The Relationship Between
Engagement at Work andOrganizational Outcomes2020 Q12® Meta-Analysis: 10 th Edition
James K. Harter, Ph.D., GallupFrank L. Schmidt, Ph.D., University of Iowa
Sangeeta Agrawal, M.S., Gallup
Anthony Blue, M.A., Gallup
Stephanie K. Plowman, M.A., Gallup
Patrick Josh, M.A., Gallup
Jim Asplund, M.A., Gallup

OCTOBER 2020

AC K N OW L E D G M E N T S

The authors thank Marie-Lou Almeida, Jeevika Galhotra, Rujuta Gandhi, Julie Griffiths, Ryan
Gottfredson, Domonique Hodge, Diana Lu, Shane McFeely, Marco Nink, John Reimnitz, Chayanun
Saransomrurtai, Puneet Singh and Ben Wigert for contributing important new research studies to thisongoing meta-analysis.

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

The Relationship Between Engagement at Workand Organizational Outcomes2020 Q12® Meta-Analysis: 10th Edition
TA B L E O F C O N T E N T S
Executive Summary 2
Objective
Methods
Results2

2

2

Conclusion

3

Introduction 4
Foreword

4

Development of the Q12 5Introduction to the Study

Description of the Q12 9

7

Meta-Analysis, Hypothesis, Methods and Results 14
Meta-Analysis

14

Hypothesis and Study Characteristics
Meta-Analytic Methods Used
Results

24

21

14

Utility Analysis: Practicality of the Effects 29
Discussion 32
References 34
Appendix A: Reliabilities of Business/Work Unit Outcomes 39
Appendix B: Test-Retest Reliabilities of Employee Engagement 40

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Executive Summary
Objective
Business and work units in the same organization vary substantially in their levels of
engagement and performance. The purpose of this study was to examine the:1) true relationship between employee engagement and performance in

276 organizations

2) consistency or generalizability of the relationship between employee engagement

and performance across organizations3) practical meaning of the findings for executives and managers

Methods
We accumulated 456 research studies across 276 organizations in 54 industries, with
employees in 96 countries. Within each study, we statistically calculated the business-/work-unit-level relationship between employee engagement and performance outcomesthat the organizations supplied. In total, we studied 112,312 business and work units that
included 2,708,538 employees. We studied 11 outcomes: customer loyalty/engagement,
profitability, productivity, turnover, safety incidents, absenteeism, shrinkage, patient safetyincidents, quality (defects), wellbeing and organizational citizenship.
Individual studies often contain small sample sizes and idiosyncrasies that distort theinterpretation of results. Meta-analysis is a statistical technique that is useful in combining
results of studies with seemingly disparate findings, correcting for sampling, measurement
error and other study artifacts to understand the true relationship with greater precision.
We applied Hunter-Schmidt meta-analysis methods to 456 research studies to estimate
the true relationship between engagement and each performance measure and to test for
generalizability. After conducting meta-analysis, we examined the practical meaning of therelationships by conducting utility analysis.
Results
Employee engagement is related to each of the 11 performance outcomes studied.
Results indicate high generalizability, which means the correlations were consistent acrossdifferent organizations. The true score correlation between employee engagement andcomposite performance is 0.49. Across companies, business/work units scoring in the top
half on employee engagement more than double their odds of success compared withthose in the bottom half. Those at the 99th percentile have nearly five times the successrate of those at the first percentile.

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Median percent differences between top-quartile and bottom-quartile units were:• 10% in customer loyalty/engagement• 23% in profitability• 18% in productivity (sales)• 14% in productivity (production records and evaluations)• 18% in turnover for high-turnover organizations (those with more than 40%
annualized turnover)• 43% in turnover for low-turnover organizations (those with 40% or lower
annualized turnover)• 64% in safety incidents (accidents)• 81% in absenteeism• 28% in shrinkage (theft)• 58% in patient safety incidents (mortality and falls)• 41% in quality (defects)• 66% in wellbeing (thriving employees)• 13% in organizational citizenship (participation)
Conclusion
The relationship between engagement and performance at the business/work unit level issubstantial and highly generalizable across organizations. Employee engagement is related
to each of the 11 performance outcomes. This means that practitioners can apply the Q12
measure in a variety of situations with confidence that the measure captures important
performance-related information.

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Introduction
Foreword
In the 1930s, George Gallup began a worldwide study of human needs and satisfactions.
He pioneered the development of scientific sampling processes to measure public opinion.
In addition to his polling work, Dr. Gallup completed landmark research on wellbeing,
studying the factors common among people who lived to be 95 and older (Gallup & Hill,
1959). Over the next several decades, Dr. Gallup and his colleagues conducted numerous
polls throughout the world, covering many aspects of people’s lives. His early world polls
dealt with topics such as family, religion, politics, personal happiness, economics, health,education, safety and attitudes toward work. In the 1970s, Dr. Gallup reported that lessthan half of those employed in North America were highly satisfied with their work (Gallup,1976). Work satisfaction was even lower in Western Europe, Latin America, Africa and theFar East.
Satisfaction at work has become a widespread focus for researchers. In addition to Dr.
Gallup’s early work, the topic of job satisfaction has been studied and written about in morethan 10,000 articles and publications. Because most people spend a high percentage of
their waking hours at work, studies of the workplace are of great interest to psychologists,sociologists, economists, anthropologists and physiologists. The process of managing
and improving the workplace is crucial and presents great challenges to nearly everyorganization. So, it is vital that the instruments used to create change do, in fact,measure workplace dynamics that predict key outcomes — outcomes that a variety oforganizational leaders would consider important. After all, organizational leaders are in thebest position to create interest in and momentum for job satisfaction research.
Parallel to Dr. Gallup’s early polling work, Don Clifton, a psychologist and professor at the
University of Nebraska, began studying the causes of success in education and business.
Dr. Clifton founded Selection Research, Inc. (SRI) in 1969. While most psychologists werebusy studying dysfunction and the cause of disease, Dr. Clifton and his colleagues focused
their careers on the science of strengths-based psychology, the study of what makes
people flourish.
Their early discoveries led to hundreds of research studies focused on successful
individuals and teams across a broad spectrum of industries and job types. In particular,
research on successful learning and workplace environments led to numerous studies ofsuccessful teachers and managers. This work included extensive research on individual
differences and the environments that best facilitate success. Early in their studies, theresearchers discovered that simply measuring employees’ satisfaction was insufficient
to create sustainable change. Satisfaction needed to be specified in terms of its mostimportant elements, and it needed to be measured and reported in a way that could be
used by the people who could take action and create change.
Further research revealed that change happens most efficiently at a local level — at thelevel of the front-line, manager-led team. For executives, front-line teams are their direct
reports, and for plant managers, front-line teams are the people they manage each
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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Editionday. Studying great managers, Gallup scientists learned that optimal decision-making
happens when information regarding decisions is collected at a local level, close to theeveryday action.
Dr. Clifton’s work merged with Dr. Gallup’s work in 1988, when Gallup and SRI combined,
enabling the blending of progressive management science with top survey and pollingscience. Dr. Gallup and Dr. Clifton spent much of their lives studying people’s opinions,attitudes, talents and behaviors. To do this, they wrote questions, recorded the responses,
and studied which questions elicited differential responses and related to meaningful
outcomes. In the case of survey research, some questions are unbiased and elicit
meaningful opinions, while others do not. In the case of management research, somequestions elicit responses that predict future performance, while others do not.
Developing the right questions is an iterative process in which scientists write questions
and conduct analysis. The research and questions are refined and rephrased. Additional
analysis is conducted. The questions are refined and rephrased again. And the process isrepeated. Gallup has followed the iterative process in devising the survey tool that is the
subject of this report, Gallup’s Q12 instrument, which is designed to measure employee
engagement conditions.
The next section provides an overview of the many decades of research that have gone
into the development and validation of Gallup’s Q12 employee engagement instrument.
Following this overview, we present a meta-analysis of 456 research studies, exploring therelationship between employee engagement and performance across 276 organizations
and 112,312 business/work units that include 2,708,538 employees.
Development of the Q12
Beginning in the 1950s, Dr. Clifton started studying work and learning environments todetermine the factors that contribute positively to those environments and that enablepeople to capitalize on their unique talents. It was through this early work that Dr. Clifton
began using science and the study of strengths to research individuals’ frames ofreference and attitudes.
From the 1950s to the 1970s, Dr. Clifton continued his research of students, counselors,
managers, teachers and employees. He used various rating scales and interview
techniques to study individual differences, analyzing questions and factors that explain
dissimilarities in people. The concepts he studied included “focusing on strengths versus
weaknesses,” “relationships,” “personnel support,” “friendships” and “learning.” Various
questions were written and tested, including many early versions of the Q12 items. Ongoing
feedback techniques were first developed with the intent of asking questions, collecting
data and encouraging ongoing discussion of the results to provide feedback and potential
improvement — a measurement-based feedback process. To learn causes of employee
turnover, exit interviews were conducted with employees who left organizations. A
common reason for leaving an organization focused on the quality of the manager.
In the 1980s, Gallup scientists continued the iterative process by studying high-performing
individuals and teams. Studies involved assessments of individual talents and workplace
attitudes. As a starting point for questionnaire design, numerous qualitative analyses
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were conducted, including interviews and focus groups. Gallup researchers asked
top-performing individuals or teams to describe their work environments and theirthoughts, feelings and behaviors related to success.
The researchers used qualitative data to generate hypotheses and insights into the
distinguishing factors leading to success. From these hypotheses, they wrote andtested questions. They also conducted numerous quantitative studies throughout the
1980s, including exit interviews, to continue to learn causes of employee turnover.
Qualitative analyses such as focus groups and interviews formed the basis for lengthy
and comprehensive employee surveys, called “Organizational Development Audits” or“Managing Attitudes for Excellence” surveys. Many of these surveys included 100 to 200
items. Quantitative analyses included factor analyses to assess the dimensionality of thesurvey data; regression analyses to identify uniqueness and redundancies in the data;and criterion-related validity analyses to identify questions that correlate with meaningful
outcomes such as overall satisfaction, commitment and productivity. The scientists
developed feedback protocols to facilitate the feedback of survey results to managers andemployees. Such protocols and their use in practice helped researchers learn which items
were most useful in creating dialogue and stimulating change.
One outgrowth of a management research practice that was focused on talent andenvironment was the theory of talent maximization in an organization:

Per-Person Productivity = Talent x (Relationship+ Right Expectation + Recognition/Reward)
These concepts would later become embedded in the foundational elements of the Q12.
Over time, SRI and Gallup researchers conducted numerous studies of manager successpatterns that focused on the talents of the manager and the environments that best
facilitated success. By integrating knowledge of managerial talent with survey dataon employee attitudes, scientists had a unique perspective on what it takes to build
a successful workplace environment. Themes such as “individualized perception,”“performance orientation,” “mission,” “recognition,” “learning and growing,” “expectations,”
and “the right fit” continued to emerge. In addition to studies of management,
researchers conducted numerous studies with successful teachers, students andlearning environments.
In the 1990s, the iterative process continued. During this time, Gallup researchersdeveloped the first version of the Q12 (“The Gallup Workplace Audit” or GWA) in an effort
to efficiently capture the most important workplace attitudes. Qualitative and quantitative
analyses continued. In that decade, more than 1,000 focus groups were conductedand hundreds of instruments were developed, many of them with several additional
items. Scientists also continued to use exit interviews; these revealed the importanceof the manager in retaining employees. Studies of the Q12 and other survey items wereconducted in various countries throughout the world, including the United States, Canada,
Mexico, Great Britain, Japan and Germany. Gallup researchers obtained internationalcross-cultural feedback on Gallup’s core items, which provided context on the applicabilityof the items across different cultures. Various scale types were also tested, including
variations of 5-point and dichotomous response options.
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Quantitative analyses of survey data included descriptive statistics, factor analyses,
discriminant analyses, criterion-related validity analyses, reliability analyses, regression
analyses and other correlational analyses. Gallup scientists continued to study thecore concepts that differentiated successful from less successful work units and theexpressions that best captured those concepts. In 1997, the criterion-related studies
were combined into a meta-analysis to study the relationship of employee satisfactionand engagement (as measured by the Q12) to business/work unit profitability, productivity,
employee retention and customer satisfaction/loyalty across 1,135 business/workunits (Harter & Creglow, 1997). Meta-analysis also enabled researchers to study thegeneralizability of the relationship between engagement and outcomes. Results ofthis confirmatory analysis revealed substantial criterion-related validity for each of the
Q12 items.
As criterion-related validity studies are ongoing, the meta-analysis was updated in 1998
(Harter & Creglow, 1998) and included 2,528 business/work units; in 2000 (Harter &Schmidt, 2000), when it included 7,939 business/work units; in 2002 (Harter & Schmidt,
2002), when it included 10,885 business/work units; in 2003 (Harter, Schmidt, & Killham,
2003), when it included 13,751 business/work units; in 2006 (Harter, Schmidt, Killham, &Asplund, 2006), when it included 23,910 business/work units; in 2009 (Harter, Schmidt,
Killham, & Agrawal, 2009), when it included 32,394 business/work units; in 2013 (Harter,
Schmidt, Agrawal, & Plowman, 2013), when it included 49,928 business/work units; and in2016 (Harter, Schmidt, Agrawal, Plowman, & Blue, 2016), when it included 82,248 business/work units. This report provides the 10th published iteration of Gallup’s Q12 meta-analysis of
the relationship between employee engagement and performance.
As with the 2016 report, this report expands the number of business/work units andincreases the total composition of different industries and countries studied. It also
includes two new outcome variables: wellbeing and organizational citizenship.
Since its final wording and order were completed in 1998, the Q12 has been administered
to more than 43 million employees in 212 different countries or territories and in 74
languages. Additionally, a series of studies was conducted to examine the cross-culturalproperties of the instrument (Harter & Agrawal, 2011).
Introduction to the Study
The quality of an organization’s human resources is perhaps the leading indicator of its
growth and sustainability. The attainment of a workplace with high-caliber employees
starts with the selection of the right people for the right jobs. Numerous studies havedocumented the utility of valid selection instruments and systems in the selection of
the right people (Schmidt, Hunter, McKenzie, & Muldrow, 1979; Hunter & Schmidt, 1983;
Huselid, 1995; Schmidt & Rader, 1999; Harter, Hayes, & Schmidt, 2004; Schmidt, Oh, &Shaffer, 2016).
After employees are hired, they make decisions and take actions every day that canaffect the success of their organizations. Many of these decisions and actions are
influenced by their own internal motivations and drives. One can also hypothesize thatthe way employees are treated and the way they treat one another can positively affecttheir actions — or can place their organizations at risk. For example, researchers have
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found positive relationships between general workplace attitudes and service intentions,customer perceptions (Schmit & Allscheid, 1995), and individual performance outcomes(Iaffaldano & Muchinsky, 1985). An updated meta-analysis has revealed a substantial
relationship between individual job satisfaction and individual performance (Judge,
Thoresen, Bono, & Patton, 2001). Additional and more recent research illustrates that
individual job attitudes are a substantial predictor of individual employee effectiveness,
defined by both performance and withdrawal behaviors and intentions (Harrison, Newman,& Roth, 2006; Mackay, Allen, & Landis, 2017). Both of these more recent studies found that
employee engagement is best conceptualized as a higher order job attitudes construct.
This is further reinforced by Newman, Harrison, Carpenter and Rariden (2016).
There is also evidence at the business or work unit level that employee attitudes relate
to various organizational outcomes. Organization-level research has focused primarilyon cross-sectional studies. Independent studies found relationships between employee
attitudes and performance outcomes such as safety (Zohar, 1980, 2000), customer
experiences (Schneider, Parkington, & Buxton, 1980; Ulrich, Halbrook, Meder, Stuchlik,& Thorpe, 1991; Schneider & Bowen, 1993; Schneider, Ashworth, Higgs, & Carr, 1996;
Schmit & Allscheid, 1995; Reynierse & Harker, 1992; Johnson, 1996; Wiley, 1991),
financials (Denison, 1990; Schneider, 1991) and employee turnover (Ostroff, 1992). A
study by Batt (2002) used multivariate analysis to examine the relationship between
human resource practices (including employee participation in decision-making) andsales growth. Gallup has conducted large-scale meta-analyses, most recently studying
82,248 business and work units regarding the concurrent and predictive relationshipof employee attitudes (satisfaction and engagement) with safety, customer attitudes,
financials, employee retention, absenteeism, quality metrics and merchandise shrinkage(Harter et al., 2016; Harter et al., 2013; Harter et al., 2009; Harter et al., 2006; Harter et al.,
2003; Harter, Schmidt, & Hayes, 2002; Harter & Schmidt, 2002; Harter & Schmidt, 2000;
Harter & Creglow, 1998; Harter & Creglow, 1997). This meta-analysis, repeated acrosstime, has found consistently that there are positive concurrent and predictive relationships
between employee attitudes and various important business outcomes. It has also foundthat these relationships generalize across a wide range of situations (industries, business/work unit types and countries). Additional independent studies have found similar results(Whitman, Van Rooy, & Viswesvaran, 2010; Edmans, 2012). A recent meta-analysis of
employee engagement data found somewhat stronger correlations between job attitudes
and business performance during past economic recessions compared to nonrecession
years (Harter, Schmidt, Agrawal, Plowman, & Blue, 2020). Like the studies of individual
job attitudes, this study also found that the best predictor of overall business/work unit
performance was a higher order job attitudes-engagement construct.
Even though it has been much more common to study employee opinion data at theindividual level, studying data at the business or work unit level is critical because thatis where the data are typically reported (because of confidentiality concerns, employee
surveys are reported at a broader business or work unit level). In addition, businessunit-level research usually provides opportunities to establish links to outcomes thatare directly relevant to most businesses — outcomes like customer loyalty, profitability,
productivity, turnover, safety, merchandise shrinkage and quality variables that are often
aggregated and reported at the business/work unit level.

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Another advantage to reporting and studying data at the business/work unit level is that
instrument item scores are of similar reliability to dimension scores for individual-level
analysis. This is because at the business or work unit level, each item score is an averageof many individuals’ scores. This means that employee surveys reported at a businessor work unit level can be more efficient or parsimonious in length because item-levelmeasurement error is less of a concern. See Harter and Schmidt (2006) for a more
complete discussion of job satisfaction research and the advantages of conducting
unit-level analyses.
One potential problem with such business-unit-level studies is limited data as a result ofa limited number of business/work units (the number of business/work units becomes
the sample size) or limited access to outcome measures that one can compare acrossbusiness/work units. For this reason, many of these studies are limited in statistical power.
As such, results from individual studies may appear to conflict with one another. Metaanalysis techniques provide the opportunity to pool such studies together to obtain more
precise estimates of the strength of effects and their generalizability.
This paper’s purpose is to present the results of an updated meta-analysis of therelationship between employee workplace perceptions and business/work unit outcomesbased on currently available data collected with Gallup clients. The focus of this study is on
Gallup’s Q12 instrument. The Q12 items — which were selected because of their importanceat the business or work unit level — measure employee perceptions of the quality ofpeople-related management practices in their business/work units.
Description of the Q12
The development of the GWA (Q12) was based on more than 30 years of accumulated
quantitative and qualitative research. Its reliability, convergent validity and criterionrelated validity have been extensively studied. It is an instrument validated through priorpsychometric studies as well as practical considerations regarding its usefulness for
managers in creating change in the workplace.
In designing the items included in the Q12, researchers took into account that, froman actionability standpoint, there are two broad categories of employee survey items:
those that are reflective measures of attitudinal outcomes (satisfaction, loyalty, pride,customer service perceptions and intent to stay with the company) and those that areformative measures of actionable issues that drive these outcomes. The Q12 measures
the actionable issues for management — those predictive of attitudinal outcomes suchas satisfaction, loyalty, pride and so on. On Gallup’s standard Q12 instrument, after an
overall satisfaction item are 12 items measuring issues we have found to be actionable(changeable) at the supervisor or manager level — items measuring perceptions ofelements of the work situation, such as role clarity, resources, fit between abilities andrequirements, receiving feedback, and feeling appreciated. The Q12 is a formative measureof “engagement conditions,” each of which is a contributor to engagement through themeasure of its causes.

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The overall satisfaction item and Q12 items are:
Q00. (Overall Satisfaction) On a 5-point scale, where 5 means extremely satisfied and 1means extremely dissatisfied, how satisfied are you with (your company) as a place
to work?
Q01. I know what is expected of me at work.
Q02. I have the materials and equipment I need to do my work right.
Q03. At work, I have the opportunity to do what I do best every day.
Q04. In the last seven days, I have received recognition or praise for doing good work.
Q05. My supervisor, or someone at work, seems to care about me as a person.
Q06. There is someone at work who encourages my development.
Q07. At work, my opinions seem to count.
Q08. The mission or purpose of my company makes me feel my job is important.
Q09. My associates or fellow employees are committed to doing quality work.
Q10. I have a best friend at work.
Q11. In the last six months, someone at work has talked to me about my progress.
Q12. This last year, I have had opportunities at work to learn and grow.
The Gallup Q12 items (Q01-Q12) are Gallup proprietary information and are protected by law. You may
not administer a survey with the Q12 items or reproduce them without written consent from Gallup.
Copyright © 1993-1998 Gallup, Inc. All rights reserved.

The current standard is to ask each employee (a census survey; median participation rate
is 85%) to rate the Q12 statements using six response options, from 5 = strongly agree to
1 = strongly disagree, and the sixth response option — don’t know/does not apply — isunscored. Because it is a satisfaction item, the first item (Q00) is scored on a satisfaction
scale rather than on an agreement scale. Regression analyses (Harter et al., 2002) indicatethat employee engagement accounts for nearly all of the performance-related variance(composite performance) accounted for by the overall satisfaction measure. Therefore, thefocus of this report is on employee engagement, as measured by statements Q01-Q12.
While these items measure issues that the manager or supervisor can influence, only one
item contains the word “supervisor.” This is because it is realistic to assume that numerous
people in the workplace can influence whether someone’s expectations are clear, whether
the employee feels cared about and so on. The manager’s or supervisor’s position, though,allows them to take the lead in establishing a culture that values behaviors that support
these perceptions.

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

The following is a brief discussion of the conceptual relevance of each of the 13 items:
Q00. Overall satisfaction
The first item on the survey measures affective satisfaction on a scale from “extremely
dissatisfied” to “extremely satisfied.” It is an attitudinal outcome or direct reflective measureof how people feel about their organization. Given that it is a direct measure of affective
satisfaction, on its own, it is difficult to act on the results of this item. Other issues, like thosemeasured in the following 12 items, explain why people are satisfied and why they become
engaged and produce outcomes.
Q01. Expectations
Defining and clarifying the outcomes that are to be achieved is perhaps the most basic of all
employee needs and manager responsibilities. How these outcomes are defined and acted on
will vary across business/work units, depending on the goals of the business/work unit.
Q02. Materials and equipment
Getting people what they need to do their work is important in maximizing efficiency,
demonstrating to employees that their work is valued and showing that the company issupporting them in what they are asked to do. Great managers help employees see how theirrequests for materials and equipment connect to important organizational outcomes.
Q03. Opportunity to do what I do best
Helping people get into roles in which they can most fully use their inherent talents andstrengths is the ongoing work of great managers. Learning about individual differences throughexperience and assessment can help the manager position people efficiently within andacross roles and remove barriers to high performance.
Q04. Recognition for good work
Employees need constant feedback to know if what they are doing matters. Ongoing
management challenges include understanding how each person prefers to be recognized,
making recognition objective and real by basing it on performance, and recognizing
employees frequently.
Q05. Someone at work cares about me
For each person, feeling cared about may mean something different. The best managers listen
to individuals and respond to their unique needs. In addition, they find the connection between
the needs of the individual and the needs of the organization.
Q06. Someone at work encourages my development
How employees are coached can influence how they perceive their future. If the manager is
helping the employee improve as an individual by providing opportunities that are in sync withthe employee’s talents, both the employee and the company will profit.

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Q07. Opinions count
Asking for the employee’s input and considering that input can often lead to better
decision-making. This is because employees are often closer than the manager is to manyfactors that affect the overall system, whether that is the customer or the products they
are producing every day. In addition, when employees feel they are involved in decisions,they take greater ownership for the outcomes.
Q08. Mission or purpose
Great managers help people see not only the purpose of their work, but also how each
person’s work influences and relates to the purpose of the organization and its outcomes.
Reminding employees of the big-picture effect of what they do each day is important,whether their work influences the customer, safety or the public.
Q09. Associates committed to quality
Managers can influence the extent to which employees respect one another by selecting
conscientious employees, providing some common goals and metrics for quality, andincreasing associates’ frequency of opportunity for interaction.
Q10. Best friend at work
Managers vary in the extent to which they create opportunities for people at work to get
to know one another and in how much they value close, trusting relationships at work. The
best managers do not subscribe to the idea that there should be no close friendships atwork; instead, they free people to get to know one another, which is a basic human need.
This, then, can influence communication, trust and other outcomes.
Q11. Progress
Providing a structured time to discuss each employee’s progress, achievements and goals
is important for managers and employees. Great managers regularly meet with individuals,
both to learn from them and to give them guidance. This give-and-take helps managers
and employees make better decisions.
Q12. Opportunities to learn and growIn addition to having a need to be recognized for doing good work, most employees needto know that they are improving and have opportunities to build their knowledge and skills.
Great managers choose training that will benefit the individual and the organization.

More detailed discussion of the practical application of each Q12 item is provided in
Wagner and Harter (2006) and in various articles posted on Gallup.com.

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You may not administer a survey with the Q12 items or reproduce them without written consent from Gallup.

The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

As a total instrument (sum or mean of items Q01-Q12), the Q12 has a Cronbach’s alphaof 0.91 at the business/work unit level. The meta-analytic convergent validity of theequally weighted mean (or sum) of items Q01-Q12 (GrandMean) to the equally weighted
mean (or sum) of additional items in longer surveys (measuring all known facets of job
satisfaction and engagement) is 0.91. This provides evidence that the Q12, as a composite
measure, captures the general factor in longer employee surveys. Individual items correlate
to their broader dimension true-score values, on average, at approximately 0.70. While theQ12 is a measure of actionable engagement conditions, its composite has high convergent
validity with affective satisfaction and other direct measures of work engagement (seeHarter and Schmidt, 2008, for further discussion of convergent and discriminant validity
issues and the construct of “engagement”).
As previously mentioned, this is the 10th published iteration of the Q12
business-unit-level meta-analysis. Compared with the previous meta-analysis,
the current meta-analysis includes:• a larger number of studies, business/work units and countries• two new outcomes (wellbeing and organizational citizenship)• more than double the number of business/work units with absenteeism data, 79%
more business/work units with quality (defects) data, 43% more business/work units
with turnover data, 23% more business/work units with customer loyalty/engagement
data and 17% more business/work units with productivity data
As such, this study provides a substantial update of new and recent data.
The coverage of research studies includes business/work units in 96 countries, including
Australia; New Zealand; and countries in Asia, Europe, the Commonwealth of Independent
States, Latin America, the Middle East, North America, Africa and the Caribbean. Fifty-two
companies included in the current meta-analysis operate exclusively in countries outside
the U.S.
This meta-analysis includes all available Gallup studies (whether published or unpublished)and has no risk of publication bias.

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Meta-Analysis, Hypothesis, Methods and ResultsMeta-Analysis
A meta-analysis is a statistical integration of data accumulated across many different
studies. As such, it provides uniquely powerful information because it controls formeasurement and sampling errors and other idiosyncrasies that distort the results ofindividual studies. A meta-analysis eliminates biases and provides an estimate of true
validity or true relationship between two or more variables. Statistics typically calculated
during meta-analyses also allow the researcher to explore the presence, or lack, ofmoderators of relationships.
More than 1,000 meta-analyses have been conducted in the psychological, educational,
behavioral, medical and personnel selection fields. The research literature in the behavioral
and social sciences fields includes a multitude of individual studies with apparently
conflicting conclusions. Meta-analysis, however, allows the researcher to estimate
the mean relationship between variables and make corrections for artifactual sourcesof variation in findings across studies. It provides a method by which researchers candetermine whether validities and relationships generalize across various situations(e.g., across firms or geographical locations).
This paper will not provide a full review of meta-analysis. Rather, the authors encourage
readers to consult the following sources for background information and detailed
descriptions of the more recent meta-analytic methods: Schmidt and Hunter (2015);
Schmidt (1992); Hunter and Schmidt (1990, 2004); Lipsey and Wilson (1993); BangertDrowns (1986); and Schmidt, Hunter, Pearlman and Rothstein-Hirsh (1985).
Hypothesis and Study Characteristics
The hypotheses examined for this meta-analysis were as follows:
Hypothesis 1
Business-unit-level employee engagement will have positive average correlations with thebusiness/work unit outcomes of customer loyalty/engagement, profitability, productivity,
wellbeing and organizational citizenship, and negative correlations with turnover, safetyincidents, absenteeism, shrinkage, patient safety incidents and quality (defects).
Hypothesis 2
The correlations between engagement and business/work unit outcomes will generalize
across organizations for all business/work unit outcomes. That is, these correlations
will not vary substantially across organizations. And in particular, there will be few, if any,organizations with zero correlations or those in the opposite direction from Hypothesis 1.

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Gallup’s inferential database includes 456 studies conducted as proprietary researchfor 276 independent organizations. In each Q12 study, data were aggregated at thebusiness/work unit level and correlated with the following aggregate business/work unit
performance measures:• customer metrics (referred to as customer loyalty/engagement)• profitability• productivity• turnover• safety incidents• absenteeism• shrinkage• patient safety incidents• quality (defects)• wellbeing• organizational citizenshipThat is, in these analyses, the unit of analysis was the business or work unit, not theindividual employee.
Correlations (r values) were calculated, estimating the relationship of business/work unit
average measures of employee engagement (the mean of the Q12 items) to each of these11 general outcomes. Correlations were calculated across business/work units in eachcompany, and these correlation coefficients were entered into a database. The researchersthen calculated mean validities, standard deviations of validities and validity generalization
statistics for each of the 11 business/work unit outcome measures.
As with previous meta-analyses, some of the studies were concurrent validity studies,
where engagement and performance were measured in roughly the same time period orwith engagement measurement slightly trailing behind the performance measurement(because engagement is relatively stable and a summation of the recent past, such studies
are considered “concurrent”). Predictive validity studies involve measuring engagement attime 1 and performance at time 2. Predictive validity estimates were obtained for 47% of
the organizations included in this meta-analysis.
This paper does not directly address issues of causality, which are best addressed withmeta-analytic longitudinal data, consideration of multiple variables and path analysis.
Issues of causality are discussed and examined extensively in other sources (Harter,
Schmidt, Asplund, Killham, & Agrawal, 2010). Findings of causal studies suggest thatengagement and financial performance are reciprocally related, but that engagement
is a stronger predictor of financial outcomes than the reverse. The relationshipbetween engagement and financial performance appears to be mediated by its causal
relationship with other outcomes such as customer perceptions and employee retention.
That is, financial performance is a downstream outcome that is influenced by theeffect of engagement on shorter-term outcomes such as customer perceptions andemployee retention.
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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Studies for the current meta-analysis were selected so that each organization was
represented once in each analysis. For several organizations, multiple studies wereconducted. To include the best possible information for each organization represented in
the study, some basic rules were used. If two concurrent studies were conducted for thesame client (where Q12 and outcome data were collected concurrently [i.e., in the sameyear]), then the weighted average effect sizes across the multiple studies were enteredas the value for that organization. If an organization had a concurrent and a predictive
study (where the Q12 was collected in year 1 and outcomes were tracked in year 2), thenthe effect sizes from the predictive study were entered. If an organization had multiple
predictive studies, then the mean of the correlations in these studies was entered. If
sample sizes varied substantially in repeated studies for an organization, the study withthe largest sample size was used.
• For 107 organizations, there were studies that examined the relationship between
business/work unit employee perceptions and customer perceptions. Customer
perceptions included customer metrics, patient metrics and student ratings ofteachers. These metrics included measures of loyalty, satisfaction, service excellence,customer evaluation of quality of claims, net promoter scores and engagement.
The largest representation of studies included loyalty metrics (e.g., likelihood torecommend/net promoter or repeat business), so we refer to customer metrics as
customer loyalty/engagement in this study. Instruments varied from study to study.
The general index of customer loyalty was an average score of the items included ineach measure. A growing number of studies include “customer engagement” as themetric of choice, which measures the emotional connection between the customers
and the organization that serves them. For more information on the interaction of
employee and customer engagement, see Fleming, Coffman and Harter (2005), andHarter, Asplund and Fleming (2004).
• Profitability studies were available for 90 organizations. The definition of profitability
typically was a percentage profit of revenue (sales). In several companies, theresearchers used — as the best measure of profit — a difference score from the prioryear or a difference from a budgeted amount because it represented a more accurate
measure of each unit’s relative performance. As such, a control for opportunity(location) was used when profitability figures were deemed less comparable fromone unit to the next. For example, a difference variable involved dividing profit byrevenue for a business/work unit and then subtracting a budgeted percentage fromthis percentage. Or, more explicitly, in some cases, a partial correlation (r value) was
calculated, controlling for location variables when they were deemed to be relevant
to accurate comparison of business/work units. In every case, profitability variables
were measures of margin, and productivity variables (which follow) were measures ofamount produced.

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition• Productivity studies were available for 162 organizations. Measures of business/work unit productivity consisted of one of the following: financials (e.g., revenue/sales
dollars per person or patient), quantity produced (production volume), enrollments inprograms, hours/labor costs to budget, cross-sells, performance ratings or student
achievement scores (for three education organizations). In a few cases, this was adichotomous variable (top-performing business/work units = 2; less successful units= 1). The majority of variables included as “productivity” were financial measures ofsales or revenue, or growth in sales or revenue. As with profitability, in many cases, it
was necessary for the researchers to compare financial results with a performancegoal or prior-year figure to control for the differential business opportunity becauseof the location of business/work units, or to explicitly calculate a partial correlation(r value). Variables included in this category could best be summarized as financial
metrics, evaluations or production records.
• Turnover data were available for 128 organizations. The turnover measure was theannualized percentage of employee turnover for each business/work unit. In mostcases, voluntary turnover was reported and used in the analyses.
• Safety data were available for 59 organizations. Safety measures included lost
workday/time incident rate, percentage of workdays lost as a result of incidents
or workers’ compensation claims (incidents and costs), number of incidents, orincident rates.

• Absenteeism data were included for 37 organizations. Absenteeism measures
included the average number of days missed per person for each business/work unit
divided by the total days available for work. The measures of absenteeism included
sick days or hours absent or total days absent.
• Eleven organizations provided measures of shrinkage. Shrinkage is defined as thedollar amount of unaccounted-for lost merchandise, which could be the resultof employee theft, customer theft or lost merchandise. Given the varying size oflocations, shrinkage was calculated as a percentage of total revenue or a difference
from an expected target.
• Ten healthcare organizations provided measures of patient safety. Patient safetyincident measures varied from patient fall counts (percentages of total patients),
medical error and infection rates, and risk-adjusted mortality rates.

• Twenty organizations provided measures of quality. For most organizations, quality
was measured through records of defects such as unsalable/returned items/
quality shutdowns/scrap/operational efficiency/rejections per inspection rate (inmanufacturing), forced outages (in utilities), disciplinary actions, deposit accuracy(financial) and other quality scores. Because the majority of quality metrics were
measures of defects (where higher figures meant worse performance), measures of
efficiency and quality scores were reverse coded so that all variables carried the same
inferential interpretation.
• Wellbeing measures were collected by 12 organizations. In all studies, the wellbeing
measure was the Cantril Self-Anchoring Striving Scale. The scale measures
respondents’ life evaluation on the 0-10 ladder of life “at this time” and anticipated life
evaluation “about five years from now.” The scale is anchored from “best possible life”(10) to “worst possible life” (0).
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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition• Organizational citizenship measures were available for two organizations. These measuresconsisted of the percentage of participation and enrollment in company-sponsoredactivities that are intended to benefit employees, such as conferences and programs.
Wellness conferences and 401(k) enrollment are examples from the two organizations thatprovided data.
The overall study involved 2,708,538 independent employee responses to surveys
and 112,312 independent business/work units in 276 organizations, with an average of
24 employees per business/work unit and 407 business/work units per organization.
We conducted 456 research studies across the 276 organizations.
Table 1 provides a summary of industries included in this meta-analysis. It is evident that
there is considerable variation in the industry types represented, as organizations from 54
industries provided studies. Each of the general government industry classifications (via SIC
codes) is represented, with the largest number of organizations represented in services, retail,manufacturing and finance industries. The largest numbers of business/work units are in theservices, finance and retail industries. Specific subindustry frequencies are detailed in Table 1.
Table 1: Summary of Industries

Number ofOrganizations

Number of Business/
Work Units

Number of
Respondents

Commercial Banking

6

3,132

21,435

Depository

21

16,230176,430

Mortgage

1

27

985

Industry Type

Finance
CreditInsurance

Nondepository
Security

2

59

10

7,837

1

94

581

79,464
2,038

4

797

25,833

Aircraft1

3,411

37,616

Automobiles

1

30

1,453

1

928

8,203

5

289

Transactions

Manufacturing
ApparelBuilding Materials
Chemicals

Computers and Electronics
Consumer Goods
Food

Glass

Industrial Equipment
Instrument

Miscellaneous
Paper

Pharmaceutical

1

73

1

16

1

8

3
7
1

3,116

91,337

89

535

2

753

5

18

1,335
27,002

8
4

111

239

5

1

1,530

13,098
1,349
639

5,848

924

22,481

4,103

39,575

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27,025

The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Table 1: Summary of Industries (continued)

Number ofOrganizations

Number of Business/
Work Units

Number of
Respondents

Plastics

1

133

938

Ship Building

3

882

134,297

Materials and Construction

4

1,270

29,932

Automotive

4

261

13,614

Clothes

4

1,055

28,937

Industry Type

Manufacturing (continued)
Printing

Materials and Construction
RetailBuilding Materials
Department Stores
EatingElectronics

2

35

3

1,158

2

752

8

1,296

65,001
6,594

57,104

1,483

104,273

6

7,101

344,559

12

4,170

158,264

Agricultural

1

7

635

Education

10

Health

68

Nursing Home

2Entertainment
Food

Industrial Equipment
Miscellaneous

Pharmaceutical
Services
Business

Government
Hospitality

Personal Services
Real Estate
Recreation

Social Services

6

716

1

106

1

11

2

8,288

1,051
484

171,463

4

1,258

16,162

7

11,127

213,63111

1,241

190,473

1,259

14,807
508

1

424

4

321

2

49

22,142

326,483
28,768
3,226
7,924
1,969

4

1,621

28,602

Airlines

1

111

2,293

Delivery Services

1

Transportation/Public Utilities
Communications

Electric, Gas and Sanitary Services
Nonhazardous Waste Disposal
Trucking

7

4,234

46,784

5

3,183

28,887

1

100

639

1

727

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53,151
28,6006,213

The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Table 1: Summary of Industries (continued)

Number ofOrganizations

Number of Business/
Work Units

Number of
Respondents

Finance46

28,249308,296

Materials and Construction

4

1,270

29,932

Industry Type

Total

Manufacturing

47

Retail15,49649

Services

114

Total

276

Transportation/Public Utilities

25,681
32,622

16

8,994

112,312413,023
951,344840,015
165,928

2,708,538

Table 2 provides a summary of the business/work unit types included in this meta-analysis.
There is considerable variation in the types of business/work units, ranging from stores toplants/mills to departments to schools. Overall, 22 different types of business/work units
are represented; the largest number of organizations had studies of workgroups (teams),
stores or bank branches. Likewise, workgroups, stores and bank branches have the highest
proportional representation of business/work units.
Table 2: Summary of Business/Work Unit Types
Business/Work Unit TypeBank BranchCall Center

Child Care Center

Number ofOrganizations

Number of Business/
Work Units

Number of
Respondents

7

1,240

22,07620
1

Cost Center

16

Dealership

7

Country

18,118
1,562
3,675

1

26

196,481
25,66176,7582,618

423

16,940714

134,703

800

69,028Department

12

1,553

Facility

2

1,080

9

846

182,953

216

3,790

Division

Hospital
Hotel

3
7

Location

14

11,414

Patient Care Unit

8

2,825

2

113

Mall

Plant/MillRegionRestaurant

Sales Division
Sales Team
School2
8
6
6269,82952,703

100,871588

34,866420

655,182

2,106

391

6

33,132409

13,52021,722
27,543
10,496Store

37

24,124

893,781

Total

276

112,3122,708,538

Workgroup (Team)96

39,66920

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463,885

The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Meta-Analytic Methods Used
Analyses included weighted average estimates of true validity; estimates of standard
deviation of validities; and corrections made for sampling error, measurement error in
the dependent variables, and range variation and restriction in the independent variable(Q12 GrandMean) for these validities. An additional analysis was conducted, correcting
for independent-variable measurement error. The most basic form of meta-analysis
corrects variance estimates only for sampling error. Other corrections recommended by
Hunter and Schmidt (1990, 2004) and Schmidt and Hunter (2015) include correction formeasurement and statistical artifacts such as range restriction and measurement error in
the performance variables gathered. The sections that follow provide the definitions of thepreviously mentioned procedures.
Gallup researchers gathered performance-variable data for multiple time periods tocalculate the reliabilities of the performance measures. Because these multiple measures
were not available for each study, the researchers used artifact distributions meta-analysis
methods (Hunter & Schmidt, 1990, pp. 158–197; Hunter & Schmidt, 2004) to correct formeasurement error in the performance variables. The artifact distributions were basedon test-retest reliabilities, where they were available, from various studies. The procedure
followed for calculation of business/work unit outcome-measure reliabilities was consistent
with scenario 23 in Schmidt and Hunter (1996). To take into account that some change
in outcomes (stability) is a function of real change, test-retest reliabilities were calculatedusing the following formula:(r12 x r23)/r13
Where r12 is the correlation of the outcome measured at time 1 with the same outcome
measured at time 2, r23 is the correlation of the outcome measured at time 2 with theoutcome measured at time 3, and r13 is the correlation of the outcome measured at time
1 with the outcome measured at time 3.
The above formula factors out real change (which is more likely to occur from time 1 to 3
than from time 1 to 2 or 2 to 3) from random changes in business/work unit results causedby measurement error, data collection errors, sampling errors (primarily in customer andquality measures) and uncontrollable fluctuations in outcome measures. Some estimates
were available for quarterly data, some for semiannual data and others for annual data. The
average time period in artifact distributions used for this meta-analysis was consistent withthe average time period across studies for each criterion type. See Appendix A for a listingof the reliabilities used in the corrections for measurement error. Artifact distributions
for reliability were collected for customer loyalty/engagement, profitability, productivity,
turnover, safety incidents and quality (defects) measures. They were not collected forabsenteeism, shrinkage, patient safety incidents, wellbeing and organizational citizenshipbecause they were not available at the time of this study. Therefore, the assumed reliability
for these outcomes was 1.00, resulting in downwardly biased true validity estimates (theestimates of validity reported here are lower than reality). Artifact distributions for thesevariables will be added as they become available in the future.

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It could be argued that, because the independent variable (employee engagement asmeasured by the Q12) is used in practice to predict outcomes, the practitioner must livewith the reliability of the instrument being used. However, correcting for measurement error
in the independent variable answers the theoretical question of how the actual constructs(true scores) relate to each other. Therefore, we present analyses both before and after
correcting for independent variable reliability. Appendix B presents the distributions ofreliabilities for the GrandMean of Q12. These values were computed in the same manner
as were those for the performance outcomes.
In correcting for range variation and range restriction, there are fundamental theoreticalquestions that need to be considered relating to whether such correction is necessary.
In personnel selection, validities are routinely corrected for range restriction because in
selecting applicants for jobs, those scoring highest on the predictor are typically selected.
This results in explicit range restriction that biases observed correlations downward(i.e., attenuation). But in the employee satisfaction and engagement arena, one could argue
that there is no explicit range restriction because we are studying results as they exist in
the workplace. Business/work units are not selected based on scores on the predictor(Q12 scores).
However, we have observed that there is variation across companies in standard deviationsof engagement. One hypothesis for why this variation occurs is that companies vary in
how they encourage employee satisfaction and engagement initiatives and in how they
have or have not developed a common set of values and a common culture. Therefore, thestandard deviation of the population of business/work units across organizations studied
will be greater than the standard deviation within the typical company. This variation instandard deviations across companies can be thought of as indirect range restriction(as opposed to direct range restriction). Improved indirect range restriction corrections
have been incorporated into this meta-analysis (Hunter, Schmidt, & Le, 2006).
Since the development of the Q12, Gallup has collected descriptive data on more than
43 million respondents, 5.1 million business/work units and 5,076 organizations. Thisaccumulation of data indicates that the standard deviation within a given company isapproximately four-fifths the standard deviation in the population of all business/work units.
In addition, the ratio of standard deviation for a given organization relative to the population
value varies from organization to organization. Therefore, if one goal is to estimate theeffect size in the population of all business/work units (arguably a theoretically important
issue), then correction should be made based on such available data. In the observed data,
correlations are attenuated for organizations with less variability across business/workunits than the population average and vice versa. As such, variability in standard deviations
across organizations will create variability in observed correlations and is therefore an
artifact that can be corrected for in interpreting the generalizability of validities. Appendixes
in Harter and Schmidt (2000) provide artifact distributions for range-restriction/variation
corrections used for meta-analysis. These artifact distributions were updated substantiallyin 2009 and have again been updated for this meta-analysis. We have included a randomly
selected 100 organizations in our current artifact distributions. Because of the large sizeof these tables, they are not included in this report. They resemble those reported in theearlier study but include a larger number of entries.

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The following excerpt provides an overview of meta-analysis conducted using
artifact distributions:
In any given meta-analysis, there may be several artifacts for which artifact information is
only sporadically available. For example, suppose measurement error and range restriction
are the only relevant artifacts beyond sampling error. In suwch a case, the typical artifact
distribution-based meta-analysis is conducted in three stages:
1) Information is compiled on four distributions: the distribution of the observed

correlations, the distribution of the reliability of the independent variable, thedistribution of the reliability of the dependent variable and the distribution of the range
departure. There are then four means and four variances compiled from the set ofstudies, with each study providing whatever information it contains.

2) The distribution of observed correlations is corrected for sampling error.

3) The distribution corrected for sampling error is then corrected for error ofmeasurement and range variation (Hunter & Schmidt, 1990, pp. 158–159;
Hunter & Schmidt, 2004).

In this study, statistics are calculated and reported at each level of analysis, startingwith the observed correlations and then correcting for sampling error, measurement
error and, finally, range variation. Both within-organization range-variation corrections(to correct validity generalization estimates) and between-organization range-restriction
corrections (to correct for differences in variation across organizations) were made.
Between-organization range-restriction corrections are relevant in understanding how
engagement relates to performance across the business/work units of all organizations.
As alluded to, we have applied the indirect range-restriction correction procedure to thismeta-analysis (Hunter et al., 2006).
The meta-analysis includes an estimate of the mean sample-size-weighted validity andthe variance across the correlations — again weighting each validity by its sample size.
The amount of variance predicted for weighted correlations based on sampling error was
also computed. The following is the formula to calculate variance expected from sampling
error in “bare bones” meta-analyses, using the Hunter et al. (2006) technique referred
to previously:

S

2

- 2 2
=
(1- r ) / (N-1)
eResidual standard deviations were calculated by subtracting the amount of variance due
to sampling error, the amount of variance due to study differences in measurement error
in the dependent variable, and the amount of variance due to study differences in range
variation from the observed variance. To estimate the true validity of standard deviations,the residual standard deviation was adjusted for bias due to mean unreliability and mean
range restriction. The amount of variance due to sampling error, measurement error andrange variation was divided by the observed variance to calculate the total percentage
variance accounted for. Generalizability is generally assumed if a high percentage (suchas 75%) of the variance in validities across studies is due to sampling error and otherartifacts, or if the 90% credibility value (10th percentile of the distribution of true validities)
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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

is in the hypothesized direction. As in Harter et al. (2002), Harter et al. (2006), Harter etal. (2009), Harter et al. (2013) and Harter et al. (2016), we calculated the correlation ofengagement to composite performance. This calculation assumes that managers aremanaging toward multiple outcomes simultaneously and that each outcome occupies
some space in the overall evaluation of performance. To calculate the correlation to thecomposite index of performance, we used the Mosier (1943) formula to determine thereliability of the composite measure (as described in Harter et al., 2002), using reliability
distributions and intercorrelations of the outcome measures. Patient safety was combinedwith the more general “safety” category because patient safety is an industry-specificvariable. The reliability of the composite metric is 0.91. Composite performance wasmeasured as the equally weighted sum of customer loyalty/engagement, turnover (reverse
scored as retention), safety (accidents and patient safety incidents reverse scored),absenteeism (reverse scored), shrinkage (reverse scored), financials (with profitability andproductivity equally weighted) and quality (defects reverse scored). We also calculated
composite performance as the equally weighted sum of the most direct outcomes ofengagement — customer loyalty/engagement, turnover (reverse scored as retention),safety (accidents and patient safety incidents reverse scored), absenteeism (reverse
scored), shrinkage (reverse scored) and quality (defects reverse scored). The reliability ofthis composite variable is 0.89. We did not include the newly added outcomes (wellbeing
and organizational citizenship) to the composite performance estimates because we do
not have estimates of their intercorrelation with the other outcome variables.
In our research, we used the Schmidt and Le (2004) meta-analysis package (the artifact
distribution meta-analysis program with correction for indirect range restriction). The
program package is described in Hunter and Schmidt (2004).
Results
The focus of analyses for this report is on the relationship between overall employee
engagement (defined by an equally weighted GrandMean of Q12) and a variety ofoutcomes. Table 3 provides the updated meta-analytic and validity generalization
statistics for the relationship between employee engagement and performance foreach of the 11 outcomes studied. Two forms of true validity estimation follow meanobserved correlations and standard deviations. The first corrects for range variation
within organizations and dependent-variable measurement error. This range-variation
correction places all organizations on the same basis in terms of variability of employee
engagement across business/work units. These results can be viewed as estimating therelationships across business/work units within the average organization. The second
corrects for range restriction across the population of business/work units and dependentvariable measurement error. Estimates that include the latter range-restriction correction
apply to interpretations of effects in business/work units across organizations, as
opposed to effects expected within a given organization. Because there is more variationin engagement for business/work units across organizations than there is within the
average organization, effect sizes are higher when true validity estimates are calculated forbusiness/work units across organizations.

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For instance, observe the estimates relative to the customer loyalty/engagement criteria.
Without the between-organization range-restriction correction (which is relevant to theeffect within the typical organization), the true validity value of employee engagement
is 0.20 with a 90% credibility value (CV) of 0.13. With the between-organization
range-restriction correction (which is relevant to business/work units across organizations),
the true validity value of employee engagement is 0.29 with a 90% CV of 0.19.
As in the nine prior meta-analyses, findings here show high generalizability acrossorganizations in the relationships between employee engagement and customer loyalty/
engagement, profitability, productivity, turnover, safety, shrinkage and quality (defects)
outcomes. And, for the two new outcomes (wellbeing and organizational citizenship),correlations are highly generalizable. Of the 11 outcomes, the correlation between
employee engagement and wellbeing is the strongest, with a mean observed correlationof 0.56 and true validity of 0.72. Across the 11 outcomes, most of the variability incorrelations across organizations was the result of sampling error, measurement error
or range restriction in individual studies. All of the 90% credibility values are in the
hypothesized direction. The largest variability in correlations across organizations wasobserved for the absenteeism outcome. This was mainly because one very large study
was added with substantially stronger correlations than were observed in past studies.
The mean true validity of the relationship between engagement and absenteeism was-0.38, and the 90% credibility value was -0.21, indicating wide generalizability in the
direction of the relationship. The direction of the effect is predictable, but the size ofeffect across companies varies somewhat. Artifacts do not explain all of the variancein correlations of employee engagement and most outcomes, but they explain a highpercentage of the variance in nearly all outcomes. This means that the Q12 measure of
employee engagement effectively predicts these outcomes in the expected direction
across organizations, including those in different industries and in different countries.
In summary, for the composite measure of engagement shown in Table 3, the strongest
effects were found for wellbeing, patient safety incidents, absenteeism, quality (defects),
customer loyalty/engagement, safety incidents and productivity. Correlations were lower
but highly generalizable for profitability, shrinkage, turnover and organizational citizenship.

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Table 3: Meta-Analysis of Relationship Between Employee Engagement and Business/Work Unit Performance
Customer
Loyalty/
Engagement

Profitability

Productivity

Number ofBusiness/
Work Units

25,39132,298Mean Observed r

107

0.16

90

0.09

0.13

-0.08

-0.13

-0.27

-0.09

-0.43

-0.20

0.56

0.08

True Validity1

0.20

0.10

0.15

-0.12

-0.15

-0.27

-0.09

-0.43

-0.21

0.57

0.08

0.21

-0.18

-0.21

-0.38

-0.12

-0.56

-0.29

0.71

0.12

Number of r’s

Observed SD

True Validity SD1True Validity2

True Validity SD2% Variance
Accounted For —Sampling Error% Variance
Accounted For1
% Variance
Accounted For2
90% CV1
90% CV

2

0.09
0.05
0.29
0.07

0.07
4.00
0.15
0.06

Shrinkage

Patient
SafetyIncidents

Quality(Defects)

Wellbeing

Organizational
Citizenship

24,099

4,514

1,464

4,150

2,651

1,693

37

11

10

20

12

2

Turnover

SafetyIncidents

Absenteeism

53,22862,815

10,891

162

128

59

0.08
0.05
0.06

0.06
0.05
0.07

0.09
0.03
0.05

0.13
0.10
0.13

0.06
0.03
0.05

0.15
0.08
0.09

0.11
0.07
0.09

0.04
0
0

0.01
0
0

50

58

46

49

73

8

60

23

40

114

708

78

73

72

73

90

37

74

66

63

729

995

78

73

72

73

90

37

74

66

64

810

995

0.13

0.05

0.09

-0.06

-0.11

-0.14

-0.05

-0.32

-0.12

0.57

0.08

0.19

0.08

0.13

-0.09

-0.16

-0.21

-0.06

-0.44

-0.18

0.71

0.12

r = correlation
SD = standard deviation
CV = credibility value
1
Includes correction for range variation within organizations and dependent-variable measurement error
2Includes correction for range restriction across population of business/work units and dependent-variable measurement error

In the case of profitability, it is likely influenced indirectly by employee engagement and more directly by variables
such as customer loyalty/engagement, productivity, turnover, safety, absenteeism, shrinkage, patient safety
and quality. Remember, the productivity variable includes various measures of business/work unit productivity,
the majority of which are sales data. Of the two financial variables included in the meta-analysis (sales andprofit), engagement is more highly correlated with sales. This is probably because day-to-day employee
engagement has an impact on customer perceptions, turnover, quality and other variables that relate to sales.
In fact, this is what we have found empirically in our causal analyses (Harter et al., 2010). In the case of shrinkage,correlations may be somewhat lower because many factors influence merchandise shrinkage, including theft,attentiveness to inventory and damaged merchandise. The next section will explore the practical utility of theobserved relationships.
As in Harter et al. (2002), we calculated the correlation of employee engagement to composite performance.
As defined earlier, Table 4 provides the correlations and d-values for four analyses: the observed correlations;correction for dependent-variable measurement error; correction for dependent-variable measurement error andrange restriction across companies; and correction for dependent-variable measurement error, range restriction
and independent-variable measurement error (true score correlation).
As with previous meta-analyses, the effect sizes presented in Table 4 indicate substantial relationships between
engagement and composite performance.

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Business/work units in the top half on engagement within companies have 0.65 standard
deviation units’ higher composite performance compared with those in the bottom halfon engagement.
Across companies, business/work units in the top half on engagement have 0.90 standard
deviation units’ higher composite performance compared with those in the bottom halfon engagement.
After correcting for all available study artifacts (examining the true score relationship),business/work units in the top half on employee engagement have 1.12 standard
deviation units’ higher composite performance compared with those in the bottom
half on engagement. This is the true score effect expected over time across all
business/work units.
Table 4: Correlation of Employee Engagement to Composite Business/Work
Unit Performance — All Outcomes
Analysis

Correlation of Engagement to Performance

Observed r

0.30

r corrected for dependent-variablemeasurement error

0.31

d

0.63

d

0.65

r corrected for dependent-variablemeasurement error and range restriction
across companies

0.41

d

0.90

ρ corrected for dependent-variablemeasurement error, range restriction andindependent-variable measurement error

0.49

δ

1.12

r = correlationd = difference in standard deviation unitsρ = true score correlation
δ = true score difference in standard deviation units

As alluded to, some outcomes are the direct consequence of employee engagement(customer loyalty/engagement, turnover, safety, absenteeism, shrinkage and quality[defects]), and other outcomes are more of a downstream result of intermediary outcomes(sales and profit). For this reason, we have also calculated the composite correlation
to short-term outcomes. Table 5 again indicates a substantial relationship between
engagement and composite performance. Observed correlations and d-values are of thesame magnitude as those reported in Table 4.

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Table 5: Correlation of Employee Engagement to Composite Business/Work Unit Performance— Direct Outcomes (Customer Loyalty/Engagement, Turnover, Safety, Absenteeism, Shrinkage,
Quality [Defects])
Analysis

Correlation of Engagement to Performance

Observed r

0.29

r corrected for dependent-variablemeasurement error

0.31

d

0.61

d

0.65

r corrected for dependent-variablemeasurement error and range restriction
across companies

0.41

d

0.90

ρ corrected for dependent-variablemeasurement error, range restriction andindependent-variable measurement error

0.49

δ

1.12

r = correlationd = difference in standard deviation unitsρ = true score correlation
δ = true score difference in standard deviation units

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Utility Analysis: Practicality of the Effects
In the past, studies of job satisfaction’s relationship to performance have had limited
analysis of the utility of the reported relationships. Correlations have often been
discounted as trivial without an effort to understand the potential utility, in practice, of therelationships. The Q12 includes items that Gallup researchers have found to be changeable
by the local manager and others within the business/work unit. As such, understanding thepractical utility of potential changes is crucial.
The research literature includes a great deal of evidence that numerically small ormoderate effects often translate into large practical effects (Abelson, 1985; Carver, 1975;
Lipsey, 1990; Rosenthal & Rubin, 1982; Sechrest & Yeaton, 1982). As shown in Table 6, thisis, in fact, the case here. Effect sizes referenced in this study are consistent with or above
other practical effect sizes referenced in other reviews (Lipsey & Wilson, 1993).
A more intuitive method of displaying the practical value of an effect is that of binomial
effect size displays, or BESDs (Rosenthal & Rubin, 1982; Grissom, 1994). BESDs typically
depict the success rate of a treatment versus a control group as a percentage above themedian on the outcome variable of interest.
BESDs can be applied to the results of this study. Table 6 shows the percentage ofbusiness/work units above the median on composite performance for high- and lowscoring business/work units on the employee engagement (Q12) composite measure. True
validity estimates (correcting for measurement error only in the dependent variable) wereused for analysis of business/work units both within and across organizations.
One can see from Table 6 that there are meaningful differences between the top andbottom halves. The top half is defined as the average of business/work units scoring in
the higher 50% on the Q12, and business/work units scoring in the lower 50% constitutethe bottom half. It is clear from Table 6 that management would learn a great deal more
about success if it studied what was going on in top-half business/work units rather thanbottom-half units.
With regard to composite business/work unit performance, business/work units in the top
half on employee engagement have a 94% higher success rate in their own organization
and a 145% higher success rate across business/work units in all companies studied. In
other words, business/work units with high employee engagement nearly double theirodds of above-average composite performance in their own organizations and increase
their odds for above-average success across business/work units in all organizations by
2.45 times.

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Table 6: BESDs for Employee Engagement and OutcomesBusiness/Work Units
Within Company

Employee Engagement% Above Median Composite
Performance (Total)

Top Half

Bottom Half
Top Half

Business/Work Units
Across Companies% Above Median Composite
Performance (Total)

66

71

34

29

% Above Median Composite
Performance (Direct Outcomes)% Above Median Composite
Performance (Direct Outcomes)

34

29

Bottom Half

66

71

To illustrate this further, Table 7 shows the probability of above-average performancefor various levels of employee engagement. Business/work units at the highest level of
employee engagement across all business/work units in Gallup’s database have an 83%
chance of having high (above average) composite performance. This compares with a
17% chance for those with the lowest level of employee engagement. So, it is possible
to achieve high performance without high employee engagement, but the odds aresubstantially lower (in fact, nearly five times as low).
Table 7: Percentage of Business/Work Units Above the Company Median on Composite
Performance (Customer Loyalty/Engagement, Profitability, Productivity, Turnover, Safety,
Absenteeism, Shrinkage, Quality [Defects]) for Different Employee Engagement Percentiles
Employee Engagement Percentile

Percentage Above Company MedianAbove 99th

83

90th

70

95

75

th

80th

63

70

58

th

60

54

th

50

50

th

40

46

th

30

42

th

20th

37

10th
5

30
25

th

Below 117

st

Other forms of expressing the practical meaning behind the effects from this study
include utility analysis methods (Schmidt & Rauschenberger, 1986). Formulas havebeen derived for estimating the dollar-value increases in output as a result of improved
employee selection. These formulas take into account the size of the effect (correlation),
the variability in the outcome being studied and the difference in the independent variable(engagement in this case) and can be used in estimating the difference in performance30

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outcomes at different levels in the distribution of Q12 scores. Previous studies (Harter et al.,
2002; Harter & Schmidt, 2000) provided utility analysis examples, comparing differencesin outcomes between the top and bottom quartiles on the Q12. For companies includedin the 2002 meta-analysis, it was typical to see differences between top and bottom
engagement quartiles of 2 to 4 percentage points on customer loyalty/engagement,
1 to 4 points on profitability, hundreds of thousands of dollars on productivity figures permonth, 4 to 19 points in turnover for low-turnover organizations and 14 to 51 points forhigh-turnover organizations.
Gallup researchers recently conducted utility analysis across multiple organizations withsimilar outcome metric types (an update of analyses presented in Harter et al., 2002, p.
275, Table 6). Comparing top-quartile with bottom-quartile engagement, business/workunits resulted in median percent differences of:• 10% in customer loyalty/engagement• 23% in profitability• 18% in productivity (sales)• 14% in productivity (production records and evaluations)• 18% in turnover for high-turnover organizations(those with more than 40% annualized turnover)• 43% in turnover for low-turnover organizations(those with 40% or lower annualized turnover)• 64% in safety incidents (accidents)• 81% in absenteeism• 28% in shrinkage (theft)• 58% in patient safety incidents (mortality and falls)• 41% in quality (defects)• 66% in wellbeing (thriving employees)• 13% in organizational citizenship (participation)
The above differences and their utility in dollar terms should be calculated for eachorganization, given the organization’s unique metrics, situation and distribution ofoutcomes across business/work units. The median estimates represent the midpoint in the
distribution of utility analyses conducted across 426 studies based on organizational datawith similar outcome types.
One can see that the above relationships are nontrivial if the business has manybusiness/work units. The point of the utility analysis, consistent with the literature thathas taken a serious look at utility, is that the relationship between employee engagement
and organizational outcomes, even conservatively expressed, is meaningful from apractical perspective.

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Discussion
Findings reported in this updated meta-analysis continue to provide large-scalecross-validation to prior meta-analyses conducted on the Q12 instrument. The present
study expands the size of the meta-analytic database by 30,064 business/workunits (an increase of 37%), as well as the number of countries and business/work units
studied. The relationship between engagement and performance at the business/work unit level continues to be substantial and highly generalizable across companies.
Differences in correlations across companies can be attributed mostly to study artifacts.
For outcomes with sample sizes of 10,000 or more business/work units in 2016 (customer
loyalty/engagement, profitability, productivity, turnover and absenteeism), the results ofthis updated meta-analysis are almost completely replicated. For the first four outcomes,
differences in effect sizes from 2016 to 2020 ranged from 0.00 to 0.02, and evidenceof generalizability remained substantial. For absenteeism, the effect size increased by
0.16, likely the result of one large study with a substantially higher effect size than the
combination of other studies in the meta-analysis. But the direction of relationshipbetween engagement and absenteeism was highly generalizable (90% credibility
value of -0.21).
The size of this database gives us confidence in the direction of the true relationshipbetween employee engagement and business outcomes and confidence in the size ofthe relationship, which can be helpful in calculating potential return on investment fromperformance management initiatives. The consistent findings across many iterationsof meta-analysis also speak to the importance and relevance of workplace perceptions
for businesses across different economic times and even amid massive changes
in technology since 1997 when this study series began. As noted earlier, a recent
meta-analysis found somewhat higher correlations of engagement and business results
during past economic recessions (Harter et al., 2020).
The findings from this updated meta-analysis are important because they
continue to reinforce that generalizable tools can be developed and used acrossdifferent organizations with a high level of confidence that they elicit important
performance-related information.

The data from the present study provide further
substantiation to the theory that doing what isbest for employees does not have to contradict
what is best for the business or organization.
This concept is further reinforced in the present study with the newly reported strong
relationship between employee engagement and wellbeing.

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The strong association between engagement and wellbeing is supported by prior research.
In worldwide samples, we have found consistent associations between engagement atwork and life satisfaction, daily experiences and health (Gallup, 2010). A longitudinal study
found that changes in engagement predicted changes in cholesterol and triglycerides(via blood samples) after controlling for demographics, health history and medicationuse (Harter, Canedy, & Stone, 2008). Even more recently, we have observed differences
in momentary affect and cortisol when comparing engaged and disengaged employees(Harter & Stone, 2011). Consistent with the present study’s finding of association between
engagement and organizational citizenship, a previous study found that engagement
at work predicts likelihood of involvement in organization-sponsored health programs(Agrawal & Harter, 2009). A previous meta-analysis found strong associations between
job attitudes and citizenship behaviors (Whitman et al., 2010). Engagement has also beenshown to be integral to perceptions of inclusiveness across diverse groups (Jones &Harter, 2004; Badal & Harter, 2014). All together, these studies suggest that the boundaries
for the effect of an engaging workplace are quite wide.
It is also worth noting that, as Gallup consultants have educated managers and partneredwith companies on change initiatives, organizations have experienced, on average,
one-half standard deviation growth on employee engagement between the first andsecond year and often a full standard deviation growth and more after three or more years.
An important element in the utility of any applied instrument and improvement process isthe extent to which the variable under study can be changed. Our current evidence is that
employee engagement is changeable and varies widely by business/work unit.
As we demonstrated in the utility analyses presented here and in other iterations of thisanalysis, the size of the effects observed has important practical implications, particularly
given that engagement, as measured here, is quite changeable.

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The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Appendix A: Reliabilities of Business/Work Unit Outcomes
Based on Schmidt & Hunter, 1996, scenario 23, page 219
Customer
Loyalty/
Engagement

Profitability

Productivity

TurnoverReliability Frequency

Reliability Frequency

Reliability Frequency

Reliability Frequency

Reliability

Frequency

Reliability

Frequency

0.89

1

1.00

3

1.00

4

1.00

1

0.84

1

0.94

1

0.86

1

0.94

1

0.92

2

0.62

1

0.66

1

0.87
0.84
0.75
0.58
0.53
0.52
0.51
0.46
0.41
0.33

1
1
1
1
2
1
1
1
1

0.99
0.93
0.91
0.90
0.89
0.79
0.57
0.56

2
1
1
1
2
1
1

0.99
0.90
0.62
0.57
0.34

2
1
1
1
1

0.63
0.60
0.39
0.27
0.25
0.24

1
1
1
1
1
1

1

1

39

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Safety0.82
0.63

Quality (Defects)

11The Relationship Between Engagement at Work and Organizational Outcomes | 2020 Q12® Meta-Analysis: 10th Edition

Appendix B: Test-Retest Reliabilities of Employee Engagement
Based on Schmidt & Hunter, 1996, scenario 23, page 219
Engagement
Reliability

Frequency

0.97

1

0.86

1

0.92
0.84
0.83
0.82
0.81
0.80
0.79
0.78
0.77
0.76
0.75
0.74
0.71
0.70
0.69
0.66
0.65
0.63
0.61
0.60
0.55
0.47
0.45
0.35
0.27

1
1
1
3
1
3
2
1
1
1
4
1
1
1
1
2
2
1
2
1
1
2
1
1
1

40

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