Running head: MOCK STUDY 1

Mock Study

Ron Hyland

University of Phoenix

MOCK STUDY 2

Mock Study

One of the primary tasks faced by doctoral learners in

University of Phoenix’s School of Advance Studies is to complete

a dissertation. The dissertation is the culminating work of the

program. The intention of the dissertation, among other things,

is to show the academic growth of the student, mastery of

research skills, and to add to the body of knowledge in the

student’s chosen field of study.

As part of the student’s development, they must develop

research skills. One exercise aimed at this endeavor is the

completion of a mock study. The mock study provides the doctoral

learner with the opportunity to learn and practice the skills

necessary for the completion of his or her dissertation. Over a

period of eight weeks, the student completes various tasks and

gains insight into the components that make up a sound

dissertation study. What follows is the beginning of one such

exercise

Research Topic & Purpose

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Before beginning to utilize the tools and skills necessary,

the student must first select a research topic to form the basis

of the mock study. For this series of exercises, this doctoral

learner has selected a topic focused on understanding the impact

that transformational leadership has on driving employee

engagement. Much literature exists about the value of employee

engagement. Soieb, Othman, and D’Silva (2013) found that

leadership has a significant role in driving the level of

employee engagement. Robertson and Cooper (2011) found that

employee engagement levels are a direct result of the environment

that exists within an organization. . Jauhari, Sehgal, and

Sehgal (2013) found that higher levels of employee engagement

improved the performance of the organization. In all of these

cases, the authors found that the higher the measure of employee

engagement, the better the overall performance of the

organization. Engaged employees are willing to give extra effort

towards the achievement of the organization’s goals.

Transformational leadership’s roots extend from the work of

Burns who introduced the concepts of transformational and

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transactional leadership (Burns, 1978). Bass, thought by many as

one of the fathers of transformational leadership theory,

continued this work (Zahari & Shurbagi, 2012). Bass’s theory of

transformational leadership defines transformational behaviors

as: (1) idealized influence (charisma), (2) individualized

consideration, (3) intellectual stimulation, and (4)

inspirational motivation (Clawson, 2006). Transformational

leadership encompasses the leader moving followers past immediate

self-interests towards a higher purpose (Bass, 1999).

Leadership has a responsibility to maximize organizational

performance. Even with the knowledge about leadership practices

and employee engagement, there is opportunity to understand

deeper how leadership drives employee engagement. Davidson,

Azziz, Morrison, Rocha, and Braun (2012), found that leadership

practices correlate directly with organizational success.

Nevertheless, there is little emphasis in developing competencies

since leaders often do not understand the value of investing in

self-development. Ahmed, Shields, White, and Wilbert (2010)

found that a measure of successful leadership is employee

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commitment. High levels of commitment result in improvements in

organizational performance.

The purpose of this quantitative correlational design study

is to discover the relationship of transformational leadership

practices to employee engagement. To carry out this study, the

researcher will observe participants in their natural

surroundings to establish the relationship transformational

leadership has on driving higher levels of employee engagement.

Research Design

According to Christensen, Johnson, and Turner (2011), a

correlational design is the best design to utilize if the goal of

the study is to show the relationship between two variables. In

a correlational design, the researcher measures levels of the

variables and then utilizes statistics to illustrate the patterns

and relationships in the data. For the proposed study, the

design must support measuring the variables transformational

leadership and employee engagement.

The correlational study is a non-experimental quantitative

design (Christens, Johnson, & Turner, 2011). The purpose of the

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study is to describe the relationships between variables and then

use this relationship to predict future performance. The

correlational design is the most appropriate design for

description and prediction. While the design does not allow the

researcher to establish cause and effect relationships, it still

provides valuable information regarding the presence of one

variable and the ability to predict the second variable.

The correlational design is appropriate for evaluating

variables in their natural settings (Christensen, Johnson, &

Turner, 2011; Black, 1999). Often the researcher is unable to

control and manipulate the independent variable. Instead, the

researcher measures independent and dependent variables in their

natural settings. The researcher establishes patterns and

relationships from these measurements. This allows the

researcher to conduct his or her study without the need to try to

reproduce the natural environment in a laboratory setting, which

is often not possible.

Christensen, Johnson, and Turner (2011) shared that random

samples of study participants enhance the external validity of

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the study. Random samples tend to be more representative of the

general population then purposeful samples. As such, any changes

observed in a study with random sampling would likely apply to

the population at large.

The proposed study will utilize two groups of participants.

One group, will have leaders that exhibit high levels of

transformational leadership characteristics. The other group

will utilize leaders with low to normal transformational

leadership characteristics. Each group will have engagement

levels measured. In this way, the impact of transformational

leadership on employee engagement should be isolated. While the

study sample is not truly random, because of the size of the

samples, and the nature of the selection, they should be

representative of the population at large. This will enhance the

external validity of the proposed study.

The purpose of this study is to identify the relationship

between transformational leadership and employee engagement. The

correlational study design is an excellent choice for this study.

This design has known and acceptable internal and external

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validity. As such, the results should be reliable and may be

generalizable. In addition, since the purpose of the study is to

identify the relationships, a correlational design is the

appropriate choice. Finally, the environment will support

executing the selected study design. There are numerous

participants and leaders from which to choose. This means there

are enough people from which to identify the required participant

population.

Defining the Variables

Establishing the operational definition of the study

variables is a critical step in research design (Black, 1999).

Operational definition of the variables ensures alignment with

the research purpose and questions. In addition, operational

definition helps to identify the appropriate measurement

instruments. The operational definition of the variables

provides the researchers and any reviewers with a clear

understanding of what the variables are measuring. An

operational definition of the variables provides a description of

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exactly what the variables are, as well as how the researcher

proposes to measure them.

When measuring variables, it is important to understand the

level of measurement appropriate (Black, 1999; Christensen,

Johnson, & Turner, 2011). The level of measurement refers to

quantitative scale of the variable. There are four levels of

measurement for quantitative variables: nominal, ordinal,

interval, and ratio. A nominal level of measurement is the

simplest and most basic type of measurement. It uses words,

symbols, or numbers to classify data. These tags serve as

markers. There is no implied order, rank, or magnitude. The

ordinal level is a rank-order scale with an implied order to the

variables. However, there is no sense of the distance between

each variable. An ordinal scale gives a sense of which value is

higher or lower, but not a sense of the magnitude between values.

The next level is interval scale. This scale has equidistance

between adjacent values on the scale as well as ordering.

However, there is no absolute zero on an interval scale. A ratio

level scale is one in which there is order, equidistance adjacent

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members, and an absolute zero point. The levels of measurement

build in that nominal scale is the least quantitative and ratio

scale is the most quantitative level. As such, each level has

the characteristics of the previous level and adds a higher level

of quantification.

The proposed study includes a number of variables, which the

researcher will monitor and measure. The main study variables

are transformational leadership and employee engagement.

Transformational leadership is the independent variable and

employee engagement is the dependent variable. In addition, the

study will include some other demographic variables. Gender of

the leader will be included in the study variables. Furthermore,

the researcher will look at length of service of the leader as

another demographic study variable. Below is a further

examination of each of the study variables and their operational

definition.

Transformational Leadership. For the proposed study, the

researcher will measure transformational leadership

characteristics of the leaders. This will be the independent

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variable in this correlational study. To accomplish this, the

researcher will measure Idealized Attributes, Idealized

Behaviors, Inspirational Motivation, Intellectual Stimulation,

and Individualized Consideration of each leader using a survey

instrument. The overall assessment of these characteristics

provides a strong indication of transformational leadership

capabilities. The researcher will be able to look at each

measurement individually as well as the aggregate score.

Transformational Leadership is an interval level variable.

As such, it is highly quantifiable. To analyze the

transformational leadership variable, the researcher will use

descriptive statistics. The researcher will use measures of

central tendency, including the mean and the median. Central

tendency provides a sense of the typical values of a pool of

research data (Christensen, Johnson, & Turner, 2011). The

researcher will also calculate the standard deviation. Standard

deviation is a measure of variability of the data. This value

provides the researcher with a sense of how spread out the data

may be.

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These measures will help to divide the leaders into

quartiles (Q1 – Q4). Quartiles are a way of segmenting samples

into equal quarters by performance (Christensen, Johnson, &

Turner, 2011; Black, 1999). Leaders from quartile groups Q1 – Q3

will form the “low to normal transformational” leader group.

Leaders from Q4 will form the “high transformational” leader

group.

Inferential statistics allow researchers to make inferences

about a sample population (Christensen, Johnson, & Turner, 2011).

There are two types of inferential statistics, those used for

estimation, and those used for hypothesis testing. The

researcher will use a t-test for the two sample leadership groups

to establish statistical significance in the difference of the

scores on transformational leadership. This will provide an

understanding of the difference in transformational leadership

characteristics of the two selected populations.

Employee Engagement. The researcher will measure the level

of employee engagement as the dependent variable in this study.

Work engagement will provide an assessment of the level of

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employee engagement. For the purposes of this study, work

engagement is a positive attitude, characterized by vigor,

dedication, and absorption using the Utrecht Work Engagement

Scale (Schaufeli & Bakker, 2003). This scale provides an

effective and statistically significant measure of work

engagement.

Employee engagement, measured in this fashion, is a interval

level variable. The researcher will use measures of central

tendency to evaluate the mean, mode, and median scores of

employee engagement. Understanding the central tendency measures

of employee engagement for each of the tested populations is the

first step in looking for relationships between transformational

leadership and employee engagement. Once again, the researcher

will use a t-test to evaluate the statistical significance of any

difference in performance.

To understand the relationship that transformational

leadership has on driving employee engagement, the researcher

will utilize inferential statistics. Cohen’s d is a measure to

evaluate the difference between means (Christensen, Johnson, &

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Turner, 2011). This is an effect size indicator, which will show

the difference in performance between the two populations. The

researcher proposes that there is a linear relationship between

transformational leadership and employee engagement. Therefore,

the researcher intends to use Pearson’s Correlation Coefficient

to evaluate the relationship. Pearson’s Correlation Coefficient

will show the direction and magnitude of the correlation between

two quantitative variables. If a curvilinear relationship is

observed, the researcher will employ curvilinear regressions to

establish the correlation.

Gender. For the proposed study, the researcher will collect

gender and evaluate it as a moderating variable. Gender is a

nominal scale variable. The researcher is interested in

understanding the role that gender plays in driving

transformational leadership behaviors and the corresponding

impact on employee engagement. A moderating variable is one that

affects the strength of performance on another quantitative

variable (Christensen, Johnson, & Turner, 2011). To evaluate the

effect of gender on performance, the researcher will use a two

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dimensional contingency table. The researcher will create a

table with gender as one dimension, and the mean employee

engagement of each of the groups as the second dimension.

Applying a t-test to these measures will allow the researcher to

evaluate whether there is a significant difference in mean

performance of the groups. This will identify whether gender is

a moderating variable.

Length of Service in Leadership. The researcher will

collect and evaluate length of service in leadership as a

demographic variable. The purpose is to understand if this

variable is a mediating variable in driving employee engagement.

For example, do leaders who have been in leadership longer drive

higher levels of engagement. To evaluate this variable, the

researcher will divide the leaders, based on length of service,

into quartile groups. Using length of service in this fashion

makes this variable an interval scale variable. The researcher

will create a two dimensional contingency table with quartile

group as one dimension and mean employee engagement as the second

dimension. The researcher will use a t-test to obtain a

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statistical significance in performance based on length of

service in leadership.

Research Question

The area of focus for this quantitative correlational study

is the role that leadership plays in driving employee engagement.

The research aims to explore the relationship that leadership has

on employee engagement. The research question guiding this study

is:

1. What is the relationship of leadership practices and

employee engagement?

To discover the answer to the study’s research questions, the

researcher will examine the following hypotheses:

H01: There is no significant relationship between

leadership practices and employee engagement.

Ha1: There is a significant relationship between

leadership practices and employee engagement.

The study hopes to answer this question to fill the current gaps

in knowledge about how leadership practices affect levels of

employee engagement. By conducting the study, the researcher

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should be able to identify any statistically significant

relationships that may exist between the study variables.

Association of the leadership practices with corresponding

changes in employee engagement should yield deeper understanding.

Although it is the hope of the researcher to identify

leadership practices that will enhance the levels of employee

engagement, the study hypothesis Ha1 is non-directional. A non-

directional hypothesis is one in which the direction of the

relationship, positive or negative, is not specified (Black,

1999; Vogt, 2007). Because the study may just as likely identify

leadership practices that inhibit as well as enhance employee

engagement, the researcher posed the hypothesis in a non-

directional fashion.

Because the hypothesis is non-directional, the researcher

intends to use two-tailed correlational statistical tests. A

two-tailed test is one in which the researcher looks for

correlation of the study variables as shown by either an increase

or a decrease (Black, 1999; Vogt, 2007). Although the

statistical power of a one-tailed test is stronger, the nature of

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the research question and hypothesis is such that a positive or

negative relationship may exist. Therefore, the research must

use two-tailed tests for correlation. With the use of two tailed

tests, there is a lower probability of making a Type I error,

rejecting a true null hypothesis. However, there is an increased

probability of making a Type II error, failure to reject a false

null hypothesis. To account for this, the researcher will need

to pay special attention to confounding variables as well as

sample size and selection.

Research Instrument

Research instruments must meet certain criteria to be valid

for use in a study. One of the most important criteria is

internal consistency reliability. Christensen, Johnson, and

Turner (2011) wrote that internal consistency reliability is a

measure of how well the items on a test measure a single

construct. For a test to be valid, it must have a high degree of

reliability. Therefore, it is generally accepted practice to

utilize published and tested assessments.

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For this study, two such instruments are required. The

first instrument will measure the level of transformation

leadership in the leadership population. The Multifactor

Leadership Questionnaire (MLQ) is the accepted standard for

measuring transformational leadership (Harazd, & van Ophuysen,

2011). The work of Bass and Avolio form the basis for the

instrument. The test typically shows a Cronbach's alpha from .63

to .94. Cronbach’s alpha is a measure of the internal validity

of the instrument. These scores represent a high degree of

internal consistency, which makes the MLQ an appropriate

instrument to utilize for the proposed study,

The second instrument needed for the study is one that can

measure employee engagement. The Utrecht Work Engagement Scale

(UWES) is an accepted measure of employee engagement (Fong & Ng,

2012). The UWES-9 version of the questionnaire exhibits high

levels of internal consistency and reliability. Using this test,

researchers are able to arrive at a measure that accurately

represents the level of engagement.

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Utilizing these two assessments, the proposed study will

ensure internal validity. The selected instruments are both

well-known and exhibit high levels of consistency and

reliability. This makes these two assessments appropriate

selections for the study, which will increase the acceptance of

the study results among the academic community.

Sampling and Data Collection

Sampling and data collection are two very important aspects

of any research study. Sampling is a process whereby the

researcher selects a number of elements from a list of possible

elements in the population (Shaughnessy, Zechmeister, &

Zechmeister, 2002). For study results to be accurate, it is

imperative that the selected sample is representative of the

general population (Christensen, Johnson, & Turner, 2011; Black,

1999; Vogt, 2007; Shaughnessy, Zechmeister, & Zechmeister, 2002).

Generalizability of study results is critically dependent on

the representativeness of the sample population (Christensen,

Johnson, & Turner, 2011; Black, 1999; Vogt, 2007; Shaughnessy,

Zechmeister, & Zechmeister, 2002). Another way of stating

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generalizability of study results is external validity. For a

researcher to draw conclusions for the population at large based

on the study results, the researcher must apply rigor in sample

selection.

Sample selection is important as well for controlling for

confounding variables (Black, 1999). By carefully selecting the

sample, the researcher minimizes the chances of introducing new

and previously unidentified variables into the study. Since

hidden extraneous variables can corrupt study results by

influencing the independent variables, researchers must control

for unknowns. This would affect the internal validity of the

study. Internal validity is a representation of the relationship

between independent and dependent variables (Christensen,

Johnson, & Turner, 2011).

For the proposed study, the researcher intends to use a

probability sampling method. A probability sample is one in

which each member of the population has equal probability of

being selected into the sample (Vogt, 2007). There are four

different types of probability samples: random, systemic,

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stratified, and clustered. A random sample is one in which there

is equal probability of selecting each member of a population

into the sample group. A stratified sampling method is one in

which one first sub-divides the group and then use random

sampling techniques to select from this smaller population. A

cluster sample is one in which a researcher will cluster the

participants and then randomly select from those clusters. A

systemic method is an approximated random sample. To utilize

this method, a researcher will list the population, and then

select every “nth” person. The purpose of a probability sample

is to increase the likelihood of selecting participants that are

representative of the population at large. This increases the

generalizability (external validity) of the study results

(Christensen, Johnson, Turner, 2011; Black, 1999; Vogt, 2007;

Shaughnessy, Zechmeister, & Zechmeister, 2002).

The population for the proposed study consists of call

center employees for a large California based media company. The

population size is 20,000 front line employees. The ratio of

employees to leaders is 15:1. This means that there are

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approximately 1,300 leaders and 19,000 employees. The researcher

will utilize systemic sampling design. The researcher will

select systemically from a list of leaders. This should provide

a random representation of the population.

Statistical power is important for a researcher to address

in study design (Black, 1999). Statistical power is a

representation of both the strength and validity of study

results. Sampling procedures and measurement procedures (data

collection) are two components that influence statistical power.

When determining the sampling protocol, a researcher must

consider sample size and sample quality (Black, 1999;

Shaughnessy, Zechmeister, & Zechmeister, 2002). Larger sample

sizes generally reduce the chance of error. Sample quality

pertains to the homogeneity and representativeness of the sample

population with respect to the designated traits. Casually

selecting sample populations will increase error variance.

Because the researcher intends to study the correlation of

leadership on employee engagement, sample size is an important

consideration. When calculating sample size one must consider

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the desired confidence level, confidence interval, and

population. The confidence level and confidence interval work

together to determine the accuracy of results (Vogt, 2007; Black,

1999). The confidence interval is the plus or minus range on a

given data point. Another name for this is the margin of error.

For example, a confidence interval of 5 says that the answer is

between x-5 and x+5. The confidence level is a percentage that

tells how sure the answer is. It represents how often the answer

lies within the confidence interval. For example, a 95%

confidence level states that 95% of the time the answer is

between x-5 and x+5.

For studies to be statistically accurate, it is generally

acceptable to have a confidence level of 95%. For the proposed

study, the confidence interval for leadership scores is five.

Given the population size of 1300 leaders, and the aforementioned

confidence level and confidence interval, the study requires a

sample size of 297 leaders ("Sample size calculator," 2012).

Given the ratio of 15:1 employees to leaders, this yields

approximately 5000 employees in the sample population. With a

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95% confidence level, and a population of 19,000 total employees,

the confidence interval for the employee sample will be 1.19.

With respect to data collection, two important

considerations influencing statistical power are reliability of

the instrument and instrument design (Black, 1999). Reliability

refers to the consistency of scores from the measurement

instrument (Christensen, Johnson, & Turner, 2011). To ensure

accuracy of the study results, the instrument must report

consistent results in similar populations and situations.

Validity pertains to the appropriateness of the instrument for

collecting data regarding the study variables. Instrument design

ensures validity. Instrument design determines the level of

measurement of the variables, and thereby the statistical tests

used to make inferences.

The proposed study utilizes to well-known and widely

accepted instruments for data collection, the MLQ and the UWES-9.

Both instruments possess high degrees of reliability and

validity. Each instrument has a history of use in many studies

for purposes similar to those in the proposed research. As such,

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there are no concerns related to statistical power stemming from

data collection as related to the instruments. Each leader will

be administered the MLQ. Each employee will be administered the

UWES-9. To facilitate collection and analysis of the data

administration of the tests will occur electronically. SPSS

statistical software will provide analysis of the collected data.

Exploring the Instruments

Understanding the critical contribution that data collection

makes to a study justifies further exploration of the survey

instruments. Black (1999) shared that a researcher must

critically consider the appropriateness of an instrument to

achieving the study’s goals. What is clear is that a researcher

must understand the basis of instrument construction, such as the

variables measured, the instrument validity, as well as the

reliability (Christensen, Johnson, & Turner, 2011). There must

be alignment between the study’s goals, the construct of the

instrument, and the population studied.

The Multifactor Leadership Questionnaire (MLQ), based on the

work of Bass and Avolio, is one of the most often used

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instruments for measuring leadership style (Den Hartog, Van

Muijen, & Koopman, 1997). The MLQ finds its roots in Full Range

Leadership Theory (Garg & Ramjee, 2013). Full Range Leadership

Theory (FRLT) is an effective way to explain leadership and show

its multidimensional nature (Antonakis & House, 2002). FRLT

represents leadership behaviors in tangible and empirically

measurable behaviors that predict leadership outcomes.

The FRLT is an integrative approach to leadership, which

explains the leader’s ability to drive follower performance

(Antonakis & House, 2002). The leadership scales of the FRLT

hierarchically relate to leadership outcomes in that

transformational constructs and contingent rewards are positive

predictors of effectiveness. As well, the passive constructs are

negative predictors. “There is substantial and consistent

support for this hierarchy of effects of leader behaviors based

on the results of numerous studies” (p.10).

The MLQ has been improved and tested over time (Antonakis &

House, 2002). This instrument is the best-validated instrument

representing the FRLT. The MLQ is a strong predictor of leader

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performance across many different types of organizations (Garg &

Ramjee, 2013). Because of this rich history, the MLQ is a

reliable measure of the components of transformational,

transactional, and non-transactional leadership. Bass and Avolio

showed that the test is valid and reliable. The instrument

provides consistent and statistically valid results across

cultures, raters, and regions. Therefore, no modification is

required for use in the proposed study.

The MLQ contains 45 questions focused on transformational

and transactional leadership styles (Golla & Johnson, 2013). The

test, developed by Avolio and Bass, is licensed for use through

Mindgarden.com. The current version of the MLQ instrument is the

MLQ5X (Bass & Avolio, 2013). The MLQ5X is the benchmark

assessment for Transformational leadership. The license grants

the researcher the right to reproduce and administer the test

electronically.

The MLQ has been the subject of much debate in the academic

community with respect to validity and reliability. Some have

questioned the representative value of the instrument with

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respect to transformational leadership characteristics. Much

debate exists around the factorial validity of the instrument

(Heinitz, 2006). Factorial validity is a part of construct

validity. Construct validity addresses the appropriateness of an

instrument for the measurement of the variables in question

(Black, 1999; Christensen, Johnson, & Turner, 2011).

Evidence shows a high correlation between transformational

scales. Some debate exists as to whether a reduced set of

factors would provide a better representation of transformational

leadership behaviors (Heinitz, 2006). Even critics admit the

test shows statistically valid and correlated results. Bass and

Avolio addressed the concerns and modified the instrument. The

MLQ5X is valid and reliable (Antonakis & House, 2002). “Avolio

et. al. demonstrated that the MLQ5X is reliable and valid, and

offered evidence for its discriminant validity using the full

nine-factor model” (p. 18) This makes the MLQ an appropriate

choice for measuring leadership in the proposed study.

The UWES-9 is a highly reliable and consistent measure of

employee engagement (Fong & Ng, 2012). The UWES-9 measure stems

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from the work done to create the Utrecht Work Engagement Scale

(UWES) (Seppälä, Mauno, Feldt, Hakanen, Kinnunen, Tolvanen, &

Schaufeli, 2009). The UWES has been used in many different

regions, locales, and cultures. Translations exist for many

different languages. In addition, many studies with different

types of workers, from blue-collar to professional, have used the

assessment. The UWES measures work engagement across three

scales: vigor, dedication, and absorption. These factors are a

positive and stable indication of occupational well-being.

The UWES-9 is a nine-question test and takes an average five

to ten minutes to complete. (Schaufeli & Bakker, 2003).

Respondents complete the assessment by evaluating each statement

about work attitude. Each statement is rated on a scale of 0

(never) to 6 (always) with respect to job satisfaction. The

test is easy to administer and can be given in a standalone

version, or as part of a larger assessment.

The UWES scale has its roots in the work of Schaufeli and

Bakker at Utrecht University in the Netherlands (Schaufeli &

Bakker, 2003). The authors sought a measurement of true

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engagement and developed the scale as an alternative to the

traditional views, which evaluated employee burnout. The UWES-9

instrument benefits from extensive psychometric testing. The

instrument has undergone tests for factorial validity, inter-

correlation, cross-national invariance, internal consistency, and

stability.

Factorial analysis has demonstrated that the UWES’s three-

factor analysis of employee engagement is superior to the

previous one factor model (Schaufeli & Bakker, 2003). The three

dimensions of the UWES are closely correlated (correlation

coefficient = .65, with latent variable correlation between .80

and .90). The test is largely invariant across multiple

languages and cultures. The UWES has a high degree of internal

consistency (all tests yield Cronbach’s alpha values greater than

.70, with typical scores ranging between .80 and .90). The

instrument also has a high degree of stability with two-year

stability coefficients of .30, .36, and .40 for vigor,

dedication, and absorption respectively.

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Validation of the UWES instrument occurred in several

countries (Bakker & Demerouti, 2008). A confirmatory factor

analysis of the results from the various studies utilizing the

UWES scale shows the three-factor model is superior to that of

any of the alternative factor structures. The UWES-9 is valid,

captures the three-factor dimensionality of engagement, and

yields reliable scores (Mills, Culbertson, & Fullagar, 2012).

The UWES-9, with high degrees of reliability and validity and

consistency across cultures and populations, will provide strong

indications of employee engagement in the proposed study. Given

this, no modifications to the UWES-9 are required for the

proposed study.

Conclusion

Dissertations are a critical component of the doctoral

learning experience. As such, being able to successfully design

and execute a study is an important skill for the doctoral

learner to develop. The execution of a mock study provides the

student with the opportunity to hone these important skills. As

illustrated, identifying the research purpose is the first step

MOCK STUDY 33

towards executing a study. From there the researcher selects a

design. The design must be appropriate to address the research

problem and questions. The researcher then develops an

operational definition of the study variables. The researcher

formulates research questions and hypotheses driving the study.

With the design selected and operational definition complete, the

researcher must select instruments to measure the variables.

These critical components of executing a research study are

important to address correctly. From the choices made at this

juncture, the rest of the study will unfold. Spending time

developing these skills is time well invested by the doctoral

learner.

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References

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