Posted: September 20th, 2022


week4question1and2 xLeadershipandJobSatisfaction Quantitativeresearcharticle ReadingQuantitativeResearch



What considerations must be given to the selection of a quantitative methodology for a research study? Based on what you know now, which of these considerations do you believe are the most important? Why?  

QUESTION 2-    Coughlan, Cronin, and Ryan (2007) provided a step-by-step guide to critiquing quantitative literature. Using these criteria, critique the article by Barnett. What are the important markers to look for when critiquing a quantitative study?


use attached article for source to provide cited reference 

International Journal of Psychology and Educational Studies, 2017, 4 (3), 53-63

© 2014 International Journal of Psychology and Educational Studies (IJPES) is supported by Educational Researches and Publications Association (ERPA)

International Journal of Psychology and Educational


Leadership and Job Satisfaction: Adjunct Faculty at a For-Profit University

Donald Barnett1

Grand Canyon University, USA



Article History:

Received 20.06.2017

Received in revised form


Accepted 25.09.2017

Available online


There is a lack of research in the for-profit sector of higher education in the United States. Likewise,

there is a lack of research on the factors that affect the job satisfaction of adjunct faculty. To address

these gaps in knowledge, a quantitative correlational study was performed to investigate the effect

of administrative leadership on the job satisfaction of adjunct faculty who teach online classes at a

for-profit university in the United States. The Multifactor Leadership Questionnaire, which measures

perceived leadership behaviors, and Spector’s Job Satisfaction Survey, which measures job

satisfaction, were used to anonymously collect data from a sample of 77 adjunct faculty. The Full-

Range Leadership model, which is composed of transformational, transactional, and laissez-faire

leadership behaviors, was the theoretical model for leadership. Pearson’s product moment

correlational analyses were performed to investigate the bi-variate relationships between the

variables. The dependent variable of total satisfaction had a statistically significant, direct and strong

correlation with the independent variable of transformational leadership (r = .536, p < .0005). The

strength and direction of the relationship indicated that increases in the scores of total satisfaction

are associated with increases in scores in transformational leadership. Total satisfaction had a

statistically significant, indirect and moderate correlation with the independent variable of laissez-

faire leadership (r = -.372, p = .001). The strength and direction of the relationship indicated that lower

total satisfaction scores are associated with higher laissez-faire leadership scores. There was no

statistically significant relationship between transactional leadership and overall job


© 2017 IJPES. All rights reserved


Job satisfaction, Full-Range Leadership, Adjunct Faculty, For-profit University, Transformational

Leadership, Postsecondary Education.

1. Introduction

Enrollments at for-profit universities in the United States have tripled sinced 2000, with close to 1.6 million

students registered in the year 2014 (National Center for Education Statistics, 2016). This increased enrollment,

along with the expansion of online education, has amplified the demand for classes that are taught entirely

online (Allen & Seaman, 2016), and produced a need for part time, non-tenured, adjunct, faculty to facilitate

these classes (Starcher & Mandernach, 2016). Regardless of the increased use of adjunct faculty to teach online

classes, few studies have investigated adjunct development, job satisfaction, or work experiences (Datray,

Saxon, & Martirosyan, 2014; Rich, 2015). Likewise, research in the for-profit sector of post-secondary education

is sparse when compared to the non-profit sector (Chung, 2012).

Currently, there is little research on the effects of perceived leadership behaviors in post-secondary, for-profit,

education on the job satisfaction of online adjunct faculty members in the United States. This study sought to

discover if there was a correlation between the perceived use of Full-Range leadership behaviors by

administrators in post-secondary education and the overall job satisfaction of adjunct faculty members who

teach online classes at a for-profit university in the United States. Bateh and Heyliger (2014) observed that

1Corresponding author’s address: Grand Canyon University, USA


International Journal of Psychology and Educational Studies, 2017, 4 (3), 53-63


research should be conducted in the for-profit sector to determine if the job satisfaction of online adjuncts is

effected by the bahaviors of administrative leadership because the problems and concerns of for-profit

administrators are different than their colleagues in private or public universities. The absence of research on

this demographic is significant because a university’s faculty is a major contributor to the accomplishment of

organizational goals (Machado-Taylor et al., 2016). Likewise, Askling and Stensaker (2002) observed the

significance of researching higher education leadership practices.

1.1. Background

For-profit higher education in the United States, while not new, has expanded from less than 100,000 students

over 40 years ago (Wilson, 2010) to near 1.6 million by 2014 (National Center for Education Statistic, 2016).

Enrollments in the for-profit sector in the United States have increased at 9% each year over the past 30 years,

while enrollment in the non-profit sector only posted a 1.5% increase per year over the same time frame

(Wilson, 2010). Despite controversies concerning some for-profit schools (Deming, Goldin, & Katz, 2013), the

growth potential in the for-profit sector of post-secondary education remains strong, especially in career

education , adult education, and online learning (Levy, 2015). Coupled with the expansion of the for-profit

sector of post-secondary education is the increased use of part-time non-tenured, or adjunct, faculty members

(Gilpin, Saunders, & Stoddard, 2015).

Adjunct faculty typically are part-time employees who serve in a non-permanent capacity. They are non-

tenured, temporary, employees of a university who work as independent contractors. Post-secondary

institutions pay per course that the adjunct teaches, or sometimes retain their services by yearly appointment

(Bradley, 2013). In 2010, adjunct faculty accounted for 50% of all faculty in post-secondary schools in the

United States. The use of adjunct faculty has flourished because of economic concerns associated with

maintaining faculty (Dailey-Hebert, Mandernach, Donnelli-Sallee, & Norris, 2014; Eagan, Jaeger, & Grantham,

2015) and the flexibility provided by adjuncts, which is required in online programs (Starcher & Mandernach,

2016). Regardless of the importance of adjunct faculty, many universities do not adequately support their

adjunct faculty members (Kezar, 2013a). Generally, adjunct faculty members do not receive raises, and have

limited chances for advancement. Health insurance and retirement benefits are scarce, and adjuncts seldom

have a voice in university governance (Halcrow & Olson, 2011; Kezar, 2013b; Morton, 2012). Adjunct faculty

who teach online classes are especially disconnected from their full-time counterparts (Benton & Li, 2015), and

usually rely on other adjunct faculty members for support (Rich, 2015). Despite their importance to academia,

adjunct faculty are an overlooked population (Ott & Cisneros, 2015), and little research has been conducted

into factors that lead to adjunct faculty job satisfaction (Rich, 2015).

Asking and Stensaker (2002) advocated studying leadership behaviors in higher education. Moreover, Al-

Smadi and Oblan (2015) stated that depending on the type of school investigated, there are statistically

significant differences in faculty job satisfaction. Despite this, little research examining the correlation between

administrative leadership and job satisfaction in higher education has been performed (Alonderiene &

Majauskaite, 2016; Kalargyrou, Pescosolido, & Kalagrios, 2012). This research was important because of the

need for research on the effect of leadership behaviors on faculty in for-profit universities (Bateh & Heyliger,


1.2. Literature Review

1.2.1. Full Range Leadership Model. The theoretical foundation for this study was the Full-Range Leadership

Model (FRLM), which is composed of transformational, transactional, and laissez-faire leadership behaviors.

Moynihan, Pandey, and Write (2012) observed that the FRLM is one of the best-formulated leadership models.

This is true because the three leadership styles examined by the model encompass almost all leadership

behaviors exhibited by leaders (Avolio & Bass, 2004). The framework of the FRLM allows researchers to

examine the advantages and disadvantages of varying leadership behaviors when investigating

administrative leadership in post-secondary education (Asmawi, Zakaria, & Wei,


Burns (1978) coined the phrases transactional and transformational leadership while investigating the

biographies of great political and historical leaders. Bass and Avolio (1993) expanded on Burns’ work and

developed the FLRM in order to find leadership behaviors that would be effective in non-political

organizations. Bass (1985) professed that leaders do not use one exclusive style of leadership. Instead, leaders

could use aspects of transactional and transformational leadership to effectively lead their followers. Recent

Donald E. Barnett


research indicates a mixture of transactional and transformational leadership displays a positive predictive

relationship with faculty job satisfaction (Bateh & Heyliger, 2014).

The FRLM is composed of five facets of transformational leadership, three elements of transactional

leadership, and one aspect of laissez-faire leadership (Avolio & Bass, 2004). Transformational Leadership. The theory of transformational leadership was introduced in a political

context by Burns (1978). Critical revisions to the theory were made by Bass (1985) and Avolio and Bass (2004).

Since then, the theory of transformational leadership has gone through significant meta-analytic and

theoretical examinations (Banks, McCauley, Gardner, & Guler, 2016; van Knippenberg & Sitkin, 2013). Today,

it is one of the most recognizable theories on leadership behavior.

Transformational leadership represents how a leader motivates and inspires their followers to achieve their

higher potential (Burns, 1978). This style of leadership is based on encouragement, commendation,

acknowledgement, and trust (Mujkić, Šehić, Rahimić, & Jusić, 2014). Transformational leadership addresses

the needs of the followers, facilitates follower empowerment, and increases follower effort, efficiency, and

satisfaction (Bass, 2000). It is separated into four dimensions that can be distinguished theoretically and

empirically (Hobman, Jackson, Jimmieson, & Martin, 2012). These dimensions include individualized

consideration, idealized influence, inspirational motivation, and intellectual stimulation (Northouse, 2013). Idealized Influence. Omar and Hussin (2013) observed that idealized influence is associated with how

a leader is viewed by their subordinates in terms of charisma, confidence, trust, power, consistency, and ideals.

Leaders who exhibit idealized influence consider the needs of others before their own, and demonstrate high

ethical standards. They are not motivated by personal gain and set challenging, but reasonable, goals for their

followers (Northouse, 2013). To more accurately describe and measure this dimension, idealized influence has

been divided into two different dimensions: Idealized influence (behavioral) and idealized influence

(attributed), with the former denoting how the leader behaves and the latter reflecting how the leader is

perceived by their followers (Avolio & Bass, 2004). Inspirational Motivation. Sometimes referred to as inspirational leadership, inspirational motivation

entails inspiring and motivating subordinates. Inspirational leaders promote eagerness and confidence in their

followers by exhibiting dedication to the organization’s goals, communicating high expectations, and making

the employee an active part of achieving the vision of the organization (Northouse, 2013). Effective

communication of an inspiring and motivating vision is the primary component of inspirational motivation

(Avolio, Bass, & Jung, 1999), which inspires subordinates to share in, and be committed to, the organization’s

vision (Avolio & Bass, 2004). Inspirational leaders foster a climate of trust, which in turn encourages follower

loyalty to the organization, even during downturns or crisis situations (Nisar, Rehman, Shah, & Rehman,

2013). Individualized Consideration. In simple terms, individualized consideration denotes the leader’s ability

to make their followers feel special (Balyer, 2012). Leaders who display individualized consideration act as

advisor and teacher, and strive to nurture their subordinates so they reach their greatest potential (Northouse,

2013). Bass and Avolio (1993) stressed the encouraging facet of individualized consideration and the

significance of developing followers. Northouse (2013) emphasized that individualized consideration involves

teaching, mentoring, reinforcement, active listening, and offering emotional and social benefaction to the

follower. Intellectual Stimulation. Avolio et al. (1999) stated intellectual stimulation encourages independent

and critical thinking by subordinates. Leaders that exhibit intellectual stimulation encourage innovative

thinking and the discovery of new ways to complete jobs (Anjali & Anand, 2015). Intellectually stimulating

leaders never criticize the ideas of their followers when they are different from their own, and encourage

problem solving by providing assignments that are intellectually challenging (Avolio & Bass, 2004; Bass, 1990). Transactional Leadership. Burns (1978) devised the expression transactional leadership, which he

based on the 1947 work of Max Weber. Transactional leadership can be viewed as an agreement, or exchange.

Subordinates are rewarded, with pay or something else that is desired, in exchange for satisfactory

performance. Conversely, punishments are denoted for unsatisfactory performance (Bass & Riggio, 2006). The

basis for transactional leadership is the adage that everything has a price, and leaders define all benefits, codes

International Journal of Psychology and Educational Studies, 2017, 4 (3), 53-63


of discipline, and job duties (Bass & Avolio, 1994). Transactional leadership is composed of two individual

facets: management-by-exception and contingent reward. Contingent Reward. The basis for contingent reward is self-interest. Management motivates employees

by offering a set price for their work. Contingent reward ensues when an agreement is made between leader

and follower as to the rewards for successful job completion and punishment for sub-standard performance

(Bass & Avolio, 1994). Managers understand the needs of the organization, establish clear expectations and

goals, and effectively communicate organizational expectations (Bass, 1997). Management-by-exception. Management-by-exception is separated into two separate facets: active

management-by-exception and passive management-by-exception. Management-by-exception (active) occurs

when management actively monitors an employee’s work performance, acting before work declines, and

intervening if there is a violation of policy (Bass, 1997). This differs from management-by-exception (passive)

in that the passive dimension involves the leader acting only after work deteriorates or a problem occurs.

Management-by-exception (passive) often involves negative feedback, correction, criticism, or punishments

issued by management (Northouse, 2013). During the refinement of the Multifactor Leadership Questionnaire,

which measures the dimensions of the FRLM, management-by-exception (passive) was moved from a

transactional dimension to a dimension of laissez-faire, or passive-avoidant, leadership (Avolio & Bass, 2004). Laissez-Faire Leadership. Laissez-faire leadership is the lack of leadership. Laissez-faire leaders do not

act when a correction is needed. They do not offer any assistance to their subordinates and do not provide

followers with feedback that could help them reach their full potential (Northouse, 2013). Laissez-faire leaders

usually avoid taking any actions, shun responsibility, and are absent when needed (Bass, 1990). Even though

laissez-faire leadership is not usually found in entire organizations, it is still seen in the inaction of some

members of management (Bateh & Heyliger, 2014).

1.2.2. Job Satisfaction. Locke (1976) viewed job satisfaction as “a pleasurable or positive emotional state

resulting from the appraisal of one’s job or job experience” (p. 1300). Job satisfaction is often seen as a

multifaceted combination of emotions, values, and the perceptions an individual has about the tasks

associated with their job (Chamberlain, Hoben, Squires, & Estabrooks, 2016). Spector (1985) observed that job

satisfaction may be viewed as the degree an individual is dissatisfied or satisfied with their job. Moradi,

Almutairi, Idrus, and Emami (2013) stated that job satisfaction is a mixture of job characteristics, environment,

and personal traits and feelings that are dynamic and, contingent on elements such as a changing of co-

workers, supervision, or the structure of the organization, may change over time.

1.3. Research Questions and Hypotheses

Research Questions and Hypotheses. Research on the perceived effect of leadership on the job satisfaction of

non-tenured, adjunct faculty members who teach online classes is lacking in the for-profit segment of post-

secondary education. Research concerning the effect of leadership on job satisfaction in public and private

post-secondary institutions has yielded conflicting results. Bateh and Heyliger (2014) found transformational

and transactional leadership behaviors displayed a positive predictive relationship to faculty job satisfaction

at a public university in Florida, United States, but laissez-faire leadership produced negative results. Amin,

Shah, and Tatlah (2013) found transformational leadership had a positive relationship with job satisfaction.

Conversely transactional behaviors yielded a negative relationship to the job satisfaction of lecturers at a

university in Pakistan. Masum, Azad, and Beh (2015), in their research on faculty job satisfaction at a private

university in Bangladesh, found transactional behaviors yielded a positive relationship with the job

satisfaction of lecturers, while transformational leadership had no significant relationship. Given the

conflicting findings, the researcher proposes these research questions and null hypotheses:

RQ1: Does the transformational leadership style of a higher education administrator have a correlation


the overall job satisfaction of online adjunct faculty at a for-profit university in the United States?

H10: There is no statistically significant correlation between the administrators’ transformational

leadership style and the job satisfaction of online adjunct faculty at a for-profit university in the United


Donald E. Barnett


RQ2: Does the transactional leadership style of a higher education administrator have a correlation

with the overall job satisfaction of online adjunct faculty at a for-profit university in the United States?

H20: There is no statistically significant correlation between the administrators’ transactional

leadership style and the job satisfaction of online adjunct faculty in a for-profit university in the United


RQ3: Does the laissez-faire leadership style of a higher education administrator have a correlation with

the overall job satisfaction of online adjunct faculty at a for-profit university in the United States?

H30: There is no statistically significant correlation between the administrators’ laissez-faire leadership

style and the job satisfaction of online adjunct faculty at a for-profit university in the United States.

2. Method

This quantitative study used a correlational design to investigate the relationship, if any, between the

leadership style of administrators in a private, for-profit university, as perceived by the adjunct faculty who

teach online classes at the same university, and the overall job satisfaction of the same faculty. An examination

of the bi-variate relationships between the four variables was performed with a Pearson’s product moment

correlational analyses. The independent variables were overall transformational leadership, overall

transactional leadership, and overall laissez-faire leadership. The dependent variable was overall job




The study population consisted of online, non-tenured, adjunct faculty at a private, for-profit, post-secondary

school in the United States. After IRB approval, the research site invited 600 prospective participants via email

to participate in an online survey. After accepting the invitation, 85 individuals who met the criteria for the

study took the survey. Eight individuals did not complete the survey, and their responses were removed. A

total of N = 77 respondents composed the sample.

2.2. Instruments

The Multifactor Leadership Questionnaire 5x (MLQ) and Spector’s Job Satisfaction Survey (JSS) were the

instruments used in this study. The MLQ quantifies the nine different dimensions of the FRLM, using 36 total

questions that are assessed on a 5-point Likert-type scale. (Avolio & Bass, 2004). George and Mallery (2016)

stated a Cronbach’s alpha value of .90 or more is deemed excellent, .80-.89 is seen as good, .70-.79 is judged

acceptable, .60-.69 is viewed as questionable, .50-.59 is viewed as poor, and less than .50 is deemed

unacceptable. Tests performed by Avolio and Bass(2004) found reliabilities of (α = .63) to (α = .92) accross the

scales of the MLQ. Garg and Ramjee (2013) discovered the MLQ yielded an average Cronbach’s alpha

coefficient of (α = .97). For this study, the overall Cronbach alpha values were as follows: transformational

leadership (α = .95), transactional leadership (α = .69), and laissez-faire leadership (α = .79). The slightly low

Cronbach alpha value for overall transactional leadership was allowed because both dimensions of

transactional leadership displayed high Cronbach values, contingent reward (α = .73) and management-by-

exception (active) (α = .77). Moreover, the instrumentation has been used extensively and has shown

acceptable reliability in similar research and in literature; therefore, all constructs were considered acceptable

for use during inferential analysis.

Spector’s Job Satisfaction Survey (JSS) measures nine work factors, using 4 questions for each factor, on a 6-

point Likert type scale, for a total of 36 questions. Van Saane, Sluiter, Verbeek, and Frings-Dresen (2003), in

their assessment of 29 different instruments that measured job satisfaction, found the JSS met all reliability

and validity criteria, and produced Cronbach alpha values of (α = .60) to (α = .80) across the scales, and an

overall Cronbach alpha of (α = .91). For this study, the Cronbach alpha value for overall job satisfaction was

(α = .90).

3. Results

3.1. Descriptive Statistics

Descriptive data concerning the respondents and other demographic data was not collected for this study. The

descriptive analysis for the MLQ and JSS (Table 1) are as follows. The sample rated transactional leadership

International Journal of Psychology and Educational Studies, 2017, 4 (3), 53-63


as the highest perceived overall style of leadership (M = 2.87), followed by transformational leadership (M =

2.85), and laissez-faire leadership (M = 2.79). The respondents perceived the three styles of leadership being

used at almost the same frequency, which indicates all three styles were used by administrators. To measure

overall job satisfaction, Spector (1997) stated the 36-item scale, which ranges from 36 to 216, should be

interpreted as follows: ranges from 36 to 108 indicate dissatisfaction, 109 to 144 indicate ambivalence, and 145

to 216 indicate satisfaction. The overall job satisfaction for this study (M = 116.34) indicates the respondents

are ambivalent about their overall job satisfaction, expressing neither satisfaction nor dissatisfaction.

Table 1

Measures of Central Tendency for Study Instrumentation Scores (N = 77)







Transformational leadership 2.85 0.84 2.75 1.00 – 4.75

Transactional leadership 2.87 0.65 3.00 1.25 – 4.00

Laissez-faire leadership 2.79 0.77 2.88 1.38 – 4.63

Total satisfaction 116.34 19.92 115.00 69.00 – 154.00

Note. M = Mean; SD = Standard Deviation; Mdn = Median; MLQ = Multifactor Leadership Questionnaire;

JSS = Job Satisfaction Survey.

3.2. Correlational analysis.

The researcher used Pearson’s product moment correlational analyses to examine the bi-variate relationships

between the four variables (Table 2). The dependent variable of total satisfaction had a statistically significant,

direct and strong correlation with the independent variable of transformational leadership (r = .536, p < .0005).

The strength and direction of the relationship indicated increases in the scores of total satisfaction are

associated with increases in scores in transformational leadership, and conversely, lower total satisfaction

scores were associated with lower transformational leadership scores. Total satisfaction had a statistically

significant, indirect and moderate correlation with the independent variable of laissez-faire leadership (r = –

.372, p = .001). The strength and direction of the relationship indicated that increases in the scores of total

satisfaction were associated with decreases in scores of laissez-faire leadership, and conversely, lower total

satisfaction scores are associated with higher laissez-faire leadership scores. There was not a statistically

significant correlation between total satisfaction and transactional leadership.

The independent variable of transactional leadership had a statistically significant, direct and moderate

correlation with the independent variable of transformational leadership (r = .41, p < .0005). The strength and

direction of the relationship indicated that increases in the scores of transactional leadership are associated

with increases in scores in transformational leadership, and conversely, lower transactional leadership scores

were associated with lower transformational leadership scores. Transactional leadership had a statistically

significant, indirect and weak correlation with the independent variable of laissez-faire leadership (r = -.23, p

= .043). The strength and direction of the relationship indicated that increases in the scores of transactional

leadership were associated with decreases in scores of laissez-faire leadership, and conversely, lower

transactional leadership scores were associated with higher laissez-faire leadership scores. There was also a

statistically significant indirect and strong correlation between the independent variables of transformational

leadership and laissez-faire leadership (r = -.65, p < .0005). The strength and direction of the relationship

indicated that increases in the scores of transformational leadership were associated with decreases in scores

of laissez-faire leadership, and conversely, lower transformational leadership scores were associated with

higher laissez-faire leadership scores. Table 2 presents the correlation coefficients for the Pearson’s product

moment correlations.

Donald E. Barnett


Table 2

Pearson’s Product Moment Correlation Coefficients (N = 77)

Variable 1 2 3

1. Total satisfaction —

2. Transformational leadership .54** —

3. Transactional leadership -.02 .41** —

4. Laissez-faire leadership -.37** -.65** -.23*

* p < .05

** p < .01

4. Discussion

4.1. Research Question 1

The first question investigated if, and to what extent, the transformational leadership style of the administrator

affected the overall job satisfaction of online, non-tenured, adjunct faculty who teach at a for-profit university

in the United States. It was hypothesized that overall transformational leadership behaviors would have a

significant correlation with overall job satisfaction. The outcome of the Pearson’s correlation showed a

statistically significant, direct and strong correlation between overall job satisfaction and transformational

leadership (r = .54, p < .0005). The strength and direction of the relationship indicated an increase in the score

of total satisfaction is associated with an increase in the score in transformational leadership. Conversely,

lower total scores in overall job satisfaction were associated with lower transformational leadership scores.

The results denoted there was a significant correlation between transformational leadership style and overall

job satisfaction; therefore, the null hypothesis was rejected.

The researcher concluded that transformational leadership was beneficial to the overall job satisfaction of

online, non-tenured, adjunct faculty at a for-profit university in the United States. This finding is consistent

with similar research that found job satisfaction displayed a positive relationship with transformational

leadership (Aydin, Sarier, & Uysal, 2013; Banks et al., 2016). The results of this study suggest that

administrators in post-secondary for-profit institutions should make use of transformational leadership

techniques to enhance the job satisfaction of their followers, although since only one university was

researched, it is difficult to generalize the results to similar institutions.

4.2. Research Question 2

The second question sought to discover if the administrators’ transactional leadership style affected the overall

job satisfaction of online, non-tenured, adjunct faculty who teach at a for-profit post-secondary institution in

the United States. The researcher hypothesized overall transactional leadership behaviors would have a

significant correlation with overall job satisfaction. The results showed transactional leadership did not have

a significantly significant relationship with job satisfaction (r = -.021, p = .855). The null hypothesis, in this case,

was not rejected.

There was not enough evidence to show a statistically significant correlation between the administrator’s

transactional leadership behaviors and overall job satisfaction. The findings agree with previous research that

found transactional leadership to display a statistically insignificant relationship with employee job

satisfaction (Amin et al., 2013; Tetteh & Brenyah, 2016), and contradicts previous research that found

transactional leadership either advantageous (Aydin et al., 2013; Bateh & Heyliger, 2014; Sakiru et al., 2014) or

disadvantageous to employee job satisfaction (Hijazi, Kasim, & Saud, 2016; Saleem, 2015).

4.3. Research Question 3

The third question investigated if the administrators’ laissez-faire leadership style affected the overall job

satisfaction of online, non-tenured, adjunct faculty who teach at a for-profit university in the United States.

The researcher hypothesized overall laissez-faire leadership behaviors would have a significant correlation

with overall job satisfaction. Total satisfaction had a statistically significant, indirect and moderate correlation

with the independent variable of laissez-faire leadership (r = -.37, p = .001). The strength and direction of the

International Journal of Psychology and Educational Studies, 2017, 4 (3), 53-63


relationship indicated that higher laissez-faire leadership scores are associated with lower total satisfaction

scores, and vice versa. The null hypothesis was rejected.

There was sufficient evidence to denote a statistically significant correlation between the administrator’s

laissez-faire leadership behaviors and overall job satisfaction. This study confirms recent research, which

found job satisfaction had a significant negative relationship with laissez-faire leadership (Dussault & Frenette,

2015; Masum et al., 2015). The findings suggest that administrators should avoid using laissez-faire leadership

behaviors in their organization.

5. Conclusion

The findings of this research add to the body of knowledge on leadership and job satisfaction by examining

the relatively new demographic of adjunct faculty members who facilitate online classes for a private, for-

profit university in the United States. Based on this research, and previous studies, administrators in higher

education should make use of transformational leadership to enhance the job satisfaction of their followers.

Conversely, administrators should avoid laissez-faire leadership because of its negative correlation with

follower job satisfaction. Universities should also incorporate transformational leadership into their

leadership development programs. It must be noted that correlation does not equal causation, or even a

predictive relationship, but there is sufficient evidence that transformational leadership behaviors are

beneficial to job satisfaction in this sample.

Limitations for this study include the fact that only one university was investigated, and the results are not

generalizable to other institutions. In the future, it may be advantageous to investigate other, similar,

institutions of for-profit higher learning to determine if these results are unique to the organization studied.

Secondly, although a quantitative study provided valuable insight into the subject, another suggestion for

further research would be to perform a qualitative study to understand faculty motivations and opinions.

Third, this study did not examine the various demographic specifics of the sample. Future research could

examine if there was a difference between male and female faculty members, or differences in the perceptions

of adjunct faculty who teach traditional versus online classes.

Although the results of this study added to the body of knowledge, there is still significant research to be

performed in the for-profit sector of post-secondary education. Likewise, the relatively new phenomenon of

adjunct faculty who teach only online classes provides ample avenues to investigate their work experiences.

Given that online education may expand in the future, understanding factors that affect online instructors

work experiences may help universities provide a better learning environment to their faculty and students.


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Donald E. Barnett


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Step’by-step guide to critiquing
research. Part 1: quantitative research

Michaei Coughian, Patricia Cronin, Frances Ryan

When caring for patients it is essential that nurses are using the
current best practice. To determine what this is, nurses must be able
to read research critically. But for many qualified and student nurses
the terminology used in research can be difficult to understand
thus making critical reading even more daunting. It is imperative
in nursing that care has its foundations in sound research and it is
essential that all nurses have the ability to critically appraise research
to identify what is best practice. This article is a step-by step-approach
to critiquing quantitative research to help nurses demystify the
process and decode the terminology.

Key words: Quantitative research

Review process • Research

]or many qualified nurses and nursing students
research is research, and it is often quite difficult
to grasp what others are referring to when they
discuss the limitations and or strengths within

a research study. Research texts and journals refer to
critiquing the literature, critical analysis, reviewing the
literature, evaluation and appraisal of the literature which
are in essence the same thing (Bassett and Bassett, 2003).
Terminology in research can be confusing for the novice
research reader where a term like ‘random’ refers to an
organized manner of selecting items or participants, and the
word ‘significance’ is applied to a degree of chance. Thus
the aim of this article is to take a step-by-step approach to
critiquing research in an attempt to help nurses demystify
the process and decode the terminology.

When caring for patients it is essential that nurses are
using the current best practice. To determine what this is
nurses must be able to read research. The adage ‘All that
glitters is not gold’ is also true in research. Not all research
is of the same quality or of a high standard and therefore
nurses should not simply take research at face value simply
because it has been published (Cullum and Droogan, 1999;
Rolit and Beck, 2006). Critiquing is a systematic method of

Michael Coughlan, Patricia Cronin and Frances Ryan are Lecturers,
School of Nursing and Midwifery, University of Dubhn, Trinity
College, Dublin

Accepted for publication: March 2007

appraising the strengths and limitations of a piece of research
in order to determine its credibility and/or its applicability
to practice (Valente, 2003). Seeking only limitations in a
study is criticism and critiquing and criticism are not the
same (Burns and Grove, 1997). A critique is an impersonal
evaluation of the strengths and limitations of the research
being reviewed and should not be seen as a disparagement
of the researchers ability. Neither should it be regarded as
a jousting match between the researcher and the reviewer.
Burns and Grove (1999) call this an ‘intellectual critique’
in that it is not the creator but the creation that is being
evaluated. The reviewer maintains objectivity throughout
the critique. No personal views are expressed by the
reviewer and the strengths and/or limitations of the study
and the imphcations of these are highlighted with reference
to research texts or journals. It is also important to remember
that research works within the realms of probability where
nothing is absolutely certain. It is therefore important to
refer to the apparent strengths, limitations and findings
of a piece of research (Burns and Grove, 1997). The use
of personal pronouns is also avoided in order that an
appearance of objectivity can be maintained.

Credibility and integrity
There are numerous tools available to help both novice and
advanced reviewers to critique research studies (Tanner,
2003). These tools generally ask questions that can help the
reviewer to determine the degree to which the steps in the
research process were followed. However, some steps are
more important than others and very few tools acknowledge
this. Ryan-Wenger (1992) suggests that questions in a
critiquing tool can be subdivided in those that are useful
for getting a feel for the study being presented which she
calls ‘credibility variables’ and those that are essential for
evaluating the research process called ‘integrity variables’.

Credibility variables concentrate on how believable the
work appears and focus on the researcher’s qualifications and
ability to undertake and accurately present the study. The
answers to these questions are important when critiquing
a piece of research as they can offer the reader an insight
into \vhat to expect in the remainder of the study.
However, the reader should be aware that identified strengths
and limitations within this section will not necessarily
correspond with what will be found in the rest of the work.
Integrity questions, on the other hand, are interested in the
robustness of the research method, seeking to identify how
appropriately and accurately the researcher followed the
steps in the research process. The answers to these questions

658 British Journal of Nursing. 2007. Vol 16, No II


Table 1. Research questions – guidelines for critiquing a quantitative research study

Elements influencing the beiievabiiity of the research

Writing styie

Report titie

Is the report well written – concise, grammatically correct, avoid the use of jargon? Is it weil iaid out and
Do the researcher(s’) quaiifications/position indicate a degree of knowledge in this particuiar field?
Is the title clear, accurate and unambiguous?
Does the abstract offer a clear overview of the study including the research problem, sample,
methodology, finding and recommendations?

Elements influencing the robustness of the research


Logical consistency

Literature review

Theoreticai framework

research question/

Ethicai considerations

Operational definitions

Data Anaiysis / results



Is the purpose of the study/research problem clearly identified?

Does the research report foilow the steps of the research process in a iogical manner? Do these steps
naturally fiow and are the iinks ciear?
is the review Iogicaily organized? Does it offer a balanced critical anaiysis of the iiterature? is the majority
of the literature of recent origin? is it mainly from primary sources and of an empirical nature?
Has a conceptual or theoretical framework been identified? Is the framework adequately described?
is the framework appropriate?
Have alms and objectives, a research question or hypothesis been identified? If so are they clearly
stated? Do they reflect the information presented in the iiterature review?

Has the target popuiation been cieariy identified? How were the sample selected? Was it a probability
or non-probabiiity sampie? is it of adequate size? Are the indusion/exciusion criteria dearly identified?
Were the participants fuiiy informed about the nature of the research? Was the autonomy/
confidentiaiity of the participants guaranteed? Were the participants protected from harm? Was ethicai
permission granted for the study?
Are aii the terms, theories and concepts mentioned in the study dearly defined?
is the research design cieariy identified? Has the data gathering instrument been described? is the
instrument appropriate? How was it deveioped? Were reliabiiity and validity testing undertaken and the
resuits discussed? Was a piiot study undertaken?
What type of data and statisticai analysis was undertaken? Was it appropriate? How many of the sampie
participated? Significance of the findings?
Are the findings iinked back to the iiterature review? if a hypothesis was identified was it supported?
Were the strengths and limitations of the study including generalizability discussed? Was a
recommendation for further research made?
Were ali the books, journais and other media aliuded to in the study accurateiy referenced?

will help to identify the trustworthiness of the study and its
applicability to nursing


Critiquing the research steps
In critiquing the steps in the research process a number
of questions need to be asked. However, these questions
are seeking more than a simple ‘yes’ or ‘no’ answer. The
questions are posed to stimulate the reviewer to consider
the implications of what the researcher has done. Does the
way a step has been applied appear to add to the strength
of the study or does it appear as a possible limitation to
implementation of the study’s findings? {Table 1).

Eiements influencing beiievabiiity of the study
Writing style
Research reports should be well written, grammatically
correct, concise and well organized.The use of jargon should
be avoided where possible. The style should be such that it
attracts the reader to read on (Polit and Beck, 2006).

The author(s’) qualifications and job title can be a useful
indicator into the researcher(s’) knowledge of the area
under investigation and ability to ask the appropriate
questions (Conkin Dale, 2005). Conversely a research
study should be evaluated on its own merits and not
assumed to be valid and reliable simply based on the
author(s’) qualifications.

Report title
The title should be between 10 and 15 words long and
should clearly identify for the reader the purpose of the
study (Connell Meehan, 1999). Titles that are too long or
too short can be confusing or misleading (Parahoo, 2006).

The abstract should provide a succinct overview of the
research and should include information regarding the
purpose of the study, method, sample size and selection. of Nursing. 2007. Vol 16. No 11 659

the main findings and conclusions and recommendations
(Conkin Dale, 2005). From the abstract the reader should
be able to determine if the study is of interest and whether
or not to continue reading (Parahoo, 2006).

Eiements influencing robustness
Purpose of the study/research problem
A research problem is often first presented to the reader in
the introduction to the study (Bassett and Bassett, 2003).
Depending on what is to be investigated some authors will
refer to it as the purpose of the study. In either case the
statement should at least broadly indicate to the reader what
is to be studied (Polit and Beck, 2006). Broad problems are
often multi-faceted and will need to become narrower and
more focused before they can be researched. In this the
literature review can play a major role (Parahoo, 2006).

Logical consistency
A research study needs to follow the steps in the process in a
logical manner.There should also be a clear link between the
steps beginning with the purpose of the study and following
through the literature review, the theoretical framework, the
research question, the methodology section, the data analysis,
and the findings (Ryan-Wenger, 1992).

Literature review
The primary purpose of the literature review is to define
or develop the research question while also identifying
an appropriate method of data collection (Burns and
Grove, 1997). It should also help to identify any gaps in
the literature relating to the problem and to suggest how
those gaps might be filled. The literature review should
demonstrate an appropriate depth and breadth of reading
around the topic in question. The majority of studies
included should be of recent origin and ideally less than
five years old. However, there may be exceptions to this,
for example, in areas where there is a lack of research, or a
seminal or all-important piece of work that is still relevant to
current practice. It is important also that the review should
include some historical as well as contemporary material
in order to put the subject being studied into context. The
depth of coverage will depend on the nature of the subject,
for example, for a subject with a vast range of literature then
the review will need to concentrate on a very specific area
(Carnwell, 1997). Another important consideration is the
type and source of hterature presented. Primary empirical
data from the original source is more favourable than a
secondary source or anecdotal information where the
author relies on personal evidence or opinion that is not
founded on research.

A good review usually begins with an introduction which
identifies the key words used to conduct the search and
information about which databases were used. The themes
that emerged from the literature should then be presented
and discussed (Carnwell, 1997). In presenting previous
work it is important that the data is reviewed critically,
highlighting both the strengths and limitations of the study.
It should also be compared and contrasted with the findings
of other studies (Burns and Grove, 1997).

Theoretical framework
Following the identification of the research problem
and the review of the literature the researcher should
present the theoretical framework (Bassett and Bassett,
2003). Theoretical frameworks are a concept that novice
and experienced researchers find confusing. It is initially
important to note that not all research studies use a defined
theoretical framework (Robson, 2002). A theoretical
framework can be a conceptual model that is used as a
guide for the study (Conkin Dale, 2005) or themes from
the literature that are conceptually mapped and used to set
boundaries for the research (Miles and Huberman, 1994).
A sound framework also identifies the various concepts
being studied and the relationship between those concepts
(Burns and Grove, 1997). Such relationships should have
been identified in the literature. The research study should
then build on this theory through empirical observation.
Some theoretical frameworks may include a hypothesis.
Theoretical frameworks tend to be better developed in
experimental and quasi-experimental studies and often
poorly developed or non-existent in descriptive studies
(Burns and Grove, 1999).The theoretical framework should
be clearly identified and explained to the reader.

Aims and objectives/research question/
research hypothesis
The purpose of the aims and objectives of a study, the research
question and the research hypothesis is to form a link between
the initially stated purpose of the study or research problem
and how the study will be undertaken (Burns and Grove,
1999). They should be clearly stated and be congruent with
the data presented in the literature review. The use of these
items is dependent on the type of research being performed.
Some descriptive studies may not identify any of these items
but simply refer to the purpose of the study or the research
problem, others will include either aims and objectives or
research questions (Burns and Grove, 1999). Correlational
designs, study the relationships that exist between two or
more variables and accordingly use either a research question
or hypothesis. Experimental and quasi-experimental studies
should clearly state a hypothesis identifying the variables to
be manipulated, the population that is being studied and the
predicted outcome (Burns and Grove, 1999).

Sample and sample size
The degree to which a sample reflects the population it
was drawn from is known as representativeness and in
quantitative research this is a decisive factor in determining
the adequacy of a study (Polit and Beck, 2006). In order
to select a sample that is likely to be representative and
thus identify findings that are probably generalizable to
the target population a probability sample should be used
(Parahoo, 2006). The size of the sample is also important in
quantitative research as small samples are at risk of being
overly representative of small subgroups within the target
population. For example, if, in a sample of general nurses, it
was noticed that 40% of the respondents were males, then
males would appear to be over represented in the sample,
thereby creating a sampling error. The risk of sampling

660 Britishjournal of Nursing. 2007. Vol 16. No II


errors decrease as larger sample sizes are used (Burns and
Grove, 1997). In selecting the sample the researcher should
clearly identify who the target population are and what
criteria were used to include or exclude participants. It
should also be evident how the sample was selected and
how many were invited to participate (Russell, 2005).

Ethical considerations
Beauchamp and Childress (2001) identify four fundamental
moral principles: autonomy, non-maleficence, beneficence
and justice. Autonomy infers that an individual has the right
to freely decide to participate in a research study without
fear of coercion and with a full knowledge of what is being
investigated. Non-maleficence imphes an intention of not
harming and preventing harm occurring to participants
both of a physical and psychological nature (Parahoo,
2006). Beneficence is interpreted as the research benefiting
the participant and society as a whole (Beauchamp and
Childress, 2001). Justice is concerned with all participants
being treated as equals and no one group of individuals
receiving preferential treatment because, for example, of
their position in society (Parahoo, 2006). Beauchamp and
Childress (2001) also identify four moral rules that are both
closely connected to each other and with the principle of
autonomy. They are veracity (truthfulness), fidelity (loyalty
and trust), confidentiality and privacy.The latter pair are often
linked and imply that the researcher has a duty to respect the
confidentiality and/or the anonymity of participants and
non-participating subjects.

Ethical committees or institutional review boards have to
give approval before research can be undertaken. Their role
is to determine that ethical principles are being applied and
that the rights of the individual are being adhered to (Burns
and Grove, 1999).

Operational definitions
In a research study the researcher needs to ensure that
the reader understands what is meant by the terms and
concepts that are used in the research. To ensure this any
concepts or terms referred to should be clearly defined
(Parahoo, 2006).

Methodology: research design
Methodology refers to the nuts and bolts of how a
research study is undertaken. There are a number of
important elements that need to be referred to here and
the first of these is the research design. There are several
types of quantitative studies that can be structured under
the headings of true experimental, quasi-experimental
and non-experimental designs (Robson, 2002) {Table 2).
Although it is outside the remit of this article, within each
of these categories there are a range of designs that will
impact on how the data collection and data analysis phases
of the study are undertaken. However, Robson (2002)
states these designs are similar in many respects as most
are concerned with patterns of group behaviour, averages,
tendencies and properties.

Methodology: data collection
The next element to consider after the research design
is the data collection method. In a quantitative study any
number of strategies can be adopted when collecting data
and these can include interviews, questionnaires, attitude
scales or observational tools. Questionnaires are the most
commonly used data gathering instruments and consist
mainly of closed questions with a choice of fixed answers.
Postal questionnaires are administered via the mail and have
the value of perceived anonymity. Questionnaires can also be
administered in face-to-face interviews or in some instances
over the telephone (Polit and Beck, 2006).

Methodology: instrument design
After identifying the appropriate data gathering method
the next step that needs to be considered is the design
of the instrument. Researchers have the choice of using
a previously designed instrument or developing one for
the study and this choice should be clearly declared for
the reader. Designing an instrument is a protracted and
sometimes difficult process (Burns and Grove, 1997) but the
overall aim is that the final questions will be clearly linked
to the research questions and will elicit accurate information
and will help achieve the goals of the research.This, however,
needs to be demonstrated by the researcher.

Table 2. Research designs




e.g. descriptive and
Includes: cross-sectional.
iongitudinal studies


2 or more groups

One or more groups

One or more groups




Not applicable


• Groups get
different treatments

• One variable has not
been manipuiated or
controlled (usually
because it cannot be)

• Discover new meaning
• Describe what already

• Measure the relationship

between two or more


• Cause and effiect relationship

• Cause and effect relationship
but iess powerful than

• Possible hypothesis for
future research

• Tentative explanations

Britishjournal of Nursing. 2007. Vol 16. No 11 661

If a previously designed instrument is selected the researcher
should clearly establish that chosen instrument is the most
appropriate.This is achieved by outlining how the instrument
has measured the concepts under study. Previously designed
instruments are often in the form of standardized tests
or scales that have been developed for the purpose of
measuring a range of views, perceptions, attitudes, opinions
or even abilities. There are a multitude of tests and scales
available, therefore the researcher is expected to provide the
appropriate evidence in relation to the validity and reliability
of the instrument (Polit and Beck, 2006).

Methodology: validity and reliability
One of the most important features of any instrument is
that it measures the concept being studied in an unwavering
and consistent way. These are addressed under the broad
headings of validity and reliability respectively. In general,
validity is described as the ability of the instrument to
measure what it is supposed to measure and reliability the
instrument’s ability to consistently and accurately measure
the concept under study (Wood et al, 2006). For the most
part, if a well established ‘off the shelf instrument has been
used and not adapted in any way, the validity and reliability
will have been determined already and the researcher
should outline what this is. However, if the instrument
has been adapted in any way or is being used for a new
population then previous validity and reliability will not
apply. In these circumstances the researcher should indicate
how the reliability and validity of the adapted instrument
was established (Polit and Beck, 2006).

To establish if the chosen instrument is clear and
unambiguous and to ensure that the proposed study has
been conceptually well planned a mini-version of the main
study, referred to as a pilot study, should be undertaken before
the main study. Samples used in the pilot study are generally
omitted from the main study. Following the pilot study the
researcher may adjust definitions, alter the research question,
address changes to the measuring instrument or even alter
the sampling strategy.

Having described the research design, the researcher should
outline in clear, logical steps the process by which the data
was collected. All steps should be fully described and easy to
follow (Russell, 2005).

Analysis and results
Data analysis in quantitative research studies is often seen
as a daunting process. Much of this is associated with
apparently complex language and the notion of statistical
tests. The researcher should clearly identify what statistical
tests were undertaken, why these tests were used and
what •were the results. A rule of thumb is that studies that
are descriptive in design only use descriptive statistics,
correlational studies, quasi-experimental and experimental
studies use inferential statistics. The latter is subdivided
into tests to measure relationships and differences between
variables (Clegg, 1990).

Inferential statistical tests are used to identify if a
relationship or difference between variables is statistically
significant. Statistical significance helps the researcher to

rule out one important threat to validity and that is that the
result could be due to chance rather than to real differences
in the population. Quantitative studies usually identify the
lowest level of significance as PsO.O5 (P = probability)
(Clegg, 1990).

To enhance readability researchers frequently present
their findings and data analysis section under the headings
of the research questions (Russell, 2005). This can help the
reviewer determine if the results that are presented clearly
answer the research questions. Tables, charts and graphs may
be used to summarize the results and should be accurate,
clearly identified and enhance the presentation of results
(Russell, 2005).

The percentage of the sample who participated in
the study is an important element in considering the
generalizability of the results. At least fifty percent of the
sample is needed to participate if a response bias is to be
avoided (Polit and Beck, 2006).

The discussion of the findings should Oow logically from the
data and should be related back to the literature review thus
placing the study in context (Russell, 2002). If the hypothesis
was deemed to have been supported by the findings,
the researcher should develop this in the discussion. If a
theoretical or conceptual framework was used in the study
then the relationship with the findings should be explored.
Any interpretations or inferences drawn should be clearly
identified as such and consistent with the results.

The significance of the findings should be stated but
these should be considered within the overall strengths
and limitations of the study (Polit and Beck, 2006). In this
section some consideration should be given to whether
or not the findings of the study were generalizable, also
referred to as external validity. Not all studies make a claim
to generalizability but the researcher should have undertaken
an assessment of the key factors in the design, sampling and
analysis of the study to support any such claim.

Finally the researcher should have explored the clinical
significance and relevance of the study. Applying findings
in practice should be suggested with caution and will
obviously depend on the nature and purpose of the study.
In addition, the researcher should make relevant and
meaningful suggestions for future research in the area
(Connell Meehan, 1999).

The research study should conclude with an accurate list
of all the books; journal articles, reports and other media
that were referred to in the work (Polit and Beck, 2006).
The referenced material is also a useful source of further
information on the subject being studied.

The process of critiquing involves an in-depth examination
of each stage of the research process. It is not a criticism but
rather an impersonal scrutiny of a piece of work using a
balanced and objective approach, the purpose of which is to
highlight both strengths and weaknesses, in order to identify

662 Uritish Journal of Nursinii. 2007. Vol 16. No II


whether a piece of research is trustworthy and unbiased. As

nursing practice is becoming increasingly more evidenced

based, it is important that care has its foundations in sound

research. It is therefore important that all nurses have the

ability to critically appraise research in order to identify what

is best practice. HH

Russell C (2005) Evaluating quantitative researcli reports. Nephrol Nurs J
32(1): 61-4

Ryan-Wenger N (1992) Guidelines for critique of a research report. Heart
Lung 21(4): 394-401

Tanner J (2003) Reading and critiquing research. BrJ Perioper Nurs 13(4):

Valente S (2003) Research dissemination and utilization: Improving care at
the bedside.J Nurs Care Quality 18(2): 114-21

Wood MJ, Ross-Kerr JC, Brink PJ (2006) Basic Steps in Planning Nursing
Research: From Question to Proposal 6th edn. Jones and Bartlett, Sudbury

Bassett C, B.issett J (2003) Reading and critiquing research. BrJ Perioper
NriK 13(4): 162-4

Beauchamp T, Childress J (2001) Principles of Biomedical Ethics. 5th edn.
O.xford University Press, Oxford

Burns N, Grove S (1997) The Practice of Nursing Research: Conduct, Critique
and Utilization. 3rd edn.WB Saunders Company, Philadelphia

Burns N, Grove S (1999) Understanding Nursing Research. 2nd edn. WB
Saunders Company. Philadelphia

Carnell R (1997) Critiquing research. Nurs Pract 8(12): 16-21
Clegg F (1990) Simple Statistics: A Course Book for the Social Sciences. 2nd edn.

Cambridge University Press. Cambridge
Conkin DaleJ (2005) Critiquing research for use in practice.J Pediatr Health

Care 19: 183-6
Connell Meehan T (1999) The research critique. In:Treacy P, Hyde A, eds.

Nursing Research and Design. UCD Press, Dublin: 57-74
Cullum N. Droogan J (1999) Using research and the role of systematic

reviews of the literature. In: Mulhall A. Le May A. eds. Nursing Research:
Dissemination and Implementation. Churchill Livingstone, Edinburgh:

Miles M, Huberman A (1994) Qualitative Data Analysis. 2nd edn. Sage,
Thousand Oaks. Ca

Parahoo K (2006) Nursing Research: Principles, Process and Issties. 2nd edn.
Palgrave Macmillan. Houndmills Basingstoke

Polit D. Beck C (2006) Essentials of Nursing Care: Methods, Appraisal and
Utilization. 6th edn. Lippincott Williams and Wilkins, Philadelphia

Robson C (2002) Reat World Research. 2nd edn. Blackwell Publishing,


I Many qualified and student nurses have difficulty

understanding the concepts and terminology associated

with research and research critique.

IThe ability to critically read research is essential if the

profession is to achieve and maintain its goal to be

evidenced based.

IA critique of a piece of research is not a criticism of

the wori<, but an impersonai review to highlight the

strengths and iimitations of the study.

I It is important that all nurses have the ability to criticaiiy

appraise research In order to identify what is best


Critiquing Nursing Research

2nd edition

Nursing Research

2nd edition

ISBN-W; 1- 85642-316-6; lSBN-13; 978-1-85642-316-8; 234 x 156 mm; p/back; 224 pages;
publicatior) November 2006; £25.99

By John R Cutdiffe and Martin Ward

This 2nd edition of Critiquing Nursing Research retains the features which made the original
a ‘best seller’ whilst incorporating new material in order to expand the book’s applicability. In
addition to reviewing and subsequently updating the material of the original text, the authors
have added two further examples of approaches to crtitique along with examples and an
additonal chapter on how to critique research as part of the work of preparing a dissertation.
The fundamentals of the book however remain the same. It focuses specifically on critiquing
nursing research; the increasing requirement for nurses to become conversant with research,
understand its link with the use of evidence to underpin practice; and the movement towards
becoming an evidence-based discipline.

As nurse education around the world increasingly moves towards an all-graduate discipline, it
is vital for nurses to have the ability to critique research in order to benefit practice. This book
is the perfect tool for those seeking to gain or develop precisely that skill and is a must-have
for all students nurses, teachers and academics.

John Cutclitfe holds the ‘David G. Braithwaite’ Protessor of Nursing Endowed Chair at the University of Texas (Tyler); he is

also an Adjunct Professor of Psychiatric Nursing at Stenberg College International School of Nursing, Vancouver, Canada.

Matin Ward is an Independent tvtental Health Nurse Consultant and Director of tvlW Protessional Develcpment Ltd.

To order your copy please contact us using the details below or visit our website where you will also tind details ot other Quay Books otters and titles.

John Cutcliffe and Martin Ward



Quay Books Division I MA Healthcare Limited
Jesses Farm I Snow Hill I Dinton I Salisbury I Wiltshire I SP3 5HN I UK

Tel: 01722 716998 I Fax: 01722 716887 I E-mail: I Web:



Uritishjoiirnnl of Nursinji;. 2OO7.V0I 16. No 11 663

By Cristie McClendon, Scott Greenberger, and Stacey Bridges

Reading Quantitative Research

Essential Questions

1. What types of research problems are suitable for quantitative research?

2. How does a researcher select a quantitative design?

3. What are the GCU core designs for quantitative research?

4. How does one select appropriate measures or instruments for quantitative research?

5. What sampling approaches are used in quantitative research?

6. What are the most common approaches used in quantitative data analysis?


Quantitative research is frequently used in the social sciences because it is quick, relatively inexpensive, and

considered a valid method of inquiry by researchers and academicians. The goals of quantitative research are

to describe the attributes of a group of people, to measure differences between groups, to determine if a

relationship exists between variables, or to predict if one event or factor causes another.

Quantitative studies contain measurable and quanti�able data, a

statistically appropriate sample, use of statistical techniques, and

a structured data collection plan to ensure that the study can be

replicated. Additionally, quantitative studies require the use of

valid and reliable instruments, surveys, or databases to quantify

variables. The research method is deductive, very structured, and

in�exible as often the goal of the researcher is to generalize or

apply the results to other groups and populations besides those

participating in the study. Ultimately, quantitative research offers a systematic and structured process for

answering research questions (Balnaves & Caputi, 2001).

Critically Reading Quantitative Research

Doctoral learners must go through a process of learning how to critically read empirical research. While

reading is a familiar skill to learners, at the doctoral level, it takes on new depth as learners transition to the

mindset of a researcher. The required reading materials will be more dif�cult to read, take more time, and

require learners to improve their reading ef�ciency and critical-thinking skills. Having ample time built in for

reading is crucial to the success of a doctoral student. Reading is the foundation to a dissertation research

project. The �rst 2 years before a proposal is accepted will be spent reading peer-reviewed articles,

dissertations, books, and other scholarly sources that can potentially contribute to the dissertation project. At

the same time, the reading of these materials directly contributes to subject matter expertise of the learner

helping to make him or her an expert in the �eld of study. Unfortunately, there is not a speci�c number of

Schedule enough time to read


resources that a learner must read to transform into an expert. The reading process in a doctoral program is an

ongoing, self-directed, independent project that begins in the �rst course and does not end until the

dissertation is approved. Even then, the learner who has transitioned to a researcher will continue to read on

the topic in the years after graduation in order to remain current with the literature. Those who also become

published scholars will continue to contribute to the literature with their own publications.

Successful completion of a doctoral dissertation requires signi�cant amounts of independent, critical reading

on the research topic. This allows the doctoral researcher to become familiar with the scope of the topic and to

identify problem spaces within the existing literature that become the source of the

dissertation research.

Researchers read research differently in order to save time, often employing a nonlinear approach.

Nonetheless, doctoral learners should schedule enough time

to critically read a research article. Articles should be read for

relevance, critique, and reference sourcing, which takes time,

as many articles will need to be read multiple times to garner

a reasonable understanding. Quinton and Smallbone (2006)

offered additional information regarding how to develop a critical approach.

Reading Quantitative Research

Learners intending to examine large samples to generalize outcomes to a population or a subpopulation should

familiarize themselves with quantitative research frameworks. In order to critique a source, understanding the

research framework is needed. This chapter provides information on quantitative research frameworks to

assist learners in identifying quantitative research and understanding the multiple techniques encountered

when reading research. The article “Step-by-Step Guide to Critiquing Research. Part 1: Quantitative Research”

by Coughlan, Cronin, and Ryan (2007) provides in-depth coverage of critiquing research to supplement the

discussion of quantitative research frameworks. McGregor (2008) offered a summary of how to critique an

article in Figure 1.1 (

research/i1401.xml). The following information is intended to serve as a starting point for learners as they

strive to improve their reading skills to gather sources for their dissertations. Broad techniques are provided

along with resources for learners to further explore information independently.

Following is a process of reading for relevancy and reading strategically to evaluate �ndings. It includes

information on how to review the references. Although, reading research saves the researcher time in the

early phases of the research project, a researcher should expect to read the article more than once as the

research project progresses.

Determine Relevance of the Article

While being able to distinguish an empirical article from a nonempirical source is an important skill to learn,

it is also meaningful to look further into the types of articles and papers that can be encountered when reading

literature on a topic. Any doctoral learner that has searched the library for sources can attest that there are

multiple types of articles that can be found in peer-reviewed journals. The �rst chapter of the Publication

Manual of the American Psychological Association (2020) details the diversity of sources that can be found in

a typical journal. What learners may be most familiar with are scienti�c journal articles that report original

research, also known as primary research. There are also methodological and theoretical articles that describe

advancements in theories or methods and do not present research. Literature reviews and meta-analyses

review or synthesize the �ndings from primary research. Some additional types of articles that are published

in journals include white papers, book reviews, reports, and letters to the editors; these types of articles are not

considered primary research and do not factor into the discussion of identifying parts of research articles.

Parts of an Empirical Article



Key Words






Author Information

Regardless of the methodology of a study, when evaluating a source, a learner should determine the credibility

and rigor of the work. Understanding how to evaluate sources now can save time when making design

choices for dissertation research. A learner must �rst decide if an article is relevant to add to the reading list.

The �rst page of an article contains the most important sections of an article to determine relevancy to a

current research project.

Publication Information: The publication information includes where the article was published.

What type of source is this? Is it original, peer-reviewed, empirical research? For more information,

view the video “Finding Empirical Research Articles (�nding-

empirical-research-articles/v1.1/)” (Grand Canyon University, n.d.) or read “Evaluating Sources: What Is a

Scholarly Source (” (Grand Canyon

University, n.d.).

When was it published? While a seminal work is relevant to a proposed study no matter when it was

published, the problem space must be substantiated on current research in the �eld. This means that

when trying to establish a problem space, articles published within the last 3–5 years are the most

relevant. If looking for articles to provide contextual relevance for the review of literature, the

publication date may not be as relevant as understanding the historical importance of an article in

developing the �eld of literature. Not all articles are going to be published this year; however, the

references used in the dissertation literature review should be mostly current.

Where was it published? Not everything published meets the highest standards of rigor. What is the

journal’s impact factor? Is this a reputable journal or a predatory journal? For more information, read

“Citation Analysis ( “(Grand Canyon University,

n.d.) or “Journal Impact Factors (” (Grand Canyon

University, n.d.).

Use the Cabell’s Directory of Publishing Opportunities to �nd out more about the journal title. Cabell’s

has a list of predatory publishers and journals. Make sure the publication is not on that list. Cabell’s

Predatory Reports ( identi�es predatory journals

and can be accessed through the GCU Library website. Learners can search by title and �nd out about

peer review, acceptance rates, and other guidelines/policies.

Title: The title of an article is important because it brie�y summarizes the study.

Abstract: Although there are multiple types of abstracts based upon journal guidelines, typically an abstract

brie�y summarizes the key features of a research study.

Keywords: Depending on the journal, there should be three to ten keywords for an article. These keywords are

important because they are used in research database searches and should be related to the major concepts of

the study.

The following questions can be used after reading the title, abstract, and keywords to evaluate the relevance of

an article for inclusion on a reading list.

Does the title suggest variables that are relevant to the reading list for the study?

Does the summary provided by the abstract and keywords indicate that the research relates in some

way to the proposed study? Information in the abstract and keywords generally speaks to what the

authors wanted to know and the short version of their �ndings. After reading the abstracts and

keywords, a reader should be able to quickly determine how relevant an article is to the proposed

dissertation research.

One key to ef�cient reading is knowing when to stop reading an article because it is not relevant. A keyword

search may result in hundreds or thousands of sources, which can lead a researcher down the proverbial

“rabbit hole.” Critically reading the �rst page of an article can save time by allowing the reader to discard

articles that are not relevant to the study and should not be placed on a reading list.

Reading Strategically to Evaluate Findings

Most learners read articles from start to �nish; however, researchers generally take a nonlinear approach to

reading an article. In the early stages of research project development, researchers invest a substantial amount

of time surveying the literature of the �eld of study, quickly evaluating sources that are pertinent. This

nonlinear approach helps to further re�ne the reading list, culling out articles that are less relevant to the

project. Later, when a key article is discovered, researchers may read that article multiple times to understand

how the article contributes to the �eld of literature and how the article can be used in their work. The strategy

used to read the article will differ based on the goals of the reader.

It is also important to practice reading strategically to improve ef�ciency. After the title, abstract, and

keywords, readers should move to the sections at the end of the article such as the discussion and conclusion.

Keep in mind that the sections of an article may vary depending on the journal and on the type of research;

these sections focus on questions that are important for quantitative studies. Here, readers will �nd it more

useful to �rst review the discussion of the �ndings rather than the results section because, in quantitative

studies, the results section is �lled with charts, �gures, tables, and statistics. Learners will �nd it easier to start

with the narrative description found in the discussion of �ndings rather than the data analysis.

Discussion: In the discussion section, the authors interpret the meaning of the study results and explain how

the �ndings answer the research questions. This section also presents how the �ndings of the current study

relate to other research in the �eld. Implications of the �ndings for future research as well as limitations of the

study will be included.

Conclusion: If a conclusion section is included, authors will tell the readers why the study matters. This

section will summarize the study, the results, and how those results can be used in real-world applications.

After reading the discussion and conclusion sections of an article, the reader has another opportunity to

decide whether to stop reading and remove the article from the reading list. The following questions can assist

a reader in determining if the reader should keep an article and continue reading.

Is the sample relevant? One way to evaluate the relevance of a study is to look at the overall sample of

the study. If a study is about the classroom motivation factors for undergraduate college students and

the article is about the classroom motivation factors of elementary school students, the information may

be tangentially relevant because of the variables, but ultimately, it is not relevant enough for inclusion in

a study of undergraduate college students. Similarly, there is always a discussion of when and how to

include studies that have been completed on international samples. Context will always drive the

evaluation of the source as relevant. For instance, a study investigating the leadership practices of

Australian CEOs may be relevant to include as context for a study being completed in the United States

because leadership practices may be similar in these organizations based on the size and multinational

context. Educational systems, on the other hand, do change based on location and culture, so a study

about Nicaraguan school children may not be useful to include in a dissertation that examines the

American school system. When considering international studies, it is important to evaluate whether

the study should be included based on where the data is being collected. In other words, how similar are

the participants in this completed study to those that are going to be used in the proposed study?

Are the �ndings important? Along with the discussion above about the sample, the �ndings may not be

important to the dissertation project. In discussion and conclusion sections, the author(s) will explain

what the key �ndings are, why they are important, and any practical implications for their use.

Looking Deeper into Quantitative Methods

As a learner continues to evaluate a source, the next sections to consider are the methods and results. By the

time a reader is looking at the methods section, an article should be deemed relevant to the proposed study

and be saved to the reading list. Reading these sections in a quantitative study may be the most dif�cult for

learners to comprehend let alone critique. This is because most doctoral learners are still novices in

developing their research skills. Understanding these sections will become easier with more exposure and

experience with the quantitative research framework. These two sections are the mechanics of how the

author(s) conducted the study, speci�cally how they collected and analyzed the data.

Methods: The methods section of an article is where the authors of a study discuss how they conducted the

study. Information about how data were collected and analyzed will be presented, including what instruments

were used. The information about any instrument, survey, scale, or checklist is provided along with associated

information on the reliability and validity of these measures. In qualitative research, the variables and how

they are measured must be clearly de�ned. In this section the independent, dependent, and other variables

will be discussed along with the parameters of measurement.

Results: This section tells readers the �ndings from the study, including demographic information and key

�ndings that pertain to the research questions. Figures, charts, and tables will likely be presented, and the

information there will also be discussed within the text. In quantitative studies, this is where the speci�c

statistical tests performed will be addressed.

The following questions should be explored for the methods and results section.

Is the selected research design appropriate? The methods section should include information on

research design and justi�cation for the choice.

Is the sample representative? Sampling methods should be appropriate for the speci�c design. In

quantitative study, random selection is better than convenience sampling but not always available to

researchers. The researchers should explain their sampling strategies and discuss the demographics of

their participants. The sample should be representative of the population so that generalization can

occur. If these conditions have not been met, the authors should address the issues as limitations in the

discussion section.

Is the sample large enough? In the methods section, a power analysis should appear to verify that the

researchers sampled enough participants to achieve statistical signi�cance. Additionally, inferential

statistical analysis requires enough participants so that the variables being tested can be normally


Is there a control group? Not all quantitative designs require a control group, and, in some cases, it is not

possible to use one. However, the decision should be described in detail. If there is not a control group,

what steps have been taken to limit confounding factors?

The mean age of a sample of

on-campus students may be 20,

while the mean age of a sample

of online students may be 43.

What were the inclusion and exclusion criteria for the sample? Because quantitative studies focus on

generalization from a representative sample to a larger population, understanding sampling strategy is

of key import. The authors should be able to detail who was included and who was excluded from the

study and why these criteria were used.

Have the statistical analyses been appropriately chosen? Most studies provide descriptive statistics to

describe the sample. These are relevant factors about the participants such as age, gender, marital status,

class year, etc. To ensure that a sample is representative, it

would be crucial that the research give basic descriptive

statistics such as the mean and standard deviation on the

key demographics. For instance, the mean age of a sample

of on-campus students may be 20, while the mean age of a

sample of online students may be 43. Inferential statistics

are used to show correlations or make predictions about a

population based on a sample. While descriptive statistics are to be expected, they are not enough to test

most hypotheses. If a variable is not normally distributed or the variables are ranked or grouped, then

nonparametric tests must be used.

Have the variables been clearly de�ned?

Is reliability presented? Any surveys, scales, or instruments presented should be psychometrically

validated. Several types of reliability exist and describe the consistency of results. Reliability should be

presented in at least one manner, and internal consistency reported as Cronbach’s Alpha is the most

common. Included in the write up should be a description of the effect size from a seminal source.

Is validity presented? Validity is the degree to which the instrument accurately measures what it

intends to measure. The manuscript should include information about the validity of the measurement

and ideally address more than one type of validity, such as content, construct, and criterion validity.

Analyzing the Need for the Study

Learners are often advised to improve their writing skills by emulating the academic tone of the research

articles that they read. Experienced learners will realize that every research article is like a mini dissertation,

and as such, other skills, such as organizing an argument and providing context for a study can be garnered

from the reading of an article. Looking at the introduction is a great way to study how a researcher identi�es a

problem space, situates the current study within the relevant research of the �eld, and concisely builds a case

for the need for the study. More will be presented on argumentation later in this book, but for now, this will

help learners to understand the importance of the introduction.

Introduction: The purpose of an introduction is for authors to relate why they did the study. The introduction

sets up the problem space that they identi�ed and contextualizes the current study within the �eld of literature

on a topic. This is where the problem statement, purpose statement, and hypotheses are found. Generally

speaking, the review of literature will often be found in the introduction, occasionally the review of literature

is presented as a separate section depending on journal guidelines. When reading the introduction, consider

the following:

Is the problem clearly stated?

Is the purpose clearly stated, and is it aligned to the problem statement?

Is there a compelling need for the study? The introduction section for any article should provide a

compelling argument for the need for a study. This textbook includes a chapter on problem spaces and a

chapter on argumentation. The author must include context to situate the study within the framework

and demonstrate a gap in the literature (or problem space) that needed to be �lled with the current work.

How closely is the literature reviewed in the study related to previous literature?

Are the purpose and research questions clearly stated? In the introduction, the purpose of the research

and the associated research questions and hypotheses should be stated clearly so that the statistical

choices can be evaluated.

Sourcing References

When researchers read academic literature, they have “a thumb on the back page.” That is, they keep the

reference section readily accessible while reading the article. This technique allows them to investigate the

reference page of the study while reading the article. This is one way of gathering additional resources that

could be used for their own research projects. Using a form of citation analysis, the reader can evaluate the

contribution of the study and the reference list.

References: The references are a list of sources the authors cited in the article but is also an important place

for learners to �nd additional sources to add to their own literature searches. Consider the following when

sourcing from references:

Is the literature review recent? Are there any outstanding references (those of vital conceptual

signi�cance) left out?

Does the reference list include seminal works from the �eld of literature?

Across several articles on the topic, which studies or researchers are repeatedly mentioned? Citation

analysis may be helpful in determining seminal works or key authors on a topic.


Finally, the author information is an item that will be found in all research studies. There are guidelines

governing authorship. Who conducted the research is an important consideration when reading an article.

Speci�cally, is the author well known in the �eld as a proli�c contributor or an expert on a topic? Is this the

only study by this author in this line of research, or is the author established in this domain?

Author Information: This is a list of people who contributed to either conducting the study, writing the

manuscript, or both. The sequence of authorship is important because it denotes the contribution of work that

each author provided. The �rst author did the most work in developing, conducting, or writing the research,

while the last author contributed the least. In citations, the sequence of authors cannot be changed. For

instance, Smith and Hatmaker (2011) should never be cited as Hatmaker and Smith due to scholarly

conventions governing providing authorship credit. Often, one author will be listed as the contributing author,

or the author to contact for questions or permissions regarding the study. McGregor (2008) offered additional

information regarding evaluating author information.

De�nition of Quantitative Research
Quantitative research could be de�ned using an etymological approach and simply discussing it as the use of

statistics to explore a phenomenon. Another approach would be to think about the different branches of

quantitative research, which include descriptive and inferential statistics. This approach is useful because it

provides an outline of the kinds of data generated from statistical analysis. Similarly, characteristics can help

to de�ne the �eld of quantitative research (Gelo, Braakmann, & Benetka, 2008). Lastly, one could simply

describe the different types of inquiry within this �eld of research, such as nonexperimental and

experimental. To combine the explanatory bene�t of characteristics and types of inquiry, this review will

explore both methodological characteristics and predominant approaches. The scienti�c method is the

foundation of quantitative research, which includes both a desire to describe and explain phenomena.

Quantitative research, as it is today, has several enduring characteristics that help to de�ne its approach,

including observing and de�ning objects of inquiry thoughtfully, making predictions about the phenomena of

interest, checking these predictions using hypothesis testing and statistical analysis, and generalizing the

results to a target


In quantitative research, the creation of variables by attributing characteristics to phenomena is a �rst step in

the de�ning process. Researchers often use abstract or hypothetical ideas, known as constructs, to label

phenomena (Dew, 2008; Freedman et al., 2007; Cronbach & Meehl, 1955). For example, intelligence is a construct

that can vary by person. The variation in human behavior, both observed and hypothetical, makes assigning

variables a useful way to organize numerical data.

Freedman et al. (2007) provided a useful way of understanding types of variables. In their taxonomy, there are

two top-level types of variables: quantitative and qualitative. Both quantitative and qualitative variables have

two subtypes: discrete and continuous (quantitative), and nominal and ordinal (qualitative). Discrete

characteristics have a limited number of values. An example of a discrete variable is the number of birthdays

celebrated by a study participant. Here a participant could not have celebrated a half or three-quarters of a

birthday. Conversely, the participant’s age is a continuous variable because age can be calculated to in�nitely

small fractions of a second. A person’s age can be 36 years, 3 months, 6 days, 2 hours, 19 minutes, ad in�nitum.

Qualitative variables, also known as categorical variables, have categories, and, by de�nition, all categorical

variables are discrete because their categories can be well de�ned (Freedman et al., 2007). For example, hair

color is a categorical variable (blonde, brown, black, or other). Categorical variables can either be nominal or

ordinal. Hair color is nominal in that there is no inherent order of hair color. An ordinal categorical variable

requires some sort of ranking system such as military rank. The categories have order. In order to know which

combination of variables to establish for research, it is necessary both to identify such variables and to predict

likely relationships between those


In doctoral research, predictions come from research questions, which arise from gathering and reviewing

previous scienti�c literature on a topic. In most cases, a literature review will include both exploring relevant

theories and a systematic assessment of empirical studies on a topic. Most empirical research articles and

dissertations provide recommended questions for future research. Answering these recommended questions

may address a problem space in the literature for which the propositions and resulting hypotheses will

provide answers (Boote & Beile, 2005). Testing these hypotheses results in accepting or rejecting predictions.

Hypotheses are predictions that have the requirement of being tested. In quantitative research, hypothesis

testing involves using a statistical procedure to determine whether predictions are correct (Burdess, 2010).

This is done by creating both an alternative (experimental) hypothesis and a null hypothesis for each research

question and deductively testing them using probability—either accepting or rejecting the null hypotheses

(Wilkinson, 2013). Formulation of hypotheses will be discussed in more detail in later chapters.

The terms population and target population are important to consider when putting forth hypotheses. In

social science, population is a term that describes some group of people. The target population is some subset

of the entire population of people. For example, suppose the researcher were exploring a relationship between

teaching method in freshman university science courses and learner academic achievement (Wiersma, 2000).

The total population would be all freshman university science classes. The target population would be

freshman science teachers (and their learners), teaching at a particular kind of university (large public), and in

a speci�c region (Midwestern United States). The sample would be the actual teachers participating in the

study, which would be a subset of the target population. Here, a null hypothesis might predict that there is no

relationship between teaching method and learner academic achievement while the alternative hypothesis is

what the researcher expects to �nd a relationship between teaching method and learner academic

achievement for the speci�c target population. The goal is either to accept or reject the null hypothesis. If the

null is rejected, the alternative hypothesis would be accepted, and vice versa.

Predictions in quantitative research seek to determine to what degree the sample is representative of the

entire target population. Generalization is the term used to refer to this goal. As Reichhardt (2011) stated, “an

effect is generalizable to the extent it varies relatively little across a given range of treatments, recipients,

settings, times, or outcome variables” (p. 51). Generalizability is important in quantitative research because, if

the sample is representative of the target population, researchers can then make predictions about the target


Research Problems Suited for Quantitative Research

Doctoral researchers �rst identify a broad �eld of interest that they may want to study in the dissertation.

Sources used to identify research problems can include literary articles, social, political, or religious issues,

practical workplace situations, topics of personal interest and experience, or extensions of prior research

studies. The most important aspect of identifying the research problem is to thoroughly read the available

research and literature in the �eld of interest to �nd out what has not been studied. This leads to the elusive

problem space that the doctoral study can �ll. A topic of interest does not always make a feasible research

study. Some problems suited for quantitative research may include:

Determining whether the climate of an organization is impacted by the level of emotional intelligence

displayed by the leader.

Determining whether high stakes test scores were improved by learner participation in an accelerated

math curriculum over a 3-year period.

Determining whether the level of moral reasoning of employees is related to or correlates with the

number of years they have spent with a company.

The steps for conducting a quantitative study closely resemble the steps of the scienti�c method:

Identify a problem or problem space based on prior research that is appropriate for a dissertation study.

1. Establish research objectives.

2. Identify the appropriate methodology and design.

3. Plan for and collect data.

4. Process and analyze data.

5. Report and interpret data.

Overview of Quantitative Core Designs
While there are a number of designs that are appropriate for quantitative studies, GCU has endorsed speci�c

designs that facilitate a smooth dissertation journey for learners. As shown in Table 4.1, these fall into three

broad categories: experimental research, quasi-experimental research, and nonexperimental research.

Nonexperimental studies can be further classi�ed as descriptive, correlational, and


GCU Core Quantitative Designs



Descriptive (Survey)



Table 4.1

Quantitative Designs, Descriptions, and Examples

Design Description Example

Experimental Used to test an idea, treatment, or program
to see if it makes a difference.

The effect of a new
discipline plan on student
incidences of

Determines if there is an effect/outcome of
some form of treatment(s) using random
assignment of subjects to treatment and
control groups.

A comparison of the
effect of direct instruction
vs. cooperative groups on
students’ ability to
compute multistep math
equations.There is a control group and a test group.

Individuals are assigned randomly to the
two groups.

One group gets the treatment (test group)
and the other group (control group) does not
get the treatment.

There is a pretest and posttest for both
groups in a traditional experimental design.

Standardization of all aspects of research
procedures employed to ensure conditions
are the same for all participants.

Designed to demonstrate unambiguous
cause-and-effect relationship between

Quasi-Experimental Designed to demonstrate cause-and-effect
relationship between variables.

Determines if there is an effect/outcome of
some form of treatment(s) using preexisting
groups of subjects assigned to treatment
and control groups.

Does not meet all requirements of an
experimental design, thus cannot produce
an unambiguous cause-and-effect

It is the same as experiment in that there is
a control and test group; however, current
groups are used as is rather than randomly
assigning people to the two groups.

Both groups receive the pretest and posttest
in a traditional design. Typically no random
assignment—participants are in preexisting
groups or groups that are formed naturally.

Inclusion of participants in the control or
treatment group is determined by conditions
beyond the control of the researcher.

Conducted with similar rigor and control as
experimental studies with clearly defined

Design contains a confounding variable or
factor that prevents the research from
obtaining an absolute cause-and-effect

Nonexperimental Descriptive

Describes the opinions,
attitudes, or trends of a
population numerically.

A description of how
parents participate in
school activities.

Provides a description of
individual variables but is
not concerned with the
relationship between

A description of the
extent to which high
school teachers integrate
technology into math

Uses a process of
surveying a sample to
generalize to the

Correlational Determines if there is a
relationship between two
or more variables on a
single group of
participants with the intent
of predicting or defining a

The relationship between
employee perceptions of
servant leadership and
job satisfaction.

Observes relationships
between variables in a
naturally occurring

The relationship between
teacher collaboration and
student achievement.

Valid approaches to data
collection such as
validated surveys or

Process for Selecting a Quantitative Design
Selecting a method and design for a research study depends on what one wants to accomplish. The focus of

the research must be considered as well as the desired outcomes. Quantitative research is sometimes based on

worldviews of realism or positivism, attempting to disclose an existing reality in a world that functions based

on predictable patterns of cause and effect. The researcher knows a certain truth exists and attempts to

There is a theoretical or
logical explanation that
can be used to predict a

Variables should not or
cannot be manipulated.

The intent is to determine
if and to what degree the
variables are related.

It does not imply one
causes the other.


Compare two groups with
the intent of
understanding the
reasons or causes for the
two groups being

The effect of preschool
attendance on reading
ability at the end of the
third grade.

Determines the causes of
differences that already
exist between or within
two or more groups on
two or more variables.

The effect of gender on
math achievement.

Identify one or more
groups that serve as the
independent variable.

The effect of single-
gender schools and
student achievement.

Define the dependent
variable on which the
groups will be compared.

Select sample groups that
are as homogeneous as

Note. Adapted from “Nonexperimental quantitative research designs,” by J. H. McMillan, 2012, Educational Research:
Fundamentals for the Consumer, Chapter 7. Copyright 2012 by Pearson.

uncover facts in a systematic, objective manner (Balnaves & Caputi, 2001). Thus, quantitative research is not in

opposition to the Christian worldview, which asserts that truth can be known. Ultimately, the researcher will

select a method and design based on the problem of the study and the stated research questions.

If the goal of the researcher is to address research questions with a quantitative answer, to be able to

generalize the results of a study to a larger population, or to test a theory numerically, the researcher will select

a quantitative method and design. Suppose a GCU doctoral learner works in a hospital and wants to determine

whether there is a relationship between registered nurses’ (RN) perceptions of their nursing manager’s

leadership style and RN job satisfaction. This study is an attempt to determine whether a relationship exists

between variables; therefore, a quantitative method and correlational design can be used. The researcher

would include hypotheses that serve as a possible explanation for a factual situation that merits investigation

Thus, quantitative research is appropriate for research questions that need to be answered quantitatively,

when numerical changes are studied or a hypothesis is tested or when one wants to describe the current state

of a situation or explain a phenomenon (Balnaves & Caputi, 2001).

Variables and Subvariables

Variables are the building blocks or foundation of quantitative research. Variables represent the

characteristics of an individual, an event, a group, or an organization that can assume different values or

amounts and can be numerically measured through instruments, surveys, or observations. Variables can vary

in degree or amount (e.g., income level, temperature) or by type or kind (e.g., gender, marital status). Some

variables differ by degree, amount, or level of measurement. Other variables take on a speci�c role, such as

explaining how the world functions, or are used in the implementation of speci�c research designs.

Variables That Differ by Degree, Amount, or

Level of Measurement

Nominal variables are considered the most rudimentary level of measurement, and are categorical in nature,

meaning that they are made up of different categories. They cannot be ordered in any speci�c manner; they

are just different. Nominal variables simply name the characteristic being measured, with no ranking. Gender,

religion, marital status, and political party are other types of nominal variables. The attribute is simply named,

but one is not ranked over the other (Trochim,


Ordinal measures represent variables that can be rank ordered, such as socioeconomic status, education

levels, or grades received in classes; however, the distance between the groups or levels has no meaning. For

example, there is no speci�c or de�ned difference between levels of education. If a researcher were to ask

customers how satis�ed they were with their service, they could offer a survey in which the customers

selected answers on a scale of 1 to 5 with 1 being very satis�ed to 5 being very dissatis�ed. The researcher

could not say that the difference between satis�ed to somewhat satis�ed is the same as being very

dissatis�ed. All he or she could say is that some customers were more satis�ed than others (Trochim, 2006).

Interval variables can be rank ordered, but the distance between the levels or categories has a speci�c

meaning. For instance, temperature has speci�c distance between degrees, or numbers on the

Richter scale

that measure the intensity of an earthquake can be interpreted, as they are a speci�ed distance apart (Trochim,


Finally, ratio variables are similar to interval data, but the ratio data has an absolute zero or has no numbers

below zero. Height and weight are examples of ratio data. If one wants to measure an individual’s weight in

pounds, there are speci�c quantities that can be measured in equal units and that measurement cannot go

below zero (Trochim, 2006).

Table 4.2

Nature of Numerical Data Variables

Variables That Take on a Speci�c Role

There are two main types of variables that describe the way phenomena work or that researchers use when

conducting quantitative studies: independent and dependent. Independent variables can stand alone, but they

can also cause changes in other variables. On the other hand, a dependent variable depends on, relates to, or is

caused by other factors. It changes as the independent variable changes. Therefore, independent variables are

the cause of other variables, whereas dependent variables represent the outcome or effect. The dependent

variable is a phenomenon one is attempting to explain or predict. In experimental studies, the independent

variable is the treatment, intervention, or the manipulated variable. In nonexperimental studies, no variables

are manipulated, so the independent variable explains or predicts the dependent variable.

In some quantitative studies, there are extra variables that are not a primary focus but may be related to the

independent or dependent variables. These are called extraneous variables. For example, a researcher wants

to study the relationship between pay and job satisfaction; however, he or she also believes that the workers’

motivation may impact their job satisfaction. The extraneous variable would be motivation. In other studies,

moderator variables may impact the strength of relationship between the independent and dependent

variable. An example of a moderator variable in the above example may be organizational climate.

Sometimes a researcher wants to investigate how one variable affects or impacts the other. An intervening,

mediating, or mediator variable is a causal link between two variables. For example, excessive food

consumption may cause obesity, but another effect can be that the individual becomes diabetic, which is an

intervening variable (Frankfort-Nachmias & Leon-Guerrero, 2006; Trochim, 2006).

Nature Table

Binary Variable Variables that often are listed as zero and one. These are variables
that exist in two different states—yes/no, completed/not completed,
effective/not effective, exists/doesn’t exist.

Categorical Variable Categorical data. Examples—gender (male/female), married status
(married/single/divorced/widowed), employment status
(employed/self-employed/not employed), etc. Variables are assigned
numerical values, e.g. male=0, female=1. AKA: Nominal Variable.

Continuous Variable A variable that demonstrates continuous movement of time, range
and space (e.g. age range, time, size intervals, IQ ranges). AKA:
Interval Variable

Dichotomous Variable See Binary Variable

Discrete Variable Variables that can take on a finite number of values (e.g. responses
on a five-point rating scale, specific number of integers, finite
values). All qualitative variables are discrete.

Interval Variable See Continuous Variable

Nominal Variable See Categorical Variable

Ordinal Variable A variable for which order matters (e.g. scales of measurements,
Likert scales)

Table 4.


Role of Relative Position Variables

Role Table

Confounding Variable An extraneous variable, the presence of which in the study could
damage the validity of the research if the researcher fails to control
or eliminate it.

Control Variable A variable that the researcher does not want to examine in the study.
The variable is controlled.

Criterion Variable The predicted outcome variable in correlational research or a
nonexperimental study.

Dependent Variable The predicted outcome variable (attribute or characteristic) of a
study. The dependent variable is influenced by independent

Dummy Variable A bucket of binary variables with more than two variables in two
categories of variables. For example, marital status—married, single,
divorced, widowed—can be bucketed into married or not married.

Endogenous Variable Used in a causal model. It is a variable that is changed by one of the
functional relationships within the study or model. For example,
changing the income demand curve using quantity and price
(variables within the model).

Exogenous Variable A variable that is not within the study or model. The variable is either
endogenous (from within the study/model) or exogenous (outside of
the study/model).

Independent Variable A variable that affects the outcome of a dependent variable or an
outcome. For example, in a study examining the effects of sleep on
test scores, sleep is the independent variable.

Intervening Variable A variable that provides a link (causal) between other variables.

Latent Variable A variable that cannot be observed. They exist to define/explain
other variables. For example, patterns that underlie specific
behaviors related to voting for a president—Republican/Democratic.

Manifest Variable This variable is the opposite of a latent variable. Variables that can
be directly observed.

Manipulated Variable An independent variable that is manipulated to determine a particular
effect. For example, the amount of helium in a balloon would be the
manipulated variable.

Mediating Variable A variable that creates a link between two variables that is causally

Standardized Measurements and Instruments

After a researcher has de�ned the variables present in a situation, he or she must decide how to measure them;

therefore, measures and indicators must be considered. Measures are instruments that directly assess

quantities. For example, if one wanted to ask participants about their level of income, weight, or age, responses

to a survey would measure these variables. Other questions are considered indicators, as they are not direct

numeric measurements.

Selecting a valid and reliable instrument is required for a quantitative study, as one goal of the researcher is to

be able to generalize the results of the study to a larger population. First, the researcher needs to determine the

type of data to be collected by reviewing the stated research questions, determining the scope or parameters of

the study, reviewing the methods that were used in prior research on the topic, and the nature of the data to be

collected (qualitative, quantitative, or both).

At GCU, learners as researchers should become familiar with the instruments used in studies on their

dissertation topic through a thorough review of literature on the topic. This occurs as they develop Chapter 2 of

the dissertation, the literature review. As a doctoral learner considers the proper selection of instruments for

his or her dissertation study, he or she should consider how data on the topic was gathered in prior research

studies, whether the identi�ed instrument is appropriate for the nature or type of data needed, whether the

instrument will collect data to answer all research questions, and whether the instrument is valid and reliable.

Challenges arise when the learner or researcher does not conduct a robust review of prior studies on the topic

and is unaware of the existing critiques of instruments. At times, though better or more robust instruments

may exist, a researcher attempts to use an instrument that has not been tested for reliability and validity, or

the researcher uses an instrument for a population or sample for which the instrument is not intended. At

GCU, learners are discouraged from designing their own quantitative instruments as calibrating the

instrument, �eld testing the questions, and establishing validity and reliability is a study in and of itself, and

outside of the scope of the GCU program timeframe.

Moderating Variable A variable that increases or decreases the proven effect of the
independent variable on the dependent variable.

Outcome Variable A variable that is the result the researcher compares in a study or
experiment. AKA: Dependent Variable, Response Variable

Polychotomous Variable Variables with more than two possible values. Includes binary
variables and categorical variables with multiple categories.

Predictor Variable Predicted cause on a nonexperimental study. Typically used in
correlational studies. For example, if studying whether GPA predicts
intent to go to college, GPA is the predictor variable.

Treatment Variable See Independent Variable

Table 4.4

Types of Variables Categorized by Level of Measurement

Type of Variable Characteristics Example

Level of Measurement

Nominal/Categorical Most rudimentary level of

Categorical in nature

Cannot be ordered in any
specific manner



Marital status

Political party

Ordinal Can be rank ordered

Distance between the
groups or levels has no

Socioeconomic status

Education levels

Grades received in

Interval Can be rank ordered

Distance between the
levels or categories has a
specific meaning


Richter scale

Ratio Can be rank ordered

Has an absolute zero or
no numbers below zero



Role Taken by Variable

Independent Can stand alone

Can cause changes in
other variables

In experimental studies,
the independent variable
is the treatment or
intervention, or the
manipulated variable.

In nonexperimental
studies, the independent
variable explains or
predicts the dependent

Quantitative data can be gathered from a variety of sources; however, they must be collected systematically

from a prede�ned protocol. Additionally, the instrument used to gather data must be valid and reliable in order

to ensure the study can be replicated and that the results can be generalized to other populations or settings

(Golafshani, 2003). As shown in Table 4.5, data collection instruments can be categorized into two groups:

those the researcher completes, and those participants complete.

Dependent Depends on, relates to, or
is caused by other factors

Changes as the
independent variable

Represents the outcome
or effect

The variable is a
phenomenon one is
attempting to explain or

Mediating/Intervening Causal link between two

Moderator Impacts the strength of
relationship between the
independent and
dependent variable

Table 4.5

Quantitative Data Collection Instruments

Sampling Approaches for Quantitative Research

When the researcher is ready to apply the selected data collection instruments, he or she needs to consider

who is going to participate in the study and who will complete those instruments. First, the researcher needs

to always consider the purpose of the study and the practicality as well as strengths and weaknesses of

different sampling methods.

Probability and Nonprobability Sampling

Probability sampling – in this strategy, the sample is speci�cally selected and directly

re�ects the characteristics of this population. Probability sampling provides the most

credible (valid) results because it directly represents the population. Examples of

probability sampling include simple random sampling, strati�ed sampling, and

multistage cluster sampling.

Nonprobability sampling – this strategy is less desirable than probability sampling, as

the sample may not represent the population. This strategy is used when the

researcher does not care about the direct representation, they are not able to obtain a

sample suf�cient for the research, or it is too expensive to obtain a random sample.

Examples of nonprobability sampling include convenience, purposive, quota, or

snowball sampling.

Researcher-Completed Instruments Subject-Completed Instruments

Rating scales, archival databases, media sources Questionnaires, tests, surveys

Interview or focus-group guides Self-checklists

Tally sheets Attitude scales

Flowcharts Personality inventories

Performance checklists Achievement or aptitude tests

Time-and-motion logs Projective devices

Observation forms Stoichiometric devices

Note. Adapted from Research Rundowns > Quantitative Methods > Instrumentation, Validity, Reliability by Research Rundowns,
2009. Retrieved from https://researchrundowns.�

Every researcher has to select a sample or participants from a larger population. For the dissertation study, the

population includes the people or units that will be addressed by the research problem and research questions.

For example, the population of interest to the researcher may include teenage males in K-12 schools in the

United States, if the goal of a study is to determine whether a relationship exists between socioeconomic

status and academic achievement. In quantitative studies, the researcher should strive to select a

representative sample from the population, meaning that the group of individuals selected will produce results

that can be generalized to that larger population.

The sampling frame includes the group of people who can be realistically selected for the sample, or to be

recruited to participate in the study. For instance, in the previous example about teenage males in K-12

schools, the researcher may only have access to learners in one school district located in Georgia; therefore,

only learners in those schools will be recruited to participate in the study. This sample represents some bias as

the researcher wants to be able to generalize the results of the study but the demographics of the learner

sample from one district in Georgia may be different than other schools in the United States. The researcher

will, therefore, acknowledge the bias, which is relatively common in dissertation studies. Ultimately, the

sample includes the actual individuals who consent to participate in the study. Figure 4.1 includes a graphic of

the population, target population, sample, and data collection.

Questions to Consider When Selecting an Instrument

Consider the following questions as you make your decision:

Will you use an existing instrument?

What permissions do you need if you are using an existing instrument?

Are you considering creating your own instrument?

Will you access data from a database?

Do you have permission to use existing database information, or does the

database contain public information?

Are you using pretests and posttests (for experiment or quasi-experiment designs)?

Do you need permission to use the tests for research?

Are the tests valid?

Are the tests suitable for retesting at different time points?

Is there another source for data other than an instrument, database, or tests?

What is the source of data?

Sample Size

The sample size of a study is determined by the required or desired level of statistical signi�cance. Larger

samples reduce the risk of statistical errors and improve the statistical power and con�dence of the data. In

the most simplistic form, a sample should be equivalent to √N where N is the size of the population. For

example, if the population is the 150 �fth-grade learners at ABC Elementary School, a reasonable sample would

be 150≈12.24 learners. Because the measure of number of learners is a discrete measure, the minimum sample

size required is 13 learners. Advanced data analysis techniques require more advanced calculations of sample


Sampling Strategies

There are two basic kinds of sampling: random and nonrandom. Random sampling means that every unit in

the established sampling frame has an equal chance of being selected. This should result in the sample

representing the larger population from which it is drawn. There are four types of random sampling: strati�ed,

systematic, cluster, and multistage. Strati�ed random samples occur when the population is divided into strata

or levels, and the sample is randomly selected from each level. In systematic sampling, the researcher

systematically selected every nth individual from a list of people. Cluster sampling occurs when the

population is divided into groups, and individuals are then randomly selected from each group. Multistage

sampling occurs when the researcher wants to combine different random sampling strategies hierarchically

from this group.

Figure 4.1

Population, Target Population, and Sample

When a researcher employs nonprobability sampling, he or she does not use random techniques. This

sampling strategy is not as reliable or powerful as random sampling, as the results cannot always be

generalized to a larger population; therefore, the researcher relies on more haphazard methods of recruiting

people to participate in the study. There are several types of nonprobability sampling. At GCU these usually

include convenience, purposive, or snowball samples. When applying a convenience sampling strategy, the

researcher selects units or individuals from a group that is readily available. A purposive sample is comprised

of a prespeci�ed group of individuals that the researcher speci�cally seeks out to recruit for participation in a

study. Usually these individuals have speci�c characteristics, such as males age 18 and older attending

alternative schools in Georgia. Finally, snowball sampling is used when the researcher recruits initial

individuals who participate in a study, and then those individuals may provide names of other people who

could be contacted to complete data collection instruments. Table 6.6 contains the most common sampling

strategies used at GCU.

Reliability and Validity in Quantitative Studies

Reliability in quantitative research focuses on the degree to which the variable is consistently measured.

Because instruments such as surveys are used to measure variables, then a reliable instrument is one that will

gather reasonable and plausible data and produce similar results consistently over time. Test-retest reliability

and inter-rater reliability are two types of reliability that researchers address in quantitative studies. In order

Table 4.6

Quantitative Sampling Strategies

Type of Sampling Strategy De�nition

Random sample Every participant has an equal chance of
being selected.

Accidental, haphazard, or convenience sampling Participants are sampled according to what
is conveniently, accidentally, or haphazardly

Researcher selects participants who are
readily available.

Purposive sample Participants from a prespecified group are
purposively sought out and sampled

Researcher selects participants based on
predefined criteria.

Snowball sample Initial participants are sampled, and then
they identify more people to sample, and so

to establish test-retest reliability, the researcher administers the same instrument at two given times, and one

person scores the instrument. In order to establish inter-rater reliability, one version of the instrument is

administered at one time, and two people score it. Then, their scores are compared (Golafshani, 2003).

Validity refers to the degree the �ndings of the research can be applied or trusted. Whereas reliability is

concerned with the consistency of which a variable is measured, validity focuses on whether an instrument

measures what it is intended to measure. For instance, if a participant’s height is measured three times, it is

likely that the measurements will be consistent or reliable. However, a researcher could not measure a

participant’s height and claim that this is an accurate or valid measure of intelligence. Therefore, a valid

instrument allows the researcher to draw meaningful and useful inferences from the scores on the

instrument. If a participant took the SAT �ve times, the scores should be roughly consistent and, therefore,

reliable; the SAT should also be a valid measure of what a student learned in high school. In general,

quantitative researchers must consider three types of validity. Content validity refers to whether the questions

are representative of all questions on the topic. Criterion related validity refers to how well the scores on an

instrument relate to and predict an outcome. The criterion is the condition or standard by which people differ.

Construct validity refers to what the scores mean (Golafshani, 2003).

Validity assumes reliability. If a researcher has an unreliable or unstable variable, it is not valid. An unreliable

variable can change over time and cannot provide a true indication of what it is supposed to measure. If

variables are internally unreliable, then they measure more than one concept and then do not accurately

measure the concept under study (Golafshani, 2003).

Common Approaches to Quantitative Data Analysis

Researchers collect quantitative data and then analyze that data to discover and describe patterns.

Types of Variables

When determining what statistical tests to run on a variable, it will be important to know

both the nature of the numerical data provided by the variable and the speci�c variable type

identi�ed by your data source for the variable.

There are speci�c terms that describe variables used in research studies. There are two

different sets of terminology for variables. The �rst set of terminology describes the nature of

the numerical data that you collect and is de�ned by the data source you use. These

Table 4.7

Three Kinds of Analysis

Univariate Analysis Descriptive statistics describe
the distribution of variables

Bivariate Analysis Analysis of the relationship
between two variables

Multivariate Analysis Analysis of the relationship between
more than two variables

variables include binary/dichotomous variables, categorical/nominal variables,

continuous/interval variables, discrete variables, or ordinal variables. For example, in a two-

way ANOVA analysis of learner test score, you may group the learners into two different

instructional methods and two gender groups within each instructional method, so

instructional method and gender are the categorical variables (or binary/dichotomous

variables if there are only two levels within each category) for the study, while test score is

the continuous/interval (or “scale” in SPSS language).

The second set of terminology describes the role of/or relative position of the variable(s) in

your study. These variables include confounding, control, criterion, dependent, dummy,

exogenous, independent, intervening, latent, manifest, manipulated, mediating, moderating,

outcome, polychotomous, predictor, and treatment variables. Depending on the research

design, you will use the relevant terms to describe the variables in your study. For example,

in a multiple regression analysis of learner test scores, you could use hours of study,

instructional method, gender as predictor variables and the test score as an outcome or

criterion variable.

Univariate Analysis

In Chapter 4 of the dissertation at GCU, the researcher will use descriptive statistics to describe the sample and

some of the data collected. Often, tables and graphs are used for this purpose. The sample, for example, may be

described in a table displaying the age, gender, job role, and years of experience of the sample. Other graphics,

such as histograms, are used to show the distribution of data along a continuum with no problem spaces. The

researcher may use a histogram to show the years of education for the sample. Frequency distributions are

often used to demonstrate how often a particular score or range of scores appears in the data set.

Measures of central tendency are used to summarize the mean, median, and mode of a data set. The mode is

the most frequently occurring value, the median is the middle value (i.e., divides the distribution in half), and

the mean represents the average value. Other summary statistics can include the range and distribution of

data. The range includes the entire possible set of data from the lowest to highest point. The standard

deviation, or distance, from the mean is often calculated.

Parametric vs. Nonparametric Tests

Based on the way data are distributed, the researcher will conduct parametric or nonparametric tests.

Parametric tests are used when the data assume a normal distribution, meaning that half the points fall

equally on either side of the mean. Nonparametric tests are used when the data are not normally distributed.

Therefore, no assumptions can be made about the form or boundaries of the population distribution from

which the study sample was recruited. When data are normally distributed and parametric tests are used,

then tests of assumptions must be calculated including normality tests to see if the data is normally

distributed, (approximating a normal or “bell-shaped” curve) and possibly homoscedasticity tests (required for

some statistical analyses such as ANOVA) to see if the data values are spread out to about the same degree. If

the assumptions are not met, the researcher may need nonparametric tests instead of typical inferential

statistical tests. The researcher may need to normalize the data if using different tests to allow for the

comparison of the raw scores on different tests. In this case, the researcher would calculate the z-scores to

normalize or standardize the data from the two tests.

Bivariate and Multivariate Analysis

Bivariate and multivariate analyses are used to determine if a relationship exists between two or more

variables, respectively. Inferential statistics estimate the degree of con�dence that can be placed in

generalizations from a sample to the population from which the sample was selected.

Steps for Conducting Quantitative Data Analysis
The following steps are followed for conducting quantitative analysis of data:

1. Clean and prepare data.

2. Compute descriptive statistics.

3. Conduct assumptions testing (not used with descriptive/survey design).

a. Normality

b. Homoscedasticity

c. Nonparametric tests

4. Calculate z- or t-scores.

5. Perform inferential statistics if assumptions are met.

6. Use nonparametric techniques if assumptions are not met.

Table 4.8

Parametric and Nonparametric Tests

Table 4.9 shows the inferential statistical tests most commonly performed. For example, the t-test is used to

see whether there is a signi�cant difference for the means between two samples or groups.


Type Example Parametric


Bivariate Compare the
between two
different groups.

Is there a difference in
the mean math scores
of students who
participate in a
technology-rich math
class versus those who
do not?

Two sample t-

Wilcoxon rank-sum

Bivariate Compare two
completed or
taken from the
same person.

Is there a significant
change in the mean
math scores of
students who
participate in a
technology-rich math
class for a 16-week

Paired t-test Wilcoxon signed-
rank test

Multivariate Compare means
between three or
more distinctively
different groups.

If the math class has
three groups (those
who experience
traditional instruction,
those who have access
to technology in the
classroom on a daily
basis, and those who
go to the computer lab
once a week), how will
their mean scores differ
over time?

Analysis of

Kruskal-Wallis test

Bivariate Estimate the
degree of
between two

Is student math
associated with

coefficient of

Spearman’s rank

Table 4.9

Parametric vs. Nonparametric Tests

There are resources and information doctoral learners can use to determine the best approach for their

dissertation study. These include:

1. Books from methodology, design, and statistical analysis classes,

2. SAGE research resources (i.e., books and articles) in the GCU Library databases, and

3. Resources such as YouTube.

In all cases, the credibility of the resource must be considered.

Designs Using Quantitative Data Analysis in Quantitative

Sometimes researchers will use quantitative data in qualitative studies. As noted earlier, the researcher often

uses descriptive statistics to provide a summary of the sample in terms of demographics such as age, gender,

and number in each category. In qualitative studies, such as case studies and grounded theory studies, the

researcher will often use descriptive statistics to provide counts of words in interview transcripts to establish

patterns and codes. Inferential statistics and causal statistics can also be used in qualitative case studies.

Quantitative research offers a systematic and structured process for answering research questions.

Quantitative studies are often used in the social sciences, as they are quick, considered to be scienti�cally

valid, and establish important �ndings for the researcher. Quantitative studies require the use of statistical

techniques and a structured data collection plan to ensure that the study can be replicated. Additionally,

quantitative studies require the use of valid and reliable instruments, surveys, or databases to quantify


There are several quantitative designs GCU suggests researchers use. These include experimental,

nonexperimental, and quasi-experimental studies. Under the quasi-experimental umbrella, researchers can

select descriptive, correlational, or causal-comparative designs. Ultimately, the method and design will be

determined by the goals of the research and research questions. Quantitative researchers must carefully

Parametric Tests Nonparametric Tests

t-test (independent means) Mann-Whitney U test

t-test (correlated means) Kruskal-Wallis one-way analysis of variance
Sign tests

ANOVA (ANalysis Of VAriance) Friedman two-way analysis of variance

ANCOVA (Analysis Of COVAriance) Chi square (for categorical data)

MANOVA (Multivariate Analysis Of Variance)

t-test for r

t-test for difference in proportions (for categorical

construct a data collection plan, use valid and reliable instruments, consider appropriate sampling strategies,

and understand the nature of the variables used in the study. These factors will dictate the statistical analysis

conducted in efforts to glean results that can be generalized to a larger population.

Checks for Understanding
1. What are the GCU core designs for quantitative research?

2. How does a researcher know which quantitative design is right for the research project, if one is right at


3. Why is instrument selection important in quantitative research?

4. How are sample size and sampling approach determined in quantitative research?

5. How are quantitative data analysis methods selected?

American Psychological Association. (2020). Types of articles and papers. In American Psychological

Association (Ed.), Publication manual of the American Psychological Association (7th ed.) (pp. 3–10).


Balnaves, M., & Caputi, P. (2001). Introduction to quantitative research methods: An investigative approach.


Boote, D. N., & Beile, P. (2005). Scholars before researchers: On the centrality of the dissertation literature review

in research preparation. Educational Researcher, 34(6), 3–15.

Burdess, N. (2010). Starting statistics: A short, clear guide. SAGE.

Coughlan, M., Cronin, P., & Ryan, F. (2007). Step-by-step guide to critiquing research. Part 1: quantitative

research. British Journal of Nursing, 16(11), 658–663.

Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4),


Dew, D. (2008). Construct. In P. J. Lavrakas (Ed.), Encyclopedia of survey research method (pp. 134–135). SAGE.

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2006). Social statistics for a diverse society. Pine Forge Press.


1. They are experimental, quasi-experimental, descriptive (survey), correlational, and


2. If the goal of the researcher is to address research questions with a quantitative answer,

to be able to generalize the results of a study to a larger population, or to test a theory

numerically, the researcher will select a quantitative method and design.

3. This is signi�cant because one goal of the researcher is to be able to generalize the

results of the study to a larger population.

4. The sample size of a study is determined by the required or desired level of statistical

signi�cance. Sampling strategy is largely determined by the availability of sample

populations related to the study.

5. The researcher must know both the nature of the numerical data provided by the

variable and the speci�c variable type identi�ed by the data source for the variable.

Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.). W. W. Norton & Company.

Gelo, O., Braakmann, D., & Benetka, G. (2008). Quantitative and qualitative research: Beyond the debate.

Integrative Psychological and Behavioral Science, 42(3), 266–290.


Golafshani, N. (2003). Understanding reliability and validity in qualitative research. The Qualitative Report,

8(4), 597–607.

Grand Canyon University (n.d.). Citation analysis.

Grand Canyon University (n.d.). Evaluating sources: What is a “scholarly” source?

Grand Canyon University (n.d.). Finding empirical research articles [Video �le].�nding-empirical-research-articles/v1.1/

Grand Canyon University (n.d.). Journal impact factors.

McGregor, S. (2018). Critical research literacy. In S. McGregor Understanding and evaluating research (pp. 3–

18). SAGE Publications.

McMillan, J. H. (2012). Nonexperimental quantitative research designs. In Educational research: Fundamentals

for the consumer (chapter 7). Pearson.

Quinton, S. & Smallbone, T. (2006). How to read critically. In S. Quinton, & T. Smallbone, Sage Study Skills:

Postgraduate research in business (pp. 81–96). SAGE. https://dx-doi-

Reichardt, C. S. (2011). Criticisms of and an alternative to the Shadish, Cook, and Campbell validity typology.

New Directions for Evaluation, 130, 43–53. http://doi:10.1002/ev.364

Trochim, W. M. K. (2006). Levels of measurement. Research methods knowledge base.

Wiersma, W. (2000). Research methods in education (7th ed.). Pearson.

Wilkinson, M. (2013). Testing the null hypothesis: The forgotten legacy of Karl Popper? Journal of Sports

Sciences, 31(9), 919–920.

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