Running head Organization behaviorOrganization behavior 2.docx
- 1. Running head: Organization behavior Organization behavior 2 Organization behavior Name: Institution: Course: Date: Organizational behavior analyzes the environment in different perspectives in order to come up with policies which make the organization convenient in its business operations. The organization must analyze various factors which affect it in order to frame the different policies. This means finding out the challenges or problems which an individual face in an organization and also the problems that groups faces in the organization. In this context, organization behavior is simply the way which an organization uses to solve the problems in its environment (Kreitner 2012). This discussion will involve Apple Inc. One of the challenges facing Apple Inc. is managing human resources. Human resources in Apple Inc. are an invaluable
- 2. asset and are always associated with the organization. Apple had experienced problems in managing its human resources. Some of the issues it experienced include failing to retain employees’ talents, not observing diverse recruitment to its fullest, non-performance among employees and employees not getting their benefits appropriately (O'Grady 2015). This went hand in hand with violation of rules governing employees, code of conduct and features which keep the value of team and organization high. The individuals’ and organization’s wellbeing depend highly on each other. This means that what people do while in the organization should reflect what is in their mind. The organizational value highly depends on social responsibility which the organization is portraying. They should put up policies for protecting the organizational environment. The issue has affected the behavior of Apple and the human resource management sorted them out (O'Grady 2015). Managing human resources and employees ethics is a very important issue and a backbone of any organization. If managed well, the organization is likely to succeed easily. If not managed well, the issues will spoil the organization’s reputation completely and the organization may not undergo dissolution (Kreitner 2012). References Kreitner, Angelo Kinicki & Robert. 2012. Organization behavior. New York: Wiley. O'Grady, Jason D. 2015. Apple Inc. Westport, Conn: Greenwood Press. DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The ongoing question that the weekly
- 3. assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BSBA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the
- 4. larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0S tandard Deviation8.48.76.4Range28.030.021.03What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that is how many values are above and below a comparable value.For each group (overall, females, and males) find:OverallFemaleMaleAThe value that cuts off the top 1/3 salary value in each group"=large" functioniThe z score for this value within each group?Excel's standize functioniiThe normal curve probability of exceeding this score:1-normsdist functioniiiWhat is the empirical probability of being at or exceeding this salary value?BThe value that cuts off the top 1/3 compa value in each group.iThe z score for this value within each group?iiThe normal curve probability of exceeding this score:iiiWhat is the empirical probability of being at or exceeding this compa value?CHow do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question?5. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? What is the difference between the sal and compa measures of pay?Conclusions from looking at salary results:Conclusions from looking at compa results:Do both salary measures show the same results?Can we make any conclusions about equal pay for equal work yet? Week 2 Week 2Testing means - T-testsIn questions 2, 3, and 4 be sure to include the null and alternate hypotheses you will be testing. In the first 4 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.1Below are 2 one-sample t-tests comparing male and
- 5. female average salaries to the overall sample mean. (Note: a one-sample t-test in Excel can be performed by selecting the 2- sample unequal variance t-test and making the second variable = Ho value - a constant.)Note: These values are not the same as the data the assignment uses. The purpose is to analyze the results of t-tests rather than directly answer our equal pay question.Based on these results, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?MalesFemalesHo: Mean salary =45.00Ho: Mean salary =45.00Ha: Mean salary =/=45.00Ha: Mean salary =/=45.00Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances, having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.MaleHoFemaleHoMean5245Mean3845Variance3160Variance 334.66666666670Observations2525Observations2525Hypothesi zed Mean Difference0Hypothesized Mean Difference0df24df24t Stat1.9689038266t Stat-1.9132063573P(T<=t) one- tail0.0303078503P(T<=t) one-tail0.0338621184t Critical one- tail1.7108820799t Critical one-tail1.7108820799P(T<=t) two- tail0.0606157006P(T<=t) two-tail0.0677242369t Critical two- tail2.0638985616t Critical two-tail2.0638985616Conclusion: Do not reject Ho; mean equals 45Conclusion: Do not reject Ho; mean equals 45Note: the Female results are done for you, please complete the male results.Is this a 1 or 2 tail test?Is this a 1 or 2 tail test?2 tail- why?- why?Ho contains =P-value is:P-value is:0.0677242369Is P-value < 0.05 (one tail test) or 0.025 (two tail test)?Is P-value < 0.05 (one tail test) or 0.025 (two tail test)?NoWhy do we not reject the null hypothesis?Why do we not reject the null hypothesis?P-value greater than (>) rejection alphaInterpretation of test outcomes:2Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)Ho: Male salary
- 6. mean = Female salary meanHa: Male salary mean =/= Female salary meanTest to use:t-Test: Two-Sample Assuming Equal VariancesP-value is:Is P-value < 0.05 (one tail test) or 0.025 (two tail test)?Reject or do not reject Ho:If the null hypothesis was rejected, calculate the effect size value:If calculated, what is the meaning of effect size measure:Interpretation:b.Is the one or two sample t-test the proper/correct apporach to comparing salary equality? Why?3Based on our sample data set, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.)Again, please assume equal variances for these groups.Ho:Ha:Statistical test to use:What is the p-value:Is P-value < 0.05 (one tail test) or 0.025 (two tail test)?Reject or do not reject Ho:If the null hypothesis was rejected, calculate the effect size value:If calculated, what is the meaning of effect size measure: Interpretation: 4Since performance is often a factor in pay levels, is the average Performance Rating the same for both genders?NOTE: do NOT assume variances are equal in this situation.Ho:Ha:Test to use:t- Test: Two-Sample Assuming Unequal VariancesWhat is the p- value:Is P-value < 0.05 (one tail test) or 0.025 (two tail test)?Do we REJ or Not reject the null?If the null hypothesis was rejected, calculate the effect size value:If calculated, what is the meaning of effect size measure:Interpretation:5If the salary and compa mean tests in questions 2 and 3 provide different results about male and female salary equality, which would be more appropriate to use in answering the question about salary equity? Why?What are your conclusions about equal pay at this point? Week 3Week 3Paired T-test and ANOVAFor this week's work, again be sure to state the null and alternate hypotheses and use alpha = 0.05 for our decisionvalue in the reject or do not reject decision on the null hypothesis.1Many companies consider the grade midpoint to be the "market rate" - the salary needed to hire a new employee.SalaryMidpointDiffDoes the company, on average, pay its existing employees at or above the market rate?Use the data columns at the right to set up the paired data
- 7. set for the analysis.Null Hypothesis:Alt. Hypothesis:Statistical test to use:What is the p-value:Is P-value < 0.05 (one tail test) or 0.025 (two tail test)?What else needs to be checked on a 1- tail test in order to reject the null?Do we REJ or Not reject the null?If the null hypothesis was rejected, what is the effect size value:If calculated, what is the meaning of effect size measure:Interpretation of test results:Let's look at some other factors that might influence pay - education(degree) and performance ratings.2Last week, we found that average performance ratings do not differ between males and females in the population.Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?(Assume variances are equal across the grades for this ANOVA.)Here are the data values sorted by grade level.The rating values sorted by grade have been placed in columns I - N for you.ABCDEFNull Hypothesis:Ho: means equal for all grades9080100908570Alt. Hypothesis:Ha: at least one mean is unequal807510065100100Place B17 in Outcome range box.1008090759595907080905595809580959095858095956590 90707595956090909575809590100Interpretation of test results:What is the p-value:0.57If the ANVOA was done correctly, this is the p-value shown.Is P-value < 0.05?Do we REJ or Not reject the null?If the null hypothesis was rejected, what is the effect size value (eta squared):Meaning of effect size measure:What does that decision mean in terms of our equal pay question:3While it appears that average salaries per each grade differ, we need to test this assumption. Is the average salary the same for each of the grade levels? Use the input table to the right to list salaries under each grade level. (Assume equal variance, and use the analysis toolpak function ANOVA.) Null Hypothesis:If desired, place salaries per grade in these columnsAlt. Hypothesis:ABCDEFPlace B51 in Outcome range box.Note: Sometimes we see a p-value in the format of 3.4E-5; this means move the decimal point left 5 places. In this example, the p-value is 0.000034What is the p- value:Is P-value < 0.05?Do we REJ or Not reject the null?If the
- 8. null hypothesis was rejected, calculate the effect size value (eta squared):If calculated, what is the meaning of effect size measure:Interpretation:4The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results.Note: These values are not the same as the data the assignment uses. The purpose of this question is to analyze the result of a 2-way ANOVA test rather than directly answer our equal pay question.BAMAHo: Average compas by gender are equalMale1.0171.157Ha: Average compas by gender are not equal0.8700.979Ho: Average compas are equal for each degree1.0521.134Ha: Average compas are not equal for each degree1.1751.149Ho: Interaction is not significant1.0431.043Ha: Interaction is significant1.0741.1341.0201.000Perform analysis:0.9031.1220.9820.903Anova: Two-Factor With Replication1.0861.0521.0751.140SUMMARYBAMATotal1.052 1.087MaleFemale1.0961.050Count1212241.0251.161Sum12.349 12.925.2491.0001.096Average1.02908333331.0751.0520416667 0.9561.000Variance0.0066864470.00651981820.00686604171.0 001.0411.0431.043Female1.0431.119Count1212241.2101.043Su m12.79112.78725.5781.1871.000Average1.06591666671.06558 333331.065751.0430.956Variance0.0061024470.00421281060.0 049334131.0431.1291.1451.149TotalCount2424Sum25.1425.68 7Average1.04751.0702916667Variance0.00647034780.0051561 286ANOVASource of VariationSSdfMSFP-valueF critSample0.002255020810.00225502080.38348211710.5389389 5074.0617064601 (This is the row variable or gender.)Columns0.006233520810.00623352081.06005396090.3 0882956334.0617064601 (This is the column variable or Degree.)Interaction0.006417187510.00641718751.09128776640 .30189150624.0617064601Within0.25873675440.0058803807To tal0.273642479247Interpretation:For Ho: Average compas by gender are equalHa: Average compas by gender are not equalWhat is the p-value:Is P-value < 0.05?Do you reject or not reject the null hypothesis:If the null hypothesis was rejected, what is the effect size value (eta squared):Meaning of effect
- 9. size measure:For Ho: Average compas are equal for all degrees Ha: Average compas are not equal for all gradesWhat is the p- value:Is P-value < 0.05?Do you reject or not reject the null hypothesis:If the null hypothesis was rejected, what is the effect size value (eta squared):Meaning of effect size measure:For: Ho: Interaction is not significantHa: Interaction is significantWhat is the p-value:Is P-value < 0.05?Do you reject or not reject the null hypothesis:If the null hypothesis was rejected, what is the effect size value (eta squared):Meaning of effect size measure:What do these three decisions mean in terms of our equal pay question:Place data values in these columns5. Using the results up thru this week, what are your conclusions about gender equal pay for equal work at this point?Dif Week 4Week 4Confidence Intervals and Chi Square (Chs 11 - 12)For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions.For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed.1Using our sample data, construct a 95% confidence interval for the population's mean salary for each gender. Interpret the results. MeanSt error t valueLow to HighMalesFemales<Reminder: standard error is the sample standard deviation divided by the square root of the sample size.>Interpretation:2Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population. How does this compare to the findings in week 2, question 2?DifferenceSt Err.T valueLow to HighYes/NoCan the means be equal?Why?How does this compare to the week 2, question 2 result (2 sampe t- test)?Results are the same - means are not equal.a.Why is using a two sample tool (t-test, confidence interval) a better choice than using 2 one-sample techniques when comparing two samples?3We found last week that the degree values within the population do not impact compa rates. This does not mean that degrees are distributed evenly across the grades and genders.Do males and females have athe same distribution of degrees by
- 10. grade?(Note: while technically the sample size might not be large enough to perform this test, ignore this limitation for this exercise.)Ignore any cell size limitations.What are the hypothesis statements:Ho: Ha:Note: You can either use the Excel Chi-related functions or do the calculations manually.Data InTablesThe Observed Table is completed for you.OBSERVEDA BCDEFTotalIf desired, you can do manual calculations per cell here.M Grad11115312A BCDEFFem Grad53111213M GradMale Und22215113Fem GradFemale Und71121012Male Und1575512650Female UndSum =EXPECTEDM GradFor this exercise - ignore the requirement for a correctionFem Gradfor expected values less than 5.Male UndFemale UndInterpretation:What is the value of the chi square statistic: What is the p-value associated with this value: Is the p-value <0.05?Do you reject or not reject the null hypothesis: If you rejected the null, what is the Cramer's V correlation:What does this correlation mean?What does this decision mean for our equal pay question: 4Based on our sample data, can we conclude that males and females are distributed across grades in a similar patternwithin the population?Again, ignore any cell size limitations.What are the hypothesis statements:Ho: Ha:Do manual calculations per cell here (if desired)A BCDEFA BCDEFOBS COUNT - mMOBS COUNT - fFSum = EXPECTEDWhat is the value of the chi square statistic: What is the p-value associated with this value: Is the p-value <0.05?Do you reject or not reject the null hypothesis: If you rejected the null, what is the Phi correlation:If calculated, what is the meaning of effect size measure:What does this decision mean for our equal pay question: 5. How do you interpret these results in light of our question about equal pay for equal work? Week 5Week 5 Correlation and Regression1. Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.)a. Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)?b.
- 11. Place table here (C8):c.Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables aresignificantly related to Salary?To compa?d.Looking at the above correlations - both significant or not - are there any surprises -by that I mean any relationships you expected to be meaningful and are not and vice- versa?e.Does this help us answer our equal pay for equal work question?2Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of expressing an employee’s salary, we do not want to have both used in the same regression.)Plase interpret the findings.Note: These values are not the same as the data the assignment uses. The purpose is to analyze the result of a regression test rather than directly answer our equal pay question.Ho: The regression equation is not significant.Ha: The regression equation is significant.Ho: The regression coefficient for each variable is not significant Note: technically we have one for each input variable.Ha: The regression coefficient for each variable is significant Listing it this way to save space.SalSUMMARY OUTPUTRegression StatisticsMultiple R0.9915590747R Square0.9831893985Adjusted R Square0.9808437332Standard Error2.6575925726Observations50ANOVAdfSSMSFSignificanc e FRegression617762.29967387432960.383278979419.151611129 41.8121523852609E- 36Residual43303.70032612577.062798282Total4918066Coeffic ientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept-1.74962121233.6183676583- 0.48353881570.6311664899-9.04675504275.547512618- 9.04675504275.547512618Midpoint1.21670105050.0319023509 38.13828811638.66416336978111E- 351.15236382831.28103827271.15236382831.2810382727Note: These values are not the same as in the data the assignment uses. The purpose is to analyze the result of a 2-way ANOVA
- 12. test rather than directly answer our equal pay question.Age- 0.00462801020.065197212-0.07098478760.9437389875- 0.13611071910.1268546987- 0.13611071910.1268546987Performace Rating- 0.05659644050.0344950678-1.64071109710.1081531819- 0.12616237470.0129694936- 0.12616237470.0129694936Service- 0.04250035730.0843369821-0.50393500330.6168793519- 0.21258209120.1275813765- 0.21258209120.1275813765Gender2.4203372120.86084431762. 81158528040.00739661880.6842791924.1563952320.68427919 24.156395232Degree0.27553341430.79980230480.34450190090 .732148119-1.33742165471.8884884833- 1.33742165471.8884884833Note: since Gender and Degree are expressed as 0 and 1, they are considered dummy variables and can be used in a multiple regression equation.Interpretation:For the Regression as a whole:What is the value of the F statistic: What is the p-value associated with this value: Is the p-value <0.05?Do you reject or not reject the null hypothesis: What does this decision mean for our equal pay question: For each of the coefficients:InterceptMidpointAgePerf. Rat.ServiceGenderDegreeWhat is the coefficient's p-value for each of the variables: NAIs the p-value < 0.05?NADo you reject or not reject each null hypothesis: NAWhat are the coefficients for the significant variables?Using the intercept coefficient and only the significant variables, what is the equation?Salary =Is gender a significant factor in salary:If so, who gets paid more with all other things being equal?How do we know? 3Perform a regression analysis using compa as the dependent variable and the same independentvariables as used in question 2. Show the result, and interpret your findings by answering the same questions.Note: be sure to include the appropriate hypothesis statements.Regression hypothesesHo:Ha:Coefficient hyhpotheses (one to stand for all the separate variables)Ho:Ha:Place c94 in output box.Interpretation:For the Regression as a whole:What is the value of the F statistic: What
- 13. is the p-value associated with this value: Is the p-value < 0.05?Do you reject or not reject the null hypothesis: What does this decision mean for our equal pay question: For each of the coefficients: InterceptMidpointAgePerf. Rat.ServiceGenderDegreeWhat is the coefficient's p-value for each of the variables: NAIs the p-value < 0.05?NADo you reject or not reject each null hypothesis: NAWhat are the coefficients for the significant variables?Using the intercept coefficient and only the significant variables, what is the equation?Compa = Is gender a significant factor in compa:Regardless of statistical significance, who gets paid more with all other things being equal?How do we know? 4Based on all of your results to date, Do we have an answer to the question of are males and females paid equally for equal work?Does the company pay employees equally for for equal work? How do we know?Which is the best variable to use in analyzing pay practices - salary or compa? Why?What is most interesting or surprising about the results we got doing the analysis during the last 5 weeks?5Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question?What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?