{"id":1203,"date":"2023-03-31T17:37:40","date_gmt":"2023-03-31T17:37:40","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introductiontopsychology\/chapter\/13-1-2-learn-it-interviewing\/"},"modified":"2026-01-15T20:09:56","modified_gmt":"2026-01-15T20:09:56","slug":"13-1-2-learn-it-interviewing","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introductiontopsychology\/chapter\/13-1-2-learn-it-interviewing\/","title":{"raw":"Hiring and Evaluating Employees: Learn It 3\u2014Interviewing","rendered":"Hiring and Evaluating Employees: Learn It 3\u2014Interviewing"},"content":{"raw":"
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Assessing Applicants<\/h2>\r\n

Once an organization has recruited an applicant pool, the organization must decide how to assess the applicants, and the formal hiring process begins. The process used to evaluate job candidates and decide which ones to hire is typically referred to as personnel selection. Personnel selection is one of the oldest topics in I-O psychology, dating back to the very roots of the field at the start of the 20th century (Farr & Tippins, 2010; Ployhart, Schmitt, & Tippins, 2017).<\/p>\r\n

Selection Methods<\/h3>\r\n

Selection typically involves administering a series of instruments\u2014such as tests or interviews\u2014to job applicants. These instruments are often scored and combined with other information, such as letters of recommendation, to identify the best candidates. The selection instruments an organization uses are commonly called predictors<\/strong>, and helping organizations develop effective predictors is one of the most common roles for I-O consultants. Common predictors include tests of various qualities (such as intelligence, personality, and other traits) and interviews (Cascio & Aguinis, 2011).<\/p>\r\n

Determining the right combination of predictors involves many factors, including cost, time, legality, validity, reliability, practicality, and acceptance in the business world. Selection often occurs in multiple stages: initial screening assessments \"weed out\" unqualified applicants, and subsequent stages attempt to select the optimal candidate from those who remain.<\/p>\r\n<\/section>\r\n

What Predicts Job Performance?<\/h2>\r\n

General Mental Ability<\/h3>\r\n

For decades, one of the most consistent findings in I-O psychology has been that general mental ability<\/strong> (GMA), or intelligence, is a strong predictor of job performance\u2014particularly for complex jobs (Schmidt & Hunter, 2004). GMA helps predict a person's ability to learn new information and skills, a critical component of success in virtually any job.<\/p>\r\n

However, recent research has prompted a significant reevaluation of just how strong this relationship is. A landmark meta-analysis by Sackett, Zhang, Berry, and Lievens (2022) found that previous estimates of predictor validity had been systematically inflated due to inappropriate statistical corrections. Their revised analysis suggests that while GMA remains a useful predictor, its validity is lower than earlier estimates indicated\u2014and structured interviews may actually be the strongest predictor of job performance.<\/p>\r\n

Personality<\/h3>\r\n

Personality tests, particularly those measuring the Big Five traits, can also predict which applicants will be effective employees. The trait conscientiousness<\/strong> predicts performance in a wide variety of jobs\u2014not surprisingly, since people high in this trait are typically hard-working, reliable, and organized (Barrick, Mount, & Judge, 2001).<\/p>\r\n

Recent research confirms that conscientiousness remains the strongest personality predictor across performance outcomes, including task performance, organizational citizenship behavior, and counterproductive work behavior (see review by relevant authors, 2025).<\/p>\r\n

Work Samples and Simulations<\/h3>\r\n

Simulations and work samples assess a person's ability to handle actual job-related tasks in realistic settings (Scott & Reynolds, 2010). These methods allow employers to see how candidates perform on tasks they would actually encounter in the job.<\/p>\r\n

\r\n

Determining the right combination of predictors to give applicants for a given job is a central topic for consultants that assist with personnel selection, and involves the consideration of many factors, including cost, time, legality, validity, reliability, practicality, and acceptance in the business world. Selection often occurs in multiple stages. During the initial stage, it is common for applicants to participate in some initial screening assessments to \u201cweed out\u201d unqualified applicants. Following this, subsequent stages in the selection process attempt to select the optimal candidate from the qualified applicants that remain after screening.<\/p>\r\n

Interviews<\/h2>\r\n\r\n[caption id=\"attachment_4319\" align=\"alignright\" width=\"482\"]\"Two Figure 2<\/strong>. Studies of job interviews show that they are more effective at predicting future job performance when they are structured.[\/caption]\r\n\r\n

Most jobs for mid-size to large-size businesses in the United States require a personal interview as a step in the selection process. Because interviews are commonly used, they have been the subject of considerable research by industrial psychologists.<\/p>\r\n

Information derived from job analysis usually forms the basis for the types of questions asked. Interviews can provide a more dynamic source of information about the candidate than standard testing measures. Importantly, social factors and body language can influence the outcome of the interview. These include influences, such as the degree of similarity of the applicant to the interviewer, and nonverbal behaviors, such as hand gestures, head nodding, and smiling (Bye, Horverak, Sandal, Sam, & Vivjer, 2014; Raki\u0107, Steffens, & Mummendey, 2011).<\/p>\r\n<\/section>\r\n

\r\n

Structured vs. Unstructured Interviews<\/h3>\r\n

There are two main types of interviews: unstructured<\/strong> and structured<\/strong>.<\/p>\r\n

In an unstructured interview<\/strong>, the interviewer may ask different questions of each candidate. One candidate might be asked about career goals while another is asked about previous work experience. Questions are often unspecified beforehand, and responses are generally not scored using a standard system. This makes it harder to compare candidates fairly.<\/p>\r\n

In a structured interview<\/strong>, the interviewer asks the same questions of every candidate, the questions are prepared in advance based on job analysis, and the interviewer uses a standardized rating system for each response. This approach allows accurate comparison between candidates. Meta-analyses have consistently found that structured interviews are more effective at predicting subsequent job performance (McDaniel, Whetzel, Schmidt, & Maurer, 1994). Recent research by Sackett and colleagues (2022) suggests that well-designed structured interviews may be the single most valid selection tool available.<\/p>\r\n

\r\n

Preparing for the Job Interview<\/h3>\r\n

You might wonder whether psychology can help applicants perform better in interviews. While most research focuses on helping organizations select effectively, some studies offer insights relevant to candidates.<\/p>\r\n

Nonverbal behavior and impression management<\/b><\/h4>\r\n

Research shows that nonverbal cues<\/b><\/span> influence interviewer ratings. For example:<\/p>\r\n

    \r\n\t
  • reduced eye contact and smiling can lower applicant evaluations (Liden et al., 1993)<\/li>\r\n\t
  • self-promotion<\/span> (clearly describing accomplishments) is often associated with more positive interview outcomes, especially when tied to job fit (Gilmore & Ferris, 1989)<\/li>\r\n<\/ul>\r\n

    However, self-promotion can be overdone<\/span>, particularly with experienced interviewers (Howard & Ferris, 1996).<\/p>\r\n

    Barrick, Swider, and Stewart (2010) found that first impressions formed during early rapport-building<\/span> were related to job offers. Importantly, these impressions reflected judgments about competence<\/span>, not just likability.<\/p>\r\n

    Gender, nonverbal cues, and complexity<\/b><\/h4>\r\n

    Some research suggests that nonverbal behaviors may be interpreted differently depending on gender and context. For example:<\/p>\r\n

      \r\n\t
    • smiling generally increases perceived likability<\/li>\r\n\t
    • eye contact has been shown to affect evaluations differently for men and women in some studies (Levine & Feldman, 2002)<\/li>\r\n<\/ul>\r\n

      These findings highlight why structured interviews<\/b><\/span> are preferred\u2014they reduce the impact of subjective impressions that can disadvantage certain candidates.<\/p>\r\n

      In real-world interviews, DeGroot and Gooty (2009) found that interviewer judgments were positively influenced by visual and vocal cues<\/b><\/span> signaling conscientiousness, openness, and extroversion\u2014traits relevant to many jobs.<\/p>\r\n<\/section>\r\n<\/section>\r\n

      \r\n
      \r\n

      Artificial Intelligence in Hiring<\/h3>\r\n

      AI-based selection tools are increasingly common, with surveys suggesting that a majority of employers now use some form of technology-driven assessment. These tools can screen resumes, conduct initial assessments, analyze interview responses, and even predict job performance.<\/p>\r\n

      However, AI selection tools have raised significant concerns about algorithmic bias. Research has documented cases where AI systems disadvantaged candidates based on race, age, gender, or disability\u2014sometimes because the algorithms were trained on historically biased data. In August 2023, the Equal Employment Opportunity Commission (EEOC) settled its first AI hiring discrimination lawsuit against a company whose recruitment software automatically rejected older candidates.<\/p>\r\n

      In response, new regulations are emerging. New York City enacted a law in 2023 requiring employers to conduct independent bias audits of AI hiring tools before use. The EU's proposed AI Act classifies AI in hiring as a \"high-risk\" application requiring rigorous standards. Research suggests that effective use of AI in selection requires diverse training data, ongoing monitoring for bias, transparency with candidates, and human oversight of algorithmic decisions (Chen, 2023; K\u00f6chling et al., 2022).<\/p>\r\n<\/section>\r\n

      \r\n
      [ohm2_question height=\"300\"]4423[\/ohm2_question]<\/section>\r\n<\/div>\r\n<\/section>\r\n<\/section>","rendered":"
      \n
      \n

      Assessing Applicants<\/h2>\n

      Once an organization has recruited an applicant pool, the organization must decide how to assess the applicants, and the formal hiring process begins. The process used to evaluate job candidates and decide which ones to hire is typically referred to as personnel selection. Personnel selection is one of the oldest topics in I-O psychology, dating back to the very roots of the field at the start of the 20th century (Farr & Tippins, 2010; Ployhart, Schmitt, & Tippins, 2017).<\/p>\n

      Selection Methods<\/h3>\n

      Selection typically involves administering a series of instruments\u2014such as tests or interviews\u2014to job applicants. These instruments are often scored and combined with other information, such as letters of recommendation, to identify the best candidates. The selection instruments an organization uses are commonly called predictors<\/strong>, and helping organizations develop effective predictors is one of the most common roles for I-O consultants. Common predictors include tests of various qualities (such as intelligence, personality, and other traits) and interviews (Cascio & Aguinis, 2011).<\/p>\n

      Determining the right combination of predictors involves many factors, including cost, time, legality, validity, reliability, practicality, and acceptance in the business world. Selection often occurs in multiple stages: initial screening assessments “weed out” unqualified applicants, and subsequent stages attempt to select the optimal candidate from those who remain.<\/p>\n<\/section>\n

      What Predicts Job Performance?<\/h2>\n

      General Mental Ability<\/h3>\n

      For decades, one of the most consistent findings in I-O psychology has been that general mental ability<\/strong> (GMA), or intelligence, is a strong predictor of job performance\u2014particularly for complex jobs (Schmidt & Hunter, 2004). GMA helps predict a person’s ability to learn new information and skills, a critical component of success in virtually any job.<\/p>\n

      However, recent research has prompted a significant reevaluation of just how strong this relationship is. A landmark meta-analysis by Sackett, Zhang, Berry, and Lievens (2022) found that previous estimates of predictor validity had been systematically inflated due to inappropriate statistical corrections. Their revised analysis suggests that while GMA remains a useful predictor, its validity is lower than earlier estimates indicated\u2014and structured interviews may actually be the strongest predictor of job performance.<\/p>\n

      Personality<\/h3>\n

      Personality tests, particularly those measuring the Big Five traits, can also predict which applicants will be effective employees. The trait conscientiousness<\/strong> predicts performance in a wide variety of jobs\u2014not surprisingly, since people high in this trait are typically hard-working, reliable, and organized (Barrick, Mount, & Judge, 2001).<\/p>\n

      Recent research confirms that conscientiousness remains the strongest personality predictor across performance outcomes, including task performance, organizational citizenship behavior, and counterproductive work behavior (see review by relevant authors, 2025).<\/p>\n

      Work Samples and Simulations<\/h3>\n

      Simulations and work samples assess a person’s ability to handle actual job-related tasks in realistic settings (Scott & Reynolds, 2010). These methods allow employers to see how candidates perform on tasks they would actually encounter in the job.<\/p>\n

      \n

      Determining the right combination of predictors to give applicants for a given job is a central topic for consultants that assist with personnel selection, and involves the consideration of many factors, including cost, time, legality, validity, reliability, practicality, and acceptance in the business world. Selection often occurs in multiple stages. During the initial stage, it is common for applicants to participate in some initial screening assessments to \u201cweed out\u201d unqualified applicants. Following this, subsequent stages in the selection process attempt to select the optimal candidate from the qualified applicants that remain after screening.<\/p>\n

      Interviews<\/h2>\n
      \"Two
      Figure 2<\/strong>. Studies of job interviews show that they are more effective at predicting future job performance when they are structured.<\/figcaption><\/figure>\n

      Most jobs for mid-size to large-size businesses in the United States require a personal interview as a step in the selection process. Because interviews are commonly used, they have been the subject of considerable research by industrial psychologists.<\/p>\n

      Information derived from job analysis usually forms the basis for the types of questions asked. Interviews can provide a more dynamic source of information about the candidate than standard testing measures. Importantly, social factors and body language can influence the outcome of the interview. These include influences, such as the degree of similarity of the applicant to the interviewer, and nonverbal behaviors, such as hand gestures, head nodding, and smiling (Bye, Horverak, Sandal, Sam, & Vivjer, 2014; Raki\u0107, Steffens, & Mummendey, 2011).<\/p>\n<\/section>\n

      \n

      Structured vs. Unstructured Interviews<\/h3>\n

      There are two main types of interviews: unstructured<\/strong> and structured<\/strong>.<\/p>\n

      In an unstructured interview<\/strong>, the interviewer may ask different questions of each candidate. One candidate might be asked about career goals while another is asked about previous work experience. Questions are often unspecified beforehand, and responses are generally not scored using a standard system. This makes it harder to compare candidates fairly.<\/p>\n

      In a structured interview<\/strong>, the interviewer asks the same questions of every candidate, the questions are prepared in advance based on job analysis, and the interviewer uses a standardized rating system for each response. This approach allows accurate comparison between candidates. Meta-analyses have consistently found that structured interviews are more effective at predicting subsequent job performance (McDaniel, Whetzel, Schmidt, & Maurer, 1994). Recent research by Sackett and colleagues (2022) suggests that well-designed structured interviews may be the single most valid selection tool available.<\/p>\n

      \n

      Preparing for the Job Interview<\/h3>\n

      You might wonder whether psychology can help applicants perform better in interviews. While most research focuses on helping organizations select effectively, some studies offer insights relevant to candidates.<\/p>\n

      Nonverbal behavior and impression management<\/b><\/h4>\n

      Research shows that nonverbal cues<\/b><\/span> influence interviewer ratings. For example:<\/p>\n

        \n
      • reduced eye contact and smiling can lower applicant evaluations (Liden et al., 1993)<\/li>\n
      • self-promotion<\/span> (clearly describing accomplishments) is often associated with more positive interview outcomes, especially when tied to job fit (Gilmore & Ferris, 1989)<\/li>\n<\/ul>\n

        However, self-promotion can be overdone<\/span>, particularly with experienced interviewers (Howard & Ferris, 1996).<\/p>\n

        Barrick, Swider, and Stewart (2010) found that first impressions formed during early rapport-building<\/span> were related to job offers. Importantly, these impressions reflected judgments about competence<\/span>, not just likability.<\/p>\n

        Gender, nonverbal cues, and complexity<\/b><\/h4>\n

        Some research suggests that nonverbal behaviors may be interpreted differently depending on gender and context. For example:<\/p>\n

          \n
        • smiling generally increases perceived likability<\/li>\n
        • eye contact has been shown to affect evaluations differently for men and women in some studies (Levine & Feldman, 2002)<\/li>\n<\/ul>\n

          These findings highlight why structured interviews<\/b><\/span> are preferred\u2014they reduce the impact of subjective impressions that can disadvantage certain candidates.<\/p>\n

          In real-world interviews, DeGroot and Gooty (2009) found that interviewer judgments were positively influenced by visual and vocal cues<\/b><\/span> signaling conscientiousness, openness, and extroversion\u2014traits relevant to many jobs.<\/p>\n<\/section>\n<\/section>\n

          \n
          \n

          Artificial Intelligence in Hiring<\/h3>\n

          AI-based selection tools are increasingly common, with surveys suggesting that a majority of employers now use some form of technology-driven assessment. These tools can screen resumes, conduct initial assessments, analyze interview responses, and even predict job performance.<\/p>\n

          However, AI selection tools have raised significant concerns about algorithmic bias. Research has documented cases where AI systems disadvantaged candidates based on race, age, gender, or disability\u2014sometimes because the algorithms were trained on historically biased data. In August 2023, the Equal Employment Opportunity Commission (EEOC) settled its first AI hiring discrimination lawsuit against a company whose recruitment software automatically rejected older candidates.<\/p>\n

          In response, new regulations are emerging. New York City enacted a law in 2023 requiring employers to conduct independent bias audits of AI hiring tools before use. The EU’s proposed AI Act classifies AI in hiring as a “high-risk” application requiring rigorous standards. Research suggests that effective use of AI in selection requires diverse training data, ongoing monitoring for bias, transparency with candidates, and human oversight of algorithmic decisions (Chen, 2023; K\u00f6chling et al., 2022).<\/p>\n<\/section>\n

          \n