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How to fit averaged data to obtain a single psychometric function?

How to fit averaged data to obtain a single psychometric function?



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I have psycho-physical data from a motion discrimination task in order to obtain PSE (point of subjective equality). I am using psignifit and have constructed individual psychometric logistic functions. How can I construct a distribution with averaged data?


Short answer
Averaging psychometric curves may not be the preferred way to pool psychophysical data.

background
Typically, extracted gold-standard outcome measures from the psychometric curves will be pooled and averaged to perform statistical analyses and so on.

For example, in the visual sciences a much-used outcome measure is the visual acuity where, for example, gratings in four orientations are shown using standard psychophysical tests. Since it's a 4AFC task, the 62.5% correct score will be taken as a measure of the threshold, where the gratings will be varied in their width, measures in cycles per degree (cpd) or related measure of visual angle.

To pool and average individual subject scores the acuity scores will be averaged, measured in cpd, and not the psychometric curves. For examples see Nau et al. (2013) and Bach et al. (1996).

If you would really insist, you could pool every measurement in case the method of constant stimuli or related paradigm was used. Then one single psychometric fit could be performed on the congregate data. Problem with this approach, as opposed to the preferred method described above, is that the fit will yield awesome outcomes in terms of superbly small variances in the fitted outcome parameters, as well as favorable descriptive statistical parameters, such as the correlation coefficient, simply because there are so many data points and hence many degrees of freedom. Further, random errors occurring in one, or a few subjects will now affect the overall fit and 'weird' data points will tend to be obscured by the multitude of data points per x value.

A better approach in terms of statistical descriptive outcomes would be to first average every data point and then do the fit. However, also here outliers will be obscured because of the averaging procedure. The power of psychometric fits is that individual subjects can be analyzed.

In case an adaptive method is used above procedure won't hold as each subject will have different x values. Adaptive procedures in general do not lend themselves very well for curve fitting as the data points around threshold are dense, but the trials targeting chance level or 100% correct are sparse or nonexistent altogether. Hence the asymptotes are ill-defined. You could average these data, if you insist, by averaging each of the fitted parameters and generate a 'master' fit out of those. Beware of logarithmic values, though, as averaging those is not arbitrary. Again, descriptive statistical parameters become obscure in such a master fit, and statistical analyses become difficult.

In all, my advice is to stick with pooling the single outcome measures obtained from the psychometric curve, i.e, stick to pooling your PSE outcomes.

References
- Nau et al., Transl Vis Sci Technol (2013); 2(3): 1
- Bach et al., optom Vis Sci (1996); 73(1): 49-53


INTRODUCTION

Insomnia is a highly prevalent condition and carries significant burden in terms of functional impairment, health care costs, and increased risk of depression. 1 𠄷 Despite its high prevalence and significant morbidity, insomnia often remains unrecognized and untreated, partly due to several barriers to assessment. Accurate case identification is important for deriving valid estimates of prevalence/incidence and for assessing burden of disease in the population. Identifying clinically significant insomnia is also important to intervene early and reduce morbidity. Thus, reliable and valid instruments are needed to assist investigators and clinicians in evaluating insomnia in various research and clinical contexts.

The assessment of insomnia is multidimensional and should ideally include a clinical evaluation and be complemented by self-report questionnaires and daily sleep diaries. While a clinical evaluation remains the gold standard for making a valid insomnia diagnosis, 8,9 such an evaluation can be time-consuming in routine clinical practice and may discourage some health practitioners from systematically inquiring about sleep in all of their patients. Brief and valid questionnaires can facilitate the initial screening and formal evaluation of insomnia. The patient's perspective is also of critical importance to monitor progress and evaluate outcome after initiating treatment. From a regulatory perspective, patient-reported outcomes are becoming increasingly used to substantiate evidence of treatment effectiveness in clinical trials. There is a need for assessment tools that are brief, practical, and psychometrically sound both for screening purposes and treatment outcome evaluation.

There are currently several patient-reported questionnaires available for assessing insomnia symptoms, severity, correlates, and a variety of constructs presumed to contribute to the etiology of insomnia. 8,10 With regard to screening for insomnia and evaluating treatment outcome, there are fewer choices available. Some of the most widely used instruments for these purposes include, for example, the Insomnia Severity Index, 11 the Pittsburgh Sleep Quality Index, 12 the Insomnia Symptom Questionnaire, 13 and the Athens Insomnia Scale. 14 While the number of items, response format, and time frame varies across instruments, they are generally aimed at assessing the patient's perception and at quantifying subjective dimensions of insomnia. Each of these instruments has its own advantages and limitations (for reviews see Buysse et al., Martin et al., Morin, and Moul et al.). 10,15 � The Insomnia Severity Index (ISI) is a brief instrument that was designed to assess the severity of both nighttime and daytime components of insomnia. It is available in several languages and is increasingly used as a metric of treatment response in clinical research. While its psychometric properties using classical test theory have been documented previously, 11,18 � the present paper reports further validation using item response theory (IRT) analyses to examine response patterns on individual ISI items and receiver-operating curves (ROC) to identify optimal cut points for case finding in a community sample and for assessing treatment response in a clinical sample.


The History of Psychometric Testing

Whether you are going through the recruitment process or simply thinking about applying for a new role, you’ve probably come across the all-important psychometric test. Psychometric tests may seem new, in the sense that most employers are now beginning to utilize them in recruitment efforts across the board, but what most people don’t realize is the lengthy history behind psychometric tests themselves, which have developed throughout human history to be the psychometric tests we take today.

From the dawn of human history

Psychometric tests are found throughout human history, appearing across cultures and religions. In ancient China, candidates were required to take examinations in order to obtain prized occupations which involved the need to be competent in areas such as fiscal policies, revenue, agriculture, military, and law as well as tests that determined physical capability of potential soldiers.

Early forms of psychometric tests were not easy. Rather, they were a test of skill and intelligence, as well as endurance. An early psychometric test required the candidate to attend testing for a full day and night – imagine that next time you are taking a not-so-simple assessment spanning a couple of hours! To make matters worse, these tests were so challenging that they had a pass rate of little more than 7%. You could almost say these psychometric tests were not just about assessing competency they were about pushing candidates to their limits to find the absolute best.

While it may seem like it would be ideal to be in that 7%, unfortunately being at that elite level did not mean the candidate was successful. Rather, it meant they moved on to the final round of psychometric testing, which had a pass rate of about 3%. The lucky few that achieved this entered the much sought after public official roles. This procedure was eliminated in 1906, and a fairer but still difficult test was chosen in its place, but this type of testing still exists today in modern China, as well as other nearby countries such as the Republic of South Korea.

The importance of accuracy

Interestingly, the Bible[1] also makes a mention of an informal psychometric test, which involved a group of people pronouncing a single word – proving that sometimes just that little bit of preparation is all you need to gain an extra edge. These kinds of psychometric tests exist today, especially when it comes to roles which require exact, clear pronunciation or a type of language specific to one area. It can also be seen in occupations where accuracy is essential, such as the military, and perhaps to a greater extent the medical profession, where accurate and clear communication can be a life-or-death situation.

Although we have evidence of psychometric-type tests coming from ancient sources, researchers agree that the first true psychometric test, in terms of how we identify it today, was developed by Francis Galton, who in the 1880s created a framework of tests to gauge participants’ intelligence based on an examination of their sensory and motor skills. In fact, it was Francis Galton who created the term “psychometric” and his work in developing this efficient sensory and motor skill psychometric test went on to influence noted psychologist Dr. James Cattell, who is renowned for developing psychometric tests further at that time than they had ever been before, when Galton’s work was criticized as not being very useful when it came to predicting educational outcomes.

Toward modern psychometric testing

The modern type of psychometric test we know today has roots in France in the 19 th century and was devised to allow physicians to identify and separate patients with mental deficiencies and those experiencing mental illness.

Three renowned psychologists, Alfred Binet, Victor Henri, and Theodore Simon, got together to work on developing a psychometric test that could identify young children affected by mental deficiencies. It took them 15 years to develop their groundbreaking assessment tool, which looked at participants’ verbal skills and then assessed their level of mental capacity. Referred to as “mental retardation” in their day, the test became known as the Binet-Simon test, and remarkably, is still in use today.

Now known as the Stanford-Binet test, it is in its fifth edition, having been updated and released in 2003 in conjunction with Stanford researcher Lewis M. Terman to address the challenges of diagnosing children in the modern era. Terman used the original Binet-Simon Intelligence Scale, but removed problematic cultural assumptions, such as a task which required the child to select the “prettiest looking” person, which could clearly be affected by cultural bias. With significant revision, but based on the heart of the original work, the resulting test is now able to identify developmental deficiencies as well as intellectual challenges.

The roots of personality testing

Psychometric tests include aptitude tests (cognitive, IQ tests, and other tests that assess aptitude rather than knowledge or a skill set), ability tests (tests that assess learned knowledge and skills – this could be a spelling & grammar test, a typing test or an MS Office test), and personality tests. Personality tests are very popular in today’s recruitment, with plenty of employers looking to find candidates’ Myers-Briggs personality type, regardless of the fact that many psychologists no longer believe[2] the results are meaningful.

Before the popular Myers-Briggs, and other in-house personality types which give a better indication of how someone would behave in a team functioning inside a workplace environment, personality tests were rather unfortunate, especially if you weren’t what society termed “an ideal beauty.” The now debunked practice of phrenology assessed candidates’ personalities by looking at their physical features, in particular the face and head. Created by Dr. Franz Joseph Gall, phrenology has long been debunked, but nonetheless would have contributed toward many unlucky candidates losing out on opportunities simply because a candidate with more “desirable” physical features had also applied. Interestingly, researchers at the University of Oxford have put phrenology to the test[3] and found no link whatsoever between a patient’s personality with shape or measurements of their face and head.

Addressing the needs of war

As we have mentioned, ancient China was the first civilization on record to take a psychometric approach when recruiting, and this extended to their military selection. Western armies followed suit, and were able to select soldiers with what was considered the most suitable personality with a test which was known as the Woodworth Personality Data Sheet (1917).

However, rather than being an administered clinical test, it was a self-reported inventory that gave candidates some leeway on how they represented their personality. Initially designed to ensure candidates were not at risk of developing shell shock, the test became popular as a general personality test and paved the way for personality tests used in recruitment today.

The test consisted of 116 questions[4] where the candidate could respond “yes” or “no” and included revealing questions that helped recruiters identify people at risk of stress. Answering “yes” to “Are you troubled with dreams about your work?” may have put candidates into a pool not best suited for military life, as they would be too affected by what they saw and did on a daily basis. Plenty of modern psychometric tests, such as the Symptom Checklist 90, ask questions that have come directly from Woodworth’s diagnostic test.

Psychometric testing today

Most employers make use of psychometric testing to ensure they are selecting candidates with the right mix of skills, knowledge, and capabilities as well as the capacity to learn more on the job, adapt to changes instantaneously, and the ability to function well in the face of stress – which most workers deal with as roles become much more demanding.

The psychometric test industry has evolved to suit the needs of the employer, who is faced with increasing numbers of applications as well as a desire to assess all candidates objectively. So instead of just facing a personality or intelligence test, candidates may be asked to take an aptitude test covering cognitive skill, an IQ test, or another test that assesses aptitude in general rather than knowledge or an established skill set.

Employers can choose to administer an aptitude test alone, or combine it with an ability test which assesses the candidates’ learned knowledge and skills – this could be a punctuation test, a word processing test, or an Excel test. Finally, some employers still choose to use personality tests, which can actually be a good thing for you as a candidate as it helps you determine which environment is right for you. Remember, a job interview is a good time to see whether you want to work in the environment the potential employer offers, so don’t hesitate to use the insight you receive about your skills and tendencies to make a choice that is a good fit for you.

With such a fascinating history, psychometric tests continue to reveal insight into how people work, and with a little preparation, can help you land a role that perfectly matches the unique set of skills you’ve developed over your working life – what could be better than that?


Sensation Seeking: Behavioral Expressions and Biosocial Bases

6 Psychophysiology

Differences in the psychophysiological responses of the brain and autonomic nervous system as a function of stimulus intensity and novelty have been found and generally replicated (Zuckerman 1990 ). The heart rate response reflecting orienting to moderately intense and novel stimuli is stronger in high sensation seekers than in lows, perhaps reflecting their interest in novel stimuli (experience seeking) and disinterest in repeated stimuli (boredom suceptibility).

The cortical evoked potential (EP) reflects the magnitude of the brain cortex response to stimuli. Augmenting–reducing is a measure of the relationship between amplitude of the EP as a function of the intensity of stimuli. A high positive slope (augmenting) is characteristic of high sensation seekers (primarily those of the disinhibition type) and very low slopes, sometimes reflecting a reduction of response at the highest stimulus intensities (reducing), is found primarily in low sensation seekers. These EP augmenting–reducing differences have been related to differences in behavioral control in individual cats and strains of rats analogous to sensation seeking behavior in humans (Siegel and Driscoll 1996 ).


Conclusion

We measured psychometric functions for detection and discrimination with and without flankers using a robust psychophysical method. Our results confirm that psychometric functions for detection are flatter in the presence of flankers, that this flattening is mildly present in psychometric functions for discrimination near the detection threshold, and that it virtually disappears well above the detection threshold. When plotted in TvC form, our discrimination data describe a pattern that is distinctly different from two other patterns that have been reported in the literature, although the differences are reasonably attributed to the different psychophysical methods used across the studies that reported these three patterns.

Our results did not replicate the most common finding of earlier studies, namely that at high-contrast levels, discrimination thresholds with flankers are higher than those without flankers. Because our method eliminated Type-A order effects that spuriously broaden psychometric functions, one might speculate that, by comparison, what previous studies have actually shown is that flankers increase the magnitude of order effects, and thus produce spuriously higher discrimination thresholds. The origin of Type-B order effects still found in our data is unclear, although they have been reported to have different forms and magnitudes in different conditions (Ulrich & Vorberg, 2009). Although only a speculation at this point, flanker-contingent Type-B order effects do not seem untenable. Hopefully, further research designed also to eliminate Type-A order effects will clarify whether Type-B order effects in 2AFC discrimination tasks are actually larger with flankers than without them and, ideally, will also identify their causes and devise means for the elimination of their contaminating influence.

Our discussion of current models of flanker facilitation effects has questioned the validity of the hypothesis that flankers reduce uncertainty about the location of the target. Also, the widespread claim that flankers alter the contrast response function has been shown to reflect only the natural outcome of the modeler’s decision to attribute this particular role to the flankers by the arbitrary choice of fitting additive noise models to the data (and succeeding at that). We have shown that the alternative choice of fitting a multiplicative noise model also succeeds at accounting for the data equally accurately, and in this type of model, the contrast response function is the same with and without flankers, whereas the variance function differs in either case. The functional equivalence of these alternative explanations reveals that the cause of flanker effects cannot be determined until experimental procedures are devised that allow separate estimation of the contrast response and variance functions.


Characteristics and Analysis of Big Data

Characteristics of Big Data

There is no clear consensus on neither who coined the term 𠇋ig Data” nor the definition of it (Diebold, 2012). In general one could say big data refers to datasets that cannot be perceived, acquired, managed, and processed by traditional IT and software/hardware tools within a tolerable time (Chen et al., 2014). We adopt this definition on big data. We define large data as datasets that are large in comparison to conventional datasets in psychological research. Researchers can still analyze large datasets with their standard computers but it may take more time to process the data, such that efficient data-analysis is desirable. It should be noted that these definitions are all relative to the computing facilities. A dataset of 10 GB, e.g., the Airlines data in the illustration, is considered as big data in typical computers with 8 GB RAM. The same dataset is no longer big for workstations with 128 GB RAM.

One of the first to describe big data was probably Laney (2001), who used three dimensions, namely Volume, Velocity, and Variety (the 3 Vs), to describe the challenges with big data. High volume data means that the size of the dataset may lead to problems with storage and analysis. High velocity data refers to data that come in at a high rate and/or have to be processed within as short an amount of time as possible (e.g., real-time processing). High variety data are data consisting of many types, often unstructured, such as mixtures of text, photographs, videos, and numbers.

A fourth V that is often mentioned is Veracity, indicating the importance of the quality (or truthfulness) of the data (Saha and Srivastava, 2014). Veracity is different in kind from the other three Vs, as veracity is not a characteristic of big data per se. That is, data quality is important for all datasets, not only big ones. However, due to the methods that are used to gather big data, the scale of the problems with respect to the veracity of data may be larger with big datasets than with small ones. Therefore, with big data it may be even more important to consider whether the conclusions based on the data are valid than with carefully obtained smaller datasets (Lazer et al., 2014 Puts et al., 2015)

As big data analyses are mainly performed in the physical sciences and business settings, and not commonly in the social sciences, the quality of the data is often not considered in terms of reliability and validity of the constructs of interest, but in terms of screening for duplicate cases and faulty entries. By focusing on the reliability and validity of the data, the veracity of big data is an area where psychology can really contribute to the field of big data. In the illustrations, we demonstrate how reliability and validity can be evaluated in big and large datasets. Example 1 shows how the reliability and the construct validity of the measures can be studied, while Example 2 illustrates how various regression techniques that are often used to study predictive validity, can be applied to big and large datasets.

In order to analyze large volumes of data properly using a typical computer, the size of the dataset cannot be larger than the amount of random-access memory (RAM), which will often be 4 or 8 GB on typical computers. The present study focuses exclusively on how to handle the large volume and the veracity of data in psychology so that psychologists may begin to analyze big data in their research.


Identifying steep psychometric function slope quickly in clinical applications

Knowledge of an observer’s psychometric function slope is potentially useful in clinical visual psychophysics (for example, perimetry), however, the short test times necessary in a clinical setting typically prevent slope estimation. We explore, using computer simulation, the performance of several possible procedures for estimating psychometric function slope within limited presentations (aiming for approximately 30 or 140 trials). Procedures were based on either adaptive staircase or Bayesian techniques, and performance was compared to a Method of Constant Stimuli. An adaptation of the Ψ algorithm was best performing, being able to reliably identify steep from flat psychometric functions in less than 30 presentations, however reliable quantification of shallow psychometric functions was not possible.

Research highlights

► We present clinically viable algorithms for finding psychometric function slope ► An adaption of the Psi algorithm of Konstevich and Tyler works well ► Steep slopes can be reliably identified in less than 30 presentations.


Covert attention affects the psychometric function of contrast sensitivity

We examined the effect of transient covert attention on the psychometric function for contrast sensitivity in an orientation discrimination task when the target was presented alone in the absence of distracters and visual masks. Transient covert attention decreased both the threshold (consistent with a contrast gain mechanism) and, less consistently, the slope of the psychometric function. We assessed performance at 8 equidistant locations (4.5° eccentricity) and found that threshold and slope depended on target location—both were higher on the vertical than the horizontal meridian, particularly directly above fixation. All effects were robust across a range of spatial frequencies, and the visual field asymmetries increased with spatial frequency. Notwithstanding the dependence of the psychometric function on target location, attention improved performance to a similar extent across the visual field.

Given that, in this study, we excluded all sources of external noise, and that we showed experimentally that spatial uncertainty cannot explain the present results, we conclude that the observed attentional benefit is consistent with signal enhancement.


Other considerations [ edit | edit source ]

A criticism of the Rasch model is that it is overly restrictive or prescriptive because it does not permit each item to have a different discrimination. A criticism specific to the use of multiple choice items in educational assessment is that there is no provision in the model for guessing because the left asymptote always approaches a zero probability in the Rasch model. These variations are available in models such as the two and three parameter logistic models (Birnbaum, 1968). However, the specification of uniform discrimination and zero left asymptote are necessary properties of the model in order to sustain sufficiency of the simple, unweighted raw score.

In the two-parameter logistic model (2PL-IRT Lord & Novick, 1968) the weighted raw score is theoretically sufficient for person parameters, where the weights are given by model parameters referred to as discrimination parameters. Lord & Novick's one-parameter logistic model, 1PL, appears similar to the Rasch model in that it does not have discrimination parameters, but 1PL has different motivation and subtly different parameterization. The 1PL is a descriptive model which summarizes the sample as a normal distribution. The dichotomous Rasch model is a measurement model which parameterizes each member of the sample individually. There are other technical differences. [How to reference and link to summary or text]

Verhelst & Glas (1995) derive Conditional Maximum Likelihood (CML) equations for a model they refer to as the One Parameter Logistic Model (OPLM). In algebraic form it appears to be identical with the 2PL model, but OPLM contains preset discrimination indexes rather than 2PL's estimated discrimination parameters. As noted by these authors, though, the problem one faces in estimation with estimated discrimination parameters is that the discriminations are unknown, meaning that the weighted raw score "is not a mere statistic, and hence it is impossible to use CML as an estimation method" (Verhelst & Glas, 1995, p. 217). That is, sufficiency of the weighted "score" in the 2PL cannot be used according to the way in which a sufficient statistic is defined. If the weights are imputed instead of being estimated, as in OPLM, conditional estimation is possible and the properties of the Rasch model are retained (Verhelst, Glas & Verstralen, 1995 Verhelst & Glas, 1995). In OPLM, the values of the discrimination index are restricted to between 1 and 15. A limitation of this approach is that in practice, values of discrimination indexes must be preset as a starting point. This means some type of estimation of discrimination is involved when the purpose is to avoid doing so.


Affiliations

Department of Psychology, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

Center for Perceptual Systems, University of Texas at Austin, Austin, Texas 78712, USA

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

Contributions

J.B. and W.S.G. developed the analysis, designed the experiments and wrote the paper. J.B. conceived the project, analysed the data and performed the experiments.

Corresponding author


Covert attention affects the psychometric function of contrast sensitivity

We examined the effect of transient covert attention on the psychometric function for contrast sensitivity in an orientation discrimination task when the target was presented alone in the absence of distracters and visual masks. Transient covert attention decreased both the threshold (consistent with a contrast gain mechanism) and, less consistently, the slope of the psychometric function. We assessed performance at 8 equidistant locations (4.5° eccentricity) and found that threshold and slope depended on target location—both were higher on the vertical than the horizontal meridian, particularly directly above fixation. All effects were robust across a range of spatial frequencies, and the visual field asymmetries increased with spatial frequency. Notwithstanding the dependence of the psychometric function on target location, attention improved performance to a similar extent across the visual field.

Given that, in this study, we excluded all sources of external noise, and that we showed experimentally that spatial uncertainty cannot explain the present results, we conclude that the observed attentional benefit is consistent with signal enhancement.


Conclusion

We measured psychometric functions for detection and discrimination with and without flankers using a robust psychophysical method. Our results confirm that psychometric functions for detection are flatter in the presence of flankers, that this flattening is mildly present in psychometric functions for discrimination near the detection threshold, and that it virtually disappears well above the detection threshold. When plotted in TvC form, our discrimination data describe a pattern that is distinctly different from two other patterns that have been reported in the literature, although the differences are reasonably attributed to the different psychophysical methods used across the studies that reported these three patterns.

Our results did not replicate the most common finding of earlier studies, namely that at high-contrast levels, discrimination thresholds with flankers are higher than those without flankers. Because our method eliminated Type-A order effects that spuriously broaden psychometric functions, one might speculate that, by comparison, what previous studies have actually shown is that flankers increase the magnitude of order effects, and thus produce spuriously higher discrimination thresholds. The origin of Type-B order effects still found in our data is unclear, although they have been reported to have different forms and magnitudes in different conditions (Ulrich & Vorberg, 2009). Although only a speculation at this point, flanker-contingent Type-B order effects do not seem untenable. Hopefully, further research designed also to eliminate Type-A order effects will clarify whether Type-B order effects in 2AFC discrimination tasks are actually larger with flankers than without them and, ideally, will also identify their causes and devise means for the elimination of their contaminating influence.

Our discussion of current models of flanker facilitation effects has questioned the validity of the hypothesis that flankers reduce uncertainty about the location of the target. Also, the widespread claim that flankers alter the contrast response function has been shown to reflect only the natural outcome of the modeler’s decision to attribute this particular role to the flankers by the arbitrary choice of fitting additive noise models to the data (and succeeding at that). We have shown that the alternative choice of fitting a multiplicative noise model also succeeds at accounting for the data equally accurately, and in this type of model, the contrast response function is the same with and without flankers, whereas the variance function differs in either case. The functional equivalence of these alternative explanations reveals that the cause of flanker effects cannot be determined until experimental procedures are devised that allow separate estimation of the contrast response and variance functions.


The History of Psychometric Testing

Whether you are going through the recruitment process or simply thinking about applying for a new role, you’ve probably come across the all-important psychometric test. Psychometric tests may seem new, in the sense that most employers are now beginning to utilize them in recruitment efforts across the board, but what most people don’t realize is the lengthy history behind psychometric tests themselves, which have developed throughout human history to be the psychometric tests we take today.

From the dawn of human history

Psychometric tests are found throughout human history, appearing across cultures and religions. In ancient China, candidates were required to take examinations in order to obtain prized occupations which involved the need to be competent in areas such as fiscal policies, revenue, agriculture, military, and law as well as tests that determined physical capability of potential soldiers.

Early forms of psychometric tests were not easy. Rather, they were a test of skill and intelligence, as well as endurance. An early psychometric test required the candidate to attend testing for a full day and night – imagine that next time you are taking a not-so-simple assessment spanning a couple of hours! To make matters worse, these tests were so challenging that they had a pass rate of little more than 7%. You could almost say these psychometric tests were not just about assessing competency they were about pushing candidates to their limits to find the absolute best.

While it may seem like it would be ideal to be in that 7%, unfortunately being at that elite level did not mean the candidate was successful. Rather, it meant they moved on to the final round of psychometric testing, which had a pass rate of about 3%. The lucky few that achieved this entered the much sought after public official roles. This procedure was eliminated in 1906, and a fairer but still difficult test was chosen in its place, but this type of testing still exists today in modern China, as well as other nearby countries such as the Republic of South Korea.

The importance of accuracy

Interestingly, the Bible[1] also makes a mention of an informal psychometric test, which involved a group of people pronouncing a single word – proving that sometimes just that little bit of preparation is all you need to gain an extra edge. These kinds of psychometric tests exist today, especially when it comes to roles which require exact, clear pronunciation or a type of language specific to one area. It can also be seen in occupations where accuracy is essential, such as the military, and perhaps to a greater extent the medical profession, where accurate and clear communication can be a life-or-death situation.

Although we have evidence of psychometric-type tests coming from ancient sources, researchers agree that the first true psychometric test, in terms of how we identify it today, was developed by Francis Galton, who in the 1880s created a framework of tests to gauge participants’ intelligence based on an examination of their sensory and motor skills. In fact, it was Francis Galton who created the term “psychometric” and his work in developing this efficient sensory and motor skill psychometric test went on to influence noted psychologist Dr. James Cattell, who is renowned for developing psychometric tests further at that time than they had ever been before, when Galton’s work was criticized as not being very useful when it came to predicting educational outcomes.

Toward modern psychometric testing

The modern type of psychometric test we know today has roots in France in the 19 th century and was devised to allow physicians to identify and separate patients with mental deficiencies and those experiencing mental illness.

Three renowned psychologists, Alfred Binet, Victor Henri, and Theodore Simon, got together to work on developing a psychometric test that could identify young children affected by mental deficiencies. It took them 15 years to develop their groundbreaking assessment tool, which looked at participants’ verbal skills and then assessed their level of mental capacity. Referred to as “mental retardation” in their day, the test became known as the Binet-Simon test, and remarkably, is still in use today.

Now known as the Stanford-Binet test, it is in its fifth edition, having been updated and released in 2003 in conjunction with Stanford researcher Lewis M. Terman to address the challenges of diagnosing children in the modern era. Terman used the original Binet-Simon Intelligence Scale, but removed problematic cultural assumptions, such as a task which required the child to select the “prettiest looking” person, which could clearly be affected by cultural bias. With significant revision, but based on the heart of the original work, the resulting test is now able to identify developmental deficiencies as well as intellectual challenges.

The roots of personality testing

Psychometric tests include aptitude tests (cognitive, IQ tests, and other tests that assess aptitude rather than knowledge or a skill set), ability tests (tests that assess learned knowledge and skills – this could be a spelling & grammar test, a typing test or an MS Office test), and personality tests. Personality tests are very popular in today’s recruitment, with plenty of employers looking to find candidates’ Myers-Briggs personality type, regardless of the fact that many psychologists no longer believe[2] the results are meaningful.

Before the popular Myers-Briggs, and other in-house personality types which give a better indication of how someone would behave in a team functioning inside a workplace environment, personality tests were rather unfortunate, especially if you weren’t what society termed “an ideal beauty.” The now debunked practice of phrenology assessed candidates’ personalities by looking at their physical features, in particular the face and head. Created by Dr. Franz Joseph Gall, phrenology has long been debunked, but nonetheless would have contributed toward many unlucky candidates losing out on opportunities simply because a candidate with more “desirable” physical features had also applied. Interestingly, researchers at the University of Oxford have put phrenology to the test[3] and found no link whatsoever between a patient’s personality with shape or measurements of their face and head.

Addressing the needs of war

As we have mentioned, ancient China was the first civilization on record to take a psychometric approach when recruiting, and this extended to their military selection. Western armies followed suit, and were able to select soldiers with what was considered the most suitable personality with a test which was known as the Woodworth Personality Data Sheet (1917).

However, rather than being an administered clinical test, it was a self-reported inventory that gave candidates some leeway on how they represented their personality. Initially designed to ensure candidates were not at risk of developing shell shock, the test became popular as a general personality test and paved the way for personality tests used in recruitment today.

The test consisted of 116 questions[4] where the candidate could respond “yes” or “no” and included revealing questions that helped recruiters identify people at risk of stress. Answering “yes” to “Are you troubled with dreams about your work?” may have put candidates into a pool not best suited for military life, as they would be too affected by what they saw and did on a daily basis. Plenty of modern psychometric tests, such as the Symptom Checklist 90, ask questions that have come directly from Woodworth’s diagnostic test.

Psychometric testing today

Most employers make use of psychometric testing to ensure they are selecting candidates with the right mix of skills, knowledge, and capabilities as well as the capacity to learn more on the job, adapt to changes instantaneously, and the ability to function well in the face of stress – which most workers deal with as roles become much more demanding.

The psychometric test industry has evolved to suit the needs of the employer, who is faced with increasing numbers of applications as well as a desire to assess all candidates objectively. So instead of just facing a personality or intelligence test, candidates may be asked to take an aptitude test covering cognitive skill, an IQ test, or another test that assesses aptitude in general rather than knowledge or an established skill set.

Employers can choose to administer an aptitude test alone, or combine it with an ability test which assesses the candidates’ learned knowledge and skills – this could be a punctuation test, a word processing test, or an Excel test. Finally, some employers still choose to use personality tests, which can actually be a good thing for you as a candidate as it helps you determine which environment is right for you. Remember, a job interview is a good time to see whether you want to work in the environment the potential employer offers, so don’t hesitate to use the insight you receive about your skills and tendencies to make a choice that is a good fit for you.

With such a fascinating history, psychometric tests continue to reveal insight into how people work, and with a little preparation, can help you land a role that perfectly matches the unique set of skills you’ve developed over your working life – what could be better than that?


Characteristics and Analysis of Big Data

Characteristics of Big Data

There is no clear consensus on neither who coined the term 𠇋ig Data” nor the definition of it (Diebold, 2012). In general one could say big data refers to datasets that cannot be perceived, acquired, managed, and processed by traditional IT and software/hardware tools within a tolerable time (Chen et al., 2014). We adopt this definition on big data. We define large data as datasets that are large in comparison to conventional datasets in psychological research. Researchers can still analyze large datasets with their standard computers but it may take more time to process the data, such that efficient data-analysis is desirable. It should be noted that these definitions are all relative to the computing facilities. A dataset of 10 GB, e.g., the Airlines data in the illustration, is considered as big data in typical computers with 8 GB RAM. The same dataset is no longer big for workstations with 128 GB RAM.

One of the first to describe big data was probably Laney (2001), who used three dimensions, namely Volume, Velocity, and Variety (the 3 Vs), to describe the challenges with big data. High volume data means that the size of the dataset may lead to problems with storage and analysis. High velocity data refers to data that come in at a high rate and/or have to be processed within as short an amount of time as possible (e.g., real-time processing). High variety data are data consisting of many types, often unstructured, such as mixtures of text, photographs, videos, and numbers.

A fourth V that is often mentioned is Veracity, indicating the importance of the quality (or truthfulness) of the data (Saha and Srivastava, 2014). Veracity is different in kind from the other three Vs, as veracity is not a characteristic of big data per se. That is, data quality is important for all datasets, not only big ones. However, due to the methods that are used to gather big data, the scale of the problems with respect to the veracity of data may be larger with big datasets than with small ones. Therefore, with big data it may be even more important to consider whether the conclusions based on the data are valid than with carefully obtained smaller datasets (Lazer et al., 2014 Puts et al., 2015)

As big data analyses are mainly performed in the physical sciences and business settings, and not commonly in the social sciences, the quality of the data is often not considered in terms of reliability and validity of the constructs of interest, but in terms of screening for duplicate cases and faulty entries. By focusing on the reliability and validity of the data, the veracity of big data is an area where psychology can really contribute to the field of big data. In the illustrations, we demonstrate how reliability and validity can be evaluated in big and large datasets. Example 1 shows how the reliability and the construct validity of the measures can be studied, while Example 2 illustrates how various regression techniques that are often used to study predictive validity, can be applied to big and large datasets.

In order to analyze large volumes of data properly using a typical computer, the size of the dataset cannot be larger than the amount of random-access memory (RAM), which will often be 4 or 8 GB on typical computers. The present study focuses exclusively on how to handle the large volume and the veracity of data in psychology so that psychologists may begin to analyze big data in their research.


Identifying steep psychometric function slope quickly in clinical applications

Knowledge of an observer’s psychometric function slope is potentially useful in clinical visual psychophysics (for example, perimetry), however, the short test times necessary in a clinical setting typically prevent slope estimation. We explore, using computer simulation, the performance of several possible procedures for estimating psychometric function slope within limited presentations (aiming for approximately 30 or 140 trials). Procedures were based on either adaptive staircase or Bayesian techniques, and performance was compared to a Method of Constant Stimuli. An adaptation of the Ψ algorithm was best performing, being able to reliably identify steep from flat psychometric functions in less than 30 presentations, however reliable quantification of shallow psychometric functions was not possible.

Research highlights

► We present clinically viable algorithms for finding psychometric function slope ► An adaption of the Psi algorithm of Konstevich and Tyler works well ► Steep slopes can be reliably identified in less than 30 presentations.


Sensation Seeking: Behavioral Expressions and Biosocial Bases

6 Psychophysiology

Differences in the psychophysiological responses of the brain and autonomic nervous system as a function of stimulus intensity and novelty have been found and generally replicated (Zuckerman 1990 ). The heart rate response reflecting orienting to moderately intense and novel stimuli is stronger in high sensation seekers than in lows, perhaps reflecting their interest in novel stimuli (experience seeking) and disinterest in repeated stimuli (boredom suceptibility).

The cortical evoked potential (EP) reflects the magnitude of the brain cortex response to stimuli. Augmenting–reducing is a measure of the relationship between amplitude of the EP as a function of the intensity of stimuli. A high positive slope (augmenting) is characteristic of high sensation seekers (primarily those of the disinhibition type) and very low slopes, sometimes reflecting a reduction of response at the highest stimulus intensities (reducing), is found primarily in low sensation seekers. These EP augmenting–reducing differences have been related to differences in behavioral control in individual cats and strains of rats analogous to sensation seeking behavior in humans (Siegel and Driscoll 1996 ).


Other considerations [ edit | edit source ]

A criticism of the Rasch model is that it is overly restrictive or prescriptive because it does not permit each item to have a different discrimination. A criticism specific to the use of multiple choice items in educational assessment is that there is no provision in the model for guessing because the left asymptote always approaches a zero probability in the Rasch model. These variations are available in models such as the two and three parameter logistic models (Birnbaum, 1968). However, the specification of uniform discrimination and zero left asymptote are necessary properties of the model in order to sustain sufficiency of the simple, unweighted raw score.

In the two-parameter logistic model (2PL-IRT Lord & Novick, 1968) the weighted raw score is theoretically sufficient for person parameters, where the weights are given by model parameters referred to as discrimination parameters. Lord & Novick's one-parameter logistic model, 1PL, appears similar to the Rasch model in that it does not have discrimination parameters, but 1PL has different motivation and subtly different parameterization. The 1PL is a descriptive model which summarizes the sample as a normal distribution. The dichotomous Rasch model is a measurement model which parameterizes each member of the sample individually. There are other technical differences. [How to reference and link to summary or text]

Verhelst & Glas (1995) derive Conditional Maximum Likelihood (CML) equations for a model they refer to as the One Parameter Logistic Model (OPLM). In algebraic form it appears to be identical with the 2PL model, but OPLM contains preset discrimination indexes rather than 2PL's estimated discrimination parameters. As noted by these authors, though, the problem one faces in estimation with estimated discrimination parameters is that the discriminations are unknown, meaning that the weighted raw score "is not a mere statistic, and hence it is impossible to use CML as an estimation method" (Verhelst & Glas, 1995, p. 217). That is, sufficiency of the weighted "score" in the 2PL cannot be used according to the way in which a sufficient statistic is defined. If the weights are imputed instead of being estimated, as in OPLM, conditional estimation is possible and the properties of the Rasch model are retained (Verhelst, Glas & Verstralen, 1995 Verhelst & Glas, 1995). In OPLM, the values of the discrimination index are restricted to between 1 and 15. A limitation of this approach is that in practice, values of discrimination indexes must be preset as a starting point. This means some type of estimation of discrimination is involved when the purpose is to avoid doing so.


Affiliations

Department of Psychology, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA

Center for Perceptual Systems, University of Texas at Austin, Austin, Texas 78712, USA

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Contributions

J.B. and W.S.G. developed the analysis, designed the experiments and wrote the paper. J.B. conceived the project, analysed the data and performed the experiments.

Corresponding author


INTRODUCTION

Insomnia is a highly prevalent condition and carries significant burden in terms of functional impairment, health care costs, and increased risk of depression. 1 𠄷 Despite its high prevalence and significant morbidity, insomnia often remains unrecognized and untreated, partly due to several barriers to assessment. Accurate case identification is important for deriving valid estimates of prevalence/incidence and for assessing burden of disease in the population. Identifying clinically significant insomnia is also important to intervene early and reduce morbidity. Thus, reliable and valid instruments are needed to assist investigators and clinicians in evaluating insomnia in various research and clinical contexts.

The assessment of insomnia is multidimensional and should ideally include a clinical evaluation and be complemented by self-report questionnaires and daily sleep diaries. While a clinical evaluation remains the gold standard for making a valid insomnia diagnosis, 8,9 such an evaluation can be time-consuming in routine clinical practice and may discourage some health practitioners from systematically inquiring about sleep in all of their patients. Brief and valid questionnaires can facilitate the initial screening and formal evaluation of insomnia. The patient's perspective is also of critical importance to monitor progress and evaluate outcome after initiating treatment. From a regulatory perspective, patient-reported outcomes are becoming increasingly used to substantiate evidence of treatment effectiveness in clinical trials. There is a need for assessment tools that are brief, practical, and psychometrically sound both for screening purposes and treatment outcome evaluation.

There are currently several patient-reported questionnaires available for assessing insomnia symptoms, severity, correlates, and a variety of constructs presumed to contribute to the etiology of insomnia. 8,10 With regard to screening for insomnia and evaluating treatment outcome, there are fewer choices available. Some of the most widely used instruments for these purposes include, for example, the Insomnia Severity Index, 11 the Pittsburgh Sleep Quality Index, 12 the Insomnia Symptom Questionnaire, 13 and the Athens Insomnia Scale. 14 While the number of items, response format, and time frame varies across instruments, they are generally aimed at assessing the patient's perception and at quantifying subjective dimensions of insomnia. Each of these instruments has its own advantages and limitations (for reviews see Buysse et al., Martin et al., Morin, and Moul et al.). 10,15 � The Insomnia Severity Index (ISI) is a brief instrument that was designed to assess the severity of both nighttime and daytime components of insomnia. It is available in several languages and is increasingly used as a metric of treatment response in clinical research. While its psychometric properties using classical test theory have been documented previously, 11,18 � the present paper reports further validation using item response theory (IRT) analyses to examine response patterns on individual ISI items and receiver-operating curves (ROC) to identify optimal cut points for case finding in a community sample and for assessing treatment response in a clinical sample.


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