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The new PMC design is here! Learn more about navigating our updated article layout. The PMC legacy view will also be available for a limited time. Federal government websites often end in. The site is secure. Developing self-report Likert scales is an essential part of modern psychology. However, it is hard for psychologists to remain apprised of best practices as methodological developments accumulate. To address this, this current paper offers a selective review of advances in Likert scale development that have occurred over the past 25 years.

We reviewed six major measurement journals e. We supplemented this review with an in-depth discussion of five particular advances: 1 conceptions of construct validity, 2 creating better construct definitions, 3 readability tests for generating items, 4 alternative measures of precision [e. The Supplementary Material provides further technical details on these advances and offers guidance on software implementation.

This paper is intended to be a resource for psychological researchers to be informed about more recent psychometric progress in Likert scale creation. Psychological data are diverse and range from observations of behavior to face-to-face interviews. However, in modern times, one of the most common measurement methods is the self-report Likert scale Baumeister et al.

Likert scales provide a convenient way to measure unobservable constructs, and published tutorials detailing the process of their development have been highly influential, such as Clark and Watson and Hinkin being cited over 6, and 3, times, respectively, according to Google scholar.

Notably, however, it has been roughly 25 years since these seminal papers were published, and specific best-practices have changed or evolved since then. Recently, Clark and Watson gave an update to their article, integrating some newer topics into a general tutorial of Likert scale creation.

However, scale creation—from defining the construct to testing nomological relationships—is such an extensive process that it is challenging for any paper to give full coverage to each of its stages. The authors were quick to note this themselves several times, e. Therefore, a contribution to psychology would be a paper that provides a review of advances in Likert scale development since classic tutorials were published. This paper would not be a general tutorial in scale development like Clark and Watson , , Hinkin , or others.

Instead, it would focus on more recent advances and serve as a complement to these broader tutorials. The present paper seeks to serve as such a resource by reviewing developments in Likert scale creation from the past 25 years. However, given that scale development is such an extensive topic, the limitations of this review should be made very explicit.

The first limitations are with regard to scope. This is not a review of psychometrics , which would be impossibly broad, or advances in self-report in general , which would also be unwieldy e. This is a review of the initial development and validation of self-report Likert scales.

Therefore, we also excluded measurement topics related the use self-report scales, like identifying and controlling for response biases. Importantly, like Clark and Watson , , Hinkin , this paper was written at the level of the general psychologist, not methodologists, in order to benefit the field of psychology most broadly. This also meant that our scope was to fine articles that were broad enough to apply to most cases of Likert scale development.

As a result, we omitted articles, for example, that only discussed measuring certain types of constructs [e. The second major limitation concerns its objectivity. The majority of the papers we reviewed were fairly easy to decide on. For example, we included Simms et al.

By contrast, we excluded Permut et al. However, other papers were more difficult to decide on. Our method of handling this ambuity is described below, but we do not try claim that subjectivity did not play a part of the review process in some way. Additionally, a we did not survey every single journal where advances may have been published 2 and b articles published after were not included.

Despite all these limitations, this review was still worth performing. Self-report Likert scales are an incredibly dominant source of data in psychology and the social sciences in general. The divide between methodological and substantive literatures—and between methodologists and substantive researchers Sharpe, —can increase over time, but they can also be reduced by good communication and dissemination Sharpe, The current review is our attempt to bridge, in part, that gap.

The full text of any potentially relevant article was reviewed by either the first or second author, and any borderline cases were discussed until a consensus was reached. For inclusion, our criteria were that the advance had to be: a related to the creation of self-report Likert scales seven excluded , b broad and significant enough for a general psychological audience 23 excluded , and c not superseded or encapsulated by newer developments 11 excluded. The advances we included are shown in Table 1 , along with a short descriptive summary of each.

Scale developers should not feel compelled to use all of these techniques, just those that contribute to better measurement in their context. More specific contexts e. To supplement this literature review, the remainder of the paper provides a more in-depth discussion of five of these advances that span a range of topics. These were chosen due to their importance, uniqueness, or ease-of-use, and lack of general coverage in classic scale creation papers. These are: 1 conceptualizations of construct validity, 2 approaches for creating more precise construct definitions, 3 readability tests for generating items, 4 alternative measures of precision e.

These developments are presented in roughly the order of what stage they occur in the process of scale creation, a schematic diagram of which is shown in Figure 2. Schematic diagram of Likert scale development with advances in current paper, bolded. Psychologists recognize validity as the fundamental concept of psychometrics and one of the most critical aspects of psychological science Hood, ; Cizek, In particular, there are two divergent perspectives on the definition.

The first major perspective defines validity not as a property of tests but as a property of the interpretations of test scores Messick, ; Kane, This view can be therefore called the interpretation camp Hood, or validity as construct validity Cronbach and Meehl, , which is the perspective endorsed by Clark and Watson , and standards set forth by governing agencies for the North American educational and psychological measurement supracommunity Newton and Shaw, Construct validity is based on a synthesis and analysis of the evidence that supports a certain interpretation of test scores, so validity is a property of interpretive inferences about test scores Messick, , p.

Because the context of measurement affects test scores Messick, , pp. The other major perspective Borsboom et al. In other words, on this view, validity is a property of tests rather than interpretations. To be true, it requires a that Y exists and b that variations in Y cause variations in X Borsboom et al. This definition can be called the test validity view and finds ample precedent in psychometric texts Hood, Ultimately, this disagreement does not show any signs of resolving, and interested readers can consult papers that have attempted to integrate or adjudicate on the two views Lissitz and Samuelson, ; Hood, ; Cizek, Rather, there is only construct validity, and different validation procedures and types of evidence all contribute to making inferences about score meaning Messick, ; Binning and Barrett, ; Borsboom et al.

Despite the agreement that validity is a unitary concept, psychologists seem to disagree in practice; as of , there were distinct subtypes of validity Newton and Shaw, , many of them named after the fourth edition of the Standards that stated that validity-type language was inappropriate American Educational Research Association et al. For instance, showing that the focal construct is empirically discriminable from similar constructs would constitute strong evidence for the inference of discriminability Messick, Defining the construct one is interested in measuring is a foundational part of scale development; failing to do so properly undermines every scientific activity that follows T.

Thorndike, ; Kelley, ; Mackenzie, ; Podsakoff et al. However, there are lingering issues with conceptual clarity in the social sciences. To support this effort, we surveyed key papers on construct clarity and integrated their recommendations into Table 2 , adding our own comments where appropriate.

In addition to clearly articulating the concept, there are other parts to defining a psychological construct for empirical measurement. Another recent development demonstrates the importance of incorporating the latent continuum in measurement Tay and Jebb, Briefly, many psychological concepts like emotion and self-esteem are conceived as having degrees of magnitudes e.

The continuum was originally a primary focus in early psychological measurement, but the advent of the convenient Likert -type scaling Likert, pushed it into the background. However, defining the characteristics of this continuum is needed for proper measurement. For instance, what do the poles i. Is the lower pole its absence , or is it the presence of an opposing construct i. And, what do the different continuum degrees actually represent? If the construct is a positive emotion, do they represent the intensity of experience or the frequency of experience?

Quite often, scale developers do not define these aspects but leave them implicit. Tay and Jebb discuss different problems that can arise from this. In addition to defining the continuum, there is also the practical issue of fully operationalizing the continuum Tay and Jebb, This involves ensuring that the whole continuum is well-represented when creating items.

It also means being mindful when including reverse-worded items in their scales. These items may measure an opposite construct , which is desirable if the construct is bipolar e. Finally, developers should choose a response format that aligns with whether the continuum has been specified as unipolar or bipolar.

Tay and Jebb also discuss operationalizing the continuum with regard to two other issues, assessing dimensionality of the scale and assuming the correct response process. The current psychometric practice is to keep item statements short and simple with language that is familiar to the target respondents Hinkin, Instructions like these alleviate readability problems because psychologists are usually good at identifying and revising difficult items. However, professional psychologists also have a much higher degree of education compared to the rest of the population.

Census Bureau, Researchers can probably catch and remove scale items that are extremely verbose e. Social science samples frequently consist of university students Henrich et al. In addition to asking respondents directly see Parrigon et al.

Readability tests are formulas that score the readability of some piece of writing, often as a function of the number of words per sentence and number of syllables per word.

These tests only take seconds to implement and can serve as an additional way to check item language beyond the intuitions of scale developers. When these tests are used, scale items should only be analyzed individually , as testing the readability of the whole scale together can hide one or more difficult items.

If an item receives a low readability score, the developer can revise the item. These operate in much the same way, outputting an estimated grade level based on sentence and word length.

 
 

 

– Usa gov jobs official websites likert scale

 

Interestingly, with computer technology, survey designers can create continuous measure scales that do provide interval responses as an alternative to a Likert scale. The various continuous measures for pain are well-known examples of this figure 1. Please tell us your current pain level by sliding the pointer to the appropriate point along the continuous pain scale above.

In the medical education literature, there has been a long-standing controversy regarding whether ordinal data, converted to numbers, can be treated as interval data. When conducting research, we measure data from a sample of the total population of interest, not from all members of the population.

Parametric tests make assumptions about the underlying population from which the research data have been obtained—usually that these population data are normally distributed. Nonparametric tests are less powerful than parametric tests and usually require a larger sample size n value to have the same power as parametric tests to find a difference between groups when a difference actually exists.

Descriptive statistics, such as means and standard deviations, have unclear meanings when applied to Likert scale responses. This clustering of extremes is common, for example, in trainee evaluations of experiences that may be very popular with one group and perceived as unnecessary by others eg, an epidemiology course in medical school.

Other non-normal distributions of response data can similarly result in a mean score that is not a helpful measure of the data’s central tendency. Because of these observations, experts over the years have argued that the median should be used as the measure of central tendency for Likert scale data. Fortunately, Dr. Geoff Norman, one of world’s leaders in medical education research methodology, has comprehensively reviewed this controversy. He provides compelling evidence, with actual examples using real and simulated data, that parametric tests not only can be used with ordinal data, such as data from Likert scales, but also that parametric tests are generally more robust than nonparametric tests.

Often this practice is recommended, particularly when researchers are attempting to measure less concrete concepts, such as trainee motivation, patient satisfaction, and physician confidence—where a single survey item is unlikely to be capable of fully capturing the concept being assessed. Now that many experts have weighed in on this debate, the conclusions are fairly clear: parametric tests can be used to analyze Likert scale responses.

However, to describe the data, means are often of limited value unless the data follow a classic normal distribution and a frequency distribution of responses will likely be more helpful. Furthermore, because the numbers derived from Likert scales represent ordinal responses, presentation of a mean to the th decimal place is usually not helpful or enlightening to readers.

In summary, we recommend that authors determine how they will describe and analyze their data as a first step in planning educational or research projects. Then they should discuss, in the Methods section or in a cover letter if the explanation is too lengthy, why they have chosen to portray and analyze their data in a particular way.

Reviewers, readers, and especially editors will greatly appreciate this additional effort. Gail M. J Grad Med Educ. Artino, Jr , PhD.

Author information Copyright and License information Disclaimer. Corresponding author: Gail M. Open in a separate window. The site is secure. Developing self-report Likert scales is an essential part of modern psychology. However, it is hard for psychologists to remain apprised of best practices as methodological developments accumulate.

To address this, this current paper offers a selective review of advances in Likert scale development that have occurred over the past 25 years. We reviewed six major measurement journals e. We supplemented this review with an in-depth discussion of five particular advances: 1 conceptions of construct validity, 2 creating better construct definitions, 3 readability tests for generating items, 4 alternative measures of precision [e.

The Supplementary Material provides further technical details on these advances and offers guidance on software implementation. This paper is intended to be a resource for psychological researchers to be informed about more recent psychometric progress in Likert scale creation. Psychological data are diverse and range from observations of behavior to face-to-face interviews. However, in modern times, one of the most common measurement methods is the self-report Likert scale Baumeister et al.

Likert scales provide a convenient way to measure unobservable constructs, and published tutorials detailing the process of their development have been highly influential, such as Clark and Watson and Hinkin being cited over 6, and 3, times, respectively, according to Google scholar. Notably, however, it has been roughly 25 years since these seminal papers were published, and specific best-practices have changed or evolved since then. Recently, Clark and Watson gave an update to their article, integrating some newer topics into a general tutorial of Likert scale creation.

However, scale creation—from defining the construct to testing nomological relationships—is such an extensive process that it is challenging for any paper to give full coverage to each of its stages. The authors were quick to note this themselves several times, e. Therefore, a contribution to psychology would be a paper that provides a review of advances in Likert scale development since classic tutorials were published.

This paper would not be a general tutorial in scale development like Clark and Watson , , Hinkin , or others. Instead, it would focus on more recent advances and serve as a complement to these broader tutorials. The present paper seeks to serve as such a resource by reviewing developments in Likert scale creation from the past 25 years. However, given that scale development is such an extensive topic, the limitations of this review should be made very explicit.

The first limitations are with regard to scope. This is not a review of psychometrics , which would be impossibly broad, or advances in self-report in general , which would also be unwieldy e. This is a review of the initial development and validation of self-report Likert scales.

Therefore, we also excluded measurement topics related the use self-report scales, like identifying and controlling for response biases. Importantly, like Clark and Watson , , Hinkin , this paper was written at the level of the general psychologist, not methodologists, in order to benefit the field of psychology most broadly.

This also meant that our scope was to fine articles that were broad enough to apply to most cases of Likert scale development. As a result, we omitted articles, for example, that only discussed measuring certain types of constructs [e. The second major limitation concerns its objectivity. The majority of the papers we reviewed were fairly easy to decide on. For example, we included Simms et al. By contrast, we excluded Permut et al.

However, other papers were more difficult to decide on. Our method of handling this ambuity is described below, but we do not try claim that subjectivity did not play a part of the review process in some way. Additionally, a we did not survey every single journal where advances may have been published 2 and b articles published after were not included. Despite all these limitations, this review was still worth performing. Self-report Likert scales are an incredibly dominant source of data in psychology and the social sciences in general.

The divide between methodological and substantive literatures—and between methodologists and substantive researchers Sharpe, —can increase over time, but they can also be reduced by good communication and dissemination Sharpe, The current review is our attempt to bridge, in part, that gap.

The full text of any potentially relevant article was reviewed by either the first or second author, and any borderline cases were discussed until a consensus was reached. For inclusion, our criteria were that the advance had to be: a related to the creation of self-report Likert scales seven excluded , b broad and significant enough for a general psychological audience 23 excluded , and c not superseded or encapsulated by newer developments 11 excluded.

The advances we included are shown in Table 1 , along with a short descriptive summary of each. Scale developers should not feel compelled to use all of these techniques, just those that contribute to better measurement in their context.

More specific contexts e. To supplement this literature review, the remainder of the paper provides a more in-depth discussion of five of these advances that span a range of topics. These were chosen due to their importance, uniqueness, or ease-of-use, and lack of general coverage in classic scale creation papers. These are: 1 conceptualizations of construct validity, 2 approaches for creating more precise construct definitions, 3 readability tests for generating items, 4 alternative measures of precision e.

These developments are presented in roughly the order of what stage they occur in the process of scale creation, a schematic diagram of which is shown in Figure 2. Schematic diagram of Likert scale development with advances in current paper, bolded. Psychologists recognize validity as the fundamental concept of psychometrics and one of the most critical aspects of psychological science Hood, ; Cizek, In particular, there are two divergent perspectives on the definition.

The first major perspective defines validity not as a property of tests but as a property of the interpretations of test scores Messick, ; Kane, This view can be therefore called the interpretation camp Hood, or validity as construct validity Cronbach and Meehl, , which is the perspective endorsed by Clark and Watson , and standards set forth by governing agencies for the North American educational and psychological measurement supracommunity Newton and Shaw, Construct validity is based on a synthesis and analysis of the evidence that supports a certain interpretation of test scores, so validity is a property of interpretive inferences about test scores Messick, , p.

Because the context of measurement affects test scores Messick, , pp. The other major perspective Borsboom et al. In other words, on this view, validity is a property of tests rather than interpretations. To be true, it requires a that Y exists and b that variations in Y cause variations in X Borsboom et al. This definition can be called the test validity view and finds ample precedent in psychometric texts Hood, Ultimately, this disagreement does not show any signs of resolving, and interested readers can consult papers that have attempted to integrate or adjudicate on the two views Lissitz and Samuelson, ; Hood, ; Cizek, Rather, there is only construct validity, and different validation procedures and types of evidence all contribute to making inferences about score meaning Messick, ; Binning and Barrett, ; Borsboom et al.

Despite the agreement that validity is a unitary concept, psychologists seem to disagree in practice; as of , there were distinct subtypes of validity Newton and Shaw, , many of them named after the fourth edition of the Standards that stated that validity-type language was inappropriate American Educational Research Association et al. For instance, showing that the focal construct is empirically discriminable from similar constructs would constitute strong evidence for the inference of discriminability Messick, Defining the construct one is interested in measuring is a foundational part of scale development; failing to do so properly undermines every scientific activity that follows T.

Thorndike, ; Kelley, ; Mackenzie, ; Podsakoff et al. However, there are lingering issues with conceptual clarity in the social sciences. To support this effort, we surveyed key papers on construct clarity and integrated their recommendations into Table 2 , adding our own comments where appropriate. In addition to clearly articulating the concept, there are other parts to defining a psychological construct for empirical measurement.

Another recent development demonstrates the importance of incorporating the latent continuum in measurement Tay and Jebb, Briefly, many psychological concepts like emotion and self-esteem are conceived as having degrees of magnitudes e.

The continuum was originally a primary focus in early psychological measurement, but the advent of the convenient Likert -type scaling Likert, pushed it into the background. However, defining the characteristics of this continuum is needed for proper measurement. For instance, what do the poles i. Is the lower pole its absence , or is it the presence of an opposing construct i.

And, what do the different continuum degrees actually represent? If the construct is a positive emotion, do they represent the intensity of experience or the frequency of experience?

Quite often, scale developers do not define these aspects but leave them implicit. Tay and Jebb discuss different problems that can arise from this. In addition to defining the continuum, there is also the practical issue of fully operationalizing the continuum Tay and Jebb, This involves ensuring that the whole continuum is well-represented when creating items.

It also means being mindful when including reverse-worded items in their scales. These items may measure an opposite construct , which is desirable if the construct is bipolar e. Finally, developers should choose a response format that aligns with whether the continuum has been specified as unipolar or bipolar. Tay and Jebb also discuss operationalizing the continuum with regard to two other issues, assessing dimensionality of the scale and assuming the correct response process.

The current psychometric practice is to keep item statements short and simple with language that is familiar to the target respondents Hinkin, Instructions like these alleviate readability problems because psychologists are usually good at identifying and revising difficult items.

However, professional psychologists also have a much higher degree of education compared to the rest of the population. Census Bureau, Researchers can probably catch and remove scale items that are extremely verbose e. Social science samples frequently consist of university students Henrich et al. In addition to asking respondents directly see Parrigon et al. Readability tests are formulas that score the readability of some piece of writing, often as a function of the number of words per sentence and number of syllables per word.

These tests only take seconds to implement and can serve as an additional way to check item language beyond the intuitions of scale developers. When these tests are used, scale items should only be analyzed individually , as testing the readability of the whole scale together can hide one or more difficult items.

If an item receives a low readability score, the developer can revise the item. These operate in much the same way, outputting an estimated grade level based on sentence and word length. We reviewed their formulas and reviews on the topic e. At the outset, we state that no statistic is univocally superior to all the others. It is possible to implement several tests and compare the results. However, we recommend the Flesch-Kincaid Grade Level Studies test because it a is among the most commonly used, b is expressed in grade school levels, and c is easily implemented in Microsoft Word.

The score indicates what United States grade level the readability is suited. Given average reading grade levels in the United States, researchers can aim for a readability score of 8.

There are several examples of scale developers using this reading test. Lubin et al. Ravens-Sieberer et al. As our own exercise, we took three recent instances of scale development in the Journal of Applied Psychology and ran readability tests on their items. This analysis is presented in the Supplementary Material. A major focus of scale development is demonstrating its reliability, defined formally as the proportion of true score variance to total score variance Lord and Novick, The most common estimator of reliability in psychology is coefficient alpha Cronbach, However, alpha is sometimes a less-than-ideal measure because it assumes that all scale items have the same true score variance Novick and Lewis, ; Sijtsma, ; Dunn et al.

Violating this assumption makes alpha underestimate true reliability. Often, this underestimation may be small, but it will increase for scales with fewer items and with greater differences in population factor loadings Raykov, ; Graham, A proposed solution to this is to relax this assumption and adopt the less stringent congeneric model of measurement.

The most prominent estimator in this group is coefficient omega McDonald, , 4 which uses a factor model to obtain reliability estimates. However, one caveat is that the estimator requires a good-fitting factor model for estimation. McNeish provides a software tutorial in R and Excel [see also Dunn et al.

Alpha, omega, and other reliability estimators stem from the classical test theory paradigm of measurement, where the focus is on the overall reliability of the psychological scale.

Although they are analogous concepts, information IRT and reliability are different. Whereas traditional reliability is only assessed at the scale-level, information IRT can be assessed at three levels: the response category, item, and test. Information IRT is a full mathematical function which shows how the precision changes across latent trait levels. These features translate into several advantages for the scale developer.

First, items can be evaluated for how much precision they have. Items that are not informative can be eliminated in favor of items that are for a tutorial, see Edelen and Reeve, Second, the test information function shows how precisely the full scale measures each region of the latent trait. If a certain region is deficient, items can be added to better capture that region or removed, if the region has been measured enough.

Finally, suppose the scale developer is only interested in measuring a certain region of the latent trait range, such as middle-performers or high and low performers.

In that case, information IRT can help them do so. Further details are provided in the Supplementary Material. Increasingly, psychologists wish to use short scales in their work Leite et al. To date, the most common approaches aim to maintain reliability Leite et al.

However, these strategies can incidentally impair measurement Janssen et al. Instead of just maximizing reliability, this method can incorporate any number of evaluative criteria, such as associations with variables, factor model fit, and others. When reducing a Big 5 personality scale, Olaru et al.

Since ACO has been introduced to psychology, it has been used in the creation of real psychological scales for proactive personality and supervisor support Janssen et al.

The logic of ACO comes from how ants resolve the problem of determining the shortest path to their hive when they find food Deneubourg et al. The ants solve it by a randomly sampling different paths toward the food and b laying down chemical pheromones that attract other ants. The paths that provide quicker solutions acquire pheromones more rapidly, attracting more ants, and thus more pheromone.

Ultimately, a positive feedback loop is created until the ants converge on the best path the solution. The ACO algorithm works similarly. Over repeated iterations, the items that led to good performance will become increasingly weighted for selection, creating a positive feedback loop that eventually converges to a final solution. Thus, ACO, like the ants, does not search and test all possible solutions. Instead, it uses some criterion for evaluating the items and then uses this to update the probability of selecting those items.

ACO is an automated procedure, but this does not mean that researchers should accept its results automatically. Foremost, ACO does not guarantee that the shortened scale has satisfactory content Kruyen et al. Therefore, the items that comprise the final scale should always be examined to see if their content is sufficient. We also strongly recommend that authors using ACO be explicit about the specifications of the algorithm.

Authors should always report a what criteria they are using to evaluate short form performance and b how these are mathematically translated into pheromone weights. Authors should also report all the other relevant details of conducting the algorithm e. In the Supplementary Material , we provide further details and a full R software walkthrough.

For more information, the reader can consult additional resources Marcoulides and Drezner, ; Leite et al. Measurement in psychology comes in many forms, and for many constructs, one of the best methods is the psychological Likert scale.

A recent review suggests that, in the span of just a few years, dozens of scales are added to the psychological science literature Colquitt et al. Thus, psychologists must have a clear understanding of the proper theory and procedures for scale creation. This present article aims to increase this clarity by offering a selective review of Likert scale development advances over the past 25 years. Classic papers delineating the process of Likert scale development have proven immensely useful to the field Clark and Watson, , ; Hinkin, , but it is difficult to do justice to this whole topic in a single paper, especially as methodological developments accumulate.

Though this paper reviewed past work, we end with some notes about the future. As methods progress, they become more sophisticated, but sophistication should not be mistaken for accuracy. This applies even to some of the techniques discussed here, such as ACO, which has crucial limitations e. Second, we are concerned with the problem of construct proliferation , as are other social scientists e. Finally, as psychological theory progresses, it tends to become more complex. One issue with this increasing complexity is the danger of creating incoherent constructs.

Borsboom , p. Although no common construct exists among these items, the scale can certainly be scored and will probably even be reliable, as the random error variance will be low Borsboom, Therefore, measures of such incoherent constructs can display good psychometric properties, and psychologists cannot merely rely on empirical evidence for justifying them.

Thus, the challenges of scale development of the present and future are equally empirical and theoretical. LT conceived the idea for the manuscript and provided feedback and editing. AJ conducted most of the literature review and wrote much of the manuscript. VN assisted with the literature review and contributed writing. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. McNeish provides an exceptional discussion of alternatives to alpha, including software tutorials in R and Excel. Short forms are a type of short scales, but of course, not all short scales were taken from a larger measure. In this section, we are concerned with the process of developing a short form from an original scale only.

Front Psychol. Published online May 4. Andrew T. Author information Article notes Copyright and License information Disclaimer. Jebb, moc. This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology. Received Dec 3; Accepted Apr The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

No use, distribution or reproduction is permitted which does not comply with these terms. Abstract Developing self-report Likert scales is an essential part of modern psychology.

Keywords: measurement, psychometrics, validation, Likert, reliability, scale development. Introduction Psychological data are diverse and range from observations of behavior to face-to-face interviews. Open in a separate window. Also included is a critical review of several more recent statistical approaches for testing validity e.

First, Coh-Metrix computes a syntactic simplicity score based on multiple variables e. Second, the Question Understanding Aid QUAID was designed specifically to examine the readability of survey instruments, and can identify potential issues like vague wording, jargon, and working memory overload. Both are freely available at websites listed in the paper. Respondent comprehension Hardy and Ford Good survey data requires that respondents interpret the survey items as the scale developer intended.

However, the authors describe how both a specific words and b the sentences in items can contribute to respondent miscomprehension. The authors provide evidence for this in popular scales and then discuss remedies, such as reducing words and phrases with multiple or vague meanings and collecting qualitative data from respondents about their interpretations of items.

Number of response options and labels Weng and Simms et al. These benefits stopped after six response options, and 0—1, visual analog scales did not show benefits, either. Including or removing a middle point e. Weng also found higher internal consistency and test-retest reliability when all response options had labels compared to when only endpoints of the scale had labels.

Item format Zhang and Savalei The authors further research on the expanded scale format as a way to gain the benefit of including reverse worded items i. Each Likert-type item has their response options turned into a set of statements; respondents select one statement from each set.

Item stability Knowles and Condon The stability of item properties should not be assumed when it is placed in different testing contexts. There are available methods from classical test theory, factor analysis, and item response theory to examine the stability of items when applied to new conditions or test revisions. Presentation of items in blocks Weijters et al.

For instance, items from different scales can all be randomized and presented in the same block, or each scale can be given its own block. The authors showed the effects of splitting a unidimensional scale into two blocks with other scales administered in between.

Scale items in different blocks had lower intercorrelations, and two factors emerged that corresponded to the two blocks. The authors recommend that assessments of discriminant validity should be mindful of scale presentation and that how scales are presented in surveys should be consistently reported. Content validation Guidelines for reporting Colquitt et al.

Both approaches ask subjects to rate how well each proposed item matches the construct definition, as well as the definitions of similar constructs. The authors also offer several new statistics for indexing content validity, provide standards for conducting content validation e. Guidelines for assessment Haynes et al. The authors also provide guidelines for assessing content validity, such as using multiple judges of scales, examining the proportionality of item content in scales, and using subsequent psychometric analyses to indicate the degree of evidence for content coverage.

Consulting focus groups Vogt et al. One method to do this is to use focus groups, moderator-facilitated discussions that generate qualitative data. However, having subject matter experts engage in pairwise item similarity comparisons is labor-intensive.

Conducting pilot studies Sample size considerations Johanson and Brooks Provides a cost-benefit analysis of increasing sample size relative to decreasing confidence intervals in correlation, proportion, and internal consistency estimates i.

Found that most reductions in confidence intervals occurred at sample sizes between 24 and Measurement precision Limits of reliability coefficients Cronbach and Shavelson Although coefficient alpha is the most widely used index of measurement precision, the authors argue that any coefficient is a crude marker that lacks the nuance necessary to support interpretations in current assessment practice.

Instead, they detail a reliability analysis approach whereby observed score variance is decomposed into population or true score , item, and residual variance, the latter two of which comprise error variance.

The authors argue that the standard error of measurement should be reported along with all variance components rather than a coefficient. Given that testing applications often use cut scores, the standard error of measurement offers an intuitive understanding to all stakeholders regarding the precision of each score when making decisions based on absolute rather than comparative standing.

The authors offer a software package in the R statistical computing language that allows for estimates of both alpha and omega that are robust against outliers and missing data. Confidence intervals Kelley and Pornprasertmanit Because psychologists are interested in the reliability of the population, not just the sample, estimates should be accompanied by confidence intervals. The authors review the many methods for computing these confidence intervals and run simulations comparing their efficacies.

Ultimately, they recommend using hierarchical omega as a reliability estimator and bootstrapped confidence intervals, all of which can be computed in R using the ci.

Because coefficient alpha is computed from a single time point, it cannot correct for transient error and may overestimate reliability. Both articles provide an alternative reliability statistic that controls for transient error, test-retest alpha Green, , and the coefficient of equivalence and stability Schmidt et al.

Test-retest reliability DeSimone Test-retest correlations between scale scores are limited for assessing temporal stability. The author introduces several new statistical approaches: a computing test-retest correlations among individual scale items, b comparing the stability of interitem correlations SRMR TC and component loadings CL TC , and c assessing the scale instability that is due to respondents D 2 pct rather than scale itself.

Barchard Test-retest correlations do not capture absolute agreement between scores and can mislead about consistency. The author discusses several statistics for test-retest reliability based on absolute agreement: the root mean square difference [RMSD A,1 ] and concordance correlation coefficient [CCC A,1 ].

These measures are used in other scientific fields e. Item-level reliability Zijlmans et al. This can help identify unreliable items for removal.

The authors investigate four methods for calculating item-level reliability and find that the correction for attenuation and Molenaar—Sijtsma methods performed best, estimating item reliability with very little bias and a reasonable amount of variability.

Guidance is provided for a selecting proper indicators e. The authors conclude with a discussion of two alternatives to traditional factor analysis: exploratory structural equation modeling and bifactor modeling. Exploratory factor analysis Henson and Roberts The authors briefly review four main decisions to be made when conducting exploratory factor analysis. Then they offer seven best practice recommendations for reporting how an exploratory factor analysis was conducted after reviewing reporting deficiencies found in four journals.

Exploratory factor analysis for scale revision Reise et al. Specifically, they offer guidance on a introducing new items, b sample selection, c factor extraction, d factor rotation, and e evaluating the revised scale.

However, researchers first need to articulate why the revision is needed and pinpoint where the construct resides in the conceptual hierarchy. The end product is a tree-like graphic that represents the relations among the scale items.

The authors claim this method is useful compared to alternatives e.

 
 

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