Q12 Meta-Analysis:
The Relationship Between Engagement at
Work and Organizational Outcomes®

James K. Harter, Ph.D., GallupFrank L. Schmidt, Ph.D., University of Iowa
Emily A. Killham, M.A., Gallup
Sangeeta Agrawal, M.S., Gallup
August 2009

Copyright Standards
This document contains proprietary research, copyrighted materials, and literary property of Gallup, Inc. It is for the guidanceof your company only and is not to be copied, quoted, published, or divulged to others outside of your organization. Gallup®,
Q12®, Selection Research, Inc.®, and SRI® are trademarks of Gallup, Inc. All other trademarks are the property of theirrespective owners.
This document is of great value to both your organization and Gallup, Inc. Accordingly, international and domestic laws
and penalties guaranteeing patent, copyright, trademark, and trade secret protection protect the ideas, concepts, andrecommendations related within this document.
No changes may be made to this document without the express written permission of Gallup, Inc.
Acknowledgments
The authors thank Jim Asplund, Nikki Blacksmith, James Court-Smith, Kirti Kanitkar, Mike Lemberger, Eric Olesen, andStephanie Plowman for contributing important research studies and database information to this meta-analysis update.

Contents
Executive Summary...........................................................................................................................................3
Objective.................................................................................................................................................3
Methods...................................................................................................................................................3
Results.....................................................................................................................................................3
Conclusion..............................................................................................................................................3
Introduction.........................................................................................................................................................4
Foreword.................................................................................................................................................4
Development of the Q12......................................................................................................................5
Introduction to the Study...................................................................................................................7
Description of the Q12..........................................................................................................................8
Meta-Analysis, Hypothesis, Methods, and Results................................................................................12
Meta-Analysis......................................................................................................................................12
Hypothesis and Study Characteristics.........................................................................................12
Meta-Analytic Methods Used..........................................................................................................17
Results...................................................................................................................................................20
Utility Analysis: Practicality of the Effects...............................................................................................25
Utility Analysis.....................................................................................................................................25
Discussion..........................................................................................................................................................28
References.........................................................................................................................................................29
Appendix A.........................................................................................................................................................32
Appendix B.........................................................................................................................................................33

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

1

Executive Summary
Objective
Business and work units within the same organization vary
substantially in their levels of engagement and performance.
The purpose of this study was to examine:
1. The true relationship between employee engagement
and performance in 152 organizations.
2. The consistency or generalizability of the relationshipbetween employee engagement and performanceacross organizations.
3. The practical meaning of the findings for executives
and managers.
Methods
We accumulated 199 research studies across 152
organizations in 44 industries and 26 countries. Withineach study, we statistically calculated the business/workunit level relationship between employee engagement andperformance outcomes that the organizations supplied. In
total, we were able to study 32,394 business/work units
including 955,905 employees. Nine outcomes were studied:customer loyalty/engagement, profitability, productivity,
turnover, safety incidents, shrinkage, absenteeism, patient
safety incidents, and quality (defects).
Individual studies often contain small sample sizes andidiosyncrasies that distort the interpretation 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, andother study artifacts to understand the true relationship withgreater precision. Hunter-Schmidt meta-analysis methods
were applied to 199 research studies to estimate the true
relationship between engagement and each performancemeasure and to test for generalizability. After conducting

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

meta-analysis, we examined the practical meaning of therelationships by conducting utility analysis.
Results
Employee engagement is related to each of the nine
performance outcomes studied. Results indicate highgeneralizability, which means the correlations wereconsistent across different organizations. The true score
correlation between employee engagement and composite
performance is .48. Business/work units scoring in the
top half on employee engagement essentially double
their odds of success in comparison to those in thebottom half. Those at the 99th percentile have nearlyfive times the success rate as those at the 1st percentile.
Median differences between top-quartile and bottomquartile units were: 12% in customer ratings, 16% in
profitability, 18% in productivity, 25% in turnover (highturnover organizations), 49% in turnover (low-turnoverorganizations), 49% in safety incidents, 27% in shrinkage,
37% in absenteeism, 41% in patient safety incidents, and
60% in quality (defects).
Conclusion
The relationship between engagement and performanceat the business/work unit level is substantial and highly
generalizable across organizations. Employee engagement
is related to each of nine different performance outcomes.
This means that practitioners can apply the Q12 measure
in a variety of situations with confidence that the measurecaptures important performance-related information.

3

Introduction
Foreword
In the 1930s, George Gallup began a worldwide studyof human needs and satisfactions. He pioneered thedevelopment of scientific sampling processes to measure
popular opinion. In addition to his polling work, Dr. Gallup
completed landmark research on well-being, studying
the factors common among people who lived to be 95 orolder (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 less than half of those employedin North America were highly satisfied with their work
(Gallup, 1976). Work satisfaction was even lower in Western
Europe, Latin America, Africa, and the Far East.
Satisfaction at work has become a widespread focus forresearchers. In addition to Dr. Gallup’s early work, the topic
of job satisfaction has been studied and written about inmore than 10,000 articles and publications. Because mostpeople spend a high percentage of their waking hours atwork, studies of the workplace are of great interest forpsychologists, sociologists, economists, anthropologists, andphysiologists. The process of managing and improving theworkplace is crucial and presents great challenges to nearly
every organization. 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. Afterall, organizational leaders are in the best position to create
interest in and momentum for job satisfaction research.
Parallel to Dr. Gallup’s early polling work, Donald O.
Clifton, a psychologist and professor at the University of
Nebraska, began studying the causes of success in education4and business. Dr. Clifton founded Selection Research,
Incorporated (SRI) in 1969. While most psychologists
were busy studying dysfunction and the cause of disease,
Dr. Clifton and his colleagues focused their careers on thescience of strengths-based psychology, the study of whatmakes 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, researchon successful learning and workplace environments led tonumerous studies of successful teachers and managers. This
work included extensive research on individual differences
and the environments that best facilitate success. Earlyin their studies, the researchers discovered that simply
measuring the satisfaction of employees was insufficient tocreate sustainable change. Satisfaction needed to be specified
in terms of its most important elements, and it needed to bemeasured and reported in a way that could be used by thepeople who could take action and create change.
Further research revealed that change happens mostefficiently at a local level — at the level of the frontline,
manager-led team. For an executive, the frontline teamis his or her direct reports, and for a plant manager, thefrontline team is the people he or she manages each day.
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

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

research, some questions elicit responses that predict future
performance, while others do not.
Developing the right questions is an iterative process inwhich scientists write questions and analysis is conducted.
The research and questions are refined and rephrased.
Additional analysis is conducted. The questions are refinedand rephrased again. And the process is repeated. Gallup has
followed the iterative process in devising the survey tool thatis the subject of this report, Gallup’s Q12 instrument, which
is designed to measure employee engagement conditions.
The next sections will provide 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 metaanalysis of 199 research studies exploring the relationshipbetween employee engagement and performance across 152
organizations and 32,394 business or work units containing
955,905 employees.
Development of the Q12
Beginning in the 1950s, Dr. Clifton began studying workand learning environments to determine the factors that
contribute positively to those environments and that enablepeople to capitalize on their unique talents. It was throughthis early work that Dr. Clifton began using science and thestudy of strengths to research individuals’ frames of reference
and attitudes.
From the 1950s to the 1970s, Dr. Clifton continued his
research of students, counselors, managers, teachers, andemployees. He used various rating scales and interview
techniques to study individual differences, analyzingquestions and factors that explain differences 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

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

intent of asking questions, collecting data, and encouraging
ongoing discussion of the results to provide feedback andpotential improvement — a measurement-based feedback
process. To learn causes of employee turnover, exit interviews
were conducted with employees who left organizations. A
common reason centered 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 andworkplace attitudes. As a starting point for questionnaire
design, numerous qualitative analyses were conducted,
including interviews and focus groups. Gallup researchers
asked top-performing individuals or teams to describe
their work environments and their thoughts, feelings, andbehaviors related to success.
The researchers used qualitative data to generate hypotheses
and insights into the distinguishing factors leading tosuccess. From these hypotheses, they wrote and tested
questions. They also conducted numerous quantitative
studies throughout the 1980s, including exit interviews, tocontinue to learn causes of employee turnover. Qualitative
analyses such as focus groups and interviews formed thebasis 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 the survey data;
regression analyses to identify uniqueness and redundancies
in the data; and criterion-related validity analyses to identify
questions that correlate with meaningful outcomes suchas overall satisfaction, commitment, and productivity. The
scientists developed feedback protocols to facilitate thefeedback of survey results to managers and employees. Suchprotocols, and their use in practice, helped researchers learn
which items were most useful in creating dialogue andstimulating change.

5One outgrowth of a management research practice that was
focused on both talent and environment was the theory oftalent maximization within an organization:

Per-person productivity =Talent x (Relationship +Right Expectation + Recognition/Reward)
These concepts would later become imbedded in thefoundational elements of Q12.
Over time, SRI and Gallup researchers conductednumerous studies of manager success patterns that focusedboth on the talents of the manager and the environmentsthat best facilitated success. By integrating knowledge ofmanagerial talent with survey data on employee attitudes,
scientists had a unique perspective on what it takes tobuild a successful workplace environment. Themes suchas “individualized perception,” “performance orientation,”“mission,” “recognition,” “learning and growing,”“expectations,” and “the right fit” continued to emerge. In
addition to studies of management, researchers conductednumerous studies with successful teachers, students, andlearning environments.
In the 1990s, the iterative process continued. During thistime, Gallup researchers developed the first version of theQ12 (“The Gallup Workplace Audit” or GWA), in an effort toefficiently capture the most important workplace attitudes.
Both qualitative and quantitative analyses continued. Morethan 1,000 focus groups were conducted in that decade, andhundreds of instruments were developed, many of them withseveral additional items. Scientists also continued to use exit
interviews; these revealed the importance of the manager
in retaining employees. Studies of Q12 and other survey
items were conducted in various countries throughout theworld, including the United States, Canada, Mexico, GreatBritain, Japan, and Germany. Gallup researchers obtained

6

international cross-cultural feedback on Gallup’s core
items, which provided context on the applicability of theitems across different cultures. Various scale types were also
tested, including variations of 5-point and dichotomous
response options.
Quantitative analyses of survey data included descriptive
statistics, factor analyses, discriminant analyses, criterionrelated validity analyses, reliability analyses, regression
analyses, and other correlational analyses. Gallup scientists
continued to study the core concepts that differentiatedsuccessful from less successful work units and the expressions
that best captured those concepts. In 1997, the criterionrelated studies were combined into a meta-analysis to study
the relationship of employee satisfaction and engagement(as measured by Q12) to business or work unit profitability,
productivity, employee retention, and customer satisfaction/
loyalty across 1,135 business/work units (Harter & Creglow,1997). Meta-analysis also enabled researchers to study thegeneralizability of the relationship between engagement
and outcomes. Results of this confirmatory analysis revealed
substantial criterion-related validity for each of the Q12
items.
As criterion-related validity studies are ongoing, the metaanalysis 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/workunits; 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/workunits; and in 2006 (Harter, Schmidt, Killham, & Asplund,2006), when it included 23,910 business/work units. This
report provides the seventh published iteration of Gallup’s
Q12 meta-analysis focusing on the relationship between
employee engagement and performance.
As with the 2006 report, this report expands the number ofbusiness or work units and outcomes studied, in addition toincreasing the number of industries and countries studied.
The previous meta-analysis examined the relationship

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

between employee engagement and customer loyalty,
profitability, productivity, employee turnover, accidents,
absenteeism, and merchandise shrinkage (theft and lost
merchandise). We now include patient safety incidents(particularly important to the healthcare industry, composedof mortality and patient accidents such as falls) and quality(primarily measured through recorded product defects).
Since its final wording and order were completed in 1998, Q12
has been administered to more than 15 million employees
in 169 different countries and 65 languages.

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 rightjobs. Numerous studies have documented the utility of valid
selection instruments and systems in the selection of theright people (Schmidt, Hunter, McKenzie, & Muldrow,
1979; Hunter & Schmidt, 1983; Huselid, 1995; Schmidt &Rader, 1999; Harter, Hayes, & Schmidt, 2004).
After employees have been hired, they make decisions andtake actions every day that can affect the success of theirorganizations. Many of these decisions and actions are
influenced by their own internal motivations and drives.
One can also hypothesize that the way employees aretreated and the way they treat one another can positively
affect their actions — or can place their organizations at risk.
For example, researchers have found positive relationships
between general workplace attitudes and service intentions,customer perceptions (Schmit & Allscheid, 1995), andindividual performance outcomes (Iaffaldano & Muchinsky,1985). An updated meta-analysis has revealed a substantial
relationship between individual job satisfaction andindividual performance ( Judge, Thoresen, Bono, & Patton,2001). To date, the vast majority of job satisfaction research

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

and subsequent meta-analyses have collected and studied
data at the individual employee level.
There is also evidence at the workgroup or business unit
level that employee attitudes relate to various organizational
outcomes. Organizational-level research has focused
primarily on cross-sectional studies. Independent studies
found relationships between employee attitudes andperformance 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)and sales growth. Gallup has conducted large-scale metaanalyses, most recently studying 23,910 business and work
units regarding the concurrent and predictive relationshipof employee attitudes (satisfaction and engagement) withsafety, customer attitudes, financials, employee retention,
absenteeism, and merchandise shrinkage (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 metaanalysis, repeated across time, has found consistently that
there are positive concurrent and predictive relationships
between employee attitudes and various important businessoutcomes. It has also found that these relationships generalize
across a wide range of situations (industries, business/workunit types, and countries).
Even though it has been much more common to study
employee opinion data at the individual level, studying dataat the business unit or workgroup level is critical because thatis where the data are typically reported (due to anonymity
concerns, employee surveys are reported at a broader business
unit or workgroup level). In addition, business-unit-level7research usually provides opportunities to establish links tooutcomes that are directly relevant to most businesses —outcomes like customer loyalty, profitability, productivity,
employee turnover, safety incidents, merchandise shrinkage,and quality variables that are often aggregated and reported
at the business unit level.
Another advantage to reporting and studying data at thebusiness unit or workgroup level is that instrument item
scores are of similar reliability to dimension scores forindividual-level analysis. This is because at the business
unit or workgroup level, each item score is an average ofmany individuals’ scores. This means that employee surveys
reported at a business unit or workgroup level can be more
efficient or parsimonious in length, i.e., because item-levelmeasurement error is less of a concern. See Harter andSchmidt (2006) for a more complete discussion of job
satisfaction research and the advantages of conducting unitlevel analyses.
One potential problem with such business-unit-level studies
is limited data due to a limited number of business units(the number of business units becomes the sample size) or
limited access to outcome measures that one can compare
across business units. For this reason, many of these studies
are limited in statistical power, and as such, results fromindividual studies may appear to conflict with one another.
Meta-analysis techniques provide the opportunity to poolsuch studies together to obtain more precise estimates of thestrength of effects and their generalizability.
This paper’s purpose is to present the results of an updated
meta-analysis of the relationship between employee
workplace perceptions and business unit outcomes, based 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 importance atthe business unit or workgroup level — measure employee
perceptions of the quality of people-related management
practices in their business units.

8

Description of the Q12
In short, the development of the GWA (Q12) was basedon more than 30 years of accumulated quantitative andqualitative research. Its reliability, convergent validity, andcriterion-related validity have been extensively studied. It is
an instrument validated through prior psychometric 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, from an actionability standpoint, there
are two broad categories of employee survey items: thosethat measure attitudinal outcomes (satisfaction, loyalty,
pride, customer service perceptions, and intent to stay withthe company) and those that measure actionable issues thatdrive the above outcomes. The Q12 measures the actionableissues for management — those predictive of attitudinaloutcomes such as satisfaction, loyalty, pride, and so on.
On Gallup’s standard Q12 instrument, following an overallsatisfaction item, are 12 items measuring issues we havefound to be actionable (changeable) at the supervisor or
manager level — items measuring perception of elements of
the work situation, such as role clarity, resources, fit between
abilities and requirements, receiving feedback, and feelingappreciated. The Q12 measures “engagement conditions,”
each of which is a causal contributor to engagement throughthe measure of its causes.
The Q12 statements are:
Q00. (Overall Satisfaction) On a five-point scale, where“5” is extremely satisfied and “1” is 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 mywork right.

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

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 careabout me as a person.
Q06. There is someone at work who encourages mydevelopment.
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 todoing quality work.
Q10. I have a best friend at work.
Q11. In the last six months, someone at work has talked tome about my progress.
Q12. This last year, I have had opportunities at work tolearn and grow.
These statements (Q01-Q12) are proprietary andcopyrighted by Gallup, Inc. They cannot be reprinted or
reproduced in any manner without the written consent
of Gallup, Inc. Copyright © 1993-1998, Gallup, Inc.
All rights reserved.
The current standard is to ask each employee to rate theabove statements (a census survey; median participation rate
is 86%) using six response options (from 5=strongly agree to
1=strongly disagree; the sixth response option — don’t know/
does not apply — is unscored). Because it is a satisfaction
item, the first item (Q00) is scored on a satisfaction scalerather than on an agreement scale. Regression analyses(Harter, Schmidt, & Hayes, 2002) indicate that employee
engagement accounts for nearly all of the performancerelated variance (composite performance) accounted for

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

by the overall satisfaction measure. Therefore, the focus of
this report is on employee engagement (as measured bystatements Q01-Q12).
While these items measure issues that the manager orsupervisor can influence, only one item contains the word
“supervisor.” This is because it is realistic to assume thatnumerous people in the workplace can influence whether
someone’s expectations are clear, whether he or she feels
cared about, and so on. The manager’s or supervisor’s
position, though, allows him or her to take the lead inestablishing a culture that values behaviors that support
these perceptions. The following is a brief discussion of theconceptual 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 measure of
how people feel about their organization. Given 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 those measured in the following
12 items, explain why people are satisfied and why
they become engaged and affect outcomes.
Q01. Expectations. Defining and clarifying the outcomesthat are to be achieved is perhaps the most basicof all employee needs and manager responsibilities.
How these outcomes are defined and acted on willvary across business/work units, depending on thegoals of the business/work unit.
Q02. Materials and equipment. Getting people what theyneed to do their work is important in maximizing
efficiency, in demonstrating to employees that theirwork is valued, and in showing that the company issupporting them in what they are asked to do. Great
managers help employees see how their requests
for materials and equipment connect to importantorganizational outcomes.

9Q03. Opportunity to do what I do best. Helping people
get into roles in which they can most fully use
their inherent talents is the ongoing work of greatmanagers. Learning about individual differences
through experience and assessment can help the
manager position people efficiently within and acrossroles and remove barriers to high performance.
Q04. Recognition for good work. Employees need constantfeedback to know if what they are doing matters. An
ongoing management challenge is to understandhow each person prefers to be recognized, to makerecognition objective and real by basing it on
performance, and to do it 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 respondto their unique needs. In addition, they find theconnection between the needs of the individual andthe needs of the organization.
Q06. Encourages my development. How employees arecoached can influence how they perceive their future.
If the manager is helping the employee improve as
an individual by providing opportunities that are insync with the employee’s talents, both the employee
and the company will profit.
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 to many factors that affect the overall systemthan the manager is, 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 theoutcomes.
Q08. Mission/Purpose. Great managers often help people
see not only the purpose of their work, but also
how each person’s work influences and relates to10

the purpose of the organization and its outcomes.
Reminding employees of the big-picture impact ofwhat they do each day is important, whether it is
how their work influences the customer, safety, or
the public.
Q09. Associates committed to quality. Managers caninfluence the extent to which employees respect
one another by selecting conscientious employees,
providing some common goals and metrics for
quality, and increasing associates’ frequency ofopportunity for interaction.
Q10. Best friend. Managers vary in the extent to which
they create opportunities for people at work to
get to know one another and in how much theyvalue close, trusting relationships at work. The best
managers do not subscribe to the idea that there
should be no close friendships at work; instead,they free people to get to know one another, which
is a basic human need. This, then, can influencecommunication, trust, and other outcomes.
Q11. Progress. Providing a structured time to discusseach employee’s progress, achievements, and goals
is important for managers and employees. Great
managers regularly meet with individuals, both tolearn from them and to give them guidance. This
give and take helps managers and employees makebetter decisions.
Q12. Learn and grow. In addition to having a need to berecognized for doing good work, most employees
need to know that they are improving and haveopportunities to build their knowledge and skills.
Great managers choose training that will benefit
the individual and the organization.
More detailed discussion of each of the Q12 items is provided
in Wagner and Harter (2006).

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

As a total instrument (sum or mean of items Q01-Q12),
the Q12 has a Cronbach’s alpha of .91 at the business unit
level. The meta-analytic convergent validity of the equallyweighted 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 .91. This provides evidence
that the Q12, as a composite measure, captures the generalfactor in longer employee surveys. Individual items correlate
to their broader dimension true-score values, on average,
at approximately .70. While Q12 is a measure of actionableengagement conditions, its composite has high convergent
validity with affective satisfaction and other direct measuresof work engagement (see Harter & Schmidt, 2008, forfurther discussion of convergent and discriminant validity
issues and the construct of “engagement”).

the Middle East (United Arab Emirates), and North
America (Canada and the United States).
This meta-analysis also includes updated estimates ofreliabilities across business units and includes all available
Gallup studies (whether published or unpublished) and has
no risk of publication bias.

As previously mentioned, this is the seventh publishediteration of Q12 business-unit-level meta-analysis.
Compared to the previous meta-analysis, the current metaanalysis includes a larger number of studies, business units,
industries, and countries and two additional outcomes(patient safety and quality). This meta-analysis includes
more than four times more business units with merchandise
shrinkage as a business outcome and more than double thenumber of business units with absenteeism data. It also
includes 32% more business units with customer measures,
30% more with turnover, 27% more with safety incident
data, 23% more with profitability data, and 18% more withproductivity data.
The meta-analysis also now includes data from 26 countries,
including 23 studies exclusively outside the United States.
The coverage of research studies now includes business unitsin Asia (Hong Kong, Japan, Korea, Thailand), Australia, New
Zealand, Europe (Netherlands, Germany, United Kingdom,
Ireland, France, Austria, Italy, Spain, Belgium, and Greece),
former communist countries (Russia, Hungary, Lithuania,
Czech Republic, Poland), Latin America (Brazil, Mexico),

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

11

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 for measurement
and sampling errors and other idiosyncrasies that distort theresults of individual studies. A meta-analysis eliminates biases
and provides an estimate of true validity or true relationshipbetween two or more variables. Statistics typically calculated
during meta-analyses also allow the researcher to explore
the presence, or lack, of moderators of relationships.
More than 1,000 meta-analyses have been conducted in
the psychological, educational, behavioral, medical, andpersonnel selection fields. The research literature in the
behavioral and social sciences fields includes a multitude ofindividual studies with apparently conflicting conclusions.
Meta-analysis, however, allows the researcher to estimate themean relationship between variables and make corrections
for artifactual sources of variation in findings across studies.
It provides a method by which researchers can determine
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 both background information and detailed
descriptions of the more recent meta-analytic methods:
Schmidt (1992); Hunter and Schmidt (1990, 2004); Lipsey
and Wilson (1993); Bangert-Drowns (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 the business unit
outcomes of customer loyalty, productivity, and profitability, andnegative correlations with employee turnover, employee safetyincidents (accidents), absenteeism, shrinkage (theft), patient
safety incidents (mortality and falls), and quality (defects).
Hypothesis 2: The correlations between engagement andbusiness unit outcomes will generalize across organizations
for all business unit outcomes. That is, these correlations will
not vary substantially across organizations. And in particular,
there will be few, if any, organizations with zero correlationsor those in the opposite direction from Hypothesis 1.
A total of 199 studies for 152 independent organizations
are included in Gallup’s inferential database — studies
conducted as proprietary research for various organizations.
In each Q12, one or more of the Q12 items was used (as a partof standard policy starting in 1997, all items were included
in all studies), and data were aggregated at the business unit
level and correlated with the following aggregate business
unit performance measures:•• customer metrics (referred to as customer loyalty)•• profitability•• productivity•• turnover•• safety incidents•• absenteeism•• shrinkage•• patient safety incidents•• quality (defects)
That is, in these analyses, the unit of analysis was the businessor work unit, not the individual employee.

12

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

Pearson correlations were calculated, estimating therelationship of business/work unit average measures of
employee engagement (the mean of the Q12 items) to eachof these nine general outcomes. Correlations were calculated
across business/work units within each company, andthese correlation coefficients were entered into a database.
The researchers then calculated mean validities, standard
deviations of validities, and validity generalization statistics
for each of the nine business/work unit outcome measures.
As with previous meta-analyses, some of the studies
were concurrent validity studies, where engagement andperformance were measured within roughly the same time
period or with engagement measurement slightly trailingthe performance measurement (because engagement isrelatively stable and a summation of the recent past, suchstudies are considered “concurrent”). Predictive validity
studies involve measuring engagement at time 1 andperformance at time 2. “Predictive” validity estimates
were obtained for approximately 46% 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, 2005).
Studies for the current meta-analysis were selected so thateach organization was represented once in each analysis. For
several organizations, multiple studies were conducted. 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 the same client(where Q12 and outcome data were collected concurrently,
i.e., in the same year), then the weighted average effect sizes
across the multiple studies were entered as the value for thatorganization. If an organization had both a concurrent anda predictive study (where the Q12 was collected in year 1
and outcomes were tracked in year 2), then the effect sizes
from the predictive study were entered. If an organization
had multiple predictive studies, then the mean of the
Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

correlations in these studies was entered. If sample sizes
varied substantially in repeated studies for an organization,
the study with the largest of the sample sizes was used.

•• For 65 organizations, there were studies thatexamined the relationship between business unit
employee perceptions and customer perceptions.
Customer
perceptions
includedcustomer
metrics, patient metrics, and student ratings ofteachers. These metrics included measures ofloyalty, satisfaction, and engagement. The largest
representation of studies included loyalty metrics(i.e., likelihood to recommend or repeat business),so we refer to customer metrics as customer loyalty
in this study. Instruments varied from study to
study. The general index of customer loyalty wasan average score of the items included in eachmeasure. A growing number of studies include“customer engagement” as the metric 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), and Harter, Asplund,and Fleming (2004).

•• Profitability

studies were available for 71
organizations. Definition of profitability typically
was a percentage profit of revenue (sales). In several
companies, the researchers used — as the best
measure of profit — a difference score from theprior year or a difference from a budgeted amountbecause it represented a more accurate measureof each unit’s relative performance. As such, acontrol for opportunity (location) was used when
profitability figures were deemed less comparablefrom one unit to the next. For example, a difference
variable involved dividing profit by revenue for abusiness unit and then subtracting a budgeted
percentage from this percentage. In every case,profitability variables were measures of margin, and13

productivity variables (which follow) were measuresof amount produced.

•• Productivity

studies were available for 97
organizations. Measures of business unit
productivity consisted of one of the following:
financials (i.e., revenue/sales dollars per person
or patient), quantity produced (production
volume), or student achievement scores (for threeeducation organizations). In a few cases, this wasa dichotomous variable (top-performing business
units = 2; less successful units = 1). The majority ofvariables included as “productivity” were financial
measures of sales or revenue or growth in sales orrevenue. As with profitability, in many cases, it wasnecessary for the researchers to compare financial
results to a performance goal or prior year figure
in order to control for the differential business
opportunity due to the location of business units.

•• Turnover data were available for 65 organizations.
The turnover measure was the annualized percentageof employee turnover for each business unit. In mostcases, voluntary turnover was reported and used in
the analyses.

•• Safety data were available for 35 organizations.
Safety measures included lost workday/time
incident rate, percentage of workdays lost due toincidents or workers’ compensation claims, numberof incidents, or incident rates.

•• Absenteeism

data were included for 10
organizations. Absenteeism measures included theaverage number of days missed per person for eachwork unit divided by the total days available forwork. This included either a measure of sick days orhours or total absenteeism.

•• Eight organizations provided measures of shrinkage.
Shrinkage is defined as the dollar amount of
unaccounted-for lost merchandise, which could

14

be due to employee theft, customer theft, or lost
merchandise. Given the varying size of locations,shrinkage was calculated as a percentage of total
revenue or a difference from an expected target.

•• Five healthcare organizations provided measuresof patient safety. Patient safety incident measuresvaried from patient fall counts (percentages of total
patients), medical error and infection rates, andrisk-adjusted mortality rates.

•• Twelve organizations provided measures of quality.
For most organizations, quality was measured
through records of defects such as unsaleable/
returned items/quality shutdowns/scrap/operational
efficiency (in manufacturing), forced outages (inutilities), disciplinary actions, deposit accuracy(financial), and other quality scores. Because themajority of quality metrics were measures of defects(where higher figures meant worse performance),measures of efficiency and quality scores werereverse coded so that all variables carried the same
inferential interpretation.

•• The overall study involved 955,905 independent

employee responses to surveys and 32,394
independent business/work units in 152
organizations, with an average of 30 employees
per business unit and 213 business/work units
per organization. One hundred ninety-nine (199)
research studies were conducted across the 152
organizations.

•• Table 1 provides a summary of organizations sorted

by industry. It is evident that there is considerable
variation in the industry types represented,
as organizations from 44 industries provided
studies. Each of the general government industry
classifications (via SIC codes) is represented, withthe largest number of organizations represented
in services, retail, financial, and manufacturing

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

industries. The largest numbers of business units
are in the retail and financial industries. Of thespecific industry classifications, these are the mostfrequently represented: Finance — Depository;

Services — Health; Transportation/Public Utilities— Communications; Retail — Food; and Finance— Insurance.

Table 1 — Summary of Studies by Industry
Industry Type

Number ofOrganizations

Number ofBusiness/Work
Units

Number of
Respondents

Finance — Commercial Banking
Finance — Credit
Finance — Depository
Finance — Insurance
Finance — Mortgage
Finance — Non-depository
Finance — Security
Finance — Transactions
Manufacturing — Building Materials
Manufacturing — Consumer Goods
Manufacturing — Food
Manufacturing — Glass
Manufacturing — Industrial Equipment
Manufacturing — Instrument
Manufacturing — Paper
Manufacturing — Pharmaceutical
Manufacturing — Plastics
Manufacturing — Printing
Manufacturing — Ship Building
Materials and Construction
Real Estate
Retail — Automotive
Retail — Building Materials
Retail — Clothes
Retail — Department Stores
Retail — Eating
Retail — Electronics
Retail — Entertainment
Retail — Food
Retail — Industrial Equipment
Retail — Miscellaneous
Retail — Pharmaceutical

2
2
11
4
1
1
3
1
1
3
4
1
1
7
1
2
1
2
1
3
2
2
2
3
2
6
6
1
4
1
9
1

996
59
5,709
3,104
27
94
86
73
8
146
161
5
89
87
60
124
133
35
34
502
185
105
793
279
503
732
1,461
106
3,745
11
3,048
180

7,419
581
68,36741,358
985
2,038
2,785
1,530
1,335
5,797
20,062
1,349
639
2,004
17,243
992
938
716
2,517
23,3483,790
8,313
43,763
16,795
6,594
37,191
104,273
1,051
73,630484
119,723
3,004

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

15

Table 1 — Summary of Studies by Industry (continued)

Services — Business
Services — Education
Services — Government
Services — HealthServices — Hospitality
Services — Hotels
Services — Nursing Home
Services — Recreation

1
7
4
30
1
6
2
1

Number ofBusiness/Work
Units
20
310
240
3,084
30
426
398
14

Transportation/Public Utilities —Non-Hazardous Waste Disposal

1

727

28,600Transportation/Public Utilities — Trucking

1

96

6,213

Transportation/Public Utilities —Communications

5

4,138

43,633

Transportation/Public Utilities — Electric

2

231

4,574

Total FinanceTotal Manufacturing
Total Materials and Construction
Total Real Estate
Total Retail
Total Services
Total Transportation/Public Utilities
Total

25
24
3
2
37
52
9
152

10,148882
502
185
10,963
4,522
5,192
32,394125,063
53,592
23,3483,790
414,821
252,271
83,020955,905

Industry Type

Number ofOrganizations

Table 2 provides a summary of organizations sorted bybusiness or work unit type. There is also considerable
variation in the types of business/work units, ranging fromstores to plants/mills to departments to schools. Overall, 21
different types of business/work units are represented; the16

Number of
Respondents
600
10,746
8,336
115,395
2,612
86,703
27,591288

largest number of organizations had studies of workgroups,
stores, or bank branches. Likewise, workgroups, stores, andbank branches have the highest proportional representationof business/work units.

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

Table 2 — Summary of Business/Work Unit Types
Business/Work Unit Type

Number ofOrganizations

Bank BranchCall Center
Call Center Department
Cost Center
Dealership
Department
Division
Facility
Hospital
Hotel
Location
Mall
Patient Care Unit
Plant/MillRegionRestaurant
Sales Division
Sales Team
SchoolStore
Workgroup
Total

Meta-Analytic Methods Used
Analyses included weighted average estimates of true
validity; estimates of standard deviation of validities; andcorrections made for sampling error, measurement error in
the dependent variables, and range variation and restrictionin the independent variable (Q12 GrandMean) for thesevalidities. An additional analysis was conducted, correcting
for independent-variable measurement error. The mostbasic form of meta-analysis corrects variance estimates
only for sampling error. Other corrections recommended
by Hunter and Schmidt (1990, 2004) include correction
for measurement and statistical artifacts such as range
Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

15
3
4
2
2
5
2
2
4
5
9
2
2
6
1
5
4
5
6
30
38
152

Number ofBusiness/Work
Units
6,929
247
120
68
105
306
29
1,080
248
325
3,265
185
177
269
96
369
53
365
296
10,780
7,082
32,394

Number of
Respondents
78,328
8,539
2,409
10,597
8,313
9,468
5,090
55,182
31,16785,89051,2593,790
4,433
40,442
6,213
21,183
1,418
19,936
10,496
389,718
112,034
955,905

restriction and measurement error in the performancevariables gathered. The definitions of the above proceduresare provided in the sections that follow.
Gallup researchers gathered performance-variable data for
multiple time periods to calculate the reliabilities of theperformance measures. Since 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
for measurement error in the performance variables. The
artifact distributions developed were based on test-retest
reliabilities, where they were available, from various studies.
17

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 accountthat some change in outcomes (stability) is a function ofreal change, test-retest reliabilities were calculated using thefollowing 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 correlationof the outcome measured at time 2 with theoutcome measured at time 3; and r13 is thecorrelation 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 period 1-3 than from time
periods 1-2 or 2-3) from random changes in business
unit results caused by measurement error, data collection
errors, sampling errors (primarily in customer and quality
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 thismeta-analysis was consistent with the average time period
across studies for each criterion type. See Appendix A
for a listing of the reliabilities used in the corrections formeasurement error. Artifact distributions for reliability werecollected for customer, profitability, productivity, turnover,safety, and quality measures. But they were not collected forabsenteeism, shrinkage, and patient safety because they were
not available at the time of this study. Therefore, the assumed
reliability for absenteeism, shrinkage, and patient safety was
1.00, resulting in downwardly biased true validity estimates(the estimates of validity reported here are lower thanreality). Artifact distributions for these three variables will
be added to upcoming reports as they become available.
18

It could be argued that, because the independent variable(employee engagement as measured by Q12) is used inpractice to predict outcomes, the practitioner must live withthe reliability of the instrument he or she is using. 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 forindependent variable reliability. Appendix B presents the
distributions of reliabilities 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 theoretical questions that need to beconsidered relating to whether such correction is necessary.
In personnel selection, validities are routinely corrected forrange 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). 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. Work units are notselected based on scores on the predictor (Q12 scores).
However, in studying companies, we have observed that
there is variation across companies in standard deviations of
indices. One hypothesis for why this variation occurs is thatcompanies vary in how they encourage employee satisfactionand engagement initiatives and in how they have or havenot developed a common set of values and a common
culture. Therefore, the standard deviation of the populationof business units across organizations studied will be greaterthan the standard deviation within the typical company.
This variation in standard deviations across companies canbe thought of as indirect range restriction (as opposed todirect range restriction). Improved indirect range restriction
corrections have been incorporated into this meta-analysis(Hunter, Schmidt, & Le, 2006).

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

Since the development of the Q12, Gallup has collected
descriptive data on more than 15 million respondents, 1.6
million business units or workgroups, and 948 organizations.
This accumulation of data indicates that the standard
deviation within a given company is approximately 8/10
the standard deviation in the population of all business/work units. In addition, the ratio of standard deviation fora given organization relative to the population value varies
from organization to organization. Therefore, if one goal is toestimate the effect size in the population of all business 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 lessvariability across business/work units than the population
average, and vice versa. As such, variability in standard
deviations across organizations will create variability inobserved correlations and is therefore an artifact that can becorrected for in interpreting the generalizability of validities.
Appendices in Harter and Schmidt (2000) provide artifact
distributions for range-restriction/variation corrections used
for meta-analysis. These artifact distributions have since been
updated substantially for this meta-analysis. We have included
a randomly selected 100 organizations in our current artifact
distributions. Due to the increased size of these tables, they
are not included in this report. They resemble those reported
in the earlier study, but with a larger number of entries. 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 sporadicallyavailable. For example, suppose measurement error andrange restriction are the only relevant artifacts beyond
sampling error. In such a case, the typical artifact
distribution-based meta-analysis is conducted in threestages:•• First, information is compiled on four distributions:the distribution of the observed correlations, thedistribution of the reliability of the independent

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

variable, the distribution of the reliability of thedependent variable, and the distribution of therange departure. There are then four means and four
variances compiled from the set of studies, with eachstudy providing whatever information it contains.

•• Second, the distribution of observed correlations iscorrected for sampling error.

•• Third, the distribution corrected for sampling error

is then corrected for error of measurement andrange variation (Hunter & Schmidt, 1990, pp. 158159; Hunter & Schmidt, 2004).

In this study, statistics are calculated and reported at eachlevel of analysis, starting with the observed correlations andthen correcting for sampling error, measurement error, andfinally, range variation. Both within-organization rangevariation corrections (to correct validity generalization
estimates) and between-organization range-restriction
corrections (to correct for differences in variation acrossorganizations) were made. Between-organization rangerestriction corrections are relevant in understanding how
engagement relates to performance across the business/workunits of all organizations. As alluded to, we have applied theindirect range-restriction correction procedure to this metaanalysis (Hunter et al., 2006).
The meta-analysis includes an estimate of the mean samplesize-weighted validity and the variance across the correlations— again weighting each validity by its sample size. The
amount of variance predicted for weighted correlations on
the basis of 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 above:s e = (1 - r- ) / (N - 1)2

2 2

Residual standard deviations were calculated by subtracting
the amount of variance due to sampling error, the amount19

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 tomean unreliability and mean range restriction. The amountof variance due to sampling error, measurement error,
and range variation was divided by the observed varianceto calculate the total percentage variance accounted for.
Generalizability is generally assumed if a high percentage(such as 75%) of the variance in validities across studies
is due to sampling error and other artifacts, or if the 90
percent credibility value (10th percentile of the distributionof true validities) is in the hypothesized direction. As in
Harter, Schmidt, and Hayes (2002) and Harter et al.
(2006), we calculated the correlation of engagement
to composite performance. This calculation assumes
that managers are managing toward multiple outcomes
simultaneously and that each outcome occupies some spacein the overall evaluation of performance. To calculate thecorrelation to the composite index of performance, we used
the Mosier (1943) formula to determine the reliability of
the composite measure (as described in Harter, Schmidt,
and Hayes, 2002), with updated reliability distributions
and updated intercorrelations of the outcome measures. In
addition, given the increase in number of outcomes studied,
we included quality as an outcome in the composite
performance definition. Patient safety was combined withthe more general “safety” category, since patient safety is anindustry-specific variable. The reliability of the composite
metric is .91. Composite performance was measured as theequally weighted sum of customer loyalty, turnover (reverse
scored as retention), safety (accidents and patient safetyincidents reverse scored), absenteeism (reverse scored),shrinkage (reverse scored), quality (reverse scored as defects),
and financials (with profitability and productivity equallyweighted). We also calculated composite performance as
the equally weighted sum of the most direct outcomes ofengagement — customer loyalty, turnover (reverse scored as20

retention), safety (accidents/patient safety incidents reverse
scored), absenteeism (reverse scored), quality (reverse scoredas defects), and shrinkage (reverse scored). The reliability ofthis composite variable is .89.
In our research, we used the Schmidt and Le (2004) metaanalysis 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 relationshipbetween overall employee engagement (defined by an equallyweighted GrandMean of Q12) and a variety of outcomes.
Table 3 provides meta-analytic and validity generalization
statistics for the relationship between employee engagement
and performance for each of the nine outcomes studied. Meanobserved correlations and standard deviations are followedby two forms of true validity estimation. The first corrects forrange variation within organizations and dependent-variablemeasurement error. This range-restriction correction places
all organizations on the same basis in terms of variabilityof employee engagement across business/work units. These
results can be viewed as estimating the relationships acrossbusiness/work units within the average organization. The
second corrects for range restriction across the population ofbusiness/work units and dependent-variable measurement
error. Estimates that include the latter range-restriction
correction apply to interpretations of effects in business/workunits 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.
For instance, observe the estimates relative to the customer
loyalty criteria. Without the between-organization rangerestriction correction (which is relevant to the effect within
the typical organization), the true validity value of employee
Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

engagement is .23 with a 90% credibility value (CV) of.15. With the between-organization range-restriction
correction (which is relevant to business/work units
across organizations), the true validity value of employee
engagement is .30 with a 90% CV of .20.
As in prior studies, findings here show high generalizability
across organizations in the relationship between employee
engagement and customer loyalty metrics, profitability,
productivity, employee turnover, and safety outcomes. Mostof the variability in correlations across organizations was due
to sampling error in individual studies, and for each of these
five outcomes, more than 75% of the variability in correlations
across organizations can be attributed to artifacts (sampling
error, range variation, and measurement error). In otherwords, the true validity is very similar and in the hypothesized
direction for each organization studied. Additionally, therelationship between employee engagement and “quality”
was widely generalizable, with all variance in validities acrossorganizations attributed to sampling error and artifacts. For
the remaining three outcomes (absenteeism, shrinkage, andpatient safety), results indicate high generalizability acrossthe organizations studied as indicated by the 90% credibility
value in the hypothesized direction. However, not all of thevariance in correlations of employee engagement and theselatter three outcomes is explained by artifacts. It is possiblethat this is due to a lack of reliability estimates for theseoutcomes. Once reliability estimates become available and as
more studies are added to the meta-analysis, future research
may shed light on this. Regardless, the 90% credibility values
indicate substantial evidence of generalizability for all nineoutcomes studied (Schmidt & Hunter, 1977). What thismeans is 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.

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

21

Table 3 — Meta-Analysis of Relationship Between Employee
Engagement and Business Unit Performance

Customer
Safety AbsenteePatient Safety Quality
Loyalty Profitability Productivity Turnover Incidents
ism
Shrinkage Incidents (defects)
Number ofBusiness Units

11,29817,33917,845

20,697

3,568

1,331

3,534

348

1,045

Number of r's

65

71

97

65

35

10

8

5

12

Mean Observed r
Observed SD

0.18
0.11

0.10
0.08

0.15
0.09

-0.12
0.06

-0.14
0.11

-0.20
0.13

-0.10
0.07

-0.32
0.21

-0.18
0.10

True Validity1
True Validity SD1

0.23
0.07

0.11
0.04

0.16
0.00

-0.17
0.00

-0.17
0.05

-0.20
0.08

-0.10
0.03

-0.32
0.16

-0.19
0.00

True Validity2
True Validity SD2

0.30
0.08

0.14
0.05

0.22
0.00

-0.23
0.00

-0.22
0.06

-0.26
0.10

-0.13
0.05

-0.40
0.18

-0.25
0.00

% varianceaccounted for —sampling error

49

64

73

87

74

43

53

28

107

% varianceaccounted for1

77

78

103

140

86

61

72

44

137

% varianceaccounted for2

78

78

104

141

86

61

72

45

132

90% CV1
90% CV2

0.15
0.20

0.05
0.07

0.16
0.22

-0.17
-0.23

-0.10
-0.14

-0.09
-0.13

-0.06
-0.08

-0.12
-0.17

-0.19
-0.25

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-variablemeasurement error

In summary, for the composite measure of engagement
shown in Table 3, the strongest effects were found forcustomer loyalty metrics, productivity, employee turnover,safety, absenteeism, patient safety, and quality. Correlations
were positive and generalizable relative to profitability
criteria, but of slightly lower magnitude. This may be
because profitability is influenced indirectly by employee
engagement and more directly by variables such as customer
loyalty, productivity, employee turnover, safety, absenteeism,
22

shrinkage, patient safety, and quality. Remember, theproductivity 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 and profit), engagement is most highly correlatedwith sales. This is probably because day-to-day employee
engagement has an impact on customer perceptions, turnover,quality, and other variables that are in close proximity withsales. In fact, this is what we have found empirically in our
Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

causal analyses (Harter et al., 2005). The next section will
explore the practical utility of the observed relationships.
As in Harter, Schmidt, and Hayes (2002), we calculated
the correlation of employee engagement to composite
performance. As defined earlier, Table 4 provides thecorrelations and d-values for four analyses: the observed
correlations; correction for dependent-variable measurement
error; correction for dependent-variable measurement error
and range 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.

Business units in the top half on engagement withincompanies have .53 standard deviation units’ higher
composite performance in comparison to those in thebottom half on engagement.
Across companies, business units in the top half on
engagement have .69 standard deviation units’ higher
composite performance in comparison to those in thebottom half on engagement.
After correcting for all available study artifacts (examining
the true score relationship), business units in the top halfon employee engagement have .83 standard deviation units’
higher composite performance in comparison to those in
the bottom half on engagement. This is the true score effectexpected over time, across all business units.

Table 4 — Correlation of Employee Engagement to Composite
Business Unit Performance — All Outcomes
Analysis
Observed rdr corrected for dependent-variable measurement errordr corrected for dependent-variable measurement error and range restriction
across companiesdρ corrected for dependent-variable measurement error, range restriction, andindependent variable measurement errorδCorrelation of Engagement
to Performance
0.30
0.49
0.32
0.53
0.41
0.69
0.48
0.83

r = Correlation d = Difference in standard deviation unitsρ = True score correlation δ= True score difference in standard deviation units

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

23

As alluded to, some outcomes are the direct consequence of
employee engagement (employee turnover, customer loyalty,
safety, absenteeism, shrinkage, and quality), and otheroutcomes are more of a downstream result of intermediaryoutcomes (sales and profit). For this reason, we havealso calculated the composite correlation to short-term
outcomes. Table 5 again indicates a substantial relationshipbetween engagement and composite performance. Observed
correlations and d-values are of approximately the same
magnitude as those reported in Table 4, but slightly lower(most likely because the direct outcomes do not occupy allof the performance criterion space).
Business units in the top half on engagement withincompanies have .49 standard deviation units’ higher
performance on direct outcomes in comparison to thosein the bottom half. Across companies, the difference is .64
standard deviation units. After correcting for all available
artifacts, the difference is .77 standard deviation units.

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

Analysis
Observed r
d

0.28
0.46

r corrected for dependent-variable measurement error
d

0.30
0.49

r corrected for dependent-variable measurement error and range restriction
across companies
d

0.38
0.64

ρ corrected for dependent-variable measurement error, range restriction, and

0.45

δ

0.77

independent variable measurement errorr = Correlation d = Difference in standard deviation unitsρ = True score correlation δ= True score difference in standard deviation units

24

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

Utility Analysis:
Practicality of the Effects
Utility Analysis
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 thepotential utility, in practice, of the relationships. The Q12
includes items that Gallup researchers have found to bechangeable by the local manager and others within thebusiness/work unit. As such, understanding the practicalutility of potential changes is critical.
The research literature includes a great deal of evidence thatnumerically small or moderate effects often translate intolarge practical effects (Abelson, 1985; Carver, 1975; Lipsey,
1990; Rosenthal & Rubin, 1982; Sechrest & Yeaton, 1982).
As shown in Table 6, this is, in fact, the case here. Effect sizes
referenced in this study are consistent with or above otherpractical effect sizes referenced in other reviews (Lipsey &Wilson, 1993).
A more intuitive method of displaying the practical value ofan 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 groupas a percentage above the median on the outcome variableof interest.
BESDs can be applied to the results of this study. Table 6
shows the percentage of business units above the medianon composite performance for high- and low-scoringbusiness/work units on the employee engagement (Q12)composite measure. True validity estimates (correcting formeasurement error only in the dependent variable) wereused for analysis of business/work units both within andacross organizations.

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

One can see from Table 6 that there are meaningful
differences between the top and bottom halves. The top half
is defined as the average of business/work units scoring in
the highest 50% on the Q12, and business/work units scoringin the lowest 50% comprise the 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-halfbusiness units rather than bottom-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 within their own organization
and a 145% higher success rate across business units in allcompanies studied. In other words, business/work units
with high employee engagement nearly double their odds
of above-average composite performance within their own
organizations and increase their odds for above-average
success across business/work units in all organizations by
2.4 times.
Table 6 — BESDs for Employee
Engagement and OutcomesBusiness
Units Within
Company

Business
Units AcrossCompanies% above MedianComposite
Performance(Total)% above MedianComposite
Performance(Total)Top Half

66

71

Bottom Half

34

29

% above MedianComposite
Performance(Direct
Outcomes)% above MedianComposite
Performance(Direct
Outcomes)

Top Half

65

69

Bottom Half

35

31

Employee
Engagement25

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

Percentage Above
Company MedianAbove 99th

83%

95th

75%

90th

70%

80th

63%

70th

58%

60

54%

50

50%

40

46%

30

th

42%

20th

37%

10th

30%

5th

25%

Below 1st

17%

th
th
th

Other forms of expressing the practical meaning behindthe effects from this study include utility analysis methods(Schmidt & Rauschenberger, 1986). Formulas have been
derived for estimating the dollar-value increases in output
as a result of improved employee selection. These formulas

26

take into account the size of the effect (correlation), thevariability in the outcome being studied, and the differencein the independent variable (engagement in this case) andcan be used in estimating the difference in performanceoutcomes at different levels in the distribution of Q12scores. Previous studies (Harter, Schmidt, & Hayes, 2002;
Harter & Schmidt, 2000) provided utility analysis examples,comparing differences in outcomes between the top andbottom quartiles on the Q12. For companies included in thismeta-analysis, it is typical to see differences between top
and bottom engagement quartiles of 2-4 points on customer
loyalty, 1-4 points on profitability, hundreds of thousands ofdollars on productivity figures per month, and 4-10 points
in turnover for low-turnover companies and 15-50 points
for high-turnover companies.
Gallup researchers recently conducted utility analysis
across multiple organizations with similar outcome metric
types (an update of analyses presented in Harter, Schmidt,& Hayes, 2002, p. 275, Table 6). Comparing top-quartile
to bottom-quartile engagement business units resulted inmedian percentage differences of:•• 12% in customer loyalty/engagement•• 16% in profitability•• 18% in productivity•• 25% in turnover for high-turnover companies (thosewith 60% or higher annualized turnover)•• 49% in turnover for low-turnover companies (thosewith 40% or lower annualized turnover)•• 49% in safety incidents•• 27% in shrinkage•• 37% in absenteeism•• 41% in patient safety incidents•• 60% in quality (defects)

The above differences and their utility in dollar terms should
be calculated for each company, given the company’s unique

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

metrics, situation, and distribution of outcomes acrossbusiness units. The median estimates above represent the
midpoint in the distribution of utility analyses conductedacross many studies (35 for turnover, 33 for safety, 30 for
productivity, 26 for profit, 25 for customer, 9 for quality, 7
for shrinkage, 6 for patient safety, and 5 for absenteeism),
depending on the outcome and availability of organizational
data with similar outcome types.
One can see that the above relationships are nontrivial if
the business has many business/work units. The point of theutility analysis, consistent with the literature that has takena serious look at utility, is that the relationship between
employee engagement and organizational outcomes, evenconservatively expressed, is meaningful from a practicalperspective.

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

27

Discussion

Findings reported in this updated meta-analysis provide
cross-validation to prior meta-analyses conducted on the
Q12 instrument. The present study expands the size of themeta-analytic database by 8,484 business or work units(an increase of 35%), as well as the number of countries,
industries, operating unit types, and outcomes studied. The
relationship between engagement and performance at thebusiness unit level is substantial and highly generalizable
across companies. Differences in correlations acrosscompanies can be attributed mostly to study artifacts. Thesefindings are important because they mean generalizable tools
can be developed and used across different 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 is best for employees does not have to contradict what
is best for the business or organization.

substantial linkages between employee engagement in 2008
and sick days in 2009, after controlling for demographics
and prior health conditions, including BMI (body massindex). Another longitudinal study found that changes
in engagement predicted changes in cholesterol andtriglycerides (via blood samples) after controlling for
demographics, health history, and medication use (Harter,
Canedy, & Stone, 2008). And even more recently, we haveobserved differences in momentary affect and cortisol whencomparing engaged and disengaged employees (Stone &Harter, 2009). In addition, we are collecting substantial
databases of employee engagement and subjective wellbeing variables throughout the world, such as overall life
evaluation and daily experiences and affect. Studies arebeing produced from these massive databases and will be
the subject of future reports.

It is also worth noting that, as Gallup consultants haveeducated managers and partnered with companies on change
initiatives, organizations have experienced (between the firstand second year), on average, one-half standard deviation
growth on employee engagement and often a full standard
deviation growth and more after three or more years. A very
important element in the utility of any applied instrument
and improvement process is the extent to which the variable
under study can be changed. Our current evidence is that
employee engagement is changeable and varies widely bybusiness unit or workgroup.
As we demonstrated in the utility analyses presented
here and in other iterations of this analysis, the size ofthe effects observed has important practical implications,particularly given that engagement, as measured here, isquite changeable.
Current and future Gallup research is focusing on
expanding the base of outcomes to include health andwell-being variables. For instance, one recent study found28

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

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Appendix A
Reliabilities of Business/Work Unit Outcomes
Based on Schmidt & Hunter, 1996, Scenario 23, p. 219
Customer

32

Profitability

Productivity

Turnover

SafetyQuality

Reliability

Frequency

Reliability

Frequency

Reliability

Frequency

Reliability

Frequency

Reliability

Frequency

Reliability

Frequency

0.89

1

1

3

1

4

1

1

0.84

1

0.94

1

0.87

1

0.99

2

0.99

2

0.63

1

0.82

1

0.86

1

0.94

1

0.92

2

0.62

1

0.66

1

0.84

1

0.93

1

0.90

1

0.60

1

0.63

1

0.75

1

0.91

1

0.62

1

0.39

1

0.58

1

0.90

1

0.57

1

0.27

1

0.53

2

0.89

2

0.34

1

0.25

1

0.52

1

0.79

1

0.24

1

0.51

1

0.57

1

0.46

1

0.56

1

0.41

1

0.33

1

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

Appendix B
Test-Retest Reliabilities of Employee
Engagement
Based on Schmidt & Hunter, 1996, Scenario 23, p. 219
Engagement
Reliability

Frequency

0.97

1

0.92

1

0.86

1

0.83

1

0.82

1

0.80

1

0.79

1

0.78

1

0.77

1

0.75

2

0.66

1

0.65

1

0.61

2

0.47

1

0.45

1

Copyright © 2006, 2009 Gallup, Inc. All rights reserved.

33