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Essay Instructions: READINGS:
Required Resources
Factor Analysis
Hill, T. & Lewicki, P. (2007). StatSoft Electronic Statistics Textbook Tulsa, OK: StatSoft, Inc.
Available on 26 May 2010 at
http://www.statsoft.com/textbook/stathome.html
Principal Components and Factor Analysis.
Available on 26 May 2010 at
http://www.statsoft.com/textbook/stfacan.html
Garson, G. David (2008). Statnotes: An online textbook. North Carolina State University.
Available on 26 May 2010 at
http://www2.chass.ncsu.edu/garson/pa765/statnote.htm
Factor Analysis.
Available on 26 May 2010 at
http://www2.chass.ncsu.edu/garson/pa765/factor.htm
Mead, Tim P.; Legg, David L. (1994, October) Exploratory versus Confirmatory Factor Analysis of Collegiate Physical Fitness. Paper presented at the Annual Meeting of the Midwestern Educational Research Association.
Available from via the ERIC database via the Trident U / Touro C eLibrary
Cluster Analysis
Hill, T. & Lewicki, P. (2007). StatSoft Electronic Statistics Textbook Tulsa, OK: StatSoft, Inc.
Available on 26 May 2010 at
http://www.statsoft.com/textbook/stathome.html
Cluster Analysis.
Available on 26 May 2010 at
http://www.statsoft.com/textbook/stcluan.html
Garson, G. David (2008). Statnotes: An online textbook. North Carolina State University.
Available on 26 May 2010 at
http://www2.chass.ncsu.edu/garson/pa765/statnote.htm
Cluster Analysis.
Available on 26 MY at
http://www2.chass.ncsu.edu/garson/PA765/cluster.htm



Prepare a short (5 page) paper answering the following question:
What are the roles of exploratory and confirmatory factor analysis and exploratory and confirmatory cluster analysis in dissertation research?
Expectations
The main points I want you to discuss are:
1. the purposes of and differences between Q-sort vs R-sort factor analysis and the purposes of and differences Q-sort vs. R-sort cluster analysis
2. the purposes of and differences between reflective and formative factors
3. the similarities and differences among exploratory and confirmatory factor analysis and exploratory and confirmatory cluster analysis
4. the uses and misuses of exploratory and confirmatory factor analysis and exploratory and confirmatory cluster analysis,
5. the relative importance of exploratory vs. confirmatory factor analysis and exploratory vs. confirmatory cluster analysis in dissertation research, and
6. the specific kinds of information that factor analysis vs. cluster analysis can and can't provide in pursuit of one's research goals.
The purpose of this assignment is for you to acquire experience in critically reading, examining, and analyzing research at a high level and evaluating it. In that regard, the content of your report is to demonstrate an understanding of the assigned reading and the research.
In preparing CASE4, to ensure that you demonstrate your ability to understand the theoretical bases of factor analysis and clusster analyses as well as , it is expected that you can:
Distinguish between exploratory and confirmatory factor analysis.
The background information provides a range of required and optional resources bearing on the question of various modes of factor analysis and cluster analysis.
Note that this assignment does NOT require you to prepare a detailed essay. Instead use section headings (the questions) for each of the topics you address in your paper followed by your discussion (answers) of that topic (the questions).

Customer is requesting that (cwanga) completes this order.

Essay Instructions: In the project assignment for this module you'll have an opportunity to play around with exploratory and confirmatory factor analysis, as well as cluster analyses. These are extremely valuable tools to acquire, and will vastly enhance your research repertoire.
Using two different data sets, the project assignment for this module is in three parts (the third part is optional),
Note: You may wish to bookmark the summary sheet called HOW TO DO SOME BASIC THINGS IN SPSS FOR WINDOWS.
Available on 16 November 2009 at
https://sites.google.com/site/zaydeealan/spss_use-1
The SLP is divided into two parts. You are to complete Part 1 and Part 2.
Part 1
Your job is to see if you can improve on the original analysis by Dr. Eveland, which failed to find much meaning in the multidimensionality of the data.
This part of the assignment uses data from the consulting exercise that you originally encountered in the Project for Module 1. Specifically, this is the
RELIABILITY ANALYSIS OF LEADERSHIP EFFECTIVENESS.doc
report that Dr. Eveland prepared based on survey data collected by the team of OD specialists. The items included in the survey are summarized in
questions.doc. For your analyses, you may use either
1. nordic_with_scales.sav, which is the dataset containing the unmodified data that Dr. Eveland used to compile his report
2. nordicnew2.sav, which is the dataset in which Dr. Eveland modified some of the values that were in nordic with scales (which was the dataset he analyzed for his original report) or
3. renamedvars.sav, which is the version or nordicnew2 in which I renamed the variables
Since the factor scores in nordicnew2, renamedvars, and nordic with scales are the sums of the scores on the underlying items, you can recreate the factors (scales) for yourself, if you like.
In nordicnew2 and nordic with scales the reporting groups are defined by the variable called 'Report''. In renamedvars the reporting groups are defined by the variable called 'DmgrphyRpt.' The value labels for each of the reporting groups follows the order presented in
reliability_analysis.pdf
where
1 = Self
2 = Direct Reports
3 = Peers
4 = Supervisors
Nordicnew2, nordic with scales, and renamedvars are the same data sets that were made available to you for your SLP1 activities.
Previously, you saw other parts of this report. You are now being introduced to the factor analysis portion of this report (pp. 5-8). This part reported the results of a series of confirmatory factor analyses aimed at partially validating the scales. Briefly, some of the scales were found to be appropriately unidimensional; others appeared to be two or even three-dimensional. This was not a brilliant analysis; there are some serious holes in it, which it would be nice if you found.
As a necessary first step, you are to replicate some of the reported factor analyses. Trying to replicate the whole thing is far too much work, so pick 2 or 3 of the scales that seem most interesting to you and work with those. The original analysis used principal components analysis with varimax rotation. While you should probably start with this procedure, you should experiment with other analysis techniques such as principal axis factoring (common factor analysis) and other rotations, particularly oblique forms, where the factors are allowed to be correlated. Your job is to see if you can improve on the original analysis, which failed to find much meaning in the multidimensionality of the data. Improvement might entail more clear-cut findings, better interpretations of the data, or other possibilities. Good hunting!
Click on the following which was available on 2012 May 17 at
https://sites.google.com/site/zaydeealan1/terms/factoranalysis
for a whole lot of resources of factor analysis and
https://sites.google.com/site/zaydeealan/SPSS-FactorAnalysis.zip
to access a zipped powerpoint that explains how to run a factor analysis.
Your factor analysis should be supplemented with a reliability analysis that calculates
Cronbach's alphas for each of the scales.
Please comment on the convergent validity and the discriminant validity in your factor analysis.
When you have conducted the appropriate analyses, please transfer your findings to a Word document as you have done before, and add a couple of paragraphs or so of interpretation and explanation.
Part 2
You are to carry out reliability tests and factor analyses on some of the six scales of importance used in the Community Policing study (pick a couple that interest you -- you can do more if you like, but it's not required) and put together a brief report on the quality and numerical properties of these scales.
The second part of the assignment involves data that you may remember from the police study that you encountered back in RES601. At the time, you'll recall, we found these data to be quite frustrating, because of their lack of clear-cut results despite the enormous amount of effort put into the project and the significantly large resources devoted to it.
At the time, we didn't pay much attention to the scales involved in that study, other than to use them for certain analyses. Two scales described perceived aspects of the technology; four scales summarized the attitudes of the participating police officers regarding community policing. The report is quite interesting in terms of its discussion of the origins of these scales, as much for what it does not say as for what it does. Basically, the report says the scales were made up through what they call "the logical method" -- a rather fancy name for thinking about what the dimensions might entail, knocking together some questions, running a quick-and-dirty pilot to surface obvious errors, and sending them into the field ( police computer use study.pdf pp. 16-17). At no time were the scales subjected to any quantitative analysis -- reliability assessment, factor analysis, cluster analysis, or any other form of numerical testing. There is a brief note on p. 32 of the report acknowledging this problem, but no attempt to resolve it. This might have something to do with why no articles seem to have made it out of this project and into the published literature.
Your job for this part of the assignment will be to carry out the analysis that the original authors of the report did not carry out. Specifically, you are to carry out reliability tests and factor analyses on some of the six scales (as above, pick a couple that interest you -- you can do more if you like, but it's not required) and put together a brief report on the quality and numerical properties of these scales.
technology_factors.pdf
lists the items in the two technology scales;
community_policing_factors.pdf
lists the items in the four community policing scales. To access the data set, click on
data_set_1_police_comp_use.sav
As you will have gathered from the background readings and the case, there is a certain amount of disagreement about the precise factor analysis procedures to this process. For purposes of this exercise, please try it both ways. That is, first try a confirmatory factor analysis based on the six theoretically defined factors, in which you carry out a separate analysis for each of those out of the six that you've selected and try to determine its unidimensionality.
Click on the following which was available on 2012 May 17 at

https://sites.google.com/site/zaydeealan1/terms/factoranalysis

for a whole lot of resources of factor analysis and
https://sites.google.com/site/zaydeealan/SPSS-FactorAnalysis.zip
to access a zipped powerpoint that explains how to run a factor analysis.

Your factor analysis should be supplemented with a reliability analysis that calculates Cronbach's alphas for each of the scales.
Then try an exploratory factor analysis across all 12 items for the technology scales, or all 27 items for the community policing scales, depending on which scales you selected to work with. See if you can find an empirical structure in these data that is anything like the theoretical structure that you just tested. You may need to try a variety of approaches and rotations.
When you have completed these analyses, please write them up together with a summary commentary about the process that you have carried out on these data. You might wish to formulate it as a memo to Dr. Colvin regarding how the data should be appropriately described in terms of the scales. Be kind.
Now, please try a few cluster analysis procedures, both hierarchical clustering and k-means clustering, to see if you can get a reasonable set of a few categories by reducing these variables. Since SPSS puts a limit on the number of cases (or records) it can accommodate, use the random case (record) selection feature in SPSS, explain that you did so, and run the hierarchical cluster analysis.
The police data set also includes 13 variables describing activities carried out by the police in the community policing domain. These variables are listed in
community_policing_activity_item.pdf.
For these variables, try a few cluster analysis procedures, both hierarchical clustering and k-means clustering, to see if you can get a reasonable set of a few categories by reducing these variables.
Click on the following link which was available on 16 November 2009 at
https://sites.google.com/site/zaydeealan/SPSS-ClusterAnalysis.zip
to access a zipped PowerPoint that explains how to run a cluster analysis.
In SPSS, use statistics | classify | hierarchical cluster with method | cluster method | ward and plots to generate the most effective cluster pictures, though other clustering methods are also worth experimenting with, as the readings indicate.
When you have finished your analyses, transfer your results to your document and add an appropriate summary paragraph or paragraphs reporting what you've done and conclusions you derive from it.
Expectations
The purpose of this assignment is for you to acquire experience in critically reading, examining, and analyzing research at a high level and evaluating it. In that regard, the content of your report is to demonstrate an understanding of the assigned reading and the research. You will be accomplishing this type of work throughout the course.
In preparing SLP4, to ensure that you demonstrate your ability to run a factor analysis and a clusster analyses as well as interpret the results of those analyses, it is expected that you have used Background4 to learn how:
Describe the principal procedures and alternatives involved in factor analysis, and the circumstances under which each alternative extraction procedure and rotation procedure is and is not applicable.
Describe the advantages and limitations of alternative extraction and rotation strategies
Run and interpret factor analyses in SPSS
Plan “Name that factor!” reasonably convincingly
Explain the idea of cluster analysis and identify some of the principle algorithms used for cluster analysis
Run and interpret a variety of cluster analysis procedures in SPSS
Note that this assignment does NOT require you to prepare a detailed essay. Instead use section headings (the questions) for each of the topics you address in your paper followed by your discussion (answers) of that topic (the questions).

Customer is requesting that (cwanga) completes this order.

Essay Instructions: IMPORTANT - This is a group project and I am giving you the specs for the entire group project. HOWEVER, I am only to do my individual portion which is MDS, Multi Dimensional Scaling. The entire group project instrucions are as follows but I only need the 1500 word individual portion about MDS. Please read the group project instructions so that you know what the entire project entails. Thank you.


Deliverable Length: 1500-3000 words individual portion
Details: A high-end market research firm has contacted your boss and is trying to sell some business to your organization. Upper management does not want to appear incompetent, so they have asked you to research and explain three major ways multivariate statistics are used in a business.

Small Group Discussion

Research the Library and provide at least 1 example of how a real company has used each of the following multivariate techniques: factor analysis, multi-dimensional scaling, and cluster analysis. Companies that provide statistics software websites and market research firm websites usually include case studies and customer testimonials.
Read the postings of all group members and decide as a group which technique is preferred by the group and which example best illustrates the use of this technique.



For the individual portion of this project, on your own, write a 1500-3000 word summary explaining to upper management the chosen multivariate technique, MY MULTIVARIATE TECHNIQUE THAT YOU WILL WRITE ABOUT IS MDS (MULTI DIMENSIONAL SCALING) how it is different than the other 2 techniques, how at least one other real-life company has used this technique to address a business problem and how that technique might be used at your own organization.




Objective: Explain analysis of variance, multivariate statistics, and non-parametric methods



Instructor Comments

Here is additional guidance to help you earn the highest grade possible for Phase-4 Group Project:
Individual Posting

§ *Each small group member, including the Small Group Leader, should research on the Web about his or her multivariate analysis technique assigned by the instructor and discuss about general research findings regarding that technique.

§ *Notice that companies that provide statistics software web sites and market research firm web sites usually include case studies and customer testimonials.

§ *Write a Word Document memo between one to three page long (a detailed Word Document and not an actual E-mail for this assignment and not to exceed three full pages, single or double spaced) explaining to upper management about his or her assigned multivariate technique (assigned by the Group Leader)as well as the general research findings regarding that technique.

§ *Compare the assigned multivariate technique to other two techniques and explain how it is different than the other two techniques.

§ *Provide, at least one research-based example for this technique and how it is applied to the situation along with the related reference citation.

§ *Also, discuss how this technique can be applied by Company W to reach its marketing objectives in snack food market.

§ *Post at least three viable references (some from search engines with known authors and date of publication) that you actually use in your discussion. Notice that, in order to obtain a top grade for this project, most of your selected references must be from outside course related sources and obtained by search engines.

§ *Cite all references used in your discussion right after each related statement, in APA format.

§ *You need to show your similarity score of below 25% with your modified individual posting.



iIMPORTANT****NOTICE: Please do NOT discuss about all three techniques entirely in your individual posting. You should mainly concentrate on your assigned technique. ONLY MDS!!!!

THE INFO SUPPLIED BELOW MAY NOT BE AS IMPORTANT BUT I WANTED THE WRITER TO KNOW WHAT PROJECT i AM TRYING TO HELP WITH. THE TASK BELOW IS NOT NEEDED, ONLY MY INDIVIDUAL PORTION ABOVE. INFO BELOW ONLY SHOWS THE GROUP PROJECT AND HOW I FALL INTO IT.



Final Group Project Posting

§ *The Group Leader should carefully follow the steps indicated below to submit the Final Group Project posting:

§ *The Group Leader should read the postings of all group members and ask all members about their preferred technique for Company W in order to decide as a group, which technique among factor analysis, multidimensional analysis, or cluster analysis is preferred by the majority of the group members.

§ *The group leader should provide one to three page Word Document posting, at most, about the research findings regarding the preferred technique along with members’ reasons for such selection. Notice that the group leader should rephrase the statements related to the preferred technique taken from the individual postings and cite related references in order to keep a low similarity score for the final posting.

§ *Compare the preferred technique with other two techniques, and state, in detail, about comparative advantages of the selected technique over the other two techniques.

§ *Provide some new research-based application example for the selected technique in detail that best illustrates the uses of this technique. Notice that this must a new example not used with individual postings.

§ *Provide detailed discussion as how such technique would benefit Company W with its marketing research.

§ *Post at least three viable references (including some from search engines with known authors and date of publication) in APA format that actually used in the final group project posting by the group leader. Notice that, in order to obtain a top grade for this project, most of the selected references must be from outside course related sources and obtained by search engines.

§ *Cite all references used in the posting in APA format, right after each related statement.

§ *Show the similarity score of below 25% along with the final group project posting by the group leader.

§ *Use the Small Group Discussion Board for interaction among group members and postings regarding their research progress involving their assigned technique.

§ *Only the Small Group Leader must post the final group project posting on “Small Group Files” inside your small group for grading. Please label your posting as “Final group project posting” and follow step stated below to submit the final group project posting for grading:

· Open your Small Group Web page. Then, open “Add Files” tab located inside your small group Web page and submit the final group project posting.

§ *The Group Leader should NOT discuss about all three techniques entirely in the final group project report. The discussion should concentrate on the preferred technique by the small group members and it should closely cover the indicated tasks by the instructor stated above.

Essay Instructions: Request for T.Lavinder!

THIS IS THE INSTRUCTIONS FOR THE RESEARCH CRITIQUE
NURS 225: RESEARCH
GUIDELINES FOR RESEARCH CRITIQUE

Directions for formatting the critique:

• Number all pages; title page will be page number 1 [see APA manual for format of title page, use of running head with page number, and proper citation of a journal article and book; use 12 pt. font, no bold print with APA]. Do not include an abstract with this assignment.

• For the body of this assignment you will not use a strict APA format. The form for the critique begins on page 2. Save this document as a Word file onto your desktop – there is no need to “re-type” the form. Just type in your responses to each of the italicized bulleted questions/objectives. Your responses should be typed in regular font. Double-space your narrative response.

• Use main headings [i.e., Introduction; Problem/Purpose, etc.], then respond to each of the bulleted questions/objectives. Type your responses in regular font.

• Be sure to clearly explain answers, validate “yes and no” answers with at least a comment or two. Some of your narrative responses will be more detailed. Use proper grammar and proper sentence structure.

• The critique, including title and reference pages, should be about 8–10 pages. Do not be overly concerned about the length of the paper; just be sure to clearly respond to each of the questions/objectives.

NOTE: Refer to the points on critiquing research studies that are detailed throughout the textbook. Although it’s not based on the guidelines above, you may find the example of an article critique helpful (see Module/Week 5 Additional Materials.

Points assigned to each component of critique are cited in parentheses [Total points possible: 225].

The form for the critique begins on page 2. Save this document as a word file onto your desktop – then you may format as needed and type in your responses. Do not retype the form/information.















RESEARCH CRITQUE


Introduction (5)
• Does title fit well with the content of the article?
• Are the independent/dependent or variables of interest clearly defined?
• Discuss the content of the abstract, is it a good overview of the content, is it consistent with content?

Problem/Purpose (10)
• State the problem.
• Do the authors identify the significance of the problem?
• Do they provide adequate background information to support the problem?
• Do the authors explain the purpose or aim of the study?

Literature Review (10)
• Are relevant previous described?
• Are the references current? (number of sources in the last 10 years and in the last 5 years)
• Do the authors summarize their review of the literature to reveal what is known/not known – and the need for further study?

Framework/Theoretical Perspective (10)
• Is the study based on a specific theory or theoretical framework?
• If the study is based on a specific theory, do the authors tie the framework/theory to their study – concepts/variable of interest? If so, how is this accomplished?

List the Research Question(s) OR Hypotheses (10)
• Research hypothesis or hypotheses
• Research question(s)

Identify and Define Variables (10)
• Independent variables [“intervention/treatment], identify and define variable(s), - what is the treatment or intervention and how is it implemented?
• Dependent variables [outcome of the treatment – “effect”] define the dependent variable and describe how it is measured.
• If the study does not have and independent and dependent variable, identify and define and the study variables of interest.

Demographics of Sample (5)
• Were demographics of the sample included? If yes, provide a few examples of demographics, i.e. 50% male, 50% female, etc.







Research Design (15)
• Identify the research design and define the design, i.e. nonexperimental, descriptive survey, correlational, etc.
• Is the design used in the study the most appropriate design to obtain the needed data?
• If an experimental study, identify the treatment or intervention
• Were subjects assigned to groups? If so, how was this done?
• Did the researchers conduct a pilot study? If so, what did they have to say about it – did they make changes based on the pilot study?

Sample/Setting (15)
• Sampling criteria – this is usually referred to as “inclusion criteria” – meaning what characteristics did participants need in order to be included in the study? i.e. – female, 40-50 years of age, pregnant with 1st child, etc.
• Sampling method, how did researchers obtain participants, what kind of approach was used – nonprobability [nonrandom] or probability [random]?
• Was informed consent obtained? Institutional Review Board mentioned?
• Identify the setting of the study – did the setting fit well with the study’s objectives?

Measurement, Methods & Instruments (15)
• Who developed the instruments used? The author, someone else?
• Identify the type of measurement used in the study [Likert scale, physiological measure, etc.] and the level of measurement used [remember basic stats – nominal, ordinal, interval, or ratio]
• Discuss instrument development if applicable [some studies will use established instruments developed by other researchers/scientists if so note this]
• Did the authors discuss the reliability and validity of the instruments used?

Data Collection (10)
• How were data collected?
• Timing of data collection [one time [cross-sectional] collection, longitudinal?]
• Where were the data collected?

Data Analysis (10)
• Are data analysis procres clearly described? Explain
• Are data analysis procres appropriate for the type of data collected? Explain.

Statistical Analyses (15)
• What statistical measures were used to test or report reliability and validity of the measurement methods [usually refers to the instruments used] in the study?
• What statistical measures were used to analyze the data collected [the data that “answered” the research hypotheses or research questions]?
• Was the level of significance or alpha identified? If so indicate what it was [.05; .01; or .001. – remember .05 means that the researchers are 95% confident that there was cause and effect or correlation b/w variables, .01 means that they were 99% confident, and .001 means that the researchers were 99.9% confident that their intervention was effective and directly related to the outcome – of effect.


Limitations (10)
• What limitations were identified?
• Can you identify any other limitations?

Implication of Findings (10)
• What implications for nursing were described?
• Can you think of any implications that were not described?
• What were the suggestions for further study?

Generalization of Findings (5)
• Did the author(s) generalize the findings [did they apply the findings of their study beyond the sample studied – and make application to the population in general? Remember that a random [probability] sample is considered to be generalizable whereas a nonrandom [nonprobability] sample is not.

Format (5)
• Did you discover any spelling, punctuation, or grammatical errors? What about sentence structure, organization, clarity?


Overall Evaluation (20)
• Were the steps of the research process logically linked together [did the authors’ research questions or hypotheses make sense based on the review of literature, did the methods employed, i.e., quantitative/qualitative fit well with the intent of the study, etc.?
• What are your impressions about the overall quality of the study?
• Your impressions regarding applicability of the study nursing practice and how it contributes to nursing knowledge
• Include any other points of “critique” or commentary as desired.



NOTE: Do not critique the article based on the information posted below; Quality of Work applies to the quality of your work on this critique assignment.

Quality of Work (20)
• Thoroughness
• Proper grammar and sentence structure
• Clear communication of ideas
• Depth of information
• Organization, APA formatted citation of reviewed article

THIS IS THE RESEARCH TO BE CRITIQUE

Health Policy & Systems
Factors Associated With Work Satisfaction of
Registered Nurses
Christine Kovner, Carol Brewer, Yow-Wu Wu, Ying Cheng, Miho Suzuki
Purpose: To examine the factors that influence the work satisfaction of a national sample of
registered nurses in metropolitan statistical areas (MSAs).
Design: A cross-sectional mailed survey design was used. The sample consisted of RNs randomly
selected from 40 MSAs in 29 states; 1,907 RNs responded (48%). The sample of
1,538 RNs working in nursing was used for analysis.
Methods: The questionnaire included measures of work attitudes and demographic characteristics.
The data were analyzed using ordinary least-squares regression.
Findings: More than 40% of the variance in satisfaction was explained by the various work
attitudes: supervisor support, work-group cohesion, variety of work, autonomy, organizational
constraint, promotional opportunities, work and family conflict, and distributive
justice. RNs who were White, self-perceived as healthy, and working in nursing cation
were more satisfied. RNs that were more career oriented were more satisfied. Of the
benefits options, only paid time off was related to satisfaction.
Conclusions: Work-related factors were significantly related to RNs’ work satisfaction.
JOURNAL OF NURSING SCHOLARSHIP, 2006; 38:1, 71-79. C2006 SIGMA THETA TAU INTERNATIONAL.
[Key words: work satisfaction, nurses, work attitudes]
* * *
Nursing shortages have been widely reported in the
literature both regionally (Cushman, Ellenbecker,
Wilson, McNally, & Williams, 2001) and within
healthcare organizations (Buerhaus, Staiger, & Auerbach,
2003; Grumbach, Ash, Seago, Spetz, & Coffman, 2001).
Work satisfaction is an important issue for registered nurses
(RNs) and managers in part because of its reported relationship
with RN turnover (Davidson, Folcarelli, Crawford,
Duprat, & Clifford, 1997; Francis-Felsen et al., 1996;
Gurney, Mueller, & Price, 1997; Ingersoll, Olsan, Drew-
Cates, DeVinney, & Davies, 2002; Lake, 1998; Larrabee
et al., 2003; Prevosto, 2001; Shader, Broome, Broome,West,
& Nash, 2001, which can lead to organizational shortages
and absenteeism (Siu, 2002; Song, Daly, Rudy, Douglas, &
Dyer, 1997). Results from studies about determinants of RN
work satisfaction should be of interest to both administrators
and policy makers.
Background
A substantial body of literature exists about factors associated
with RN satisfaction with work (Stamps, 1997).
Various measures of satisfaction have been used, but many
are not based on a theoretical framework. Price (2004) and
Gurney et al. (1997) proposed an integrated theoretical
model of work satisfaction and voluntary turnover (intent
to leave) that combines economic, psychological, and sociological
theories with empirical findings about the determinants
of turnover. They theorized that a variety of
work-setting characteristics and attitudes toward work are
associated with satisfaction, resulting in intent to leave jobs.
Some empirical evidence for the model (Agho, Mueller, &
Price, 1993; Davidson, Folcarelli, Crawford, Duprat, &
Clifford, 1997; Gaerter, 1999; Gurney et al., 1997) has been
presented. A modification of Gurney et al.’s model is shown
in the Figure.
Demographic characteristics have been associated with
RN work satisfaction (Blegen & Mueller, 1987; Ingersoll
Christine Kovner, RN, PhD, Upsilon, Professor, College of Nursing, New
York University, New York City; Carol Brewer, RN, PhD, Associate Professor,
School of Nursing; Yow-Wu Wu, PhD, Associate Professor, School
of Nursing; Ying Cheng, MA, Doctoral Candidate; all at University at Buffalo,
Buffalo, NY; Miho Suzuki, RN, MSN, Upsilon, Doctoral Candidate,
College of Nursing, New York University, New York City. This manuscript
was supported by the Agency for Healthcare Research and Quality, Grant
R01HS01132002. The authors of this article are responsible for its contents.
No statement in this article should be construed as an official position
of the Agency for Healthcare Research and Quality. Correspondence to Dr.
Kovner, College of Nursing, New York University, 246 Greene Street, Room
618E, New York, NY 10003. E-mail:
Accepted for publication August 7, 2005.
Journal of Nursing Scholarship First Quarter 2006 71
RN Work Satisfaction
Job satisfaction
RN characteristics
Demographic
Health
Work setting
Social support and
integration to:
Work-to-family conflict
Family-to-work conflict
Job stress
Organizational constraints
Role overload
Promotional opportunities
Professional values
Autonomy
Routinization
Disposition and orientation
Work motivation
Career orientation
Direct patient care
Job hazards (injuries)
Pay (income, benefits)
Distributive justice
Movement constraints
MSA characteristics
Figure. Factors contributing to nurses’ job satisfaction. Based on Gurney, Mueller, & Price (1997). Adapted with permission.
et al., 2002; Langemo, Anderson, & Volden, 2002; Lum,
Kervin, Clark, Reid, & Sirola, 1998; Weisman, Alexander,
& Chase, 1980), and studies have indicated both a positive
relationship between autonomy and satisfaction (Acorn,
Ratner,&Crawford, 1997; Kramer&Schmalenberg, 2003)
as well as contradictory findings (Davidson et al., 1997;
Gurney et al., 1997; McNeese-Smith & Crook, 2003). The
relationship between variety and work satisfaction is equivocal
(Gurney et al., 1997; McNeese-Smith&Crook, 2003).
Findings are contradictory about the relationship between
distributive justice and work satisfaction (Gurney et al.,
1997; Taunton, Boyle, Woods, Hansen, & Bott, 1997),
workload, organizational constraint, and work satisfaction
(Adams&Bond, 2000; Davidson et al., 1997; Gurney et al.,
1997; Hoffman & Scott, 2003; Shaver & Lacey, 2003),
supervisor and mentor support, and satisfaction (Decker,
1997; Gurney et al., 1997; Larrabee et al., 2003; McNeese-
Smith & Crook, 2003).
Work-group cohesion, also termed integration, relationship
with coworkers, and peer support (Adams & Bond,
2000; Decker, 1997; Gurney et al., 1997; Larrabee et al.,
2003; Shader et al., 2001) and promotional opportunity
satisfaction (Gurney et al., 1997; Mills & Blaesing, 2000;
Taunton et al., 1997) have been related to work satisfaction.
Work-to-family conflict and family-to-work conflict are
related concepts that have been negatively related to work
outcomes, family outcomes, and employee physical and
mental health (Frone, 2003), but they were not included
in Price et al.’s model. Family-to-work conflict (family conflicts
with work) has been positively related to job dissatisfaction,
work-related absenteeism, tardiness, and poor
job performance in various occupations (Bernas & Major,
2000; Frone, Russell, & Cooper, 1992; Frone, Yardley, &
Markel, 1997) and also among nurses (Decker, 1997). In
contrast, work-to-family conflict (work conflicts with family)
has been associated with intentions to quit one’s job
and turnover (Greenhaus, Parasuraman, & Collins, 2001;
Kirchmeyer & Cohen, 1999).
Although not included in Price et al.’s model, some
evidence exists that metropolitan statistical area (MSA)
characteristics affect nurses’ work participation behavior
(Buerhaus, 1993; Buerhaus & Staiger, 1996, 1997), but not
clear is whether these factors have any effect directly on
work satisfaction. For example, in areas with many inpatient
days, competition for RNs might be high. This competition
72 First Quarter 2006 Journal of Nursing Scholarship
RN Work Satisfaction
might force employers to improve working conditions,
which would improve RN work satisfaction. Similarly,
in areas with competition among healthcare providers, they
might compete in relation to quality or cost. If they compete
on quality, they might be satisfactory places to work. However,
if they compete on cost, they might be unsatisfactory
places to work. The purpose of the study reported here was
to empirically test the revised model shown in the Figure in
a national sample of working RNs to determine the factors
associated with RNs’ work satisfaction.
Methods
The target population for this study was all registered
nurses (RNs) in metropolitan statistical areas (areas around
and including metropolitan areas) in the United States.
About 78% of RNs live in MSAs (Spratley, Johnson,
Sochalski, Fritz, & Spencer, 2001). The sampling design included
a two-stage sample of RNs in MSAs. First, MSAs
were selected; then RNs were randomly selected from all
RNs in each MSA. Because of financial constraints for this
study, only 40 MSAs were randomly selected from the original
51 MSAs used by the Center for Studying Health System
Change in the Community Tracking Study (CTS) in 2000
(Metcalf, Kemper, Kohn, & Pickreign, 1996). The original
sampling strategy for the CTS was designed to result in a
nationally representative sample of RNs. RNs were sampled
from 29 states and the District of Columbia (AL, AR,
AZ, CA, CO, CT, FL, GA, IL, IN, KY, LA, MA, MD, DC,
MI, MO, NC, NJ, NV, NY, OH, OK, PA, SC, TN, TX, VA,
WV, and WA). The board of nursing in each area was contacted
to get an updated list of names and addresses for all
RNs. From these lists, 4,000 RNs were randomly selected
from the 40 MSAs with equal probabilities of selection. An
advantage of this method is that the statistical analyses do
not require the use of sampling weights.
After the sample of 4,000 RNs was selected, each nurse
was sent a mailed questionnaire based on a seven-stage procre
reported by Dillman (2000), including: (a) an alert
letter, (b) the first survey, (c) a postcard reminder, (d) a second
survey, (e) a third survey, (f) a follow-up phone call,
and (g) a fourth survey. Each selected RN received the first
survey with a $1.00 incentive and was eligible for one of
10 prizes of $100 in a drawing. These procres resulted
in completed questionnaires being obtained from 1,906 of
the 4,000 sampled RNs. The overall response rate was 48%
and ranged across the 40 MSAs from 30% to 51%. Fortyfive
respondents were eliminated from the analytic sample
because they had moved to an area for which we could not
obtain MSA data, and 324 were eliminated because they
were not employed in nursing. Thus, the final sample was
1,538 nurses who were working in nursing.
Four types of variables were derived from the model: (a)
RNdemographic characteristics and health (age, sex, ethnicity,
race, marital status, highest degree in nursing, living with
children, years of experience in nursing, advanced certification,
partner’s income, overall health status, current enrollment
in an cational program, and religious beliefs), (b)
MSA characteristics (medical, surgical, and other specialists
per 1,000 population, primary care practitioners per 1,000
population, index of competition, percentage of HMO hospital
services paid through fee schles, inpatient days, and
RN-to-population ratios, unemployment rate in 2002, and
MSA, and (c) RN perceptions of the labor market that represented
movement constraints (local job opportunity and
outside job opportunity). The fourth group was work setting,
which included work attitudes (autonomy, variety, distributive
justice, work group cohesion, supervisory support,
mentor support, work-family conflict, family-work conflict,
promotional opportunity, organizational constraints, quantitative
workload, work motivation, career orientation,
partner’s career orientation, and satisfaction) and characteristics
of the work (annual income, holding more than one
position for pay, work setting, position, work shift, transfer
of work unit, change in supervisor, needle sticks, strains and
back injury, paid time off benefit, medical insurance benefit,
retirement benefit, tuition reimbursement, importance of
benefits, and number of benefits). The full list of variables
is shown in Table 1.
Work attitudes were measured with scales used in previous
research (Carlson & Frone, 2003; Gurney, 1990; Quinn &
Staines, 1979; Spector & Jex, 1998). Satisfaction was measured
with the five-item Quinn and Staines’s facet-free job
satisfaction scale (Quinn&Staines, 1979), but with slightly
altered response items.We expanded the number of options
in several cases, such as from the original three-response options
(strongly recommend, have doubts about recommending,
and advise the friend against) to four-response options
(strongly recommend, somewhat recommend, somewhat advise
against, and strongly advise against). The Cronbach
alpha coefficient was .86. Quinn and Staines reported that
these indicators of job satisfaction were correlated with less
role ambiguity (?.22), depressed mood at work (?.43), and
more facet-specific job satisfaction (.55), indicating evidence
of the validity of the scale (Cook, Hepworth, Wall, & Warr,
1981). All scales were Likert-type, varying in the number
of items from 3 (for work-family conflict) to 10 (for organizational
constraints). Table 1 shows the definition, mean,
standard deviation, actual range, Cronbach alphas, and the
number of items for all scales used in the analysis. Reliability
coefficients for the scales ranged from a low of .70 for variety
to .95 for supervisory support and distributive justice.
The one-factor structure of each scale using confirmatory
factor analysis was supported in all cases except organizational
constraint. After removing one item from that scale,
a one-factor solution was supported.
Partner’s annual income was logged to normalize the distribution.
As for group two characteristics, all variables related
toMSAexcept unemployment rate were obtained from
InterStudy (2001). Unemployment rate was obtained from
the Bureau of Labor Statistics. Primary care practitioners are
physicians who provide primary care such as family practice
physicians. Index of competition is how competitive the
HMO marketplace is.
Journal of Nursing Scholarship First Quarter 2006 73
RN Work Satisfaction
Table 1. Definition, Reliability, Number of Items, Mean, Standard Deviation, and Actual Range of Work Attitude Scales (N=1,538)
Definition Alpha Number of items Mean (SD) Actual range
Local job opportunity Likelihood of obtaining jobs in local area as good, worse, or better
than current jobb
.88 2 2.95 (1.21) 1.00–5.00
Outside job opportunity Likelihood of obtaining jobs outside local area as good, worse, or
better than current jobb
.90 2 3.09 (1.15) 1.00–5.00
Supervisory support Degree to which supervisor supports and encourages employeeb .95 5 3.59 (1.03) 1.00–5.00
Mentor support Degree of adequacy of access to an appropriate experienced
professional to sponsorship, protectorship and professional
benefactorshipb
.91 6 3.00 (0.88) 1.00–5.00
Work group cohesion Degree to which employees have friends in the immediate work
environmentb
.90 4 3.81 (0.83) 1.00–5.00
Variety Degree to which job performance is repetitiveb .77 4 3.03 (0.71) 1.00–5.00
Quantitative workload Amount of performance required in a jobc .89 5 4.13 (1.16) 1.00–6.00
Autonomy Degree to which employees control their job performanceb .79 4 4.09 (0.73) 1.50–5.00
Organizational constraint Degree to which situations or things interfere with employees’ job
performancec
.89 10 2.41 (0.92) 1.00–6.00
Promotional opportunities Degree to which career structures within an organization are
available to its employeesb
.90 5 2.87 (0.92) 1.00–5.00
Work-to-family conflict Degree to which an employee’s job interferes with family lifed .94 3 3.13 (1.40) 1.00–6.00
Family-to-work conflict Degree to which an employee’s family life interferes with jobd .89 3 1.73 (0.90) 1.00–6.00
Work motivation Degree to which work is central to an employee’s lifeb .83 4 2.08 (0.74) 1.00–5.00
Distributive justice Degree to which the an employee’s rewards are related to
performance inputs into the organization b
.95 4 2.60 (0.98) 1.00–5.00
Job satisfactiona Employee’s general affective reaction to the job without reference to
any specific job facete
.86 5 ?.012 (0.80) ?2.14–1.03
Note. aThe standardized score was used for job satisfaction because the number of items varied for each question. bGurney, Mueller, & Price (1997), c Spector & Jex (1998),
d Frone, Yardley, & Markel (1997), e Quinn & Stains (1979)
Findings
As shown inTable 2, working RNs were primarily women,
White, married, and only 14.2% had children under 6 years
old living with them. 19.1% had more than one position
for pay, 61% worked in hospitals, and a similar percentage
were in direct care positions. In addition to pay, the RNs had
a variety of noncompensation benefits: 85.2% had medical
insurance, 82.6% had retirement benefits, and 83.5% said
these benefits were somewhat to very important to them for
staying in the current position. At the same time 10.9% had
transferred to another work unit and 34.5% had a change in
the immediate supervisor in the last year. Table 3 shows that
the RNs had a mean age of 46.4, 18.8 years of experience,
and $49,940 annual income.
We used ordinary least squares (OLS) regression to estimate
the model, because the dependent variable was continuous
and we were testing a linear relationship. As shown in
Table 4, the model explains 54% of the variance in work
satisfaction, with most of the variation explained by the
work setting variables. Only the significant findings are included
in the table. No other variables were significantly
related to job satisfaction. Table 4 also shows the relationships
between the predictor variables and satisfaction.
Non-Hispanic Black RNs were less satisfied than were non-
Hispanic White RNs. RNs who were in poor or fair health
were less satisfied than were those with very good health,
but injuries did not influence satisfaction. Of the MSA characteristics,
only unemployment rate was significantly related
to satisfaction. Local job opportunity was related to satisfaction,
but nonlocal job opportunity was not. Of work setting
variables, the only benefit option related to satisfaction
was not having paid time off (e.g., vacation). RNs working
in nurse cation were more satisfied than were those in
hospitals. Less career-oriented RNs were less satisfied than
were those who were more career oriented. RNs working as
managers or instructors were less satisfied than were RNs
providing direct care.
More than 40% of the variance in work satisfaction was
explained by the various attitude scales. High autonomy,
high distributive justice, high group cohesion, high promotional
opportunities, high supervisor support, high variety of
work, low work-to-family conflict, and low organizational
constraint, significantly contributed to satisfaction.
Discussion
Our sample is similar to the sample of working RNs from
the National Sample Survey of Registered Nurses (NSSRN;
74 First Quarter 2006 Journal of Nursing Scholarship
RN Work Satisfaction
Table 2. Demographic and Work-Related Characteristics of the Sample (N=1,538)
n (%)
Sex Female 1461 (95.0)
Male 77 (5.0)
Ethnicity Hispanic or Latino 38 (2.5)
Not Hispanic or Latino 1465 (97.5)
Race White 1306 (84.9)
Black 101 (6.6)
Asian 74 (4.8)
Other 57 (3.7)
Marital status Now married 1067 (69.5)
Unmarried 469 (30.5)
Live with children under age 6 Yes 219 (14.2)
No 1319 (85.8)
Live with children between age 6-11 Yes 269 (17.5)
No 1269 (82.5)
Live with children between age 12-17 Yes 415 (27.0)
No 1123 (73.0)
Live with children age over 18 Yes 480 (31.2)
No 1058 (68.8)
Overall health Poor or fair 137 (8.9)
Good 482 (31.5)
Very good 556 (36.3)
Excellent 356 (23.3)
Highest nursing degree Diploma 259 (17.1)
Associate 566 (37.3)
Baccalaureate 525 (34.6)
Master’s/doctorate 167 (11.0)
Formal cational program Currently enrolled 129 (8.4)
Not currently enrolled 1409 (91.6)
Advanced certificate Yes 413 (26.9)
(National specialty or NP certification) No 1125 (73.1)
Nursing cation in the US Yes 1448 (94.1)
No 90 (5.9)
Importance of religious beliefs Not at all/not very important 232 (15.3)
Moderately/very/extremely important 1283 (84.7)
MSA size Small (population <250,000) 181 (11.8)
Medium 402 (26.1)
Large (>1 million) 955 (62.1)
Position for pay More than one 293 (19.1)
Only one 1238 (80.9)
Work setting Hospital 938 (61.0)
Nursing home 86 (5.6)
Nursing cation program 44 (2.9)
Home health care 126 (8.2)
Ambulatory care 218 (14.2)
Other 126 (8.2)
Position Manager 282 (18.9)
Consultant 26 (1.7)
Instructor 70 (4.7)
continued.
Journal of Nursing Scholarship First Quarter 2006 75
RN Work Satisfaction
Table 2. (continued)
n (%)
Direct care 943 (63.3)
Advanced practice nurse 100 (6.7)
Other 69 (4.6)
Work shift Day 890 (59.9)
Night 302 (20.3)
Other 295 (19.8)
Transfer of work unit Yes 167 (10.9)
No 1366 (89.1)
Change in supervisor Yes 529 (34.5)
No 1003 (65.5)
RN’s career orientation Less than others 184 (12.0)
The same as others 755 (49.2)
More than others 594 (38.7)
Partner’s career orientation Less than others 132 (8.7)
The same as others 534 (35.4)
More than others 435 (28.8)
No partner 409 (27.1)
Needle sticks Never 1153 (75.0)
One time 284 (18.5)
More than one time 101 (6.6)
Strains/back injury Never 839 (54.6)
One time 311 (20.2)
More than one time 388 (25.2)
Paid time off benefit Have it and used it 552 (35.9)
Have it but not used it 778 (50.6)
Do not have it 208 (13.5)
Medical insurance benefit Have it and used it 277 (18.0)
Have it but not used it 1033 (67.2)
Do not have it 228 (14.8)
Retirement benefit Have it and used it 122 (7.9)
Have it but not used it 1149 (74.7)
Do not have it 267 (17.4)
Tuition reimbursement Have it and used it 129 (8.4)
Have it but not used it 922 (59.9)
Do not have it 487 (31.7)
Importance of benefits to stay in the position Not at all/Not very important 254 (16.5)
Somewhat/Very important 1284 (83.5)
aSample sizes smaller than 1,538 indicate missing data.
Spratley et al., 2001) with the samples respectively, male (5%
vs. 6%), White (85.0% vs. 85.3%), and married (69.5%
vs. 70.4%) RNs. Although the mean age of the workingin-
nursing RN sample from the NSSRN was not available,
our sample (M=46.4) is similar to the mean age of the total
sample of the NSSRN that was 45.2 (Spratley et al.,
2001).
One of the issues in a study such as the one described
here is how meaningful the potential changes in satisfaction
are. Although the relationships might be significant,
the cost or effort to make a change (such as increasing variety
and autonomy) might not be related to a meaningful
change in satisfaction. In this study satisfaction scores were
standardized so the mean is approximately zero. A score of 1
is one standard deviation above the mean. What proportion
of a standard deviation would be meaningful? If a one unit
change in supervisory support is related to a.081 change in
satisfaction, that is unlikely to be meaningful. On the other
hand a one-unit change in career orientation that results in
a .183 change might be meaningful.
Working as an RN is often physically and emotionally
demanding. RNs with poor or fair health might find this
76 First Quarter 2006 Journal of Nursing Scholarship
RN Work Satisfaction
Table 3. Means and Standard Deviations of Continuous
Demographic Variables and Metropolitan Statistical Area
Characteristics (N=1,538)
M SD
Age 46.4 (10.5)
Years of experience in nursing 18.8 (11.1)
RN’s annual income $49,940 (19,903)
Log of partner’s annual income 7.98 (4.83)
Number of benefits 6.29 (2.61)
Medical, surgical, and other specialists 1.81 (0.65)
per 1000 population
Primary care practitioners per 1000 population 0.23 (0.08)
Index of competition 0.68 (0.21)
Percentage of HMO hospital services paid through 13.8 (11.0)
fee schles
Unemployment rate in 2002 5.51 (0.97)
Inpatient days per 1000 population 0.98 (0.33)
RN size divided by corresponding MSA population 0.99 (0.25)
Note. Sample sizes for each variable may be smaller than 1,538 because of
missing values.
burden difficult, so that they are less satisfied than are RNs
with very good health. Why the non-Hispanic Black RNs
in our sample were less satisfied than were their White coworkers
is not clear, and Bush (1988) found race was not
related to satisfaction.
Regarding compensation, contrary to findings from some
other studies (Gurney et al., 1997; Ingersoll et al., 2002),
wages were not associated with satisfaction. However, dis-
Table 4. Ordinary Least Squares Regression Analysis of Significant Determinants of Job Satisfaction (N = 1,342)
Significant category for Unstandardized
Construct Variable (Reference Category) categorical variables coefficient R2 R2 change
Constant ?.971??
Demographic and Health Race/Ethnicity (Non-Hispanic White) Non-Hispanic Black ?.204?? .090 .090???
Overall health status (Very good) Poor or Fair ?.151?
MSA market Unemployment rate 2002 ?.040? .099 .009
Movement constraints Local job opportunity ?.042? .135 .035???
Work setting Supervisory support .081??? .541 .407???
Work-group cohesion .083??
Work setting (Hospital) Nursing cation program .355?
Position (Direct care) Manager ?.113?
Instructor ?.283?
Variety .106???
Autonomy .106???
Organizational constraint ?.154???
Promotional opportunity .091???
Work family conflict ?.077???
Career orientation Less than others ?.219???
(Same as others) More than others .183???
Paid time off benefit Not have it .227??
(Have it but not used it)
Distributive justice .087???
?p <.05, ??p <.01, ???p <.001.
tributive justice, which pertains to the fairness of pay, was
related to satisfaction. Interestingly, the only benefit associated
with satisfaction was paid time off. Possibly these other
benefits could directly affect turnover while not having an
effect on satisfaction.
Working shifts other than the day shift and shift length
were not related to satisfaction, consistent with findings
from other studies (Hoffman & Scott, 2003). The RNs’
quantitative workload was not related to satisfaction. RNs
who perceived that they had high workloads were no more
or less satisfied than were those who perceived that they had
low workloads. This finding is contrary to some findings
(Davidson et al., 1997; Gaerter, 1999; Hoffman & Scott,
2003; Sheward & Hagen, 2005), and others have found
no relationship between workload and satisfaction (Gurney
et al., 1997; Shaver & Lacey, 2003). These contradictory
findings might be related to the samples or to instrument
used to measure satisfaction. None of these studies included
the measure of satisfaction used in our study. The study reported
here had a nationally representative sample, which
none of the above studies had. The difference might be related
to the perceived fairness (distributive justice) of the
workload rather than the actual workload. If everyone is
working hard, that might not affect satisfaction. However,
if some people have higher workloads or fewer days off,
the lack of justice could lead to dissatisfaction. Although
much has been written about the need for RNs to have support
from mentors (Prevosto, 2001), this variable was not
related to satisfaction in our sample. Supervisory support,
however, was related to RN work satisfaction, as was work
group cohesion, and both of these conditions might indicate
Journal of Nursing Scholarship First Quarter 2006 77
RN Work Satisfaction
support aspects of mentoring. These work setting factors
can be influenced by employers.
Conflicts between work and family have been reported to
be related to work satisfaction. We defined two concepts:
work-to-family conflict (work interferes with family) and
family-to-work conflict (family interferes with work). When
work interfered with family, the RN work satisfaction was
lower; however, when family interfered with work no relationship
to work satisfaction was found. Work-to-family
conflict was related to satisfaction in nonnursing samples
as well. Organizational and personal initiatives to rce
work-to-family conflict would be particularly appropriate
targets to address (Frone, 2003).
Conclusions
The study reported here included a national random sample
of RNs in a variety of nursing positions and healthcare
organizations, unlike many other studies of work satisfaction
that were focused on only staff nurses in hospitals
(Adams & Bond, 2000). However, only the cational
work setting influenced satisfaction. Thus, differences in our
sample from studies focused on RNs in hospitals might account
for some differences in findings. On the other hand,
our model explained 54% of the variance inRNsatisfaction.
Thus, the model we tested, which included many variables
not analyzed in other studies, might account for some differences
from previously published studies.
Of particular interest to managers is what factors are mutable
by management or governmental policy in such a way
that they increase satisfaction. Considering the need to recruit
and retain minority nurses, managers should be particularly
sensitive to the concerns of non-Hispanic Black nurses
to determine how to increase their satisfaction. Organizational
characteristics such as paid time off, autonomy, variety,
distributive justice, supervisory support, promotional
opportunity, and organizational constraints are factors over
which organizations have a great deal of control. Interestingly
and contrary to economic literature, the amount of
wage was not significant but the fairness of the wage was
important, and this perception can be modified by employers.
Having paid time off as a benefit is a way employers
could rce work-to-family conflict; e.g., flexibility in work
schles might be an important factor in work satisfaction.
Work-to-family conflict and group cohesion might be improved
if organizations provide work environments that are
family friendly, with supervisors trained to foster activities
in work units that increase group cohesion. Improving those
organizational characteristics should lead to increased RN
satisfaction with work.
Future research should include studies with large enough
sample sizes to assess whether factors associated with satisfaction
vary by subgroup such as new graduates in the 1st
year of practice. Some measures that have been reported to
be related to satisfaction, such as communication with physicians
were not included in this study and should be included
in future research. This study was focused on individuals,
not organizations, and it included little information about
the organizations in which the RNs worked. We did not assess
organizational size or other characteristics, nor did we
include data about the dynamics of the work setting, such
as how care was organized.
Understanding satisfaction is important because it has
been linked inversely to turnover. Findings from this and
other studies indicate that organizations can do much to
increase RN satisfaction with work.
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