What is the difference between when validate item and key next item

McCoach et al. These are all based on thorough literature review and include a specifying the purpose of the domain or construct you seek to develop, and b , confirming that there are no existing instruments that will adequately serve the same purpose. Where there is a similar instrument in existence, you need to justify why the development of a new instrument is appropriate and how it will differ from existing instruments.

Then, c describe the domain and provide a preliminary conceptual definition and d specify, if any, the dimensions of the domain. Alternatively, you can let the number of dimensions forming the domain to be determined through statistical computation cf.

Steps 5, 6, and 7. Domains are determined a priori if there is an established framework or theory guiding the study, but a posteriori if none exist. Finally, if domains are identified a priori , e the final conceptual definition for each domain should be specified. Once the domain is delineated, the item pool can then be identified. There are two ways to identify appropriate questions: deductive and inductive methods This can be done through literature review and assessment of existing scales and indicators of that domain 2 , Qualitative data obtained through direct observations and exploratory research methodologies, such as focus groups and individual interviews, can be used to inductively identify domain items 5.

It is considered best practice to combine both deductive and inductive methods to both define the domain and identify the questions to assess it. While the literature review provides the theoretical basis for defining the domain, the use of qualitative techniques moves the domain from an abstract point to the identification of its manifest forms. A scale or construct defined by theoretical underpinnings is better placed to make specific pragmatic decisions about the domain 28 , as the construct will be based on accumulated knowledge of existing items.

It is recommended that the items identified using deductive and inductive approaches should be broader and more comprehensive than one's own theoretical view of the target 28 , Further, content should be included that ultimately will be shown to be tangential or unrelated to the core construct.

In other words, one should not hesitate to have items on the scale that do not perfectly fit the domain identified, as successive evaluation will eliminate undesirable items from the initial pool. Kline and Schinka et al. Others have recommended the initial pool to be five times as large as the final version, to provide the requisite margin to select an optimum combination of items We agree with Kline and Schinka et al. Further, in the development of items, the form of the items, the wording of the items , and the types of responses that the question is designed to induce should be taken into account.

It also means questions should capture the lived experiences of the phenomenon by target population Further, items should be worded simply and unambiguously. Items should not be offensive or potentially biased in terms of social identity, i.

Fowler identified five essential characteristics of items required to ensure the quality of construct measurement These include a the need for items to be consistently understood; b the need for items to be consistently administered or communicated to respondents; c the consistent communication of what constitutes an adequate answer; d the need for all respondents to have access to the information needed to answer the question accurately; and e the willingness for respondents to provide the correct answers required by the question at all times.

These essentials are sometimes very difficult to achieve. Krosnick 32 suggests that respondents can be less thoughtful about the meaning of a question, search their memories less comprehensively, integrate retrieved information less carefully, or even select a less precise response choice. All this means that they are merely satisficing, i. In order to combat this behavior, questions should be kept simple, straightforward, and should follow the conventions of normal conversation.

With regards to the type of responses to these questions, we recommend that questions with dichotomous response categories e. When a Likert-type response scale is used, the points on the scale should reflect the entire measurement continuum. Responses should be presented in an ordinal manner, i. In terms of the number of points on the response scale, Krosnick and Presser 33 showed that responses with just two to three points have lower reliability than Likert-type response scales with five to seven points.

However, the gain levels off after seven points. Therefore, response scales with five points are recommended for unipolar items, i. Seven response items are recommended for bipolar items, i. As an analytic aside, items with scale points fewer than five categories are best estimated using robust categorical methods.

However, items with five to seven categories without strong floor or ceiling effects can be treated as continuous items in confirmatory factor analysis and structural equation modeling using maximum likelihood estimations One pitfall in the identification of domain and item generation is the improper conceptualization and definition of the domain s.

This can result in scales that may either be deficient because the definition of the domain is ambiguous or has been inadequately defined It can also result in contamination, i.

Caution should also be taken to avoid construct underrepresentation, which is when a scale does not capture important aspects of a construct because its focus is too narrow 35 , Further, construct-irrelevant variance, which is the degree to which test scores are influenced by processes that have little to do with the intended construct and seem to be widely inclusive of non-related items 36 , 37 , should be avoided. Both construct underrepresentation and irrelevant variance can lead to the invalidation of the scale An example of best practice using the deductive approach to item generation is found in the work of Dennis on breastfeeding self-efficacy 38 — Dennis' breastfeeding self-efficacy scale items were first informed by Bandura's theory on self-efficacy, followed by content analysis of literature review, and empirical studies on breastfeeding-related confidence.

A valuable example for a rigorous inductive approach is found in the work of Frongillo and Nanama on the development and validation of an experience-based measure of household food insecurity in northern Burkina Faso In order to generate items for the measure, they undertook in-depth interviews with 10 household heads and 26 women using interview guides.

The data from these interviews were thematically analyzed, with the results informing the identification of items to be added or deleted from the initial questionnaire. Also, the interviews led to the development and revision of answer choices. The need for content adequacy is vital if the items are to measure what they are presumed to measure 1.

Additionally, content validity specifies content relevance and content representations, i. Guion has proposed five conditions that must be satisfied in order for one to claim any form of content validity. We find these conditions to be broadly applicable to scale development in any discipline. These include that a the behavioral content has a generally accepted meaning or definition; b the domain is unambiguously defined; c the content domain is relevant to the purposes of measurement; d qualified judges agree that the domain has been adequately sampled based on consensus; and e the response content must be reliably observed and evaluated Therefore, content validity requires evidence of content relevance, representativeness, and technical quality.

Content validity is mainly assessed through evaluation by expert and target population judges. Expert judges seem to be used more often than target-population judges in scale development work to date. Ideally, one should combine expert and target population judgment.

When resources are constrained, however, we recommend at least the use of expert judges. Expert judges evaluate each of the items to determine whether they represent the domain of interest. These expert judges should be independent of those who developed the item pool. Expert judgment can be done systematically to avoid bias in the assessment of items.

Multiple judges have been used typically ranging from 5 to 7 Their assessments have been quantified using formalized scaling and statistical procedures such as the content validity ratio for quantifying consensus 43 , content validity index for measuring proportional agreement 44 , or Cohen's coefficient kappa k for measuring inter-rater or expert agreement Among the three procedures, we recommend Cohen's coefficient kappa, which has been found to be most efficient Additionally, an increase in the number of experts has been found to increase the robustness of the ratings 25 , Another way by which content validity can be assessed through expert judges is by using the Delphi method to come to a consensus on which questions are a reflection of the construct you want to measure.

A good example of evaluation of content validity using expert judges is seen in the work of Augustine et al. After identifying a list of items to be validated, the authors consulted experts in the field of nutrition, psychology, medicine, and basic sciences. The items were then subjected to content analysis using expert judges. Two independent reviews were carried out by a panel of five experts to select the questions that were appropriate, accurate, and interpretable. Items were either accepted, rejected, or modified based on majority opinion Target population judges are experts at evaluating face validity, which is a component of content validity These end-users are able to tell whether the construct is a good measure of the domain through cognitive interviews, which we discuss in Step 3.

An example of the concurrent use of expert and target population judges comes from Boateng et al. We used the Delphi method to obtain three rounds of feedback from international experts including those in hydrology, geography, WASH and water-related programs, policy implementation, and food insecurity. Each of the three rounds was interspersed with focus group discussions with our target population, i.

In each round, the questionnaires progressively became more closed ended, until consensus was attained on the definition of the domain we were studying and possible items we could use.

Pre-testing helps to ensure that items are meaningful to the target population before the survey is actually administered, i.

Because pre-testing eliminates poorly worded items and facilitates revision of phrasing to be maximally understood, it also serves to reduce the cognitive burden on research participants. Finally, pre-testing represents an additional way in which members of the target population can participate in the research process by contributing their insights to the development of the survey.

Pre-testing has two components: the first is the examination of the extent to which the questions reflect the domain being studied.

The second is the examination of the extent to which answers to the questions asked produce valid measurements To evaluate whether the questions reflect the domain of study and meet the requisite standards, techniques including cognitive interviews, focus group discussion, and field pre-testing under realistic conditions can be used. We describe the most recommended, which is cognitive interviews.

Cognitive interviewing entails the administration of draft survey questions to target populations and then asking the respondents to verbalize the mental process entailed in providing such answers Generally, cognitive interviews allow for questions to be modified, clarified, or augmented to fit the objectives of the study. This approach helps to determine whether the question is generating the information that the author intends by helping to ensure that respondents understand questions as developers intended and that respondents are able to answer in a manner that reflects their experience 49 , This can be done on a sample outside of the study population or on a subset of study participants, but it must be explored before the questionnaire is finalized 51 , The sample used for cognitive interviewing should capture the range of demographics you anticipate surveying A range of 5—15 interviews in two to three rounds, or until saturation, or relatively few new insights emerge is considered ideal for pre-testing 49 , 51 , In sum, cognitive interviews get to the heart of both assessing the appropriateness of the question to the target population and the strength of the responses The advantages of using cognitive interviewing include: a it ensures questions are producing the intended data, b questions that are confusing to participants are identified and improved for clarity, c problematic questions or questions that are difficult to answer are identified, d it ensures response options are appropriate and adequate, e it reveals the thought process of participants on domain items, and f it can indicate problematic question order 52 , Outcomes of cognitive interviews should always be reported, along with solutions used to remedy the situation.

An example of best practice in pre-testing is seen in the work of Morris et al. They developed and validated a novel scale for measuring interpersonal factors underlying injection drug use behaviors among injecting partners. After item development and expert judgment, they conducted cognitive interviews with seven respondents with similar characteristics to the target population to refine and assess item interpretation and to finalize item structure.

Eight items were dropped after cognitive interviews for lack of clarity or importance. They also made modifications to grammar, word choice, and answer options based on the feedback from cognitive interviews. Collecting data with minimum measurement errors from an adequate sample size is imperative. A number of software programs exist for building forms on devices. Each approach has advantages and drawbacks.

Using technology can reduce the errors associated with data entry, allow the collection of data from large samples with minimal cost, increase response rate, reduce enumerator errors, permit instant feedback, and increase monitoring of data collection and ability to get more confidential data 56 — 58 , A subset of technology-based programs offers the option of attaching audio files to the survey questions so that questions may be recorded and read out loud to participants with low literacy via audio computer self-assisted interviewing A-CASI Self-interviewing, whether via A-CASI or via computer-assisted personal interviewing, in which participants read and respond to questions on a computer without interviewer involvement, may increase reports of sensitive or stigmatized behaviors such as sexual behaviors and substance use, compared to when being asked by another human.

However, as sample sizes increase, the use of PAPI becomes more expensive, time and labor intensive, and the data are exposed in several ways to human error 57 , The sample size to use for the development of a latent construct has often been contentious.

It is recommended that potential scale items be tested on a heterogeneous sample, i. For example, when the scale is used in a clinical setting, Clark and Watson recommend using patient samples early on instead of a sample from the general population The necessary sample size is dependent on several aspects of any given study, including the level of variation between the variables, and the level of over-determination i. The rule of thumb has been at least 10 participants for each scale item, i.

However, others have suggested sample sizes that are independent of the number of survey items. Clark and Watson 29 propose using respondents after initial pre-testing. Others have recommended a range of — as appropriate for factor analysis 61 , Additionally, item reduction procedures described, below in Step 5 , such as parallel analysis which requires bootstrapping estimating statistical parameters from sample by means of resampling with replacement 64 , may require larger data sets.

In sum, there is no single item-ratio that works for all survey development scenarios. A larger sample size or respondent: item ratio is always better, since a larger sample size implies lower measurement errors and more, stable factor loadings, replicable factors, and generalizable results to the true population structure 59 , A smaller sample size or respondent: item ratio may mean more unstable loadings and factors, random, non-replicable factors, and non-generalizable results 59 , Sample size is, however, always constrained by resources available, and more often than not, scale development can be difficult to fund.

The development of a scale minimally requires data from a single point in time. To fully test for the reliability of the scale cf. Steps 8, 9 , however, either an independent dataset or a subsequent time point is necessary. Data from longitudinal studies can be used for initial scale development e. Step 7 , and to assess test—retest reliability using baseline and follow-up data.

The problem with using longitudinal data to test hypothesized latent structures is common error variance, since the same, potentially idiosyncratic, participants will be involved. To give the most credence to the reliability of scale, the ideal procedure is to develop the scale on sample A, whether cross-sectional or longitudinal, and then test it on an independent sample B.

The work of Chesney et al. This study sought to investigate the psychometric characteristics of the Coping Self-Efficacy CSE scale, and their samples came from two independent randomized clinical trials. As such, two independent samples with four different time points each 0, 3, 6, and 12 months were used. The authors administered the item scale to the sample from the first clinical trial and examined the covariance that existed between all the scale items exploratory factor analysis giving the hypothesized factor structure across time in that one trial.

The obtained factor structure was then fitted to baseline data from the second randomized clinical trial to test the hypothesized factor structure generated in the first sample In scale development, item reduction analysis is conducted to ensure that only parsimonious, functional, and internally consistent items are ultimately included Therefore, the goal of this phase is to identify items that are not or are the least-related to the domain under study for deletion or modification.

CTT is considered the traditional test theory and IRT the modern test theory; both function to produce latent constructs. Each theory may be used singly or in conjunction to complement the other's strengths 15 , CTT allows the prediction of outcomes of constructs and the difficulty of items CTT models assume that items forming constructs in their observed, manifest forms consist of a true score on the domain of interest and a random error which is the differences between the true score and a set of observed scores by an individual IRT seeks to model the way in which constructs manifest themselves in terms of observable item response Comparatively, the IRT approach to scale development has the advantage of allowing the researcher to determine the effect of adding or deleting a given item or set of items by examining the item information and standard error functions for the item pool Several techniques exist within the two theories to reduce the item pool, depending on which test theory is driving the scale.

The five major techniques used are: item difficulty and item discrimination indices, which are primarily for binary responses; inter-item and item-total correlations, which are mostly used for categorical items; and distractor efficiency analysis for items with multiple choice response options 1 , 2.

The item difficulty index is both a CTT and an IRT parameter that can be traced largely to educational and psychological testing to assess the relative difficulties and discrimination abilities of test items Subsequently, this approach has been applied to more attitudinal-type scales designed to measure latent constructs.

Under the CTT framework, the item difficulty index, also called item easiness, is the proportion of correct answers on a given item, e.

It ranges between 0. A high difficulty score means a greater proportion of the sample answered the question correctly. A lower difficulty score means a smaller proportion of the sample understood the question and answered correctly. This may be due to the item being coded wrongly, ambiguity with the item, confusing language, or ambiguity with response options.

A lower difficulty score suggests a need to modify the items or delete them from the pool of items. Under the IRT framework, the item difficulty parameter is the probability of a particular examinee correctly answering any given item This has the advantage of allowing the researcher to identify the different levels of individual performance on specific questions, as well as develop particular questions to specific subgroups or populations Item difficulty is estimated directly using logistic models instead of proportions.

Researchers must determine whether they need items with low, medium, or high difficulty. For instance, researchers interested in general purpose scales will focus on items with medium difficulty 68 , i. The item discrimination index also called item-effectiveness test , is the degree to which an item correctly differentiates between respondents or examinees on a construct of interest 69 , and can be assessed under both CTT and IRT frameworks.

It is a measure of the difference in performance between groups on a construct. The upper group represents participants with high scores and the lower group those with poor or low scores. It differentiates between the number of students in an upper group who get an item correct and the number of students in a lower group who get the item correct The use of an item discrimination index enables the identification of positively discriminating items i.

The item discrimination index has been found to improve test items in at least three ways. First, non-discriminating items, which fail to discriminate between respondents because they may be too easy, too hard, or ambiguous, should be removed Second, items which negatively discriminate, e.

Third, items that positively discriminate should be retained, e. In some cases, it has been recommended that such positively discriminating items be considered for revision 70 as the differences could be due to the level of difficulty of the item. An item discrimination index can be calculated through correlational analysis between the performance on an item and an overall criterion 69 using either the point biserial correlation coefficient or the phi coefficient Item discrimination under the IRT framework is a slope parameter that determines how steeply the probability of a correct response changes as the proficiency or trait increases This allows differentiation between individuals with similar abilities and can also be estimated using a logistic model.

Under certain conditions, the biserial correlation coefficient under the CTT framework has proven to be identical to the IRT item discrimination parameter 67 , 74 , 75 ; thus, as the trait increases so does the probability of endorsing an item. A third technique to support the deletion or modification of items is the estimation of inter-item and item-total correlations, which falls under CTT.

These correlations often displayed in the form of a matrix are used to examine relationships that exist between individual items in a pool. Inter-item correlations also known as polychoric correlations for categorical variables and tetrachoric correlations for binary items examines the extent to which scores on one item are related to scores on all other items in a scale 2 , 68 , Also, it examines the extent to which items on a scale are assessing the same content Item-total correlations also known as polyserial correlations for categorical variables and biserial correlations for binary items aim at examining the relationship between each item vs.

However, the adjusted item-total correlation, which examines the correlation between the item and the sum score of the rest of the items excluding itself is preferred 1 , 2.

The distractor efficiency analysis shows the distribution of incorrect options and how they contribute to the quality of a multiple-choice item The incorrect options, also known as distractors, are intentionally added in the response options to attract students who do not know the correct answer in a test question To calculate this, respondents will be grouped into three groups—high, middle, and lower tertiles based on their total scores on a set of items.

This type of analysis is rarely used in the health sciences, as most multiple-choice items are on a Likert-type response scale and do not test respondent correct knowledge, but their experience or perception.

However, distractor analysis can help to determine whether items are well-constructed, meaningful, and functional when researchers add response options to questions that do not fit a particular experience. It is expected that participants who are determined as having poor knowledge or experience on the construct will choose the distractors, while those with the right knowledge and experience will choose the correct response options 77 , Where those with the right knowledge and experience are not able to differentiate between distractors and the right response, the question may have to be modified.

Non-functional distractors identified need to be removed and replaced with efficient distractors In addition to these techniques, some researchers opt to delete items with large numbers of cases that are missing, when other missing data-handling techniques cannot be used For cases where modern missing data handling can be used, however, several techniques exist to solve the problem of missing cases. Two of the approaches have proven to be very useful for scale development: full information maximum likelihood FIML 82 and multiple imputation When using multiple imputation to recover missing data in the context of survey research, the researcher can impute individual items prior to computing scale scores or impute the scale scores from other scale scores However, item-level imputation has been shown to produce more efficient estimates over scale-level imputation.

Thus, imputing individual items before scale development is a preferred approach to imputing newly developed scales for missing cases Factor extraction is the phase in which the optimal number of factors, sometimes called domains, that fit a set of items are determined.

This is done using factor analysis. Factor analysis is a regression model in which observed standardized variables are regressed on unobserved i. Because the variables and factors are standardized, the bivariate regression coefficients are also correlations, representing the loading of each observed variable on each factor. Thus, factor analysis is used to understand the latent internal structure of a set of items, and the extent to which the relationships between the items are internally consistent 4.

This is done by extracting latent factors which represent the shared variance in responses among the multiple items 4. The emphasis is on the number of factors, the salience of factor loading estimates, and the relative magnitude of residual variances 2.

A number of analytical processes have been used to determine the number of factors to retain from a list of items, and it is beyond the scope of this paper to describe all of them. For scale development, commonly available methods to determine the number of factors to retain include a scree plot 85 , the variance explained by the factor model, and the pattern of factor loadings 2.

Where feasible, researchers could also assess the optimal number of factors to be drawn from the list of items using either parallel analysis 86 , minimum average partial procedure 87 , or the Hull method 88 , The extraction of factors can also be used to reduce items.

With factor analysis, items with factor loadings or slope coefficients that are below 0. Hence, it is often recommended to retain items that have factor loadings of 0. Also, items with cross-loadings or that appear not to load uniquely on individual factors can be deleted. A number of scales developed stop at this phase and jump to tests of reliability, but the factors extracted at this point only provide a hypothetical structure of the scale.

The dimensionality of these factors need to be tested cf. Step 7 before moving on to reliability cf. Step 8 and validity cf. Step 9 assessment. The test of dimensionality is a test in which the hypothesized factors or factor structure extracted from a previous model is tested at a different time point in a longitudinal study or, ideally, on a new sample Tests of dimensionality determine whether the measurement of items, their factors, and function are the same across two independent samples or within the same sample at different time points.

Such tests can be conducted using independent cluster model ICM -confirmatory factor analysis, bifactor modeling, or measurement invariance. Confirmatory factor analysis is a form of psychometric assessment that allows for the systematic comparison of an alternative a priori factor structure based on systematic fit assessment procedures and estimates the relationship between latent constructs, which have been corrected for measurement errors Morin et al.

Description of model fit indices and thresholds for evaluating scales developed for health, social, and behavioral research. Bifactor modeling, also referred to as nested factor modeling, is a form of item response theory used in testing dimensionality of a scale , The bifactor model allows researchers to estimate a unidimensional construct while recognizing the multidimensionality of the construct , The bifactor model assumes each item loads onto two dimensions, i.

The first is a general latent factor that underlies all the scale items and the second, a group factor subscale. This approach allows researchers to examine any distortion that may occur when unidimensional IRT models are fit to multidimensional data , To determine whether to retain a construct as unidimensional or multidimensional, the factor loadings from the general factor are then compared to those from the group factors , Where the factor loadings on the general factor are significantly larger than the group factors, a unidimensional scale is implied , This method is assessed based on meaningful satisfactory thresholds.

Alternatively, one can test for the coexistence of a general factor that underlies the construct and multiple group factors that explain the remaining variance not explained by the general factor Another method to test dimensionality is measurement invariance, also referred to as factorial invariance or measurement equivalence Measurement invariance concerns the extent to which the psychometric properties of the observed indicators are transportable generalizable across groups or over time These properties include the hypothesized factor structure, regression slopes, intercept, and residual variances.

Measurement invariance is tested sequentially at five levels—configural, metric, scalar, strict residual , and structural , Of key significance to the test of dimensionality is configural invariance, which is concerned with whether the hypothesized factor structure is the same across groups. This assumption has to be met in order for subsequent tests to be meaningful , For example, a hypothesized unidimensional structure, when tested across multiple countries, should be the same.

This can be tested in CTT, using multigroup confirmatory factor analysis — An alternative approach to measurement invariance in the testing of unidimensionality under item response theory is the Rasch measurement model for binary items and polytomous IRT models for categorical items.

This is analogous to the conditions underpinning measurement invariance in a multi-group CFA , Whether the hypothesized structure is bidimensional or multidimensional, each dimension in the structure needs to be tested again to confirm its unidimensionality. This can also be done using confirmatory factor analysis. Appropriate model fit indices and the strength of factor loadings cf. One commonly encountered pitfall is a lack of satisfactory global model fit in confirmatory factor analysis conducted on a new sample following a satisfactory initial factor analysis performed on a previous sample.

Lack of satisfactory fit offers the opportunity to identify additional underperforming items for removal. Also, modification indices, produced by M plus and other structural equation modeling SEM programs, can help identify items that need to be modified.

Sometimes a higher-order factor structure, where correlations among the original factors can be explained by one or more higher-order factors, is needed. A good example of best practice is seen in the work of Pushpanathan et al. They tested this using three different models—a unidimensional model 1-factor CFA ; a 3-factor model 3 factor CFA consisting of sub-scales measuring insomnia, motor symptoms and obstructive sleep apnea, and REM sleep behavior disorder; and a confirmatory bifactor model having a general factor and the same three sub-scales combined.

The results of this study suggested that only the bifactor model with a general factor and the three sub-scales combined achieved satisfactory model fitness.

Based on these results, the authors cautioned against the use of a unidimensional total scale scores as a cardinal indicator of sleep in Parkinson's disease, but encouraged the examination of its multidimensional subscales Finalized items from the tests of dimensionality can be used to create scale scores for substantive analysis including tests of reliability and validity.

Scale scores can be calculated by using unweighted or weighted procedures. The unweighted approach involves summing standardized item scores or raw item scores, or computing the mean for raw item scores We have received your request and will respond promptly. Log In. Beware of false knowledge; it is more dangerous than ignorance. Thus in general it may be any dedicated key. What I should have said was a more generic "pressing a key". Lewisp: Good point.

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