were the research questions of interest in a study published in the Academy of Management Journal (October, 2015). Data were collected on n = 139 project teams working for a software company in India. Among the many variables recorded were team performance (y, measured on a 7-point scale), perceived time pressure (, measured on a 7-point scale), and whether or not the team had a strong and effective team leader (x2 = 1 if yes, 0 if no). The researchers hypothesized a curvilinear relationship between team performance (y) and perceived time pressure (), with different-shaped curves depending on whether or not the team had an effective leader (x2). A model for E(y) that supports this theory is the complete second-order model:E(y)=β0+β1x1+β2x12+β3x2+β4x1x2+β5x12x2

a. Write the equation for E(y) as a function of x1 when the team leader is not effective (x2= 0).

b. Write the equation for E(y) as a function ofwhen the team leader is effective (x2= 1).

c. The researchers reported the following b-estimates:.

β0^=4.5,β1^=0.13,β3^=0.15,β4^=0.15andβ5^=0.29Use these estimates to sketch the two equations, parts a and b. What is the nature of the curvilinear relationship when the team leaders is not effective? Effective?

Short Answer

Expert verified

Answer

  1. When x2= 0, the equation of E(y) can be written asE(y)=β0+β1x1+β2x12
  2. Whenx2= 1, the equation of E(y) can be written asE(y)=(β0+β3)+(β1+β4)x1+(β2+β5)x12
  3. The curvilinear relationship changes when team leaders are effective and not effective. When the team leaders are effective, there is a downward sloping curve observed meaning that there is a negative relationship between team performance and effectiveness of team leader. While when the team leaders are not effective, there is an upward sloping curve observed indicating positive relationship between team performance and team leader not being effective.

Step by step solution

01

Equation for E(y)

When x2= 0, the equation of E(y) can be written as

E(y)=β0+β1x1+β2x12+β3x2+β4x1x2+β5x12x2E(y)=β0+β1x1+β2x12+β3(0)+β4x1(0)+β5x12(0)E(y)=β0+β1x1+β2x12

02

Calculation for E(y)

Whenx2= 1, the equation of E(y) can be written as

E(y)=β0+β1x1+β2x12+β3x2+β4x1x2+β5x12x2E(y)=β0+β1x1+β2x12+β3(1)+β4x1(1)+β5x12(1)E(y)=(β0+β3)+(β1+β4)x1+(β2+β5)x12

03

Graph

When x2= 0, the equation of E(y) can be written asE(y)=4.5-0.13x1-0.17x12.

Whenx2 = 1, the equation of E(y) can be written asE(y)=4.65+0.02x1-0.12x12

The curvilinear relationship changes when team leaders are effective and not effective. When the team leaders are effective, there is a downward sloping curve observed meaning that there is a negative relationship between team performance and effectiveness of team leader.

While when the team leaders are not effective, there is an upward sloping curve observed indicating positive relationship between team performance and team leader not being effective.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Question: Reality TV and cosmetic surgery. Refer to the Body Image: An International Journal of Research (March 2010) study of the impact of reality TV shows on one’s desire to undergo cosmetic surgery, Exercise 12.17 (p. 725). Recall that psychologists used multiple regression to model desire to have cosmetic surgery (y) as a function of gender(x1) , self-esteem(x2) , body satisfaction(x3) , and impression of reality TV (x4). The SPSS printout below shows a confidence interval for E(y) for each of the first five students in the study.

  1. Interpret the confidence interval for E(y) for student 1.
  2. Interpret the confidence interval for E(y) for student 4

When a multiple regression model is used for estimating the mean of the dependent variable and for predicting a new value of y, which will be narrower—the confidence interval for the mean or the prediction interval for the new y-value? Why?

Question: Bordeaux wine sold at auction. The uncertainty of the weather during the growing season, the phenomenon that wine tastes better with age, and the fact that some vineyards produce better wines than others encourage speculation concerning the value of a case of wine produced by a certain vineyard during a certain year (or vintage). The publishers of a newsletter titled Liquid Assets: The International Guide to Fine Wine discussed a multiple regression approach to predicting the London auction price of red Bordeaux wine. The natural logarithm of the price y (in dollars) of a case containing a dozen bottles of red wine was modelled as a function of weather during growing season and age of vintage. Consider the multiple regression results for hypothetical data collected for 30 vintages (years) shown below.

  1. Conduct a t-test (atα=0.05 ) for each of the βparameters in the model. Interpret the results.
  2. When the natural log of y is used as a dependent variable, the antilogarithm of a b coefficient minus 1—that is ebi - 1—represents the percentage change in y for every 1-unit increase in the associated x-value. Use this information to interpret each of the b estimates.
  3. Interpret the values of R2and s. Do you recommend using the model for predicting Bordeaux wine prices? Explain

Question: Accuracy of software effort estimates. Periodically, software engineers must provide estimates of their effort in developing new software. In the Journal of Empirical Software Engineering (Vol. 9, 2004), multiple regression was used to predict the accuracy of these effort estimates. The dependent variable, defined as the relative error in estimating effort, y = (Actual effort - Estimated effort)/ (Actual effort) was determined for each in a sample of n = 49 software development tasks. Eight independent variables were evaluated as potential predictors of relative error using stepwise regression. Each of these was formulated as a dummy variable, as shown in the table.

Company role of estimator: x1 = 1 if developer, 0 if project leader

Task complexity: x2 = 1 if low, 0 if medium/high

Contract type: x3 = 1 if fixed price, 0 if hourly rate

Customer importance: x4 = 1 if high, 0 if low/medium

Customer priority: x5 = 1 if time of delivery, 0 if cost or quality

Level of knowledge: x6 = 1 if high, 0 if low/medium

Participation: x7 = 1 if estimator participates in work, 0 if not

Previous accuracy: x8 = 1 if more than 20% accurate, 0 if less than 20% accurate

a. In step 1 of the stepwise regression, how many different one-variable models are fit to the data?

b. In step 1, the variable x1 is selected as the best one- variable predictor. How is this determined?

c. In step 2 of the stepwise regression, how many different two-variable models (where x1 is one of the variables) are fit to the data?

d. The only two variables selected for entry into the stepwise regression model were x1 and x8. The stepwise regression yielded the following prediction equation:

Give a practical interpretation of the β estimates multiplied by x1 and x8.

e) Why should a researcher be wary of using the model, part d, as the final model for predicting effort (y)?

Suppose you fit the second-order model y=β0+β1x+β2x2+εto n = 25 data points. Your estimate ofβ2isβ^2= 0.47, and the estimated standard error of the estimate is 0.15.

  1. TestH0:β2=0againstHa:β20. Useα=0.05.
  2. Suppose you want to determine only whether the quadratic curve opens upward; that is, as x increases, the slope of the curve increases. Give the test statistic and the rejection region for the test forα=0.05. Do the data support the theory that the slope of the curve increases as x increases? Explain.
See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free