William Ross 1/17/2018 Written for SPSS version 25

File name (Word): Part I 2018: Post-Merger_Instructions_project_I_SPSS_2018

Consequences of a Merger at Metro Telecom

Background

In January 2017, Metro Telecom, a large regional telephone and telecommunication company with several hundred employees merged with the Smalltown Telephone System. The Smalltown Telephone System was itself a result of a merger two years earlier of 30 different telephone companies located in small communities. The Smalltown system kept a decentralized structure and allowed each location to have considerable autonomy. The new firm will keep the Metro name and will “keep the best of both” by keeping the best employees from both firms and eventually consolidate each location's operations (e.g., record keeping). If you have to name a “bidder” it is Metro and Smalltown is the “target.” However, it is supposed to be a “merger of equals.” The Metro name will be retained, but a new logo will be created.

In the year prior to the merger, voluntary turnover at the various facilities was about 20% in each firm.

Generally, individual facilities have determined their own Human Resource (HR) procedures. Consequently, there has been considerable variability in how each facility has handled HR policy issues. Top Metro administrators want to identify ‘best practices’ from various locations and eventually implement them system-wide as policies are eventually centralized. So you must soon give a report to the President of Metro about specific policies.

Layoffs

One set of policies of interest deal with how layoffs are handled. Following major organizational changes such as mergers, it is quite common to lay off workers. How layoffs are handled has varied across the various locations, as described in the Lavelle, Folger, & Mannegold (2016) reading for class [hint: read it!]. You are curious as to how variations in this procedure affect survivor attitudes and behaviors. That is, you are curious as to how those NOT laid off react.

For the sake of simplicity, you grouped organizational procedures according to two dimensions:

Type of explanation (this is relevant because laid-off workers often talk to co-workers):

No Explanation: Some managers simply told employees in individual meetings that they were being laid off because the merger had resulted in too many employees with the same knowledge and skills. The meetings were brief and the managers refused to answer any questions, referring all questions to the HR or legal departments.

Lengthy Explanation: Some managers gave employees in individual meetings a much more detailed explanation of why they were laid off in terms of specific knowledge and skills that were redundant with other employees. The managers also pointed out specific deficiencies as recorded in past performance appraisals and electronic monitoring data – and noted that these factored into the decision. [hint: see class readings on electronic monitoring]

Decision makers:

Managers only. The decision was made by a committee that consisted solely of top managers – Smalltown managers played no role in the decision, other than having previously completed performance appraisals forms, and electronic monitoring reports, that had been placed in the employee files. [hint: see class readings on performance appraisal]

Labor-mgt. committee. The decision was made by a committee that consisted of both Metro managers and Smalltown managers as well as employees from both firms. First-level managers were invited to attend selected meetings and offer verbal clarification to the committee of their previously-completed performance appraisal forms and explain the electronic monitoring reports found in the employee files.

Cultural Style

While there are many dimensions and ways to classify organizational culture, we will focus on one key dimension: Bureaucracy. Many – but not all – departments at the old Metro were highly bureaucratic, with lots of rules and procedures (similar to what Burns & Stalker call “mechanistic” firms). Because the Smalltown system was so decentralized, some of its departments and branches were also highly bureaucratic. However, other departments had an organizational culture that was very informal, with decision making by consensus and employee participation (similar to what is sometimes called an “organic” style). This means that for some departments of both organizations, the style is bureaucratic/mechanistic and for others the style is informal/organic.

You are curious about whether these layoff decision and organizational culture variables affect those who were not laid off in several ways:

Did they affect subsequent intention to quit?

Did they affect their beliefs about the fairness of the layoff procedure and its outcomes?

Did they affect their commitment to the merger?

Do you get different reactions from Smalltown vs. Metro employees?

These are the questions you will answer in your report.

Data Set Variables

First, let us consider the variables in your data set:

Contextual Variables:

Facility	This is the ID number for the facility; it is included so you can compare facilities.

Affiliation	Prior to the merger, was this department/facility affiliated with Metro Telecom? Yes =1

Or with the Smalltown telephone system? Yes =2

Cultural	1 = Very informal organizational culture (“organic”).

Style	2 = Very formal, bureaucratic, organizational culture (“mechanistic”).

Layoff Variables, under the control of supervisors or the communication facility (“Independent Variables”):

Type of	1 = No explanation of why employees were laid off (as described on previous pages).

Explanation	2 = Detailed explanation of why employees were laid off (as described on previous pages).

Decision-	1 = Managers (only) decided which workers should be laid off.

Makers	2 = Joint Labor-Mgt. committee decided which workers should be laid off.

Variables under the control of the subordinates (Dependent Variables):

The following survey was given approximately one year after the merger, so workers who

quit did not participate:

Intent	Among employees remaining at the facility how likely are they to quit?

This is an average rating, measured on a 7-point rating scale with

“1 = very unlikely to quit within the next year” and “7 = very likely to quit.”

Distrjust	Average “distributive justice” rating by facility employees, measuring the perceived

fairness of merger-related layoff outcomes. This may depend, in part, on

beliefs about performance appraisals and upon information gathered by

the electronic monitoring. This is averaged across several items. It is measured on a

7-point rating scale with “1 = very unfair” and “7 = very fair.”

Procjust	Average “procedural justice” rating, measuring the perceived fairness of merger-related

layoff procedures (which may depend, in part, upon performance appraisal and

electronic monitoring procedures). This is averaged across several items. It is measured

on a 7-point rating scale with “1 = very unfair” and “7 = very fair” (Colquitt, 2001 scales).

Interjust	Average “interpersonal justice” rating by employees, measuring the perceived fairness of

their treatment by managers (in terms of whether they felt they were treated with politeness, dignity, and respect with layoff-related matters). It is measured on a

7-point rating scale with “1 = very unfair” and “7 = very fair.”

Satisfaction	This consists of how satisfied, on average, the employees in the facility are with the merger.

This is equivalent to what Bebenroth & Thiele (2016) called “affective merger commitment.”

It is not necessarily the same as ‘job satisfaction.’ This is measured on a 7-point rating scale

with “1 = very dissatisfied with the merger” and “7 = very satisfied with the merger.”

Turnover	Some people have quit since the merger. Data are coded as 1= “Did not quit” and 2 = “Quit.”

Housekeeping Information

Remember! The format for your report is found in the SYLLABUS (pages 2-4). A tentative “Grading Rubric” is found at the end of this assignment.

These instructions assume that you are at a computer with a mouse and a printer. They also assume that you know how to work a mouse (e.g., for “dragging”) and have worked with Windows-based programs before. If these assumptions are incorrect, please see a computer consultant in the computer labs.

If you are not familiar with SPSS for Windows, please remember a few basic tips:

Data have been entered into a Data Editor file (similar to a spreadsheet).

For analyses, you will use a “horizontal menu” located at the very top of the screen of the Data Editor. Your selection will, in turn, produce other, pull-down menus. In these, you will specify the independent variables, the dependent variables, and any covariates. Then you will click on the “OK” box associated with that menu and you will be taken to the output menu.

The output menu has two parts: A summary table of contents (actually a branching tree structure) on the left, and the actual results on the right. You can print output in either “landscape” or “portrait” modes and can change it by selecting “File” and then, “Page Setup”

To toggle between the output and the Data Editor, click on the word “Window” on the menu that goes across the top of the screen. A pull-down menu will give you the choice of the Data Editor or the Results windows.

If you want to add titles, your name, etc. to your output/printouts, you can do this. Simply put your cursor where you want it in the output file and then type your comment or label. Simple, eh?

To print, either (1) click on the printer icon or, (2) from the menu at the top of the screen, select the word “file” (then select “print). Note that the output from each new analysis gets added to the end of the output file. If you print the entire output file after each specific analysis, you will get that analysis’ results but you will also get all of the output from the analyses that preceded it. For example, if you did three analyses and printed the output file after each analysis, you would wind up with three printouts of the first analysis, two printouts of the second analysis, and one printout of the third analysis. How do you avoid this problem?

Here are three different options:

(A)	If you plan to complete the entire exercise at one sitting, simply wait to print your output until you have completed the entire exercise.

(B)	When printing from the “file” menu, the computer asks if you want to print the entire file or a designated portion. The default option is the entire file. However, if you click on the “designated portion” box, you can specify exactly what you want to print. Simply drag the mouse over the block of output text that you want to print.

(C)	Perform an analysis, print the results, exit SPSS, reenter SPSS, reopen the file, and continue with the next analysis. Repeat this procedure after each printing.

Note that I’ve tried to provide thorough and up-to-date instructions. However, SPSS constantly changes the cosmetic appearance of its menus (e.g., buttons appear vertically on the right in one version will change in the next version and appear horizontally at the bottom of a dialog box; such cosmetic changes do not affect the functionality of my instructions).

Step A: Looking at “Intention to Quit” Among Current Employees

The previous analyses looked at actual turnover. But what about current employees? Do some variables (e.g., procedural justice beliefs about how layoffs were determined following the merger and interpersonal justice – how people were treated) predict that some current employees are more likely to quit? Does satisfaction with the merger moderate the relationship between these justice beliefs and the dependent variable (intention to quit)? Assume that current employees were surveyed and their data are in the database.

Recall that “Intention to Quit” is coded as “1=unlikely to quit” and “7=likely to quit within the next year.” Therefore, a higher number means the person is more likely to quit.

You are going to use “Path Analysis” to test the following relationships, shown graphically:

Procedural

Justice for

Layoffs

Satisfaction

With the	Intention to Quit

Merger

Interpersonal	(Satisfaction)

Justice for

Layoffs

To test this “full model” (with all of the arrows), you will do a series of multiple regression analyses. For each multiple regression, the dependent variable is the variable to the right of the arrows. The independent variables (predictors) are all the variables that have arrows leading to that dependent variable.

Further, you want to test these relationships separately for (1) employees who came from Metro and (2) employees who came from the Smalltown system.

To select only those who came from the Metro system:

Select	Data…	Select Cases…

Click the [IF] box

Click on the Affiliation variable in the list and click on [>] to move it to the box on the right.

To the right of the Affiliation variable type “=1” and click [Continue].

Make sure the option “Filter out unselected cases” is chosen. Click [OK]

If you look at the data set you will see diagonal lines to the left of those cases you will not be using in the next set of analyses – you will only be keeping people who were originally affiliated with Metro (Affiliation = 1).

For the full model shown above, you will need two “layers” of multiple regressions:

1. Perceived Need for Monitoring & Procedural Justice predicting Satisfaction with the Merger.

2. Need, Satisfaction, & Procedural Justice predicting Intention to Quit.

Layer #1/Analysis #1: DV is Satisfaction with the Merger (aka “Affective Merger Commitment”)

Analyze…	Regression…	Linear…

You will see a dialog box similar to this:

Variable List:	Dependent Variable:	[ Statistics	]

ID	[>]	[_______________]	[ Plots	]

Facility	[ Save	]

Affiliation	[previous] [next]	[ Options	]

Culture	[ Bootstrap	]

Explanation	[>]	Independent Variables

:	[	]

:	[	]

Note: Some versions of SPSS “switch” the location of these menu options. :	Method: [Enter]

Satisfaction

Selection Variables: [___]

Case Label [___]

WLS Weight [___]

[OK] [Paste] [Reset] [Cancel] [Help]

“Move” Satisfaction with the Merger into the Dependent Variable box by clicking on the variable and the [>] between the names of the variables and the Dependent Variable box.

Similarly, “Move” both Procedural Justice and Interpersonal Justice into the Independent Variables box using the appropriate [>].

Leave the following boxes blank:

Selection Variables: [___]

Case Label [___]

WLS Weight [___]

So after completing the dialog box, it should look like this:

Variable List:	Dependent Variable:	[ Statistics	]

ID	[>]	[Satisfaction with the Merger]	[ Plots	]

Facility	[ Save	]

Affiliation	[previous] [next]	[ Options	]

Culture	[ Bootstrap	]

Explanation	[>]	Independent Variables

:	[Procedural Justice	]

:	[Interpersonal Justice	]

Note: Some versions of SPSS “switch” the location of these menu options. :	Method: [Enter]

Selection Variables: [___]

Case Label [___]

WLS Weight [___]

[OK] [Paste] [Reset] [Cancel] [Help]

Click [Statistics] and you will see the “Linear Regression: Statistics” dialog box. Select the options that are checked. You may find some of the information in the output to be useful. Then click [Continue], then [OK].

Regression Coefficients:	Model fit

Estimates	R-square change

Confidence Intervals [95%]	Descriptives

[ ] Covariance matrix	[ ] Collinearity diagnostics

Residuals:

[ ] Durbin-Watson

[ ] Casewise diagnostics

[Continue] [Cancel] [Help]
You will get output that looks something like the following (only with different numbers):

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.627a	.393	.372	2.13499
a. Predictors: (Constant), Procedural Justice, Interpersonal Justice

A large “R” and “R-square” tell you that the two independent variables do, indeed, predict your dependent variable (Satisfaction with the Merger).

You will also see an Analysis of Variance (ANOVA) table (not shown here; more on ANOVA in another “Step” below), with an F statistic and a significance level. This tells you whether your “R” value is significantly different from zero. Basically, you want a large F and a small significance level (less than .05). If you get those, then you know that you are explaining a large percentage of the variation (39.3% in this example) that is significantly different from what you’d expect by chance (zero %).

Looking at the Predictor Weights for Layer #1:

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	2.606	.461	3.486	.001
*Procedural Justice	.098	.084	.064	.927	.355
*Interpersonal Justice	.850	.070	.729	11.123	.000
a. Dependent Variable: Satisfaction with the Merger

This table is useful because the “Beta” weights tell us how strong the relationship is between the IVs and the DV. The t-test and Significance level (Sig.) tell us whether the relationship is significantly stronger than what one would expect by chance. We can use these weights to start to fill in the diagram:

Procedural

Justice for

Layoffs	.064

Satisfaction

With the	Intention to Quit

Merger

Interpersonal	.729*	(Satisfaction)

Justice for

Layoffs

*= p < .05

Layer #2/Analysis #2: DV is Intention to Quit

“Move” Intention to Quit into the Dependent Variable box by clicking on the variable name and [>].

“Move” (1) Procedural Justice, (2) Interpersonal Justice, and (3) Satisfaction with the Merger into the Independent Variables box.

Leave the following boxes blank:

Selection Variables: [___]

Case Label [___]

WLS Weight [___]

You will get output that looks something like the following (only with different numbers):

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.552a	.305	.292	1.69696
a. Predictors: (Constant), Satisfaction with merger, Procedural Justice, Interpersonal Justice

ANOVAa
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	258.790	3	86.263	31.447	.000b
Residual	589.767	215	2.743
Total	848.557	218
a. Dependent Variable: Intention to quit within the next year
b. Predictors: (Constant), Satisfaction with merger, Procedural Justice, Interpersonal Justice
Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	6.702	.367	18.250	.000
*Procedural Justice	-.605	.065	-.530	-9.268	.000
*Interpersonal Justice	-.321	.013	-.354	-2.297	.003
*Satisfaction with merger	.052	.064	.055	.817	.415
a. Dependent Variable: Intention to quit within the next year

In this example, both Procedural Justice and Interpersonal Justice beliefs predict Intention to Quit. However, Satisfaction with the Merger is not related to Intention to Quit.

There is one more step: You need to report the “error variance” for each dependent model.

This is easy to do. The formula is:

\/¯(1-R2).

Get the R2 from the SPSS printout. In the example, the error for Satisfaction with the Merger is:

\/¯(1-.393) = \/¯.607 = .78

The error for Intention to Quit is: \/¯(1-.305) = \/¯.695 =.83

You should show this full path model like this (create your own diagrams!):

Procedural	-.53*

Justice for

Layoffs	.064

Satisfaction

With the	.055	Intention to Quit

.729*	Merger

Interpersonal	(Satisfaction)

Justice for

Layoffs	-.35*

ErrorSatisfaction = .78	ErrorIntention = .83

*= p < .05

Examining this model, we note that:

*Procedural Justice strongly and directly influences Intention to Quit. Those who thought the procedure used for determining layoffs was unfair said that they were more likely to quit.

*Paradoxically, Procedural Justice for layoffs does not influence survivor’s Satisfaction with the Merger (Affective Merger Commitment).

*Interpersonal Justice for layoff-related matters strongly relates to Satisfaction with the Merger for survivors; Interpersonal Justice beliefs also predicted Intention to Quit. People who thought that managers treated employees unfairly in terms of politeness, dignity, & respect were more likely to say that they planned to look for another job.

*Interestingly, Satisfaction with the Merger did NOT predict Intention to Quit. Thus, Satisfaction with the Merger probably doesn’t “moderate” the relationship between the Justice variables, and Quitting.

Of course, these results are just examples. You will get different numbers.

As noted on page 8, you want to test these relationships separately for (1) employees who came from Metro and (2) employees who came from the Smalltown system. The above instructions gave you the result for employees who had been affiliated with Metro prior to the merger. Next, you want to look at the same “paths” for just employees who had been affiliated with the Smalltown system prior to the merger.

To select only those who came from the Smalltown system:

Select	Data…	Select Cases…

Click the [IF] box

Click on the Affiliation variable in the list and click on [>] to move it to the box on the right.

To the right of the Affiliation variable type “=2” and click [Continue].

Make sure the option “Filter out unselected cases” is chosen. Click [OK]

If you look at the data set you will see diagonal lines to the left of those cases you will not be using in the next set of analyses – you will only be keeping people who were originally affiliated with Smalltown System (Affiliation = 2).

Now, repeat the Path Analysis (regression) instructions on pp 8-10 with this second sample.

Do you get similar findings? Or are different “paths” significant for these employees?

Extra Credit (5% max. for each sample): Re-do the Path Analysis using two other independent variables: Turnover Percentage and Distributive Justice to predict Intention to Quit. You have to report and discuss your findings to get extra credit.

Step C: Looking at Justice Beliefs Using ANOVA

Analysis of Variance (ANOVA) is a technique that is used to compare the means of two or more groups to determine if those means are significantly different from each other. Your data set has information about (1) the similarity of the two organizational cultures (2) the type of explanation given regarding layoffs, and (3) who made the decisions regarding layoffs. You will do an analysis to see if these three variables affected employee beliefs that the outcomes were fair (distributive justice). Then you will do an analysis to see if these three variables affected employee beliefs that the way in which the procedure was implemented was fair (procedural justice). The survey was given one year after the merger, so employees who quit did not participate.

ANOVA of Effects on Justice Beliefs

Analysis #1: DV = Distributive Justice

Select	Analyze…	General Linear Model…	Univariate

Univariate Variable Name:	Dependent Variable	[Model] ID Facility	[>]	[Contrasts] Affiliation Cultural Style	[Plots] Type of Explanation	Fixed Factors Decision Makers	[Post-hoc] :	[>] DV: Distributive Justice	[E.M. Means] DV: Procedural Justice DV: Interpersonal Justice	Covariates	[Save] DV: Satisfaction	[>]	[Options]	WLS Weight	[>] [OK]	[Paste]	[Reset]	[Cancel] [Help]

Select “Distributive Justice as your Dependent Variable. Click on the [>] to move it to the Dependent Variable box.

Select “Cultural Style” “Type of Explanation” and “Decision Makers” as your Fixed Factors.

You have no “Covariates” or “WLS Weight”

Select “E.M. Means” to see this dialog box:

Univariate: Options Estimated Marginal Means: Factor(s) and Factor Interactions:	Display Means for:	[>]	Compare main effects [Continue]	[Cancel]	[Help]

(OVERALL) Cultural Style Explanation Decision Makers Culture x Explain Culture x Decision…

Click on the variable names and interactions below the word “(OVERALL)” and move them to the right “Display Means for” box.

Upon returning to the main “Univariate” dialog box, select “Options” to see this dialog box:

Display:

Descriptive Statistics	Homogeneity Tests

Estimates of Effect Sizes	Spread v. Level Plots

Observed Power	Residual Plots

:	:

:	:

Significance Level: .05	Confidence Intervals are 95%

[Continue]	[Cancel]	[Help]

Left-click on box to the left of Descriptive Statistics, Estimates of Effect Sizes and Observed Power to put a check mark in them. Click on [Continue].

Analysis #2: ANOVA of Effects on Procedural Justice Beliefs

Repeat the above analysis. Remove “Distributive Justice” from the “Dependent Variable” box and replace it with “Procedural Justice”. Everything else will remain the same as you conduct this second ANOVA.

Extra Credit (up to 5%): Replace “Procedural justice” with “Interpersonal/Interactional Justice” and do a third ANOVA. Be sure to include the findings in your report and discuss what they mean.

Interpreting your ANOVA Results

You will get a table telling you how many cases you have in each condition of your study. Next, you will get a table of Between-Subject effects that looks something like this table of hypothetical results:

Tests of Between-Subjects Effects
Dependent Variable: Distributive Justice
Source	Type III Sum of Squares	df	Mean Square	F	Sig.	Partial Eta Squared	Noncent. Parameter	Observed Powerb
Corrected Model	493.615a	11	44.874	131.868	.000	.931	1450.551	1.000
Intercept	1369.461	1	1369.461	4024.337	.000	.974	4024.337	1.000
Cultural Style	316.151	1	316.151	929.050	.000	.896	929.050	1.000
Type of Explanation	2.539	2	1.269	3.730	.027	.065	7.460	.672
Decision Makers	61.536	1	61.536	180.833	.000	.626	180.833	1.000
Culture * Explain	.644	2	.322	.946	.392	.017	1.892	.210
Culture * Decision	.903	1	.903	2.653	.106	.024	2.653	.365
Explain * Decision	.817	2	.408	1.200	.305	.022	2.400	.258
Culture *Explain *Decision	2.972	2	1.486	4.366	.015	.075	8.733	.745
Error	36.752	108	.340
Total	2248.000	120
Corrected Total	530.367	119
a. R Squared = .931 (Adjusted R Squared = .924)
b. Computed using alpha = .05

What do these numbers mean? First, recognize that the “Sum of Squares” and “Mean Square” statistics are intermediate computations en route to your final destination, the F-test. So look at the F-statistic. Generally, if the F-statistic is large, the Significance (p-value) will be small. If a p value equals .000 that is good; it really means p < .001. You would expect results this large by chance less than one time in one-thousand.

So if we look only at the F-statistics for our independent variables, we see that “Cultural Style” has a main effect. So we will want to report the means for the two cultural groups. Suppose it is 5.8 for formal culture departments and 2.7 for informal cultural departments. We might say, “Employees viewed the layoff outcomes as fairer when they had a formal, bureaucratic culture than when they had an informal organizational culture (Ms = 5.8 vs. 2.7, respectively; F (1, 108) = 929.05; p < .001; Eta2 = 89.6%).” The (1, 108) refer to the degrees of freedom. The first number is the degree of freedom (df) for Monitoring. It is always smaller than the number of conditions the variable can take (there are two types of monitoring, so df = 1). The second number is the “Error” degree of freedom. You don’t have to understand it; just report it. Eta2 = 89.6% refers to the fact that Culture explains 89.6% of the variation in Distributive Justice in this example. You don’t need to worry about the “Noncentric Parameter” statistic. The “Power” statistic merely tells you whether you have sufficient power (based on sample size) to find a difference if one exists. You don’t need to report that either.

In this example, there are also main effects for Type of Explanation and Decision Makers. If you found those for your data set, you would report them just as you reported the one for “Culture” (above). There is also a complex three-way interaction which might be best explained using a table of means copied from your SPSS printout. The rest of your printout contains the “marginal means” for each condition and the “interactions” of various Independent Variables.

=======

So, to repeat and summarize, reading across the “Between-Subjects Effects” table, find the “F - statistic” column. The larger the F (generally above 2 or 3), the better. Of course, the overall mean or the “intercept” of the line of best fit also appear (sometimes called “corrected model”) and these are usually large, but not of interest to you, so for this exercise, simply ignore the overall mean and the intercept.

A large F is accompanied by a small significance level (usually less than .05). The significance level is the probability (written p <.05) that you would get an F this large simply by chance. If it is not due to chance, then it is usually a “real” finding! You want a large F and a small significance (or p) value.

Next, note the degrees of freedom. This is based on the number of conditions your Independent Variables can take and the number of subjects in your study. Usually only statisticians get excited about it, but it is proper to report it. The hypothesized degrees of freedom and then the error degrees of freedom are written just after the F statistic (in parentheses as shown here: F(hypothesized df, error df) = F value.

Example: If you had a study with a few experimental conditions and about 130 subjects, you might see something like this when reading about an F value statistic of 9.2:

F(2, 125) = 9.2.

Finally, you will get something called Eta-squared. This is the effect size. You hope that your Independent Variables have big effects, right? After all, sometimes something can be statistically significant (unlikely to happen by chance) but the effect may not be very large, so it is of little practical use. You ideally want statistics that are both significant and large in their effect sizes. The Eta-squared numbers in the computer printouts look small, but what they SPSS writers don’t tell you is that those are percentages (and are usually reported as such). So move the decimal to the right by two places and add a percentage sign.

Example: The Eta-squared for Independent Variable #1 is .099. This means it explains 9.9% of the variance in the dependent variables overall. In the world of business, that is pretty good.

Suppose you got the following HYPOTHETICAL results:

Independent Variable #1	F(16, 2) = 28.875; p < .05; Eta2 = 78.8%

This would tell you that Independent Variable #1 accounts for 78.8% of the variance in responses across the different variables. The groups differ significantly from each other. You have to look at the means from the printout to see which group has the largest mean. If there are three or more groups, the Post-hoc test can tell you exactly which group(s) differ from which other group(s).

Once you have gathered all of these results, consider these questions:

*Does this data set point you to any problem areas in the merged telephone system?

*Do different forms of monitoring matter? Is monitoring needed?

*What (if anything) do your results say about using electronic monitoring for performance appraisal?

*What do the data say about different ways of doing performance appraisal at the different locations?

*Does having an appeals mechanism matter? Does the form of the mechanism matter? Why?

*Does ‘voice’ matter? How? What else do these data suggest about “justice” (fairness) issues?

*What do these data suggest about turnover issues?

*What variables are ‘confounded’ (not measuring one thing clearly, but really measuring two things)?

*What additional analyses might you consider doing? What additional data do you wish you had?

*What are the different courses of action that one might take, given these different findings? What are the

strengths and weaknesses of each possible recommendation? What findings are most important? Why?

*What recommended HR policies should you implement? Why? Put a sample policy in an appendix.

As you write your report, don’t simply answer the questions. Write a report! See pp. 2-4 of the syllabus.

Extra Credit idea (10% max. – to receive extra credit, you must incorporate your findings into your written report and clearly identify that section as “Extra Credit” so I am sure to give you credit.)

You have considered “intention to quit” among those who stayed with the merged firm for about one year. However, some people quit after the merger. Their data has a “2” in the “Turnover” column. You can analyze actual turnover to see what factors: Culture, Explanation, and Decision Makers affected actual turnover. Look at each independently (three separate analyses).

Select Descriptive Statistics…Crosstabs…

Put one of your three independent variables (e.g., Culture) in the “Rows” box using the [>].

Put your dependent variable (“Turnover”) in the “Columns” box.

Under “Statistics” select “Chi-Square”

What do these extra three analyses tell you that you didn’t already know?

Name_______________________________________________	Part I, Spring 2018, MGT 486

Grading Rubric

Checklist:

Purpose/Overview

____Named Dependent Variables

____Mentioned Independent

Variables (I.V.s) briefly

Literature Review Topics &

Explanation & Logic;	____Did you cite ten sources?

Rationale leads to your study	____Mergers & Acquisitions

____Electronic Monitoring concepts

____Performance Appraisal issues

____Justice types

____Organizational Culture

____Explanations & ‘Voice’ in Justice

____Turnover

____Satisfaction/Org. Commitment

____Other relevant literature & logic

Method (10%)

____Mention all I.V.s

____Mention Dependent Variables

____Tell how variables are scaled

____Describe analyses briefly

Results: Note: Results must be correct to get over a “C”

_____	____Path Analysis (Satisf + Intention to Quit)

_____ ____Path Analysis graphs

_____	____Between-Subjects ANOVAs: DJ, PJ

Discussion and Recommendations

____Discusses all results

____Considers alternative courses of action

____Study Recommendations included

____Related recommendations included

____Discusses limits of the study

____Directions for future research

Reference Page

____At least ten references, all cited

____All references are complete

Figures/Results/Printouts In an

Appendix—are the answers correct?

____ All results are correct

Recommended HR Policies/Procedures in an Appendix:

Are These Relevant and Clear?	____Policy is Present & Clear

EXTRA CREDIT (10% Max.):	____Any additional Analyses

The amount of extra credit	NOTE THESE IN YOUR REPORT!

depends upon the	amount of and

quality of work. 10% max.

Note: √ is good, - is bad, (√) & (-) are in-between