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Marketing Plan Project

 

Purpose of this?

1. to improve understanding of marketing through application of the concepts and content of the course
2. to learn more about the marketing strategies and business for a chosen or assigned company/organization
3. to learn how and what research is needed to develop marketing strategies and plans
4. to learn how to write a professional and properly cited business research report

 

Preliminary Situation Analysis

1. Choose your company/organization.

First, you need to select a company/organization to analyze.  This can be a company or organization that is (1) for- profit or not-for-profit, and (2) local or national. You will use this company/organization as the foundation for the situation analysis and for your final marketing plan.  Note: if you choose a well-known for-profit national company, it will be easier to find information and research to complete this project.

Later this situation analysis along with other analyses will be used to support a new product or product line (either physical/tangible good or an intangible service) that you will recommend that the company/organization should develop and market.

You may choose any of the top brands/companies mentioned here:  https://www.businessinsider.com/top-100-millennial-brands-2018-9.  It is best to choose a company that you are most familiar with, can find research about, and will have fun being creative with a marketing plan for them.  If you prefer a company that is not on this list, then you should email the professor for advance permission to use by the end of week 3 in the course.  Be sure to include the company’s website link so a judgement for approval can be made.

For your chosen company, you will complete a preliminary situation analysis (including a SWOT analysis) to understand: (1) what the company currently has to offer in the form of products, services, and resources, etc. (the controllable), and (2) what are the forces in the marketing environment (the uncontrollable or 5Cs) that provide guidance for future marketing decisions.  This will be the requirement for the preliminary situation analysis so the professor can provide you feedback to determine if you are on track with the project (20 points).

After receiving feedback for the preliminary situation analysis, you will improve this part of the assignment and use this research to identify a market need to fill by developing a new product/product line for your chosen company.  Then you will complete the final marketing plan project with your marketing strategic decisions (STPs, 4Ps) for this new product/product line

While the content from your reading/study, quizzes, mid-term, and final exams can provide you with the needed knowledge to complete this project, it is your further study/reflection for understanding and application of the important concepts and processes that will guide you in completing the marketing plan project.

If you have any doubts about the quality of application about a potential company or a new product/product line, then email the professor at least one week before the due dates.

 

2. Begin Your Company and Situation Analysis Research Immediately.
3. You should cite current research from credible and reliable sources.

Most of your content can be found in three to five research sources.  Be sure that you use current research sources (with cites/research no older than 3 years).  Beyond 3 years would not be considered up-to-date.

Note: if you cannot easily find the date of a research source, then it is not a source you should use in this project.  Without a current and proper date of the source, the quality of the research cannot be judged, and this will negatively affect your project grade.

Company Research Guide http://libguides.unco.edu/business/company

1. Your research citations should be complete and of proper style (e.g., APA).

As you find the information needed to complete this assignment, you will also need to record the source of your research for citation.  The web links below are provided to guide you in judging quality research sources and for properly citing those sources.  Proper and complete citations are a major part of your grade for this project and failure to cite properly will likely earn a C or lower grade on the project.

Citation: http://libguides.unco.edu/content.php?pid=85068&sid=1024771

More about the APA Format for Reference Citations (for citing sources within your pager and on the reference page): http://library.duke.edu/research/citing/

Pay attention about how to properly cite research from: (1) a Full Text Article from Library Databases (Electronic) or (2) a web page.  If your citation is only a web address, then it is considered incomplete and not acceptable.  What is most important is that your reference citations are complete and with consistency of style (i.e., APA).

Note 1: If using a database to find research sources, make sure that the web link provided works and links directly to the original source (a permanent link).  While signed into a database, the link found in your browser is often a temporary link and does not work for others not signed into your database.

Note 2: GMID Passport is an excellent database for your project.  But, make sure you have a separate citation for each report used in this database (see Citation link above).  A single citation for GMID does not provide adequate detail for judging the date and source of specific data used in your paper.

Note 3: Research citations from web sources need two dates: (1) date of the source, and (2) the date retrieved or downloaded (see APA style).

Note 4: If you do not have a date for a source, then you should not use it because its quality and currency cannot be judged.  Sometimes when you cannot find an immediate date, you can look at the bottom of the page and find the copyright date.

Note 5: you may use a Bibliography Maker, such as EasyBib or BibMe, but you will need to make sure you have input the extra information often needed.

Every citation should include some or all of the following (just fill in all the missing information requested by the Bibliography Maker): (1) the author, (2) the source (e.g., name of magazine, newspaper, web site, book, database, etc.), (3) title (article title, web page title, film title, report title, etc.), (4) date published or created, (5) date accessed or retrieved (for web sites only), (6) page numbers (for printed sources only), (7) direct permanent link to the specific article (for web sites only), (8) publisher (for books or web sites only), and other information, depending on the type of source and format style.

1. You should cite your research sources where used in the written presentation.

This is called an in-text citation.

The most common way to reference a research source in the body of the presentation is to present a citation in parentheses, by author and date, and may include page numbers.

For example, if the content from this paragraph came from research source by authors McDonald and McDonald, then it is indicated as it is here (McDonald and McDonald 2018). Two references are separated by a semi-colon, e.g., (Engel and Blackwell 2010; Stocker 2011).  Page numbers are indicated by a colon, e.g., (Engel and Blackwell 1982:7), which means reference found on page 7.  Page numbers are only needed for direct quotes.  In most cases, paraphrasing should be used instead of direct quotes. Excessive use of direct quotes will count against you.

3. Complete Preliminary Situation Analysis of the Marketing Plan Project (Part 1).

You must include cover page, table of contents, headings, sub-headings, proper spacing, intext citations, reference page, etc.

1. The format for your project is as follows:
2. Cover Page that includes the title of paper and your current name, address, phone, and email. Note: I will not share your personal contact information with anyone, though it is important that I know how to reach you in case of a problem with your project submission. Make it look professional.
3. Table of Contents page that includes all headings, subheadings, and their related page numbers aligned on the right side of the page (see any book Table of Contents for an example; hint: use tabs for alignment).
4. Content:
5. Write in paragraph form, not with bullets. This is a formal report and not an outlined presentation as done in past semester classes. You do need to explain yourself as with writing a formal report.
6. 12-point font, Times New Roman recommended for easier reading.
7. Use all headings and subheadings from the project outline. Be consistent in their indentations/alignment. Note: in the outline below, the numbers represent the 1^st level headings (1, 2, 3, 4), the capital letters represent the 2^nd level headings/sub (A, B, C, D), and the lower case letters represent the 3^rd level headings/sub (a, b, c, d).
8. Use single spaces within paragraphs and double spaces between paragraphs, headings, and subheadings.
9. Cite sources where used in the paper (in-text citation) using a footnoting method by author and date (see citing reference sources section above).
10. Include visuals (images, videos, etc.) in the body of your paper to liven up the written presentation (show and tell). Be sure to provide headers/captions/labels and reference cites for all graphics/pictures used in the paper. All graphs, tables, charts, figures, etc. should be numbered and discussed in the body of the paper.  Be sure to explain any visuals that are used, and do not use the visuals as a substitute for your written content.  Note: do not cut and paste tables, charts, etc. from the web.  This is considered plagiarism.
11. Include web links to demonstrate something important, such as an advertisement or video for your brand/company. Be sure to explain any links that are used, and do not use the links as a substitute for your written content.
12. References Page.

 

1. Expected minimum length for Part 1 of the Project:

2,500-5,000 words (or about 5 to 10 total pages of content, not including cover page, table of contents, visuals, and reference cites; and with formatting explained in section 3A above)

You will upload your paper under the module for Preliminary Situation Analysis so that VeriCite can be used.

 

1. Content for completing Part 1 of the Project (Situation Analysis):

The 5Cs provide the heart of the “situation analysis” (see textbook Chapter 17 and Appendix for guidance). The first step in planning is to understand about the company/brand’s current situation.  That assessment requires our nailing down characterizations of our company, customers, context, collaborators, and competitors (the 5Cs). The ideal result of a situation analysis is to answer every part of every question as comprehensively as possible, using secondary data and research citations as extensively as possible (required). Please note that the creation of marketing plans is not perfectly linear – when you find the answers to 1 of the Cs, you might need to adjust another C.

To complete this assignment, you need to begin with a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of your company/brand.  While not required to turn in with this paper, a SWOT will guide you in completing the situation analysis.  SWOT is described in Chapter 4 on Targeting and Chapter 16 on Marketing Strategy.  Some researchers recommend that you carefully examine linkages between the four SWOT elements of strengths, weaknesses, opportunities, and threats and that you consider designing objectives that either help to maximize the impact of the linkage or minimize the impact of the linkage in to the future.  Please note, objectives are not required for the completion of this assignment.

This section draws from information gathered from the 5Cs marketing framework outlined in your textbook.  The 5Cs consist of Company, Customer, Context, Collaborators, and Competitors. The textbook chapters mapped to the marketing framework are found in Figure 1.4 in Chapter 1.  An example spreadsheet of the 5Cs is in Table A17-1 (click full book button in Cengage to get there).

 

Below outlines topics that fall under each one of the 5Cs.  A SWOT analysis is used for several aspects of the situation analysis, so it is best to work on this first to guide you in summarizing the information below.

 

 

 

 

1. Company (4 points)

Describe its origins, brief history, sales, product lines or mix, etc.

• What are we good at? Known for?  Do a SWOT to determine this!
• What do we want to become? Future strategy.

This requires research about your chosen company.  Visit their web pages.  Google search for more.  Refer to the research resources mentioned in 2B above.

This comes from the strengths and weaknesses of your company (SW of SWOT).

1. Customers (4 points)

 

The company tries to understand its customers by collecting data on them.  The company starts with studying secondary data to know the background trends, and then as an option collects fresh data on customers and potential customers.

 

• Demographics (e.g., age, income, household composition)
• Psychographics (e.g., attitude to product, attitude toward brand)
• Current levels of customer satisfaction
• Describe loyalty programs and other efforts at Customer Relationship Management (CRM)
• Buying behavior (e.g., what they buy, when they buy, what channel do they prefer, sensitivity to price?)
• Other customer issues (e.g., What changes have we seen with these customers, why don’t non-buyers buy?)
• See more in the Appendix

This requires research about your company’s customers or target markets.  Visit their web pages to find their typical customers and research the trends or growth segments for these consumers.  You may also research their competitor’s customers to identify segments that are overlooked by your company. Google search for more.  Refer to the research resources mentioned in 2B above.

This comes from the strengths and weaknesses of your company and opportunities and threats from the market (SW and OT of SWOT).

 

1. Context (4 points)

 

The Company assesses the uncontrollable macro-environmental issues that affect it.  Sample questions that the company should pose help with understanding the business context.  The company revisits these questions when it sees changes in its environments or when it changes its brand or target segments.  The following topics should be included in this section as follows.

 

• Is the economy a factor? Is it stable? Growing? What’s the consumer mood?
• Are politics a factor? Are our partners stable?
• Is legal a factor? Are any consumer laws looming?
• Is technology a threat/opportunity? Machines? IT?
• Any societal concerns? Demographic shifts? Attitude shifts?

This requires research about your company’s industry.  Visit their web pages.  Google search for more.  Refer to the research resources mentioned in 2B above.  This is about your research as support and not about your opinion.

This comes from the market opportunities and threats to your company and its marketing (OT of SWOT)

1. Collaborators (2 point)

 

The company should also study the relationship it has with its providers in the supply chain.  The following topics should be included in this section as follows.

 

• Good relations with supply chain providers?
• Good relations with distribution channel members?
• Want any modifications?

This requires research about your chosen company’s supply chain and distribution channels.  Visit their web pages.  Google search for more.  Refer to the research resources mentioned in 2B above.

This comes from all areas of the SWOT.

1. Competitors (2 point)

 

Using a SWOT analysis helps the company determine its strength relative to other providers in the marketplace. The following topics should be included in this section as follows.

 

• Who are your major competitors?
• What are our competitors’ strengths? (these are threats in a SWOT analysis)

This requires research about your company’s competitors.  Visit their web pages.  Google search for more.  Refer to the research resources mentioned in 2B above.  This is about your research as support and not about your opinion.

This comes from the market opportunities and threats to your company and its marketing (OT of SWOT).

 

 

 

1. Summary (2 points)

 

Summarize the key conclusions about the situation analysis that will serve as the foundation for future analysis and formation of marketing strategies for a new product/product line.

 

1. Format, spelling, and grammar (2 points)

 

Be sure to follow the formatting instructions provided in this assignment.   Failure to do so could result in an overall grade reduction of 10%.

1. 
   Grading Rubric for Part 1 of the Project:

This assignment is worth 20 points as outlined above for the 5Cs.  Points will be earned based on grammar and completeness of response.  Since the SWOT is the basis of some of the Cs, it would behoove you to spend some quality time on the formation of this tool.

 

Situation Analysis	Very strong evidence	Moderate Evidence	Evidence is lacking
Company / worth up to 4 points	Clear and engaging description of company and what it wants to become (4 points).	Some evidence of clear and engaging description yet a minimal amount of confusion or missing information (2 points).	Evidence is unclear and not very engaging with a lot of confusion or missing information (1 points).
Customers / worth up to 4 points	Clear and engaging description of demographics, psychographics (if appropriate), customer satisfaction, buying behavior, and other customer issues (4 points).	Some evidence of clear and engaging description yet a minimal amount of confusion or missing information (2 point).	Evidence is unclear and not very engaging with a lot of confusion or missing information (1 point)
Context / worth up to 4 points	Clear and engaging description of economic, political, legal technological, and societal issues (4 points).	Some evidence of clear and engaging description yet a minimal amount of confusion or missing information (2 point)	Evidence is unclear and not very engaging with a lot of confusion or missing information (1 point)
Collaborators / worth up to 2 points	Clear and engaging description Modifications needed with either supply chain partners or distribution channel partners (2 point)	Some evidence of clear and engaging description yet a minimal amount of confusion or missing information (1 point	Evidence is unclear and not very engaging with a lot of confusion or missing information (0.5 point)
Competitors / worth up to 2 points	Clear and engaging description of SWOT and who are the main competitors and their threats to us (2 point).	Some evidence of clear and engaging description yet a minimal amount of confusion or missing information (1 point).	Evidence is unclear and not very engaging with a lot of confusion or missing information (0.5 point)
Summary / worth up to 2 points	Clear and engaging description of key conclusions about the situation analysis that will serve as the foundation for future analysis and formation of marketing strategies (2 points).	Some evidence of clear and engaging description yet a minimal amount of confusion or missing information (1 point).	Evidence is unclear and not very engaging with a lot of confusion or missing information (0 point).
Format, Spelling, and Grammar / worth up to 2 points	Format, spelling, and grammar are perfect (2 points).	Format, spelling, and grammar show a lot of errors (1 point)	Format spelling, and grammar show an overwhelming amount of errors (0 points).

 

Identification and Analysis of Bacillus megaterium

April 19, 2019.

 

 

Identification and Analysis of Bacillus megaterium

Introduction

Background Information

            The experiment was conducted to identify bacterial species based on the gram stain reaction, morphology, and arrangement of the isolated colonies in the growth medium. Identifying the bacterial morphology, arrangement and gram reaction were pivotal to narrowing down the bacterial species and carrying specific tests that would help identify the specific bacteria. For instance, the MR/VP test was used to determine the fermentation pathway that the unknown bacteria used to utilize glucose while starch hydrolysis test identified whether the bacterial species of interest could hydrolyze amylase and amylopectin using alpha-amylase and oligo-1,6-glucosidase enzymes; thus, differentiating the genera Bacillus and Clostridium. Therefore, the utilization of specific tests and procedures were instrumental in bacterial identification and analysis.

Purpose Objective

The primary goal of the experiment was to identify unknown bacteria using specific guidelines and procedures of identifying and differentiating bacterial species.

Materials and Methods

Materials

Various materials were critical for the successful identification of the unknown. A Gram Stain was used in elucidating the Gram reaction, arrangement, and morphology of the bacterial colony. A spore test kit was instrumental in determining whether the bacteria sporulated. A starch agar helped in identifying whether the bacteria could hydrolyze glucose in the agar. Methyl Red and Voges Proskauer stains were elemental in identifying the species of bacilli bacteria that are gram-positive and exhibits a rod shape.

Procedure

The first step was describing the bacterial colony using the terms provided in the laboratory handout by indicating as side A or B. The second step involved a Gram Stain reaction procedure, determination of bacterial morphology, and the arrangement observed. In the third step, the instructor confirmed the Gram reaction before proceeding with the inoculation steps. The following step was to follow the directions on the appropriate flow chart from lab notes and manual to identify the appropriate tests to carry out. Additional tests (spore staining, starch hydrolysis test, and Methyl Red/ Voges Proskauer (MR/VP) test), inoculation on starch agar, and Voges–Proskauer (VP) test was done for Gram-positive rods that were identified during the procedure. My lab partner inoculated the identified gram-negative rods on an API 20 E strip and motility agar isolated. Finally, all the reactions used to test for the unknown gram-positive rods were recorded on a log sheet to aid in the writing of a lab report.

Results

The initial findings included observations of the colony in the growth media and recorded as Side A and B. Colony description are as follows:

Description	Side A	Side B
Form	Circular	Circular
Surface Appearance	Smooth	Smooth
Elevation	Flat	Convex
Internal Structure	Finely Granular	Amorphous
Optical Characteristics	Translucent	Translucent
Hemolysis	Beta	Alpha
Gram Reaction	Gram Positive	Gram Negative
Morphology	Rods	Rods
Arrangement	Chains	Random

 

This experiment pursued the Gram-positive rods whereby, additional stains and tests were used to determine the unknown. The first test was spore staining whereby the bacteria tested positive, showing the ability to grow spores. The outcomes observed included green spores and pink rod-like structures. The second test was MR/VP staining which tested negative affirming that the bacteria was B. megaterium and not B. cereus or B. subtilis which both test positive for VP stain and negative for MR test (Sneath, Mair, Sharpe, & Holt, 1986). Finally, the Gram-positive bacteria grew on starch agar after inoculation; thus, tested positive for starch hydrolysis test. There were clear zones observed in the growth medium after the addition of iodine.

Discussion and Conclusions

Analysis of Data

            The bacterial colony description was beta-hemolytic meaning that the organism lyses red cells entirely using hemolysins O and S (cytotoxins) produced by the bacteria. The flat characteristic described the side view elevation of the colony while smooth implies that the margins of the colony are entire. Gray defined the bacterial pigmentation during growth in the medium. Translucent appearance described the opacity of the colony meaning that it was almost clear but distorted (Kysela, Randich, Caccamo, & Brun, 2016). Finally, a Gram-positive reaction meant that the bacteria had peptidoglycan on the cell wall while rod-shaped and chain-like structures defined the morphology and arrangement of the bacteria respectively (Seltmann & Holst, 2013).

1. megaterium is an endospore-forming bacterial species, and that is why it tested positive for the spore stain test. According to Gupta, Zhou, Bailey, and Christie (2015), the spores are essential for the bacteria in responding to adverse environmental conditions because they are characteristically highly resistant and dormant; hence, facilitate bacterial survival. MR test was used to determine whether the bacteria fermented succinic, lactic, and acetic acids after glucose supplication. The test was negative because B. megaterium produced less concentration of these acids during glucose fermentation; hence the pH remained high. More so, VP helps determine that the bacteria did not produce acetoin for conversion to diacetyl during anaerobic respiration.

Noteworthy, amylopectin and amylase are large molecules and cannot pass through the bacterial cell wall, necessitating the secretion of the two enzymes that break the starch molecules into simpler glucose subunits that are capable of entering the glycolytic pathway directly. Interpreting the results of a starch hydrolysis test requires the addition of iodine into the starch agar whereby it will react with the starch to form a dark-brown color (Hansen, Wayment, Klein, & Godfrey, 2018). More so, the hydrolytic reaction creates clear zones around the bacterial growth; hence, a positive test for the bacterial species.

Critique of the Experiment

The experiment was straightforward with minimal limitations. The procedural steps were crucial to identifying the unknown bacteria. However, I will include additional tests to define specific bacteria based on research information accurately.

Clinical Significance of Bacillus megaterium

Many studies have concluded that B. megaterium is non-pathogenic and does not elicit harmful outcomes to environmental and human health. More so, it is rare that this bacterial species causes critical clinical conditions. However, in their study, Loong et al. (2017) explained that the bacteria might have severe ramifications despite its dismissal as an insignificant contaminant. The researchers also cited other studies that implicated the bacteria in the formation of a brain abscess, ovarian mass torsion, food-borne illnesses, and sepsis. Also, the bacteria can attach and invade human erythrocytes, and its isolates have been identified to contain toxin-encoding genes associated with food poisoning.

In conclusion, the experiment utilized defined procedures to define the bacterial colony isolates and carry out tests to identify the unknown. The identification of gram-positive rods prompted for additional tests including MR/VP, spore staining, and starch hydrolysis test. The experiment concluded that the unknown organism was Bacillus megaterium because it is the only bacteria in the genus Bacilli that tests negative for MR/VP.

References

Gupta, S., Zhou, K. X., Bailey, D. M. D., & Christie, G. (2015). Structure–function analysis of the Bacillus megaterium GerUD spore germinant receptor protein. FEMS Microbiology Letters, 362(24). https://doi.org/10.1093/femsle/fnv210

Hansen, C., Wayment, B., Klein, S., & Godfrey, B. (2018). Iodine–Starch test for assessment of hyperhidrosis in amputees, evaluation of different methods of application*. Disability and Rehabilitation, 40(25), 3076–3080. https://doi.org/10.1080/09638288.2017.1367965

Kysela, D. T., Randich, A. M., Caccamo, P. D., & Brun, Y. V. (2016). Diversity Takes shape: understanding the mechanistic and adaptive basis of bacterial morphology. PLOS Biology, 14(10), e1002565. https://doi.org/10.1371/journal.pbio.1002565

Loong, S. K., Teoh, B. T., Johari, J., Khor, C. S., Abd-Jamil, J., Nor’e, S. S., … AbuBakar, S. (2017). Penicillin-susceptible, oxidase-negative, nonhemolytic, nonmotile bacillus megaterium in disguise of Bacillus anthracis [Research article]. https://doi.org/10.1155/2017/2578082

Seltmann, G., & Holst, O. (2013). The Bacterial Cell Wall. Springer Science & Business Media.

Sneath, P. H. A., Mair, N. S., Sharpe, M. E., & Holt, J. G. (Eds.). (1986). Bergey’s Manual of        Systematic Bacteriology (Vol. 2). Baltimore: Williams & Wilkins.

 

Student’s Name

Professor’s Name

Course

Date

Triple Play and the Art of Relevance

According to Simon, “Something is relevance if it gives you information if it adds meaning to your life if it makes a difference to you”. Therefore, relevance creates more involvement in the arts by a higher number of people. The following is the discussion how Triple play findings supports the theory of “Arts of Relevance”.

Engagement

There are those audiences who are always excited to converse performances instantly after watching; there are much attracted in all engagement activities. According to Simon, these kinds of people are insiders. That is, the audiences are aware of the play as they show much interest. However, those that prefer to reflect privately display little interest in engaging with the new play. Therefore, if theaters need new people to come inside their institution, they need to open doors that speak to outsiders and welcome them in. It will enable them to explore new adventures from interacting with new plays. Besides, most people are much interested in receiving information directly from playwrights. It encourages interacting with new plays as they are more absorbed in narrating the play after the performances.

Marketing

According to the findings, at least 60% of respondents wanted to realize new playwrights and plays whenever they go to a theatre. It supports the theory of relevance since in discovering new plays, they learn new information, which is relevant to their lives. As a result, people will understand the needs and interests of the communities since these are individuals with shared dreams, interests, and backgrounds, hence the more we tend to understand them, the more comfortable we can unlock relevant experiences. Good brand and reputation have been linked with the increase in audiences with new plays since they realize the relevance of the play due to the company’s reputation.

As a result, these audiences identify the doors that others are offering into their own experiences. The new play creates new connections to the viewers as it adds information to their knowledge of these topics as many people enjoy going to a theatre to have fun, relax, as well as spend time with friends and families. To them, bonding is relevant as it strengthens the connection with their loved one hence relevance. Moreover, new plays enable audiences to engage in some crucial issues in the communities.

Connections to Playwrights

Although many audiences do not establish a strong relationship with the playwrights, they play a significant role in the theory of relevance. They write new plays that can trigger the audience attention. Although most viewers do not pay attention to the authors of the play, there is a connection between the two groups since play writers adapt to their organization’s missions to make it relevant to the new people. Therefore, greater access to playwrights can increase the interest of attendees in seeing new plays. In addition, theatres can foster audiences’ connections with playwright through promoting their name recognition on social media. Usually, playwrights represent the relevance of an organization to the viewers. Hence, strengthening their bond would increase the audience’s connection to the new plays.

New Play Skeptics Vs. Co-Creators

Skeptics are always inclined to doubt or question accepted opinions (Carnwath 63). Skeptics prefer to be well-conversant about what they are going to see even before arriving at the theatre. They support the theory of “art of relevant” since as Simon narrated, “relevance is the key to unlock new meanings and experiences in life”. Therefore, skeptics show much interest in the new play, which enables them to explore discoveries. Besides, their desire for advanced information allows them to engage with the original play since they reduce the sense of uncertainty that might be associated with new plays. Consequently, they enjoy watching the play, which increases their relevance in the original play. The art of relevance stresses that to improve audiences’ experiences, one has to create a better connection with the visitors.

Therefore, skeptics display much interest as they talk about the play on their way home. It indicates how the new play has added new meaning to their life; hence, relevant. In contrast with skeptics, co-creators show more interest in discussing the play immediately after the performance. It could denote that the play did not add much meaning to their life, or they want to get rid of it. Therefore, skeptics audiences discover new information and meaning compared to co-creators since distractions increase relevance in the play.

How Triple Play Findings of Motivations for Theatre Going Support Theories of Relevance

People have varied opinions concerning why they go to the theatres. However, many issues spice up their motivation to attend a theatre. For example, some Single Ticket Buyers go to the theatre to have fun, socialize, relax, and, importantly, spend time with their loved ones. Therefore, it enables them to discover new plays and playwrights, which increases their relevance since the play could be a therapy to their problems hence adding relevance in their life. As a result, the new play enables them to unlock new ways, which builds deep connections with people who do not immediately self-identify with their work.

As Simon states, “Relevance is the key to a locked room where meaning lives” due to some of attender’s views theatre-going as a social activity. “The artist behind the play was far less important than the people they experienced the play with”. They get time to socialize with other people hence unlocking their relevance doors. Some go to the theatre due to the desire to engage with essential issues in the communities. Therefore, they challenge ones’ assumptions and ideas about the world. It promotes the theory of relevance as Simon states that, “if you’re seeking to be relevant to new communities, you have to find the outsiders from those communities with whom you can collaborate”. It increases the interest of people as the value offering of the new play touches their hearts.

How People’s Engagement Preferences Support the Conversation Around Relevance

The findings in the Triple play suggests that audiences should be motivated to attend a new play if it adds relevance in their life since the key to creating broader participation and more considerable significance is unlocking more rooms. The closed doors are the ones with compelling value, experience, emotions, and information. Hence, it creates a secure connection between audiences and playwrights, which increases participation in new plays. According to Triple Play findings, greater access to playwrights can increase the interest of new plays among the attendees. Attendees’ preferences on engagement denote that people want to connect with playwrights. They had much interest in conversing with playwrights, cast directors to ask them questions about the whole script. It would bring diverse people together; hence, removing any distractions. Through bonding with attendees, the playwright would be focusing on his or her mission. Thus, the more it opens more doors, the more it will remain relevant to their audiences.

 

 

Works Cited

Carnwath, J. “Triple Play: Audience Perceptions of New Plays.” 2017

Plan and implement high performance training and recovery programs

 

Plan a training and recovery program to meet athlete requirements.

 

2.5      Incorporate principles of recovery into training program design.

 

2.6      Identify and select equipment and resource requirements for each stage of the program.

 

2.7      Include evaluation methods to monitor training program.

 

2.8       Schedule and document all aspects of training program.

Recovery

What is it

http://www.ausport.gov.au/sportscoachmag/sports_sciences/post-match_recovery_practices_for_team_sport_athletes

Recovery nutrition AIS

http://www.ausport.gov.au/ais/nutrition/factsheets/competition_and_training/recovery_nutrition

Increasing and decreasing blood flow rates

Pool

Massage

Flushing with ice

 

Recovery session example

https://www.youtube.com/watch?v=a-ifOR35YvE

 

Identify and select equipment

Different clubs and support groups will have different access to gymnasiums, university facilities, laboratories and sports grounds to support the high performance athlete. Access to such facilities should be understood by all parties and may be the subject of agreements through contracts or simple letters. This would include costs and charges and arrangements for borrowing equipment. The coach and the athlete need to know that these are regular arrangements between the organisation and the coach/ athlete and are within the policies and procedures of that organisation.

In the middle of a training schedule they do not want to be arguing about their rights of access or usage of equipment if the management of the facility suddenly changes its mind or has an internal political problem. Such issues can lead to adverse media publicity as well.

Further to these issues the coach and athlete may want access to their own equipment for travel or home use. Here is a “shopping list”:

 

• Cardiovascular equipment such as exercise bikes –
• Free weights or weights tables and systems –
• Exercise machines with various resistance loading –
• Exercise balls – Bars, steps and resistance bands –
• Pool-based equipment
• – Computer equipment
• – Cameras and video equipment
• – Timing equipment

A further consideration will be actual competition equipment such as racing bikes, helmets, other protective equipment, bats or whatever is required. Many athletes at this level can gain sponsorships for their main equipment.

Resource requirements will be different for every athlete depending on their sports and their organisational requirements. For high level team sports most resource requirements have separate managers to keep track of all requirements and make sure they arrive at venues in time.

 

In some circumstances, for example American College Leagues, there will be policies governing numbers of coaching staff per athlete and individual use of equipment.

For some individual sports there may be a team of two – the athlete and the coach. The logistics of travel should be organised by a manager but many athletes have slept in very substandard accommodation as they climb the ladder of success.

 

The coach needs to be familiar with many kinds of legislation and codes of practice. Ordinary safety legislation, anti-discrimination laws and privacy laws are there to protect players and also to protect players from their own reckless behaviour which can easily lead to ruin of reputation through the media loving to write up bad news. Above all the media loves a drugs and partying beat-up so the coach has a watching brief on all these matters.

This can be complicated by different laws when travelling overseas plus differences in cultural understanding e.g. different attitudes to women in Muslim countries and different attitudes to body exposure.

Evaluation

 

The best evaluation of success will be sports success however other forms of evaluation should be built in to the training program so that there is reliable data on progress. The kinds of data must be decided between the athlete and the coach and it is within a framework of both having considerable experience by this stage of their career.

 

Fitness assessments: These are a normal fact for any high level athlete. As well as normal fitness benchmarks, they will be measuring health and likelihood of injury and many codes will include compulsory drug assessments.

 

Lab assessments: A range of blood and hormonal physiology assessments are now standard for athletes. These should be included in planning to bring as much science as possible to the performance task.

 

Discussions: We have said that all athletes should have a sports psychologist in the team and regular discussions should be held to maintain positive motivation and survey any problems before they become critical to performance. We have also recommended periodic formal facilitated discussions between the coach and the athlete to review any matters in the relationship.

 

Motivational Coaching

 

Eric Thomas

 

https://www.youtube.com/watch?v=eI5KqBGd01M

 

Whats your why

 

https://www.youtube.com/watch?v=5XIGu3Kxg2I

 

3 cone drill

 

https://www.youtube.com/watch?v=sLs6CxMGV0A

 

agility

 

https://www.youtube.com/watch?v=Rc5WuW6EeMw

 

 

 

Training and competition performances:

 

These should all be logged in detail and should include friendly training matches as well as the real events. Where repetitive practice is required e.g. golf then the number of repetitions plus before and after video should also be part of the program.

 

Training diaries: There are many styles off diaries from specialised online systems to ordinary old-fashioned notebooks. If the athlete is making a career of their sport then a diary is a record of that career. It also helps to capture fleeting moments of experience for further reflection and discussion later.

schedule and document all aspects of the training program

 

The training program, training competitions and official competitions should all be logged in a readily accessible place – possibly on the internet so that all authorised people have access at any time. There should be room to log training reports and competition results, times, goals, runs or whatever the main scores consist of.

A special problem is entry schedules and responsibilities. There is nothing worse than missing a date of an entry because one party thought the other had control of the nomination. If there are overseas nominations the matter is further complicated by time differences and even language issues.

The Wiley Blackwell Handbook of the Psychology of Recruitment, Selection and Employee Retention,
First Edition. Edited by Harold W. Goldstein, Elaine D. Pulakos, Jonathan Passmore and Carla Semedo.
© 2017 John Wiley & Sons Ltd. Published 2017 by John Wiley & Sons Ltd.
Using Personality
Questionnaires for Selection
David J. Hughes and Mark Batey
8
Introduction
Employee selection is the process of choosing which member(s) of an applicant pool is
(are) most likely to behave in a manner that will achieve or surpass organizationally defined
metrics of success, such as selling products direct to consumers, preventing crime, building
and nurturing business‐to‐business relationships, caring for the sick and educating or
inspiring others to perform to the best of their ability. The definition of successful job
performance varies greatly across roles and organizations. Thus, while some elements of
behaviour are important for all jobs (e.g., exertion of effort), it is likely that many other
behavioural patterns will be suited to performance in some roles but not others. This
straightforward observation has led to a broad consensus from industry and academia that
personality testing, which as we discuss below is the fundamental descriptor of human
behaviour, should be useful during selection programmes. However, whether and to what
extent personality is useful remains a contested issue (see Morgeson, Campion, Dipboye,
Hollenbeck, Murphy & Schmitt, 2007a; Ones, Dilchert, Viswesvaran & Judge, 2007).
In this chapter we critically consider the evidence regarding the use of personality assessments
in selection.
We begin by setting the scene of personality test use in selection before defining personality,
considering why it should be of value in selection and briefly considering how we
arrived at the current state of knowledge in personality research generally. We then examine
the predictive validity evidence for personality in selection, considering personality as a
single predictor of job performance and as a part of a broader selection programme.
We then explore debates regarding what level of the personality hierarchy (broad factors
vs. narrow traits) is more useful during selection, whether universal job performance exists
or whether different jobs require different behaviours and thus nuanced personality
assessment, and we consider the potential utility of ‘other ratings’ of personality. We then
move on from predictive validity and discuss how and when personality measures might be
152 Selection
used within a selection programme. Finally, we suggest areas of research that offer great
promise for improving our understanding, and subsequently evidence‐based practice
within selection.
Setting the Scene
There are three key stakeholders in the personality–selection domain: academia, organizations
and test publishers. In principle, these three stakeholders share one objective: to produce
and use assessments that are reliable and valid. However, each constituency possesses
potentially conflicting drives and foci, which have led to some disarray in the development
and use of personality assessments in selection.
Academics have a primary interest in understanding the nature, theory and structure of
personality. A focus on considering what personality is and what it is not, how it is structured,
the processes underlying personality observations and the nomological net that
informs our understanding of how different aspects of personality and other individual
difference constructs relate. Organizations have a primary interest in using personality
assessments to deliver a return on investment. A focus is what predicts both productive
and counterproductive behaviour and performance in organizational contexts. Finally, test
publishers hold a primary interest in commercializing personality assessments thereby
making money from personality measures – a focus on what is marketable and useable by
those willing to pay for their assessments.
The result is a marketplace where the tools that organizations use are often at odds
with the theoretical foundations prized by academics. Further, in an effort to present
what appears to be either a unique or similar product, test publishers produce tools that
possess the same trait labels, but measure different constructs, or tools with different
trait labels that measure the same constructs. This is often referred to as the ‘Jingle Jangle
Fallacy’ (Kelley, 1927; Thorndike, 1904). Ultimately, these trends stifle scientific
progress and lead to confusion for practitioners and organizations, neither knowing
which personality measures,
if any, to use. What we have in the case of personality in
selection is a classic example of a scientist/practitioner divide and often a lack of evidencebased
practice (Rynes, Gyluk & Brown, 2007). The most theoretically and empirically
valid measures are often passed over for less‐grounded counterparts, with many test
publishers
failing to publish
their validity studies and others simply not conducting them.
These issues muddy the waters when we attempt to assess the utility of personality
measures
in selection. It is far beyond the scope of this chapter to put an end to this
confusion,
but we can at least start to address some of the important issues regarding
how useful personality testing really is and, perhaps most importantly, what can we do to
maximize its utility.
What is Personality?
Before discussing personality in selection we must first clarify what we mean by personality.
Personality has been variously defined as: ‘One’s habits and usual style’ (Cronbach, 1984,
p. 6); ‘a dynamic organization, inside the person, of psychophysical systems that create the
person’s characteristic patterns of behaviours, thoughts and feelings’ (Allport, 1961,
p. 11); ‘a person’s unique pattern of traits’ (Guildford, 1959, p. 5); and ‘relatively stable,
internal factors, which produce consistent individual differences at the emotional and
motivational level’ (Pervin & John, 2001, p. 4).
Personality Questionnaires 153
A single definition would never satisfy all stakeholders. However, a review of definitions
reveals that certain features are agreed. Personality is seen as a relatively stable and consistent
set of traits that interact with environmental factors to produce emotional, cognitive
and behavioural responses. Such theoretical views are supported by empirical evidence that
shows that there are numerous identifiable personality traits (Cattell, 1954) with some
cross‐situation stability (Funder & Ozer, 1983; Mischel, 1968), and develop through
maturation (e.g., conscientiousness and emotional stability increase with age), while
demonstrating
relative and rank‐order consistency in adulthood (Roberts & DelVecchio,
2000; Roberts & Mroczek, 2008). Importantly, measures of personality traits can be used
to explain and predict a wide range of behaviours and outcomes both cross‐sectionally
(Roberts, Kuncel, Shiner, Caspi & Goldberg, 2007) and longitudinally (Chamorro‐
Premuzic
& Furnham, 2003).
For the purposes of this chapter we suggest that personality be defined as a collection of
traits that influence a person’s typical thought patterns (e.g., how deeply one considers the
elements of a task), feelings (e.g., how anxious one is when faced with deadlines) and
behaviour (e.g., how organized one is). There are three main assumptions with regard to
the nature of personality traits that we adopt: 1) they are relatively stable (we discuss this
further below); 2) each individual has a unique constellation of traits; and 3) they drive
behaviour. Each of these assumptions is vital if personality is to predict behaviour at work.
Why should personality be relevant at work?
Given the broad agreement that personality is in part responsible for emotional, cognitive
and behavioural responses, it must be relevant for the prediction of conduct at work.
Workplace behaviour is not only defined in terms of what we can do (ability) but also how
(style) we do it. Some people work systematically, others more haphazardly; some communicate
empathetically, others in an authoritarian style; some are resilient under pressure,
others appear less so. Is it possible to achieve the same level of performance regardless of
these differences in style? Perhaps. Nevertheless, the manner in which tasks are conducted
is undoubtedly important at work.
No employee or organization can operate in a vacuum. From a single person start‐up,
through to a multinational corporation, people must interact with others. Personality has
a notable role in determining the quality and utility of these interactions. Indeed, ‘personality
clashes’ are an often‐cited cause of workplace conflict and finding like‐minded
colleagues
is an often‐cited contributor to job satisfaction.
Personality relates to the degree of enjoyment we take from certain elements of work
and thus how much motivation we have to carry out certain tasks (Ackerman, 2000).
For example, if employees are socially anxious and fearful of a negative evaluation, they
will be less motivated to speak publicly. If they are particularly anxious, it might even
reduce the quality of the communication and thus influence their job performance. Even
if they are able to manage anxiety within the presentation effectively, the emotional labour
and additional effects of the task on energy levels, well‐being and subsequent performance
could be considerable. If, however, an employee enjoys being in the limelight and finds
performing a presentation is a fun opportunity to relish, it is likely that job satisfaction and
performance will be higher.
In sum, personality influences how we approach a task, how we interact with others and
how natural or enjoyable we find a task or environment. Different approaches to these
aspects of working life may well influence job performance, yet even if they do not, variations
in these three areas are still pivotal to a wide range of other organizational variables
(organizational
commitment, citizenship behaviour, tenure, employee relations, etc.).
154 Selection
Trait, State or Type?
Above, we assumed that personality is the product of a constellation of traits, yet a number
of personality models and measures conceptualize personality through ‘types’ (e.g., Myers‐
Briggs type indicator; Myers, 1978). Personality types posit people as members of distinct
and discontinuous categories (Carver & Scheier, 1996); for example, a person is either an
extravert or an introvert. Typologies are suggested to have useful features, most notably
that they are relatively simple to grasp, which can be beneficial when discussing personality
with non‐expert individuals, as we often do within organizations. Type approaches are
often contrasted with the trait view of personality, which suggests that an individual can
fall on a continuum for each trait, so that positioning towards either extreme of the
continuum
is indicative of a stronger tendency to think, feel or behave in that manner.
A person is not simply extraverted or introverted, but rather is positioned somewhere
along a scale ranging between the two extremes. A simple consideration of human personality
and behaviour favours a continuum approach over a type approach: people do differ
in their level of extraversion (or indeed any other trait) and are not simply one type or
another. For this reason alone, we can say that trait theories are more valid than typologies.
Typologies come under further scrutiny when we consider the measures designed to assess
them. For example, the MBTI, despite being widely used, lacks internal consistency, test–
retest reliability and predictive validity (Pittenger, 2005). Thus, due to poor reliability and
questionable validity, the current authors recommend that regardless (or perhaps because)
of their simplicity, typologies be treated with caution in all organizational contexts, and
under no circumstances should be used for selection. That this point still needs to be
raised is testament to the gulf between science and practice we raised in the introduction
to this chapter.
A similarly contested yet more nuanced debate of real relevance to the personality in
selection discussion relates to personality stability and the influence of situational variables.
The extreme explanations that all behaviour is a product of the environment (if this were
true no cross‐situational consistency would exist) or that traits alone explain everything
(if this were true any cross‐situational variability would not exist) are inadequate. Indeed,
both situational variables and traits can be of equal relevance to explaining any single
behaviour. Often, traits share only modest correlations (0.3) with behaviour (Mischel, 1968),
as do situational variables (Funder & Ozer, 1983).
Thus, behaviour is not simply the product of either traits or the environment. Rather,
most behaviour is the product of complex trait × state interactions, whereby the influence
of the trait tends to be greater than that of the state in circumstances where situational
pressures are weak, and vice versa (e.g., Carver & Scheier, 1996; Judge & Zapata, 2015;
Monson, Hesley & Chernick, 1982). Thus, the influence of personality traits differs across
scenarios. Despite the role of situational variables, what we can conclude is that traits do
predict behavioural patterns across situations and time (e.g., Feist & Barron, 2003); those
who score high on measures of anxiety tend to be more anxious than those who score low
on anxiety across situations. Such consistency is essential; without it, personality would not
be a relevant construct to consider in a selection equation.
Identifying and organizing personality traits
Models of personality as they stand today are largely the result of work in two parallel
traditions:
the lexical and psychometric. The lexical hypothesis (Galton, 1869) suggests
that if a trait is important in influencing how we think, feel and act, it will be enshrined in
language;
the more important the trait, the more likely that it will be encoded in language
Personality Questionnaires 155
in a single adjective. Thus, researchers scoured dictionaries and psychological theories and
compiled lists of adjectives that describe personality (see Allport & Odbert, 1936;
Baumgarten, 1933; Cattell, 1943; Galton, 1869). This iterative work eventually culminated
in the development of Cattell’s bipolar personality–descriptor scales. These scales
represent the foundations of many currently held trait measures of personality and served
to generate, through numerous factor analyses, Cattell’s 16PF, which is today widely used
in selection.
Early attempts to replicate Cattell’s work were not wholly successful, with numerous
researchers finding that five broad personality traits consistently emerged from factor
analyses of personality ratings (Borgatta, 1964; Fiske, 1949; Norman, 1963; Tupes &
Christal, 1961). Further work in the area of personality structure continued to point
towards five broad factors and as a result led to the general consensus that ‘analyses of any
reasonably large sample of English trait adjectives in either self‐ or peer descriptions will
elicit a variant of the Big‐Five structure’ (Goldberg, 1990, p. 1223).
Today there are two main variants of these five traits: the lexical Big Five and the
psychometric
five‐factor model (FFM). The five factors are neuroticism, extraversion,
openness‐to‐experience (intellect in the lexical Big Five), agreeableness and conscientiousness
(Costa & McCrae, 1992; for a historical description of the emergence of the five
factors, see Digman, 1990). Despite some rather substantial differences in item content
and structural relations between the two models (e.g., the trait warmth is considered a
facet of extraversion in the FFM but a facet of agreeableness in the Big Five), researchers
and practitioners often conflate the two and use them interchangeably (Pace & Brannick,
2010). These differences are often given only cursory discussion but are potentially critical
in a selection environment. For example, if evidence from a job analysis or research literature
suggests warmth is one of the most important behavioural characteristics of a care
worker, how much emphasis is placed on extraversion or agreeableness in the selection
equation should depend on which inventory is being used. This example also draws on
another important debate that we will note here and consider in detail later. The bandwidth
fidelity argument concerns the question of whether narrower personality traits (e.g.,
warmth) or broader factors (e.g., agreeableness) are more useful in predicting behaviour.
Ultimately, this debate contrasts the measurement specificity one can gain using narrow
traits versus the superior reliability one can get from a broader trait. Equally, it is suggested
that predictors and outcomes should be matched in specificity, so when predicting
complex and aggregate outcome variables such as job performance, complex and aggregate
personality variables would be best.
Despite the differences between the two five‐factor approaches, there is a considerable
amount of evidence in favour of the broad five factors. In particular, the psychometric
FFM, which is argued to be ‘exhaustive of the personality sphere’ (McCrae & Costa,
1985, p. 558), is the most dominant measurement framework in research. The widespread
adoption of the FFM has undoubtedly benefited personality research. The FFM provides
a parsimonious model to guide the accumulation of research findings, allowing for crossstudy
comparison and accelerating knowledge production. The ability to empirically
aggregate research findings has ultimately resulted in the generation of meta‐analytically
derived estimates of magnitude of prediction (e.g., Barrick & Mount, 1991, 1996; Barrick,
Mount & Judge, 2001; Judge & Ilies, 2002). Meta‐analyses of the personality–job
performance relationship are very important in understanding the role personality can play
in selection.
Despite the popularity of the FFM, the adequacy of the model and even the fundamental
notion that five broad orthogonal factors top the personality hierarchy, is frequently contested.
Briefly, there are valid concerns of both a theoretical and methodological nature
156 Selection
with regard to the development of the five‐factor measures (e.g., Block, 1995, 2001,
2010). Further, research has been inconsistent in returning five factors from structural
analyses (Booth & Hughes, 2014), and where five factors have been identified there has
been debate as to whether or not these five factors are consistent (Pace & Brannick, 2010).
In addition, the FFM does not fit in confirmatory factor analyses (Vassend & Skrondal,
2011) or less restrictive exploratory structural equation models (Booth & Hughes, 2014;
Marsh, Lüdtke, Muthén, Asparouhov, Morin, Trautwein & Nagengast, 2010), suggesting
that the models are in need of some revision. These concerns may seem like excessive
academic navel‐gazing, but quite simply if the measures do not offer optimal measurement,
they are unlikely to produce optimal prediction. As a result, concerns of a structural nature
are of utmost importance to personality in the selection debate.
A further consideration relates to claims of the exhaustive nature of the FFM. This is
simply not the case. Many investigations have focused on traits that fall outside the FFM
(Ashton, Lee & Son, 2000; Jackson, Ashton & Tomes, 1996; Jackson, Paunonen, Fraboni &
Goffin, 1996; Lee & Ashton, 2004; Lee, Ashton, Hong & Park, 2000). Some of this
research has led to the development of the HEXACO model, a six‐factor model with more
facets than the FFM. There is also ample evidence of narrow, facet‐level personality traits
being omitted. For example, Paunonen and Jackson (2000) noted that traits of conventionality,
egotistical, integrity, femininity, seductiveness, manipulativeness, humour, thriftiness
and religiosity were missing. From a cursory perspective, one can see that these traits
might be of value in explaining some workplace behaviours. Further, these traits offered
incremental predictive validity over and above the FFM in relation to 19 criteria across
samples from Canada, England, Germany and Finland (Paunonen, Haddock, Forsterling &
Keinonen, 2003).
There are three main concerns to be recapped here. First, the FFM was not developed
in a theoretically or methodologically optimal manner. Second, FFM measures often provide
suboptimal measurement. Third, the FFM is not exhaustive and the traits it excludes
might be of value in selection. These limitations do not preclude the use of the FFM in
selection, but we must keep them in mind when evaluating the evidence pertaining to the
predictive validity of personality in selection. It is also important to note that these
concerns
are not exclusive to the FFM. Many other broad personality measures offer poor
measurement and miss (or incorrectly model) important aspects of personality.
What Does the Evidence Say about the Utility
of Personality within Selection?
When considering the role of personality in selection one question is of utmost importance:
is the tool a valid predictor of relevant work‐related criteria? Usually, the focus is on
job performance, but it can also span other important related criteria (e.g., training
performance, counterproductive work behaviour, citizenship behaviour). This section
addresses the vexed question: what does empirical research say about the use of personality
measures during personnel selection?
In 2007 a series of well‐respected organizational researchers considered the use of personality
in selection and concluded that ‘Due to the low validity and content of some
items, many published self‐report personality tests should probably not be used for
personnel
selection’ (Morgeson et al., 2007a, p. 720). In direct response, Ones and
colleagues
(2007, p. 1020) argued, ‘Any selection decision that does not take the key
personality
characteristics of job applicants into account would be deficient.’ Clearly, the
jury on the utility of personality measures is still out.
Personality Questionnaires 157
The use of personality measures in selection remains a contested subject and the literature
has been reviewed, in compelling fashion, to support both sides of the debate. There
is evidence to suggest that personality measures can be useful, but the same evidence tends
to suggest that their utility is limited. The current authors believe that the evidence shows
personality can add value to selection decisions, but only if used appropriately. In this
section,
we discuss the evidence for and against the predictive validity of personality in
selection, but we also address the perhaps most compelling aspect of this discussion:
how can we maximize the utility of personality measures?
Predictive validity: Meta‐analyses
The interpretation of meta‐analytic correlations between personality ratings and job
performance, often assessed by supervisor ratings, is central to the debate regarding the
use of personality during selection. Before we look at some of those relationships, we must
note that there are two main estimates of the correlation between personality and job
performance: a raw correlation and a corrected correlation. Within a meta‐analysis it is
common practice to adjust or correct correlation coefficients based on estimates of
unreliability.
Often, there is an acknowledgement that the criterion variables (e.g., job
performance metrics) and sometimes the predictor variables (in this case personality) lack
reliability, and as a result attenuate the estimated relationship. Corrections increase the
accuracy of population‐level estimates of correlations and are well supported both
theoretically
and statistically (Hunter & Schmidt, 2004; Schmidt, Shaffer & Oh, 2008).
When addressing arguments or building theories and models, the more accurate our
empirical estimates are the better. Nevertheless, despite the well‐accepted practice of
correcting
correlations in meta‐analyses, practitioners generally do not correct estimates in
selection decisions (Morgeson et al., 2007a). Thus, there is a good argument for considering
the magnitude and pattern of relationships of both the uncorrected and corrected
estimates. In this chapter, we present both estimates where applicable.
In 1991, Barrick and Mount published a seminal paper describing the meta‐analysis of
117 American and Canadian studies (undertaken between 1952 and 1988; N = 23,994).
Conscientiousness proved a reliable and valid correlate of job performance across occupations
(r = 0.13, corrected r = 0.23). The remaining traits (extraversion, neuroticism, agreeableness
and openness to experience) were unrelated to job performance en masse. Barrick
and Mount (1991) examined the correlations between the Big Five and a composite variable
of job performance, training performance and personnel data (e.g., salary, tenure) in
the whole sample, but also provided estimates for different job roles. Once again, conscientiousness
proved a valid and reliable predictor across all roles (r = 0.09–0.13, corrected
r = 0.20–0.23). Extraversion was found to be relevant for those in sales (r = 0.09, corrected
r = 0.15) or managerial roles (r = 0.11, corrected r = 0.18), while the other traits were
generally
unrelated. In a European equivalent, Salgado (1997) convergently found conscientiousness
to be a valid and generalized predictor across occupations and performance
criteria with a very similar magnitude of correlation coefficients. Divergently, Salgado reported
a role for neuroticism (r = –0.08, corrected r = –0.12) across all occupational groups,
which corresponds with other meta‐analyses (Hough, Eaton, Dunnette, Kamp & McCloy,
1990). Again, in line with Barrick and Mount (1991), Salgado found that the other personality
traits were not relevant to job performance but were relevant to some other important
organizational criteria (e.g., training performance) in specific occupational groups.
The most compelling study examining the relationship between the Big Five traits and
job performance was a meta‐analysis of meta‐analyses conducted by Barrick, Mount and
Judge (2001). Conscientiousness was found to be important across occupational groups
158 Selection
in terms of job performance (objective rating: r = 0.10, corrected r = 0.23; supervisor
rating:
r = 0.15, corrected r = 0.31) and all other job‐relevant criteria examined. Neuroticism
was also shown to be a generalizable predictor of supervisor‐rated job performance
(r = –0.07, corrected r = –0.13) but was lower in magnitude than conscientiousness and
less consistent across the other criteria examined. Thus, a relatively firm conclusion can be
made that conscientiousness is important for performance in all roles, and that in most
instances lower levels of neuroticism are also related to improved performance. The three
remaining traits, while not relevant to job performance across occupations, can be relevant
in certain roles, for example management (extraversion: r = 0.10, corrected r = 0.21),
and are related to specific work‐related behaviours such as training performance (openness
to experience: r = 0.14, corrected r = 0.33) and team working (agreeableness: r = 0.17,
corrected r = 0.34).
Thus, decades of meta‐analyses have now shown that working in an organized, responsible
and industrious manner (conscientiousness), while maintaining a degree of emotional
stability (neuroticism), is related to successful job performance across the board. Some
researchers (and indeed practitioners) have argued that while evidence of some generally
stable patterns of association between personality traits and job performance are informative,
the magnitude of the relationships raises some serious questions (Guion & Gottier,
1965; Morgeson et al., 2007a). Indeed, with uncorrected rs of –0.10–0.15 and even
corrected rs of –0.20 –0.30 the predictive validity of personality measures is roughly
equivalent to many selection methods broadly considered unusable within selection
(e.g., unstructured interviews, Schmidt & Hunter, 1998).
At a cursory level, we have to agree with Morgeson and colleagues (2007a). These
results do pose serious questions about the utility of personality within selection. The
current authors would certainly feel uncomfortable being selected, or not, based on our
conscientiousness and neuroticism alone. However, we must travel beyond a cursory level
and consider a number of important and substantial nuances within the personality–selection
debate before we reach any firm conclusions. The remainder of this section focuses on five
important nuances within this debate. First, when considering the personality of potential
employees we rarely, if ever, focus on a single trait. Thus, we must look at the combined
explanatory power of multiple personality traits not just univariate correlations. Second,
selection by personality alone (or indeed any single selection method) would be indefensible.
Thus, we must consider the relative and incremental explanatory power of personality
when considered alongside other valid selection tools. Third, broad factors of personality,
such as the FFM/Big Five, currently dominate personality assessment; we consider
whether they offer superior levels of prediction compared to their constituent lower‐order
facets. Fourth, we contest the very premise of universal job performance: that successful
job performance across occupational roles should or would require the same degree and
combination of behaviours seems an odd assumption, one that has perhaps masked the
true potential of personality in the prediction of job performance. Fifth, we consider longstanding
concerns about the measurement error produced by response distortions during
personality assessment with a special focus on the potential utility of partially ipsative
measures
and ‘other ratings’.
Personality is multidimensional
Behaviour is complex. In seeking to explain complex behaviours at work (or anywhere
else), we rarely expect a single trait to be sufficient. Rather, we identify multiple traits that
might contribute and examine their combined ability to explain the behaviour of interest.
Thus, univariate relationships between individual personality traits and job performance
Personality Questionnaires 159
may underestimate the value that personality has to offer. In the same way we would not
calculate the predictive validity of a structured interview or cognitive ability test based on
their constituent parts, we should not judge personality based on single trait associations.
This line of argument has been most convincingly put forward by Ones and colleagues
(2007), who re‐examined the meta‐analytic correlations presented by Barrick and colleagues
(2001; discussed above) and computed the multiple correlations for all of the Big
Five and job performance. The results show that personality predicts objective job
performance with a multiple r of 0.27 (uncorrected r = 0.23) and a composite overall job
performance variable of r = 0.23 (uncorrected r = 0.20). Ones and colleagues (2007) also
demonstrated that personality variables measured at the Big Five level are even more
predictive
of other important elements of workplace behaviour. For example, counterproductive
work behaviours (r = 0.44 and 0.45 for avoiding interpersonal and organizational
deviance, respectively), organizational citizenship behaviours (r = 0.31), leadership
(r = 0.45), teamwork (r = 0.37) and training performance (r = 0.40).
There is no doubt that Ones and colleagues’ (2007) evidence provides a much more
optimistic view of the role of personality in understanding workplace behaviour. Notably,
however, and despite increases from the univariate estimates, the multivariate estimates
relating to job performance – the crucial criterion for selection decisions – are still less than
impressive. Indeed, the multivariate estimate is only slightly greater than that reported for
conscientiousness alone (Barrick et al., 2001) and collectively the Big Five account for
around 5–7% of variance in job performance measures. Thus, some have argued that these
results still provide underwhelming support for the use of personality in selection (Morgeson
et al., 2007b). Again, we generally agree, explaining the same amount of variance in
job performance as unreliable selection methods such as unstructured interviews is hardly
compelling. However, we do not believe that this means that personality tests are not
or cannot be useful. Below, we continue to consider the ways in which personality
assessments
can be used effectively within selection.
Incremental predictive validity of personality measures
Selection decisions are never made based on personality assessments alone and nor should
they be. Given that personality assessments are used as a part of a selection programme,
the practical value of any validity debate pertains to the incremental predictive validity that
personality assessments offer over and above other selection methods. Personality tends to
be weakly correlated with other selection tools, and in particular other individual difference
variables such as cognitive ability. For example, in their meta‐analysis, Judge, Jackson,
Shaw, Scott and Rich (2007) show that the correlations between general mental ability
and the Big Five are small, with the largest correlation being just 0.22 with openness.
Thus, personality and cognitive ability measures capture different information about an
employee and thus personality measures may offer unique predictive validity beyond that
obtained from cognitive ability measures.
Schmidt and Hunter (1998) estimated the incremental predictive validity of 18 selection
methods beyond general mental ability using data from previous meta‐analyses. Their
analyses suggested that, when combined with general mental ability, the personality measures
of integrity (a compound trait consisting of specific traits selected due to their likely
relevance, e.g., conscientiousness, dependability, honesty) and conscientiousness offered
27% (multiple r = 0.65) and 18% (multiple r = 0.60) increases in prediction respectively.
The only other methods to offer similar levels of incremental predictive validity were structured
interviews and work samples (both multiple r = 0.63), which are typically more
expensive and time‐consuming to construct and administer than personality assessments.
160 Selection
Given the positivity of these results, it surprising that little additional empirical study has
followed. In 2006, Rothstein and Goffin, when reviewing personality and selection, found
only two studies examining this question, one demonstrating that personality assessments
offer incremental predictive validity over an assessment centre when predicting managerial
potential (Goffin, Rothstein & Johnston, 1996) and the second showing personality
supplements biodata in predicting job performance (McManus & Kelly, 1999).
In the years following Rothstein and Goffin’s (2006) review a number of researchers
have addressed this issue. In 2014, Oh and colleagues examined the incremental predictive
validity of the Big Five and honesty–humility over and above cognitive ability when
predicting task‐based performance and also contextual performance (the extent to which
employees support non‐performance‐related organizational and psychosocial aspects of
work) of 217 military officer candidates. In relation to task‐based performance, both
cognitive ability (β = 0.25), and conscientiousness (β = 0.34) were significant predictors,
with personality accounting for a 0.22 increase in the multiple correlation. When considering
contextual performance, cognitive ability was not a significant predictor but the personality
traits of conscientiousness (β = 0.32), extraversion (β = 0.16), and honesty‐humility
(β = 0.13) were, and collectively produced a multiple r of 0.37. (The figures presented here
are the uncorrected estimates; the corrected estimates provided by Oh and colleagues
(2014) show no deviation from this pattern but are generally increased in magnitude by
around 0.1–0.15.) Similar results were reported by Colodro, Garcés‐de‐los‐Fayos, López‐
García and Colodro‐Conde (2015), who showed that personality traits (assessed using the
Spanish 16PF) accounted for incremental predictive validity beyond cognitive ability, with
personality explaining three times as much variance in performance.
Despite the positive trend in the incremental validity literature, a recent advance in metaanalytic
corrections suggests that the increment offered might only be, on average, in the
region of 5% variance explained (Schmidt, Shaffer & Oh, 2008). Nevertheless, personality
does offer somewhere in the region of a 5–30% increase in predictive validity. Whether the
validity is towards the upper of lower estimates will depend on the characteristics of the
role and the quality and job relevance of the personality measurement.
Clearly, personality offers novel and useful information that can improve selection
decisions regardless of whether assessments focus on task performance or broader definitions
of performance. The incremental predictive validity offered by personality measures
is particularly valuable to organizations because, in comparison to work samples, role‐plays
or structured interviews, personality scales can be purchased and administered in a
time‐ and cost‐effective manner.
Broad factors or narrow traits
Personality models generally build from large item pools, through facets to higher‐order
factors. For example, each of the Big Five factors as measured by the NEO‐PI‐R subsumes
six narrower facets/traits, each measured by eight items. It has been suggested that there
is little added value in measuring narrow facets, when the five broad factors account for
much of the variance in their lower‐order constituents. For instance, Ones and Viswesvaran
(1996) have argued that the direct measurement of broad personality factors alone is
sufficient, and in the case where the outcome variable is itself broad or complex (e.g., job
performance), preferable, as they suggest predictor and outcome variables of similar
bandwidth give optimal prediction.
However, this approach is also the subject of debate, with suggestions that regardless of
the bandwidth of the outcome, narrow traits still offer important insights. Lower‐order
facets and the broad factors supposed to subsume them are not perfectly correlated; facet
Personality Questionnaires 161
measures possess specific and reliable (non‐random) variance that might offer increased
predictive validity (Paunonen et al., 2003), which is lost when using broad factors.
A number of studies have empirically assessed the predictive validity offered by
broad factors and narrow facets. The conclusion is that narrow facets consistently offer
better and/or incremental predictive validity regardless of the complexity of the behavioural
outcomes
(Ashton, Jackson, Paunonen, Helmes & Rothstein, 1995; Jenkins &
Griffith, 2004; Lounsbury, Sundstrom, Loveland & Gibson, 2003; Paunonen &
Ashton,
2001; Rothstein, Paunonen, Rush & King, 1994; Tett, Steele & Beauregard,
2003; Timmerman, 2006).
Perhaps the most compelling evidence for the superiority of narrow facets when predicting
job performance comes from Judge, Rodell, Klinger, Simon and Crawford’s (2013)
meta‐analysis of 1,176 studies derived from 410 independent samples. Judge and
colleagues
examined the relationships between three hierarchical levels of personality and
task performance, contextual performance and an overall composite performance variable.
At the highest level of the trait hierarchy were the Big Five factors. At the next level, each
factor split into two mid‐level factors consistent with the framework derived by DeYoung,
Quilty and Peterson (2007). At the lowest level of the hierarchy, each factor split into the
six facets defined by FFM framework (Costa & McCrae, 1992).
Judge and colleagues’ (2013) meta‐analysis reveals that optimally weighted composites
of facets resulted in greater criterion‐related validity for predicting all performance outcomes
than did the Big Five factors, often accounting for 3 or 4 times more variance in
performance. With the exception of conscientiousness, which showed similar predictive
validity at all three levels, there was a clear pattern of facets outperforming the De Young
factors, which were in turn better than the Big Five, so providing clear evidence that the
broader the factor the weaker the prediction. A summary of the correlations is shown in
Table 8.1.
The muddying effect of aggregating personality facets into broad factors is detrimental to
predictive validity. For example, when considering task‐based performance, neuroticism at
the Big Five level and facet level accounted for 0.7% and 6.4% of variance, respectively. One
might conclude that 6.4% is not particularly impressive, but as an incremental addition to
other personality traits and other selection methods such as cognitive ability it might prove
very useful. Another stark example of the obscuring effect of broad factors is observed in
the extraversion to contextual performance link where the facets accounted for 24.1% of
variance compared to just 5.4% for the DeYoung factors and 4.8% for the Big Five factor.
Table 8.1 Correlations between personality at three levels of aggregation and overall, task,
and contextual job performance.
Correlations with Job Performance
Overall Task Contextual
Trait Facet Mid Broad Facet Mid Broad Facet Mid Broad
Emotional stability 0.23 0.12 0.10 0.25 0.10 0.08 0.30 0.21 0.16
Extraversion 0.40 0.21 0.19 0.18 0.14 0.12 0.49 0.23 0.21
Openness 0.30 0.10 0.08 0.17 0.13 0.12 0.18 0.06 0.03
Agreeableness 0.19 0.17 0.17 0.24 0.11 0.10 0.33 0.18 0.18
Conscientiousness 0.26 0.27 0.26 0.24 0.25 0.25 0.33 0.32 0.32
Facet = 6 NEO facets, Mid = DeYoung et al. (2007) factors, Broad = FFM
Source: Judge et al. (2013).
162 Selection
We noted earlier that conscientiousness did not as obviously follow this pattern and that
the differences across the three levels are so marginal that they would have little practical
significance. Thus, there seems to be something unique about conscientiousness in the
selection context.
Each of the conscientiousness facets – achievement striving, competence, deliberation,
dutifulness, orderliness – is positively related to job performance in the range of 0.11 to
0.28. This is not surprising given that the aspects of personality assessed by the items (e.g.,
pays attention to detail, follows a schedule, always prepared, determined to succeed) are
clearly of importance for performance in a range of jobs. Thus, the common variance
between
these facets represented by broad conscientiousness is generally useful in every
aspect of work. However, even some conscientiousness facets are unrelated or even
negatively
related to some aspects of job performance in certain roles (e.g., Bunce & West,
1995; Driskell, Hogan, Salas & Hoskin, 1994; Tett, Jackson, Rothstein & Reddon, 1999).
In such cases, it is likely that facets would outperform aggregated factors. The same is true
but more common and extreme for facets of the other factors, which more often show
differential and even opposite relationships.
Further, from a measurement perspective the conscientiousness factor tends to fit well
relative to the other four factors in structural examinations, suggesting that the broad
factor accounts for a good proportion of the facet‐level variance (Vassend & Skrondal,
2011). The other factors often do not fit as well in structural examinations (Vassend &
Skrondal, 2011), suggesting that the facets are less closely related, which is also evident in
the differential relationships displayed with performance criteria.
Given that organizations want to maximize predictive validity, narrow facets rather than
broad factors – which lead to underestimates and/or distorted estimates of relationships
– are evidently of greater value (even in some cases for conscientiousness). However,
it is often not practically feasible or sensible to administer tests of all known personality
traits. Thus, a process of identifying the specific traits to assess is needed.
Job analysis and the selection of relevant traits
So far we have reviewed evidence pertaining to the predictive validity of personality in
predicting job performance en masse, whether that be for police officers, managers in an
investment bank, managers in an ethical bank, nurses, teachers, or military personnel. Yet
we contest the very premise of universal job performance and argue that it is not at all surprising
to find that personality is not a simple universal predictor (Tett & Burnett, 2003;
Tett et al., 1999). Cognitive ability is a linear predictor of performance, so the quicker you
can acquire and utilize information the better, regardless of the job. Yet even for cognitive
ability there are quite marked differences in the magnitude of predictive validity coefficients
across roles. Typically, cognitive ability is most valid in cognitively demanding roles,
with corrected correlations ranging from 0.3 for clerical workers and drivers to 0.7 for
professionals and engineers (Bertua, Anderson & Salgado, 2005).
We suggest that most personality traits are differentially related to performance across
job roles and that the Big Five level of aggregation is not specific enough to maximize
prediction. The diversity of the narrow facets subsumed by the Big Five – warmth and
excitement seeking in extraversion or impulsiveness and depression in neuroticism, for
example – is such that knowing exactly why one of the Big Five should or should not
correlate
with performance is rather difficult. A ski instructor would probably benefit from
scoring high on both warmth and excitement seeking; a nurse would probably benefit
most from warmth but less so from excitement seeking (excitement seeking might even
be detrimental if the nurse works on a rehabilitation ward where novelty is low); while a
Personality Questionnaires 163
soldier would probably benefit most from excitement seeking but less from warmth. So, is
extraversion relevant for ski instructors, nurses and soldiers? Some aspects are and some
are not; some might even be negatively related (Tett, Jackson & Rothstein, 1991). Put
another way, one would not ask the same structured interview questions or use the same
assessment centre tasks when selecting a ski instructor, nurse or soldier. Nor should we use
and weight personality assessments generically.
So, how do we choose which traits to measure during a selection programme? The
answer is not particularly novel: job analysis. For years, recruiters have undergone a process
of describing the characteristics of jobs in order to identify the knowledge, skills and
abilities that are relevant for performance (Brannick & Levine, 2002). Often, such job
analyses will reveal that broad sociability is important, thus a measure of extraversion is
used. We suggest that job analysis should go one step further and identify which aspects of
extraversion; is it warmth, excitement seeking, or both? By considering the role in such
detail, recruiters can then choose facet‐level measures with greater predictive validity than
the Big Five and which ones are probably more time‐efficient to administer.
Numerous authors have discussed personality‐oriented job analysis methods (e.g.,
Costa, McCrae & Kay, 1995; Goffin, Rothstein, Rieder, Poole, Krajewski, Powell & Mestdagh,
2011; Jenkins & Griffith, 2004; Raymark, Schmit & Guion, 1997; Tett, Jackson,
Rothstein & Reddon, 1999) and this area is now receiving more attention in both academia
and practice.
If organizational researchers and practitioners measure narrow facets that
are theoretically
and empirically demonstrated (through job analyses and existing research)
to be relevant to performance in a specific role, we can begin to increase the predictive
validity offered by personality within selection. Empirical support for this confirmatory
approach to personality selection is provided by Tett and colleagues (1991, 1999), who
showed that confirmatory strategies (i.e., those based on job analyses) yield predictive
validity coefficients double in magnitude (uncorrected r = 0.20, corrected r =0.30) compared
to those derived using exploratory strategies (uncorrected r = 0.10, corrected r = 0.16).
Further evidence is reported in a meta‐analysis by Hogan and Holland (2003), who show
corrected correlations between theoretically‐related facets and performance‐based criteria
ranging from 0.3 to 0.4 (uncorrected 0.1–0.3).
The use of personality‐oriented job analysis yields more detailed and precise hypotheses
regarding the associations between personality and job performance. For example, one
might find that being an effective team player is crucial to job performance. The literature
might suggest that gregariousness and assertiveness (facets of extraversion) predict team
functioning in similar industries. One can then begin to model personality appropriately
as X (gregariousness, assertiveness) → M (team effectiveness) → Y (job performance).
We know from years of mediation research that it is possible that X is modestly related to
Y but strongly related to M, while in turn M is strongly related to Y. Such process models
of personality more accurately approximate the real world and as a result are more informative
and predictive (Hampson, 2012; Hughes, 2014). In order to evaluate such models
one would also need robust measures of job performance that include ratings of the
M variables. Such measures would not be particularly difficult to generate as these variables
will be identified during job analysis. Presumably, managers would be interested in these
facets of performance as well as an overall composite. Equally, one could adopt existing,
multifaceted measures of job performance, such as the Universal Competency Framework
(Bartram, 2005; Rojon, McDowall & Saunders, 2015).
Before closing this section, it is worth noting that there is likely one large hurdle to
overcome before targeted, facet‐level programmes are widely adopted in research and
practice, namely, identifying a satisfactory list of facets that prove as marketable as the
Big Five. One initial reaction might be simply to use the facets of the five‐factor model.
164 Selection
This is certainly not a bad starting point. However, as we discussed earlier, neither the
NEO‐PI‐R facet list nor a facet list from any popular omnibus measure is exhaustive of the
personality sphere. Thus, one major goal has to be to develop such a list.
The first author of this chapter and colleagues (Booth, Irwing & Hughes, in preparation;
Irwing & Booth, 2013) are in the process of building on the work of Booth (2011),
who semantically sorted and then factor‐analysed 1,772 personality items. The items were
drawn from seven major omnibus personality inventories (NEO‐PI‐R, CPI, 16PF, MPQ,
JPI‐R, HEXACO, 6PFQ) and four specifically chosen narrow inventories with several
specific measures (social dominance orientation, right‐wing authoritarianism, Machiavellianism,
need for cognition). In total, this analysis identified 78 seemingly unique personality
facets, though it is evident that some important traits appear to be missing from this
list and further validation work is ongoing (for details contact david.hughes@mbs.ac.uk).
Should this research produce a final list of 100 or so reliable and valid traits, it can serve as
a personality trait dictionary to be utilized to measure the important aspects of personality
identified during a job analysis in a manner that reduces the general problems of the Jingle
Jangle Fallacy discussed in the introduction to this chapter.
In the meantime, regardless of whether a universal list of personality facets is available,
researchers and practitioners alike can adopt the targeted facet approach within whichever
personality framework they deem most suitable. All personality measures have facets,
which can be used to measure personality traits closely aligned with crucial aspects of job
performance.
Response Distortions
The final predictive validity discussion concerns the persistent problem of response distortions.
Personality items are designed to measure respondents’ characteristic patterns of
thinking, feeling and behaving. The utility of any measure corresponds to its reliability and
validity, both of which are attenuated by measurement error (i.e., measuring things other
than the intended target). Self‐report questionnaire items are susceptible to a wide variety
of measurement errors, from differences in item and response scale interpretations to
systematic differences in response styles (e.g., acquiescence; extreme responding; Furnham,
1986). Many of these sources of measurement error are well known. However, of more
interest in the selection literature is measurement error arising from response distortions
due to low self‐awareness and deliberate faking.
There is a number of excellent discussions of faking in the literature, covering areas such
as how much people fake, how successful people are at faking and how faking influences reliability
and validity (see Birkeland, Manson, Kisamore, Brannick & Smith, 2006; Morgeson
et al., 2007a; Mueller‐Hanson, Heggestad & Thornton, 2003; Ones & Viswesvaran, 1998;
Tett & Christiansen, 2007), and we do not intend to reproduce these discussions here.
For the current authors, the bottom line is that response distortions such as faking almost
certainly occur and that some individuals fake more than others, which is of course problematic
from measurement, validity and ethical perspectives (Birkeland et al., 2006). Nevertheless,
personality measures retain predictive validity, as discussed above, regardless of response
distortions (Ones & Viswesvaran, 1998). Thus, personality measures are still useful in a
world where response distortions are quite common. This does not mean, however, that
we should ignore response distortion or, as some have suggested, see it as a desirable social
skill (Morgeson et al., 2007a). Rather, we should aim to measure, model and prevent it.
If we can reduce response distortions, then we may be able to improve the predictive
validity of personality tests further and certainly reduce the associated ethical issues.
Personality Questionnaires 165
Numerous solutions to combat response distortions have been suggested. Solutions
such as social desirability scales (Feeney & Goffin, 2015), forced‐choice or ipsative
measures
(presenting candidates with multiple trait statements that are matched for social
desirability and allowing them only to indicate one that is most like them; Heggestad,
Morrison, Reeve & McCloy, 2006; Johnson, Wood & Blinkhorn, 1988; Meade, 2004),
and imposing time limits for candidates (Holden, Wood & Tomashewski, 2001; Komar,
Komar, Robie & Taggar, 2010). While each of these methods shows some promise, none
has shown any genuinely compelling empirical support.
That forced‐choice personality measures have little influence over social desirability
ratings,
despite appearing to be more difficult to fake, is surprising (Heggestad et al.,
2006). It is also the prevailing view in the organizational psychology community that
compared to Likert‐type formats, ipsative measures produce lower predictive validity.
Recent studies, however, provide evidence to the contrary (Bartram, 2007; Salgado,
Anderson
& Tauriz, 2014; Salgado & Tauriz, 2014).
Forced‐choice measures come in two broadly different formats: fully ipsative (e.g.,
rank order four items/traits beginning with the one most like you) and partially ipsative,
which contain a forced‐choice element while retaining some flexibility (e.g., choose from
a list of four the item/trait least and most like you; see Hicks, 1970). A recent metaanalysis
by Salgado and colleagues (2014) suggests that fully ipsative measures perform
poorly with regard to predictive validity, but that partially ipsative measures produce
impressive levels of predictive validity (Salgado et al., 2014). Compared to validity estimates
derived predominantly from Likert‐type measures (Barrick et al., 2001), partially
ipsative assessments of emotional stability, openness and conscientiousness are considerably
larger, while measures of extraversion and agreeableness are equivalent across formats
(See Table 8.2).
Salgado and colleagues (2014) also examined associations within eight job roles: clerical,
customer service, health, managerial, military, sales, skilled and supervisory. The primary
study numbers (k = 2–11) and sample sizes (N = 171–3,007) are small and vary markedly
across job roles. Equally, estimates for each of the Big Five were not available across all
roles (e.g. emotional stability not reported in customer service roles). We suggest that
these notable limitations preclude firm conclusions regarding which traits best predict
which role, however, the pattern of the results remains very interesting. Particularly
striking is the range of validities reported, which in raw correlations vary from 0 to 0.4 and
in corrected validates vary from 0 to 0.7. Table 8.3 includes the highest and lowest predictive
validity reported for each of the Big Five. The difference in variance explained between
the mean and largest validity estimates is substantial, with the largest estimates between 4
and 10 times as large as the mean.
The variation in predictive validity provides compelling support for the arguments put
forward in the ‘Job analysis and the selection of relevant traits’ section, specifically, that
universal job performance does not exist and that the nature of the role moderates the
correlations between personality and job performance. Further research using job‐relevant,
partially ipsative personality measures identified through personality‐oriented job analysis
or theoretical frameworks appears warranted.
The results from partially ipsative measures appear compelling. However, self‐report distortions
remain resilient. One potential avenue for mitigating the problems with self‐ratings
altogether is not to rely on them but instead have ‘others’ rate candidates’ personality. Two
meta‐analyses indicate that other ratings of personality might offer improved predictive
validity over self‐ratings. Connelly and Ones (2010) conducted a meta‐analysis of 44,178
targeted individuals rated across 263 independent samples. Each target participant had
at least one set of other ratings for the Big Five. Similarly, Oh, Wang and Mount (2011)
166 Selection
conducted a meta‐analysis of some 2,000 target individuals from 18 independent samples.
Table 8.3 displays a summary of the main findings of these meta‐analyses.
The predictive validities of other ratings were substantially higher than for self‐ratings,
regardless of whether or what type of correction was utilized. In many instances the predictive
validity of other ratings are 2, 3 or 4 times the magnitude of self‐ratings. In the case
of openness, the other ratings are 6 times the magnitude of self‐ratings. The magnitudes
of these relationships are impressive. If we use the estimates provided by Schmidt and
Hunter (1998) as a guide, the univariate validities are equivalent to some of our most valid
selection methods, the multivariate validity would no doubt surpass many of these other
methods and the potential incremental predictive validity over and above other methods
is substantial.
Oh and colleagues’ (2011) meta‐analysis provides two more particularly interesting
findings for the selection domain. First, it appears that combining self‐ratings with other
ratings is of little value as self‐reports offer little incremental predictive validity over other
reports. Second, while predictive validity increases in line with the number of other ratings,
the increment is generally small. Specifically, the increase from 1 to 3 other ratings ranges
from 0.04 to 0.06 (uncorrected) and 0.05 to 0.09 (corrected), suggesting that while
multiple
other ratings are optimal, the value of a single other rating is still substantial
(Oh et al., 2011).
Table 8.2 Mean, lowest and highest predictive validities of partially ipsative measures of the Big Five.
Correlations with Job Performance
Partially Ipsative Likert‐type
Trait r r1 r2 r r1 r2
Emotional stability
Highest: Supervisory 0.37 0.68 460.2
Lowest: Managerial –0.01 –0.02 00.0
Mean 0.11 0.20 40.0 0.09 0.10 10.0
Extraversion
Highest: Managerial 0.21 0.34 110.6
Lowest: Sales 0.05 0.08 00.6
Mean 0.07 0.12 10.4 0.06 0.13 10.7
Openness
Highest: Clerical –0.27 –0.44 190.4
Lowest: Sales 0.11 0.17 20.9
Mean 0.14 0.22 40.8 0.02 0.03 00.0
Agreeableness
Highest: Skilled 0.28 0.42 170.6
Lowest: Managerial –0.04 –0.07 00.5
Mean 0.10 0.16 20.6 0.07 0.17 20.9
Conscientiousness
Highest: Skilled 0.43 0.71 500.4
Lowest: Supervisory 0.09 0.18 30.2
Mean 0.22 0.38 140.4 0.10 0.23 50.3
Likert‐type estimates taken from Barrick et al. (2001); r = uncorrected correlation; r1 = corrected for unreliability
in criterion only and indirect range restriction in the predictor; r2 = percentage of variance explained.
Personality Questionnaires 167
Clearly, other ratings offer a marked improvement over self‐ratings in predicting job
performance. One likely contribution to the increase is that other ratings mitigate the
response distortions commonly associated with self‐ratings, which is highly desirable.
Perhaps
less desirable is the possibility that observer ratings and job performance ratings
are highly correlated due to an element of common method bias. It is plausible that other
ratings of personality are assessing reputation and likeability, which is arguably what supervisor
ratings of overall job performance are assessing. Whether this shared variance is a
good or bad thing remains to be debated. Nevertheless, the results from studies of other
ratings are highly promising.
Predictive validity: Conclusion
Self‐ratings of higher‐order factors of personality modestly relate to supervisor and
objective ratings of overall job performance. The magnitude of the correlations (or
corrected operational validities) typically ranges from 0.0 to 0.3 and this is true whether
personality factors are examined in univariate (single‐factor correlations) or multivariate
(as a group of five factors) fashion. With the exception of conscientiousness, many broad
personality factors appear to be generally unrelated to ratings of overall job performance.
However, broad personality factors do offer much greater levels of prediction of other
crucial elements of workplace performance, such as counterproductive behaviours,
leadership
and teamwork.
Table 8.3 Correlations between job performance and personality as assessed by self‐ratings and
other ratings.
Correlations with Job Performance
Connelly and Ones (2010) Oh et al. (2011)
Trait and rating type r r1 r2 r r3
Emotional Stability
Other rating 0.14 0.17 0.37 0.17 0.24
Self‐rating 0.06 0.11 0.12 0.09 0.14
Extraversion
Other rating 0.08 0.11 0.18 0.21 0.29
Self‐rating 0.06 0.11 0.12 0.06 0.09
Openness
Other rating 0.18 0.22 0.45 0.20 0.29
Self‐rating 0.03 0.04 0.05 0.03 0.05
Agreeableness
Other rating 0.13 0.17 0.31 0.23 0.34
Self‐rating 0.06 0.11 0.13 0.07 0.10
Conscientiousness
Other rating 0.23 0.29 0.55 0.31 0.41
Self‐rating 0.12 0.20 0.23 0.15 0.22
r = uncorrected correlation; r1 = corrected for unreliability in criterion only; r2 = corrected for unreliability in
the predictor and criterion; r3 = corrected for unreliability in the criterion measure and range restriction in
the predictor; Other ratings for Oh et al. (2011) refer to the mean predictive validity taken from three
observers.
168 Selection
At this point, some might conclude that personality is generally not fit for purpose in
the selection context (e.g., Morgeson et al., 2007a). However, that personality measures
as a stand‐alone do not offer particularly grand levels of predictive validity does not mean
that they are useless. Rather, personality measures offer significant and cost‐effective
(in terms of time and money) incremental predictive validity over other selection
methods. Notably, the combination of cognitive ability and personality is among the
most powerful combinations of selection methods. Thus, we can endorse the use of personality
as a component
of a rigorous selection programme (Schmidt & Hunter, 1998;
Schmidt et al., 2008).
Further, when we step away from meta‐analytic correlations of the Big Five the picture
is much more interesting. Narrow, lower‐order facets offer much greater predictive
validity (with the exception of conscientiousness, between 2 and 6 times more) than do
their broad composite factors. While facet‐level analyses are clearly superior to broad
factor analyses, it is also likely that our current estimates of this superiority represent
underestimates. Currently, the data we have lack nuance as they pertain to job
performance en masse across numerous industries, organizations and roles. However, as
we suggest, personality is not a universal predictor: different roles require the utilization
of different levels and combinations of behaviours. In addition, no single facet list from
popular measures of personality is exhaustive, and thus omits potentially important
personality
traits (e.g., the dark triad) and further underestimating the predictive validity
of personality.
In spite of the current limitations on our estimates, it is clear that matching a few narrow
traits on the basis of existing empirical evidence and theory leads to increased predictive
validity (Judge et al., 2013; Paunonen & Ashton, 2001). Personality‐oriented job analysis
offers an avenue to identify the narrow facets of relevance and, if utilized appropriately, can
further increase the predictive validity of self‐ratings of personality. We know of no studies
that have examined the incremental predictive validity of facet‐level personality ratings,
based on job analysis, over and above cognitive ability. We suggest that such a study is of
great importance in furthering this debate.
One of the likely limiting factors in the validity of personality measures is their susceptibility
to response distortions (e.g. low self‐awareness, faking). The evidence reviewed here
suggests that replacing self‐ratings with other ratings might mitigate self‐report response
distortions and offer substantially increased predictive validity. An intriguing question
remains just how much predictive validity increases by the simultaneous use of job analysis
to identify relevant narrow facets, which are rated by others and used in a multivariate
manner to predict nuanced measures of job performance. The evidence reviewed in this
chapter suggests that this approach could yield substantial gains in predictive validity and
ultimately improve our selection practices.
Equally, recent research suggests that partially ipsative personality measures have
improved predictive validities compared to traditional, Likert‐type measures. The utility of
partially ipsative measures is even more pronounced when the moderating effects of job
role are taken into account, with univariate relationships with performance within specific
roles ranging from 0.3 to 0.7. In addition, recent advances in the scoring and modelling
of ipsative items (Brown & Maydeu‐Olivares, 2013, in press) make the measures more
appealing and practically useful.
In sum, we believe that the predictive validity evidence suggests that personality traits are
valuable during selection. Even a simple measure of conscientiousness offers incremental
predictive validity in most selection scenarios. However, more nuanced use of personality
measures leads to even greater levels of predictive validity which, in our view, make
personality
an important component of the selection toolbox.
Personality Questionnaires 169
How and When to Use Personality Assessments in Selection
The previous sections have examined the question of whether or not personality assessments
are useful for selection. Having concluded that they can be, we now provide a
slightly more practitioner‐focused discussion of the questions how and when personality
assessments can be useful.
Employees are often the single largest cost and most complex ‘resource’ to manage;
they are also the source of the knowledge, skills and abilities needed for organizations to
thrive. Therefore, effective employee selection is a crucial component of organizational
functioning and there is an indisputable need for organizations to ensure that they manage
the flow of talented people within their organizations. The activities and processes often
identified as vital for talent management include recruitment, selection, development,
reward, performance management and succession planning. Personality data can be useful
in all these areas. Equally, selection does not refer exclusively to the selection of new
employees. Personality data can be useful for the selection of redeployed staff, short‐term
secondments, expatriate workers and future talent.
In order to elucidate how and when personality assessment can be useful in selection
and talent management more broadly, a selection paradigm is presented in Figure 8.1.
There is no established framework for the selection paradigm, but authors have agreed on
some key elements (Guion & Highhouse, 2006; Smith & Smith, 2005), which range from
identifying the needs of the organization through to the evaluation of the selected
candidate(s). As discussed above, personality‐oriented job analysis offers a very useful
framework but rather than repeat this discussion, in this section we focus on considerations
when choosing selection methods (beyond predictive validity), administering
selection
methods (initial and additional) and how to use personality data after the initial
selection decision is made.
Choosing selection methods
Selection methods should be chosen based on consideration of seven key criteria in four
main areas: 1) reliability and predictive validity; 2) legality and fairness; 3) cost and practicality;
and 4) candidate reactions. Personality assessments generally perform well against
these key criteria (Hough, Oswald & Ployhart, 2001; Mount & Barrick, 1995).
The first and most important consideration, and the main focus of this chapter, is reliability
and predictive validity. Put simply, if the method does not predict job performance,
then it is of no interest in a selection context. It is important to note here that the reliability
and predictive validity of each personality assessment will vary, and often free research
scales perform better than for‐pay scales (e.g. Hamby, Taylor, Snowden & Peterson, 2016;
Salgado, 2003). Given the detailed exploration of predictive validity discussed above, little
remains to say beyond check the research evidence pertaining to reliability and validity and
choose the measure with the best predictive validity. There are, however, four caveats.
First, make sure the test measures traits shown to be of relevance during the job analysis.
Second, ensure the measure has been tested and validated on an appropriate sample that
approximates the likely candidate pool. Third, be wary of wild claims about predictive
validity – if a self‐report measure of a construct closely approximating conscientiousness
claims predictive validities much greater than 0.3, carefully examine this evidence. Fourth,
gains in predictive validity should be considered alongside testing time and format. Choosing
a more complex, demanding, lengthy or expensive measure for a marginal increase in
validity (e.g., 0.33 vs. 0.35) makes little sense.
170 Selection
Once predictive validity has been assessed, selection method choice moves on to
concerns
of fairness and practicality. Turning to legality, there is a requirement to check
the relevant legislation in each geographical area of usage. That said, if a valid personality
assessment was chosen based on a thorough job analysis and the measures are appropriate
(e.g., are not clinical in nature), administered and interpreted by qualified personnel and
are used as part of a fuller selection programme, then there is little to suggest that using
them will be indefensible. This claim is further supported by the comprehensive body of
evidence that shows personality assessments to be less prone to adverse impact than many
other selection methods such as interviews, assessment centres or references (Hough
et al., 2001; Sackett & Lievens, 2008). In general, there are minimal racial group and age
group differences observed in personality assessments, and certainly these are much smaller
than those observed in measures of cognitive ability and situational judgement tests.
Organisational needs analysis
Job analysis
Job description
Person specification
Competency framework
Identify selection criteria
Choose selection methods
Attract candidates
Administer selection methods – initial
Administer selection methods – additional
Hiring decision
Placement
Evaluation
Figure 8.1 Overview of a typical selection paradigm.
Personality Questionnaires 171
However, there are some notable differences between men and women on personality
measures, which may not be a result of measurement error but reflections of actual group
differences (Costa, Terracciano & McCrae, 2001; Del Giudice, Booth & Irwing, 2012).
These should be considered during selection (Hough et al., 2001). It is also currently
unknown whether partially ipsative measures and other ratings of personality influence
adverse impact. Nevertheless, we can conclude that self‐ratings of personality perform
relatively well in terms of fairness and legality.
The next considerations when choosing selection methods (or perhaps first in practice)
are cost and practicality. Personality assessments are an extremely cost‐effective selection
method. They are largely inexpensive to procure and can be administered digitally without
any negative effects on response patterns (Ployhart, Weekley, Holtz & Kemp, 2003),
allowing
time‐ and cost‐efficient assessment of many candidates in multiple geographic
locations. Personality measures are among the best selection methods when considering
cost and practicality.
The final consideration is candidate reactions. Organizations do not want new employees’
first interaction with the company to be unpleasant. Equally, organizations do not want
talented but unsuccessful applicants to be deterred from applying for future vacancies. So
it is important to consider how the candidate might feel during the selection process
(
however, this is much less important than predictive validity and fairness). Despite some
concerns regarding intrusiveness and a lack of perceived job relevance (Rosse, Miller &
Stecher, 1994), personality assessments are ‘favourably evaluated’ by selection candidates
(Anderson, Salgado & Hülsheger, 2010, p. 291), but less so than interviews, work samples
and cognitive ability tests (Anderson et al., 2010). Nevertheless, personality assessments
are a ‘scientific method of selection’ (Steiner & Gilliland, 1996) and, when used, as we
suggest throughout this chapter, in conjunction with cognitive ability, they receive positive
candidate reactions (Anderson et al., 2010; Rosse et al., 1994). Indeed, organizations that
employ rigorous selection procedures and use scientific selection methods are deemed to
be more attractive to potential employees (Steiner & Gilliland, 1996), which is hugely
important in recruiting a larger applicant pool. This is of course important, as selection will
be poor, regardless of the quality of the selection methods employed, if the candidate
pool does not consist of individuals with the knowledge, skills and abilities necessary to
perform well.
Administering selection methods – Initial
An early stage of the selection process often involves the filtration of potential applicants
(Cook, 2009). Traditionally, this first sift or filter is achieved through a number of methods
which may include examination of application forms or curricula vitae, situational judgement
tests, job knowledge or skills, minimum experience or qualifications, criminal record
check or cognitive ability assessments. Personality assessment, if based on a job analysis
and used in conjunction with other selection methods (e.g. cognitive ability), can be used
to sift the initial candidate pool. This approach allows more expensive and labour‐intensive
methods to be applied to a reduced candidate pool. In general, initial assessments can be
conducted online and thus, as discussed above, become very time‐ and cost‐effective
without any reduction in response quality and an improvement in candidate reactions
(Salgado & Moscoso, 2003).
A recent trend concerns the ‘selecting out’ (removing from the candidate pool) of
candidates with specific traits. Identifying the personality traits of the ‘dark triad’ – psychopathy,
Machiavellianism and narcissism (Paulhus & Williams, 2002) – is popular in
this area given their negative influence in the workplace (e.g., Moscoso & Salgado, 2004;
172 Selection
O’Boyle, Forsyth, Banks & McDaniel, 2012). However, further research is needed
regarding selecting out in general and the potential adverse impact such a practice might
induce in the selection process before any firm practitioner points can be made. For
example, some research examining the dark triad suggests that higher scores are not
universal indicators of poor performance or delinquency (Hogan, 2007).
Administering selection methods – Additional
Following initial sifting, most organizations employ additional selection methods before
making a final selection decision. Personality assessments can be used at this stage to
improve the efficacy of the selection process. First, as we have discussed throughout this
chapter, personality assessments can be used to identify the extent to which candidates
may possess the characteristics that will help them excel against competences or duties
essential for the role. Second, the analysis of candidates’ personality profiles can be used to
identify specific interview questions which can be used alongside traditional structured
interview questions (e.g., Morgeson et al., 2007a; Schmitt & Kunce, 2002). For
example,
candidates who report an extreme tendency to be introverted might be asked to explain
how they tend to collaborate, while candidates who report an extreme tendency
towards
extraversion might be asked how they work independently. Third, personality assessments
can be used to identify values and motives in order to ascertain potential cultural fit
between
the candidate and the recruiting organization (Blackman, 2002).
Using personality assessment data post‐selection
Selection processes can be expensive and the data gathered can be of use beyond a final
selection decision. The re‐utilization of personality data after selection makes for a better
return on investment and ensures that the data collected has continuing benefits. If personality
data are to be used after selection, it is important that candidates be informed of
this prior to completing the measures. If this is done, we see personality data as useful in
four ways after selection.
First, when selecting multiple candidates, personality data can inform initial placement
by matching the candidates with mentors (Wanberg, Kammeyer‐Mueller & Marchese,
2006), teams (Morgeson, Reider & Campion, 2005) or leaders (Monzani, Ripoll & Peiró,
2015). In this approach, the personality profiles of the selected employee will be compared
with those of existing team members or managers. It is important to note that this does
not represent the ‘cloning’ of existing team members or leaders, which is generally to be
avoided, but ensuring a complementary fit of typical tendencies for thinking, feeling and
behaving.
Second, personality assessment data can inform initial employee coaching and development
(Batey, Walker & Hughes, 2012). Here, the new employee can discuss their likely strengths
and development areas on starting in their new role, and the same information can be
shared with team colleagues as part of the induction process.
Third, the personality data might indicate that new employees possess managerial
potential (Goffin et al., 1996) and are well suited to leadership positions (Judge,
Bono, Illies & Gerhardt, 2002) or expatriate roles (Caligiuri, 2000) and thus they
could be considered
for ‘high potential’ or ‘rising talent’ programmes (Silzer &
Church, 2010).
Fourth, if the role, team or department that new employees have joined is subsequently
subject to redesign, restructuring or redeployment, the personality data could partially
inform what new roles they could perform.
Personality Questionnaires 173
The key issue stressed here is that personality data collected during selection can be
effectively used later, provided the candidate is informed of these potential uses during the
selection process. Using selection data to inform placement and development offers other
advantages. Framing personality assessment during selection as the first step in a developmental
trajectory and explaining to candidates how the data are to be used increases the
face validity and job relevance of the measure, thus improving candidate reactions during
selection. It is also possible, though at this point speculative, that candidates may be more
engaged in the selection process and ‘fake’ less if they understand that the personality
assessment will influence with whom they will work and the training they will receive.
One final note is that while self‐ratings are useful, other reports appear to offer greater
predictive validity. Thus, we suggest that once a candidate has been in role for a year or so,
the company ceases to use self‐ratings and instead uses other ratings gleaned from
colleagues,
managers and subordinates as part of ongoing 360‐degree development
(Batey et al., 2012).
Future Research
To our mind, the study of personality is deeply fascinating. That we can measure the very
essence of human character is marvellous. That those measurements predict workplace
behaviour is hugely useful. The focused review and analysis in this chapter demonstrate
that research regarding personality and selection has made incredible progress over the
past 30 years and that the area remains vibrant with novel studies frequently challenging
assumptions, improving knowledge and creating a very solid platform for evidence‐based
practice. In keeping with the dynamic nature of the personality–selection field, we finish
this chapter by presenting a number of exciting avenues that are ripe for future research.
Throughout the chapter, we have noted areas of research with promising findings that
need further exploration. We will briefly recapitulate these and discuss some other areas we
believe deserve research attention.
Our first suggestion for future research – the further development of personality measures
– is unlikely to prove universally popular. Many researchers and practitioners have a
preferred tool to which they are strongly committed. However, as discussed in the early
sections of this chapter, there are limitations with currently popular measures based on the
Big Five (e.g., NEO‐PI‐R, HEXACO, HPI). Specifically, the models were developed without
a guiding theoretical framework and use suboptimal psychometric procedures, meaning
that debate remains regarding how many factors exist at each level of the personality
hierarchy and most omnibus personality measures have less than spectacular psychometric
properties (Block, 2010; Booth & Hughes, 2014). In addition, and of great importance
to the selection domain, all omnibus personality measures omit a large number of potentially
important traits (Booth, 2011; Paunonen & Jackson, 2000). Further research that
improves personality theory and measurement along these lines is very welcome.
We believe it is time to move away from producing meta‐analyses of correlations between
broad personality factors and broad measures of performance. Instead, we wish to
see more theory‐driven model testing approaches to personality–job performance
research. Particularly, process models examining the effects of mediators (e.g., teamwork,
communication, motivation) and moderators (e.g., organizational culture, team composition,
leader behaviour) within the personality–job performance link appear to be a fruitful
avenue of exploration.
In line with the argument that universal job performance does not exist and that job
roles moderate predictive validity, we call for researchers to begin building a picture of
174 Selection
role‐specific associations (e.g., leadership roles, clerical roles, sales roles, policing, teaching).
Within this call, we see a crucial role for personality‐oriented job analysis and narrow facets
of personality. In order to facilitate such research we believe that the production of a
single, exhaustive list of narrow facets would reduce the common Jingle Jangle Fallacy
problem and allow for the systematic exploration of the relations between narrow traits
and job performance. It is also important within this research that we move away from
unidimensional measures of performance and towards more realistic multidimensional
models such as that proposed by Bartram (2005). Such research would be much more
theory‐laden and have great practical value. In time, we will be able to aggregate these
studies to provide meta‐analytic estimates while retaining useful, role‐specific information.
Similar efforts have been successful in cognitive ability research (e.g., Bertua et al., 2005).
Traits do not exist or act in isolation; as discussed above, personality is multidimensional.
Currently, most multidimensional personality research adopts a simple, cumulative
regression or aggregation approach. However, we believe that the value of traits is not
simply additive. Rather, traits interact to drive motivation and behaviour. A number of
studies show that trait interactions are of value in understanding performance at work
(e.g., Blickle et al., 2013; Judge & Erez, 2007; Oh, Lee, Ashton & De Vries, 2011).
Accordingly, we call for further research in this promising area.
Similarly, the relationship between personality and job performance in some roles might
be curvilinear. It is possible that too much conscientiousness or too much extraversion
will be counterproductive in some roles (e.g., Bunce & West, 1995; Driskell et al., 1994;
Tett et al., 1999). Examinations of curvilinear relationships might increase understanding
regarding when and where traits are most relevant and potentially indicate optimal trait
levels for specific workplace tasks. Studies have been undertaken examining curvilinear
effects, but to date the results are generally inconclusive (e.g., Le, Oh, Robbins, Ilies,
Holland
& Westrick, 2011).
One area of work we have not discussed in any detail is teamwork. People often work
in teams, at least to some degree, with truly solitary work virtually unheard of in most
roles. Despite the fact that workplace interdependence is the norm, we measure only
individual traits and individual performance. While conscientiousness is the single most
important predictor for individual task performance, it is possible that other traits are very
important because they have an impact on the performance and well‐being of others.
Examining how personality enhances or suppresses group performance is a much‐needed
avenue of exploration.
Response distortions remain a problem for self‐ratings of personality. Further research
is required to understand these distortions and generate useful methods to overcome
them. Forced‐choice measures have generally offered limited utility in combating social
desirability. However, recent research suggests that some of this underwhelming
performance might be the result of suboptimal test construction, variable scoring and
analytical
procedures (Brown & Maydeu‐Olivares, 2013, in press; Meade, 2004).
Regardless of effects on social desirability, partially ipsative forced‐choice measures offer
impressive levels of predictive validity and outperform those achieved using Likert‐type
measures. Further examinations of the predictive validity of partially ipsative measures are
warranted, as are explorations of how these rating formats influence adverse impact.
In related fashion, the results from other ratings are so promising that we must continue
to examine them as a plausible measurement approach. Research must consider how other
ratings perform when using facet measures and compare these to broad measures. If the
increment in predictive validity offered by narrow traits in self‐ratings applies equally to
other ratings, then other ratings become even more attractive. We also need to explore
more thoroughly how other ratings differ from self‐ratings: do other ratings still perform
Personality Questionnaires 175
fairly across different groups (e.g., are there sex or racial differences in ratings), does the
rank order of applicants change from self‐ratings and other ratings and to what extent does
common method bias account for the increased correlations with job performance metrics?
Equally, pragmatic research regarding how to source other ratings reliably is required.
Finally, we call for a tighter integration between academia, selection practitioners and
test publishers. Practitioners have the ability to accelerate progress by adopting some of
the approaches outlined in this chapter and collecting real‐life, real‐time data which can
only serve to enhance our understanding of the personality–job performance link. Bridging
the science–practitioner divide discussed in the introduction is paramount to the fruitfulness
of our field.
Conclusion
In this chapter, we have reviewed the evidence for the utility of personality trait assessments
within selection. We conclude that personality assessments can be a very useful
component of the selection toolbox. We have also considered that the latest evidence suggests
that the use of narrow facets based on theoretical and empirical reasoning offers
superior predictive validity to broad factors and currently represent the most effective
method of utilizing self‐ratings of personality within selection. In addition, we discussed
the potential of partially ipsative measures and other ratings to bypass response distortions
and greatly increase predictive validity. Further, we have outlined a defensible and robust
paradigm for integrating personality assessments into the selection process.
What we believe this review demonstrates for practitioners is this. The utilization of personality
assessments in selection must operate concurrently with a broader selection
programme involving cognitive ability or similar selection tools. Further, if personality is
used in an off‐the‐shelf and uncritical fashion it will almost certainly yield modest values
(correlations with job performance in the region of 0.1–0.3). In fact, using personality
measures in this way is questionable given that trait measures are not linked to job
performance. However, practitioners prepared to employ more nuanced job analyses and
trait selections, within a rigorous selection paradigm, will maximize the value of personality
assessment, thus increasing the likelihood that the chosen candidate(s) will think, feel
and behave in a manner that will contribute to organizationally defined metrics of success.
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