Week 6 Discussion: Data Analysis and Planning Discussion Topic

Part 1: Review the case study data related to your research problem and describe potential analysis tools and methods you will use to answer your research question. Be specific: Reference specific descriptive and inferential statistics from your textbook that you plan to use in your analysis. To support your plan, reference a business research study that implemented this method, and explain why you believe it was appropriate.Part 2: How do your peers’ plans differ from your own, and what can you use from others’ plans that can improve or change your analysis approach?***Chapters 14 and 16 attached for reference if needed.
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MBS Direct: Research Methods For Business: A Skill Building Approach
PRINTED BY: matthew.phillips5@snhu.edu. Printing is for personal, private use only. No part of this book may be reproduced or transmitted without publisher’s prior
permission. Violators will be prosecuted.
CHAPTER 14
QUANTITATIVE DATA ANALYSIS
LEARNING OBJECTIVES
After completing Chapter 14 you should be able to:
1. Demonstrate the ability to get data ready for quantitative analysis.
2. Describe the various processes by which one can get a feel for the data in a study.
3. Describe the means by which the reliability and validity of measures can be assessed.
INTRODUCTION
After quantitative data have been collected from a representative sample of the population, the next step is to analyze them to answer
our research questions. However, before we can start analyzing the data, some preliminary steps need to be completed. These help to
ensure that the data are accurate, complete, and suitable for further analysis. This chapter addresses these preliminary steps in detail.
Subsequently, general guidelines are provided for calculating and displaying basic descriptive statistics.
The easiest way to illustrate data analysis is through a case. We will therefore introduce the Excelsior Enterprises case first.
EXAMPLE
Excelsior Enterprises is a medium-sized company, manufacturing and selling instruments and supplies needed by the health
care industry, including blood pressure instruments, surgical instruments, dental accessories, and so on. The company, with a
total of 360 employees working three shifts, is doing reasonably well but could do far better if it did not experience employee
turnover at almost all levels and in all departments. The president of the company called in a research team to study the situation
and to make recommendations on the turnover problem.
Since access to those who had left the company would be difficult, the research team suggested to the president that they talk to
the current employees and, based on their input and a literature survey, try to get at the factors influencing employees’ intentions
to stay with, or leave, the company. Since past research has shown that intention to leave (ITL) is an
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excellent predictor of actual turnover, the president concurred.
The team first conducted an unstructured interview with about 50 employees at various levels and from different departments.
Their broad statement was: “We are here to find out how you experience your work life. Tell us whatever you consider is
important for you in your job, as issues relate to your work, the environment, the organization, supervision, and whatever else
you think is relevant. If we get a good handle on the issues involved, we may be able to make appropriate recommendations to
management to enhance the quality of your work life. We would just like to talk to you now, and administer a questionnaire later.”
Each interview typically lasted about 45 minutes, and notes on the responses were written down by the team members. When
the responses were tabulated, it became clear that the issues most frequently brought up by the respondents, in one form or
another, related to three main areas: the job (employees said the jobs were dull or too complex; there was lack of freedom to do
the job as one wanted to, etc.), perceived inequities (remarks such as “I put much more in my work than I get out of it”); and
burnout (comments such as “there is so much work to be done that by the end of the day we are physically and emotionally
exhausted”; “we feel the frequent need to take time off because of exhaustion”; etc.).
A literature survey confirmed that these variables were good predictors of intention to leave and subsequent turnover. In addition,
job satisfaction was also found to be an important predictor of intention to leave. A theoretical framework was developed based
on the interviews and the literature survey, and four hypotheses (stated later) were developed.
Next, a questionnaire was designed incorporating well-validated and reliable measures for job enrichment, perceived equity,
burnout, job satisfaction, and intention to leave. Perceived equity was measured by five survey items: (1) “I invest more in my
work than I get out of it”; (2) “I exert myself too much considering what I get back in return”; (3) “For the efforts I put into the
organization, I get much in return” (reversed); (4) “If I take into account my dedication, the company ought to give me better
training”; and (5) “In general, the benefits I receive from the organization outweigh the effort I put in it” (reversed). Job enrichment
was measured on a four-item Likert scale: (1) “The job is quite simple and repetitive” (reversed); (2) “The job requires me to use
a number of complex or higher-level skills”; (3) “The job requires a lot of cooperative work with other people”; and (4) “The job
itself is not very significant or important in the broader scheme of things” (reversed). Participants responded to these items on a
five-point scale, ranging from “I disagree completely” (1) to “I agree completely” (5). Burnout was measured with The Burnout
Measure Short Version (BMS). The BMS includes ten items that measure levels of physical, emotional, and mental exhaustion of
the individual. Respondents are asked to rate the frequency with which they experience each of the items appearing in the
questionnaire (e.g., being tired or helpless) on a scale ranging from 1 (“never”) to 5 (“always”). Job satisfaction was measured by
a single-item rating of “satisfaction with your current job,” using a five-point “not at all–very much” scale. Intention to leave was
measured using two survey items: “With what level of certainty do you intend to leave this organization within the next year for
another type of job?” (item 1) “for a similar type of job?” (item 2). Participants indicated on a four-point rating scale their level of
certainty. Demographic variables such as age, education, gender, tenure, department, and work shift were also included in the
questionnaire.
The questionnaire was administered personally to 174 employees who were chosen on a disproportionate stratified random
sampling basis. The responses were entered into the computer. Thereafter, the data were submitted for analysis to test the
following hypotheses, which were formulated by the researchers:
H1: Job enrichment has a negative effect on intention to leave.
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H2: Perceived equity has a negative effect on intention to leave.
H3: Burnout has a positive effect on intention to leave.
H4: Job satisfaction mediates the relationship between job enrichment, perceived equity, and burnout on intention to leave.
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It may be pertinent to point out here that the four hypotheses derived from the theoretical framework are particularly relevant for finding
answers to the turnover issue. The results of testing the hypotheses will certainly offer insights into how much of the variance in
intention to leave can be explained by the independent variables, and what corrective action, if any, needs to be taken.
GETTING THE DATA READY FOR ANALYSIS
After data are obtained through questionnaires, they need to be coded, keyed in, and edited. That is, a categorization scheme has to be
set up before the data can be typed in. Then, outliers, inconsistencies, and blank responses, if any, have to be handled in some way.
Each of these stages of data preparation is discussed below.
Coding and data entry
The first step in data preparation is data coding. Data coding involves assigning a number to the participants’ responses so they can be
entered into a database. In Chapter 9, we discussed the convenience of electronic surveys for collecting questionnaire data; such
surveys facilitate the entry of the responses directly into the computer without manual keying in of the data. However, if, for whatever
reason, this cannot be done, then it is perhaps a good idea to use a coding sheet first to transcribe the data from the questionnaire and
then key in the data. This method, in contrast to flipping through each questionnaire for each item, avoids confusion, especially when
there are many questions and a large number of questionnaires as well.
Coding the responses
In the Excelsior Enterprises questionnaire, we have 22 items measuring perceived equity, job enrichment, burnout, job satisfaction, and
intention to leave, and six demographic variables, as shown in Figure 14.1, a sample questionnaire.
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FIGURE 14.1 Sample questionnaire
The responses of this particular employee (participant #1 in the data file) to the first 22 questions can be coded by using the actual
number circled by the respondent (1, 2, 3, 1, 4, 5, 1, 3, 3, etc.). Coding the demographic variables is somewhat less obvious. For
instance, tenure is a special case, because it is a two-category variable. It is possible to use a coding approach that assigns a 1 = parttime and a 2 = full-time. However, using 0 = part-time and 1 = full-time (this is called dummy coding) is by far the most popular and
recommended approach because it makes our lives easier in the data analysis stage. Hence, we code tenure (full-time) with 1 for
participant #1. Work shift (third shift) can be coded 3, department (production) 2, and age 54. Gender can be coded 0 (male) Finally,
education (less than high school) can be coded 1.
At this stage you should also think about how you want to code nonresponses. Some researchers leave nonresponses blank, others
assign a “9,” a “99” or a “.” All the approaches are fine, as long as you code all the nonresponses in the same way.
Human errors can occur while coding. At least 10% of the coded questionnaires should therefore be checked for coding accuracy. Their
selection may follow a systematic sampling procedure. That is, every nth form coded could be verified for accuracy. If many errors are
found in the sample, all items may have to be checked.
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Data entry
After responses have been coded, they can be entered into a database. Raw data can be entered through any software program. For
instance, the SPSS Data Editor, which looks like a spreadsheet and is shown in Figure 14.2, can enter, edit, and view the contents of
the data file.
FIGURE 14.2 The SPSS Data Editor
Each row of the editor represents a case or observation (in this case a participant of our study – 174 in the Excelsior Enterprises study),
and each column represents a variable (here variables are defined as the different items of information that you collect for your cases;
there are thus 28 variables in the Excelsior Enterprises questionnaire).
It is important to always use the first column for identification purposes; assign a number to every questionnaire, write this number on
the first page of the questionnaire, and enter this number in the first column of your
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data file. This allows you to compare the data in the data file with the answers of the participants, even after you have rearranged your
data file.
Then, start entering the participants’ responses into the data file.
Editing data
After the data are keyed in, they need to be edited. For instance, the blank responses, if any, have to be handled in some way, and
inconsistent data have to be checked and followed up. Data editing deals with detecting and correcting illogical, inconsistent, or illegal
data and omissions in the information returned by the participants of the study.
An example of an illogical response is an outlier response. An outlier is an observation that is substantially different from the other
observations. An outlier is not always an error even though data errors (entry errors) are a likely source of outliers. Because outliers
have a large impact on the research results they should be investigated carefully to make sure that they are correct. You can check the
dispersion of nominal and/or ordinal variables by obtaining minimum and maximum values and frequency tables. This will quickly reveal
the most obvious outliers. For interval and ratio data, visual aids (such as a scatterplot or a boxplot) are good methods to check for
outliers.
Inconsistent responses are responses that are not in harmony with other information. For instance, a participant in our study might have
answered the perceived equity statements as in Figure 14.3. Note that all the answers of this employee indicate that the participant
finds that the benefits she receives from the organization balance the efforts she puts into her job, except for the answer to the third
statement. From the other four responses we might infer that the participant in all probability feels that, for the efforts she puts into the
organization, she does get much in return and has made a mistake in responding to this particular statement. The response to this
statement could then be edited by the researcher.
FIGURE 14.3 Example of a possible inconsistent answer
It is, however, possible that the respondent deliberately indicated that she does not get much in return for the efforts she puts into the
organization. If such were to be the case, we would be introducing a bias by editing the data. Hence, great care has to be taken in
dealing with inconsistent responses such as these. Whenever possible, it is desirable to follow up with the respondent to get the correct
data, even though this is an expensive solution.
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Illegal codes are values that are not specified in the coding instructions. For example, a code of “6” in question 1 (I invest more in my
work than I get out of it) would be an illegal code. The best way to check for illegal codes is to have the computer produce a frequency
distribution and check it for illegal codes.
Not all respondents answer every item in the questionnaire. Omissions may occur because respondents did not understand the
question, did not know the answer, or were not willing to answer the question.
If a substantial number of questions – say, 25% of the items in the questionnaire – have been left unanswered, it may be a good idea to
throw out the questionnaire and not include it in the data set for analysis. In this event, it is important to mention the number of returned
but unused responses due to excessive missing data in the final report submitted to the sponsor of the study. If, however, only two or
three items are
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left blank in a questionnaire with, say, 30 or more items, we need to decide how these blank responses are to be handled.
One way to handle a blank response is to ignore it when the analyses are done. This approach is possible in all statistical programs and
is the default option in most of them. A disadvantage of this approach is that, of course, it will reduce the sample size, sometimes even
to an inappropriate size, whenever that particular variable is involved in the analyses. Moreover, if the missing data are not missing
completely at random, this method may bias the results of your study. For this reason, ignoring the blank responses is best suited to
instances in which we have gathered a large amount of data, the number of missing data is relatively small, and relationships are so
strong that they are not affected by the missing data (Hair, Anderson, Tatham & Black, 1995).
An alternative solution would be to look at the participant’s pattern of responses to other questions and, from these answers, deduce a
logical answer to the question for the missing response. A second alternative solution would be to assign to the item the mean value of
the responses of all those who have responded to that particular item. In fact, there are many ways of handling blank responses (see
Hair et al., 1995), each of them having its own particular advantages and disadvantages.
Note that if many of the respondents have answered “don’t know” to a particular item or items, further investigation may well be worth
while. The question might not have been clear or, for some reason, participants could have been reluctant or unable to answer the
question.
Data transformation
Data transformation, a variation of data coding, is the process of changing the original numerical representation of a quantitative value
to another value. Data are typically changed to avoid problems in the next stage of the data analysis process. For example, economists
often use a logarithmic transformation so that the data are more evenly distributed. If, for instance, income data, which are often
unevenly distributed, are reduced to their logarithmic value, the high incomes are brought closer to the lower end of the scale and
provide a distribution closer to a normal curve.
Another type of data transformation is reverse scoring. Take, for instance, the perceived inequity measure of the Excelsior Enterprises
case. Perceived inequity is measured by five survey items: (1) “I invest more in my work than I get out of it”; (2) “I exert myself too much
considering what I get back in return”; (3) “For the efforts I put into the organization, I get much in …
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