Types of Validity
Face Validity is concerned with whether a selection instrument appears to measure what it was designed to measure. Whilst face validity has no technical or statistical basis, it must not be overlooked if a test is to be accepted by the respondent. In a personality questionnaire, the question of face validity often relates to the questions asked. For example, the question “Does your soul ever drift away from your body?” (from the MMPI) would probably be felt to have low face validity for occupational use.
Faith Validity is the least defensible type of validity but the most difficult to influence. It is simply a conviction, a belief of blind faith that a selection test is valid. There is no empirical evidence and, what is more, none is wanted. This type of thinking is particularly disturbing with personality questionnaires. Clearly it is relatively easy to label a scale ‘Leadership’ or ‘Honesty’ but it is quite another matter to demonstrate whether it really works.
Content Validity (also known as logical validity) refers to the extent to which a measure represents all facets of the construct (e.g. personality, ability) being measured. For example, a depression scale may lack content validity if it only assesses the affective dimension of depression but fails to take into account the behavioural dimension. When constructing any test or questionnaire, the items should be representative of the domain to be measured. For example, a spelling test containing only the names of professional footballers would be of poor content validity as a general purpose of spelling.
Empirical Validity (or predictive validity) is the relationship between test scores and some criterion of performance obtained, e.g. job performance or academic performance. One type of validation study involves people currently employed. Thus we might test a group of computer programmers and correlate their test results with their supervisors’ ratings of work performance. Another type of study involves testing applicants, and returning after a period of time to determine the relationship between test performance and measures of job performance. Large scale empirical validation studies are difficult to undertake and people usually rely on published empirical studies (e.g. Ghiselli, Hunter & Schmidt, Smith).
Construct Validity is the extent to which a test measures some established construct or trait. Such constructs might be mechanical, verbal or spatial ability, emotional stability or intelligence. Correlations with other scales will provide useful information on a test’s construct validity (we would expect a scale of dominance to be more highly related to other measures of dominance than to traits of anxiety). Factor analysis is often used to investigate the construct validity of personality questionnaires.
Case Study: Construct Validity of a personality questionnaire
The data on “construct validity” in the 15FQ manual relates the primary global factors to correlations with 16PF global scales and Costa and McCrae’s five factor model.
|15FQ+ Global Factors Left meaning V Right meaning||16PF5 Global Factors||Correlation with NEO Five Factors (Costa McCrae)|
|Practical V Openness||-.65 (Tough Minded)||.66 (Openness)|
|Spontaneousness V Control||.79 (Self Control)||.67 (Conscientiousness)|
|Introversion V Extraversion||.88 (Extraversion)||.74 (Extraversion)|
|Independence V Agreeableness||-.81 (Independence)||.61 (Agreeableness)|
|Relaxed V aNxiety||.87 (Anxiety)||.77 (Neuroticism)|
From the table above we can infer that the correlations between 15FQ+, 16PF5 and NEO suggest the questionnaires are broadly measuring the same traits. For example we would expect someone with a sten score of 3 on “Spontaneousness V Control” (15FQ+) to have a below average score for self control (16PF5) and conscientiousness (NEO)
The Central Position of Validity in Decision Making on the Use of Psychometrics
To make appropriate judgements on the use of an assessment for selection we need to understand:
- How the traits we are measuring relate to job success (n.b. Empirical validity)
- How the specific product we are using assesses the trait (n.b. Construct Validity)
- The specific demands of the job we are selecting for (n.b. Content Validity)
All of these factors can be related to validity research. Empirical validity is particularly concerned with how the traits we are measuring relate to job success. Construct Validity is particularly concerned with how the specific product we are using assesses the trait. Finally Content Validity addresses how the specific demands of the job we are selecting for relates to the traits we are assessing.
The “Empirical Validation” of Objective Assessments in Work Environments
In the years following the end of the Second World War research focused on the predictive power of standardised assessments. Initially this research focused on ability tests and work samples, however from the late 1950’s assessment centres and structured interviews were shown to have a significant level of predictive validity. “Assessment centre” is the term applied when we use a variety of standardised assessments to provide supportive evidence for each other. Typically assessment centres will include ability tests, personality questionnaires, role plays, case studies and an interview. The table below is based on a revisiting of the Hunter & Schmidt’s 1998 research as presented by Michael Smith (Testing People at Work, 2005)
This graphic illustrates the relative effectiveness of the different types of method used in selection processes in terms of their ability to predict future job performance. A validity coefficient of 0 means that the method is no better than chance selection in terms of selecting someone who will perform successfully in the role. Perfect prediction is implausible when prediciting human behaviour.
Taylor Russell tables can help us make more appropriate inferences about future performance in a job for which a particular type of assessment is relevant using the validity coefficient and a margin of error.
|Ratio Below average/ Above Average job performance for a high assessment performer (e.g. Sten 8)
Below % / Above %
|Ratio Below average/ above Average job performance for a lower assessment performer (e.g. Sten 3)
Below % / Above %
0% / 100%
15 / 85
31 / 79
26 / 74
31 / 69
36 / 64
41 / 59
50 / 50
100% / 0%
79 / 31
74 / 26
69 / 31
64 / 36
59 / 41
55 / 45
50 / 50
Using the table above we can predict the likelihood of job success for a high assessment performer in a relevant job. For example if a graduate applicant for a marketing role had above average general reasoning ability (sten 8) we could estimate that they have 69% likelihood of above average performance (based on a validity for the assessment of .4). Similarily we can can predict the likelihood of job success for an underperformer on a career assessment relevant to their career interests. For example if student’s considering careers in different professional areas had below average general reasoning ability (sten 3) we could estimate that based on ability factors alone they have 31% likelihood of above average performance in any particular professional career (based on a validity for the assessment of .4). In considering the above it is essential that we can give feedback in an appropriately positive manner (ref section 4). Below average cognitive abilities (sten 3 or lower) mean the individual needs to make particular efforts to find an area where they have other advantages (specialist interest, motivation, complementary personality traits). A consistent pattern of below average cognitive ability on assessments should alert us to the fact that the individual will need more time to master tasks not that they cannot succeed. Others may find the career path easier but given a high level of motivation people with diverse cognitive abilities can succeed in the most cognitively challenging careers with time and effort.
Ability tests are widely accepted as being positively correlated with job performance across administrative, managerial and professional roles. (eg. Schmidt et al 1998). Appropriate technical tests are seen as having higher validity. The correlation of general reasoning ability and performance in administrative roles is widely accepted as being in the region of +.3 to + .6, while the validity of technical tests for craft roles has been found to be at least as high. While a positive correlation of .3 may sound low, even a medium correlation of this type can have significant impact on the job. See the illustration of an expectancy table below.
Verbal Reasoning Scores
Management Performance Average High
Looking at the “high” (top 30%) test scorers, 73% are high performing individuals and only 27% are low performers. This trend is reversed for “low” (bottom 30%) test scorers, where only 32% are high performers, but 68% are in the low performing group. This indicates that high test scorers are more than twice as likely as low test scorers to be high performers (73% against 32%). From validity research studies, ability/aptitude tests have been the single best predictors of training & job success. They have also been found to predict training capability slightly better, but do not predict tenure. Research also supports the idea that such tests predict better than educational level, interviews and references and comparably to bio-data, work-samples and peer evaluation.
The validity of personality questionnaires has proved to be more controversial than ability tests. Some psychologists have suggested the average validity of personality questionnaires to be as low as .10, while others claim that it could be in the region of .4 (Smith, 1988; Ghiselli, 1973). Many of these differences of opinion can be related to how effectively personality scales are related to job criteria. Validity is a key issue for any academic reviewer of a personality questionnaire. Published evidence of correlations with similar constructs (construct validity) or job performance (empirical criteria) are highly respected by professional reviewers. Instruments published with little or no validity data will be criticised in professional circles.
Limitations on carrying Out Empirical Validity Studies
One of the most important challenges to overcome in carrying out an empirical study is the quality of the criterion data available. Job performance data, for example, if it exists at all, is frequently rather subjective and can be difficult to quantify. The objectivity or reliability of the criterion data is critical to the success of a validity study. Reliability (of either measure) sets the upper limits to validity, so validity studies which have used unreliable criterion data will not show good results, irrespective of the relevance of the predictor. The use of quantifiable measures such as quantity of output, performance against target, etc, should be sought wherever possible. Behaviourally anchored rating scales designed specifically for the study are generally better then appraisal data; and two independent ratings of performance are generally better than one, as this enables an estimate to be made of inter-rater reliability. Sample size is another important consideration, and validity studies based on small samples (less than 100) should generally be avoided. Restriction in range is another common problem affecting validity studies; and this can affect both predictor and criterion variables, sometimes both. Where local validity studies cannot be undertaken larger scale studies can be used to provide justification for the relevance of an assessment tool. The landmark work of Schmidt has already created the argument of validity generalisation. It is now accepted in US courts that well administered tests of general reasoning ability are appropriate to assess analytical capabilities required for clerical, administrative, and managerial roles.
Content Validity, Fair Selection and Legality
There is an increasing requirement to justify the use of psychometric instruments from a legal point of view. This issue is particularly significant where assessment instruments are used in the process of selection (for a job, for a development opportunity etc). In this context fairness and legality require careful consideration of validity issues as well as quality assured administration processes.
The legal case for any assessment process is established by demonstrating that the assessment is relevant.
Content validation is the technical term for ensuring an assessment process is relevant to job content. This requires that a robust analysis of job content has been undertaken. The development of a job relevant competency framework usually fulfils this function. A job analysis which demonstrates a clear need for analytical capability, e.g. working with numbers and working with words, can therefore justify the use of such aptitude tests. Tests and personality questionnaires are often introduced into an assessment situation to improve the objectivity and, thus, fairness of the assessment. There is evidence to suggest that, for example, differences between ethnic groups in terms of personality are very small while interviewers continue to exhibit bias towards their own race. Nevertheless best practice is to use personality questionnaires in conjunction with an interview. By using a validation interview to test hypotheses from personality questionnaires we add breadth and depth to our interview. If you are using assessments for selection purposes, best practice is to monitor selection outcomes with respect to all of the 9 grounds*. In monitoring assessment outcomes we must watch out for adverse impact. Adverse impact is not illegal where there are relevant job demands for which one group are more capable. However where it occurs we must satisfy ourselves which element of the process is causing adverse impact and that there is a legitimate job demand underpinning the adverse impact.
* In Ireland this comes under The Employment Equality Act, 1998, 2004; which states that individuals should not be discriminated against on the grounds of their gender, marital status, family status, race, age, religion, sexual orientation, disability or membership of the traveller community.