Skip to main content
Download PDF

Validation of online psychometric instruments for common mental health disorders: a systematic review

BMC Psychiatry volume 16, Article number: 45 (2016) Cite this article

Abstract

Background

Online questionnaires for measuring common mental health disorders such as depression and anxiety disorders are increasingly used. The psychometrics of several pen-and-paper questionnaires have been re-examined for online use and new online instruments have been developed and tested for validity as well. This study aims to review and synthesise the literature on this subject and provide a framework for future research.

Methods

We searched Medline and PsycINFO for psychometric studies on online instruments for common mental health disorders and extracted the psychometric data. Studies were coded and assessed for quality by independent raters.

Results

We included 56 studies on 62 online instruments. For common instruments such as the CES-D, MADRS-S and HADS there is mounting evidence for adequate psychometric properties. Further results are scattered over different instruments and different psychometric characteristics. Few studies included patient populations.

Conclusions

We found at least one online measure for each of the included mental health disorders and symptoms. A small number of online questionnaires have been studied thoroughly. This study provides an overview of online instruments to refer to when choosing an instrument for assessing common mental health disorders online, and can structure future psychometric research.

Background

Assessment of common mental health disorders, which include depression and anxiety disorders [1], is increasingly conducted online, usually employing self-report questionnaires. Current online instruments are often paper questionnaires that have been adapted for online use [2]. Paper and online versions of the same instrument correlate strongly, but mean scores and psychometrics may differ [2] and, therefore, equivalence cannot be assumed.

Several studies have re-examined the psychometrics of paper questionnaires for use online, e.g. for measuring social phobia [3], panic and agoraphobia [4] and depression [5]. Besides the established paper instruments that are used online, new instruments are being developed and investigated for validity specifically for use online. These instruments can have technological advantages, such as the use of audio and video [6, 7], or automatically skipping irrelevant items based on previous answers [8].

To date, the psychometrics of both the digitalised paper questionnaires and newly developed online instruments have not been systematically studied. An overview and synthesis of the literature would provide a framework for future research and development, and would guide researchers, clinicians and other professionals when choosing an instrument suitable for a specific purpose. The current study aims to systematically review and synthesise the scientific literature on the psychometrics of internet-based instruments that measure common mental health disorders and related symptoms. We aim to provide an overview of the psychometric characteristics of these instruments, the evidence for these characteristics, and an indication of how these findings can be generalised to various populations.

Methods

This systematic review was conducted in accordance with the PRISMA Statement [9]. See Additional file 1. The extraction of psychometric data was based on the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) Checklist. [10]

Study selection

We conducted a comprehensive literature search in PubMed and PsycInfo, which is updated up to January 1st 2014. For the PubMed search we applied a previously developed search string for psychometric studies [11] and additional key words to focus on online assessment and common mental health disorders (Additional file 2). The PsycInfo search was a translation of the PubMed search, with additional keywords unique to PsycInfo and the omission of generic terms such as ‘methods’ and ‘instrumentation’, to increase the specificity of the search (Additional file 2).

Study inclusion

After excluding studies that were not written in English, studies were included in three a priori defined steps, as depicted in the flow chart (Fig. 1). The first inclusion step was to select all studies that applied online self-report assessments, i.e. data were collected using internet-connected devices that individuals used to fill in questions about themselves. We excluded assessments through stand-alone devices (e.g. in a clinic), or other self-report measurement within a clinic, in order to retain comparability between results. We also excluded studies on assessments through unique devices specifically developed for the study, face-to-face interviews conducted by videoconference, and interactive voice response measures by telephone. As second inclusion step, we included only those studies that aimed at assessing psychometrics and that provided data of at least one psychometric variable. The third and final inclusion step included studies that described instruments for assessing symptoms of common mental health disorders [1]. These disorders include ICD-10 [12] and DSM-5 [13] unipolar depressive disorders, social phobia, panic disorder with or without agoraphobia (PD/A), agoraphobia without panic, specific phobia, generalised anxiety disorder (GAD), post-traumatic stress disorder (PTSD) and obsessive-compulsive disorder (OCD). We also included instruments that assessed specific symptoms of these disorders or general distress that can accompany these disorders, i.e. psychological stress (only when unrelated to physical disorders), worrying, suicidal ideation and self-harm.

Fig. 1
figure1

Flow chart of included studies

Full size image

Data extraction

First, we coded the data that are relevant for generalising a study’s findings, which are the sample size, characteristics of the participants (age, gender, disease characteristics), population (e.g. patients or general population), recruitment method, country in which the study was conducted, language of the measurement instrument, any subgroups the results were reported for, and amount of missing data. Next, we extracted the psychometric data provided in the study. The following variables were entered into the tables (Tables 1, 2, 3 and 4; Additional file 3): internal consistency (usually expressed as Cronbach’s alpha); test-retest reliability (usually kappa); measurement error; factor structure, including type of analysis (exploratory or confirmatory factor analysis, or principal component analysis) and model fit or variance explained; equivalence of paper and online versions of the instrument (usually a correlation); difference in mean scores between online and paper versions; convergent validity, i.e. the relation with an instrument that measures the same construct (usually a correlation); criterion validity in terms of sensitivity, specificity (for the optimal cut-off point), Area Under the Receiver Operating Characteristic Curve (AUC), and other criterion-related outcomes (e.g. kappa); and responsiveness, i.e. the degree to which the instrument can measure change. These variables were extracted for each instrument reported in the study. When an instrument was investigated in multiple samples, e.g. when two studies on one instrument were described in one paper, we listed the sample characteristics and outcomes for each sample separately.

Table 1 Transdiagnostic online self-report instruments and the number of studies that report psychometric characteristics (between parentheses)
Full size table
Table 2 Online self-report instruments for depression and the number of studies that report psychometric characteristics (between parentheses)
Full size table
Table 3 Online self-report instruments for GAD, panic disorder and agoraphobia, social phobia, specific phobia, OCD and PTSD, and the number of studies that report psychometric characteristics (between parentheses)
Full size table
Table 4 Online self-report instruments for stress, worrying, suicidal ideation and self-harm, and the number of studies that report psychometric characteristics (between parentheses)
Full size table

Criterion validity requires a criterion such as a diagnosis that can be objectively measured, but there is no exact method to ascertain any of the included disorders. Nevertheless, some psychometric studies do aim to assess criterion validity, and the criterion is established by an interview conducted face-to-face or by telephone by a clinician or a trained interviewer. We report these data, because it is not within the scope of the present review to discuss the validity of the used criteria.

Quality assessment

Quality assessment was conducted in two ways. First, we coded variables that affect the generalisability and risk of bias of the findings, which are sample size, sample characteristics, recruitment method and amount of missing data. Second, we used the COSMIN Checklist with a 4-point scale [10, 14–17]. This checklist contains quality criteria for the psychometric variables described above. For each variable, a quality score is obtained by taking the lowest rating of any item in that list of criteria [10].

Double coding

The inclusion process was conducted by two independent raters (WvB and a research assistant). Differences between raters were solved by discussion and by conferring with the co-authors. Three of the authors of this study (WvB, JHS, PvO) and three other raters (research assistants) participated in the data extraction and quality assessment of the included studies. We double coded all extracted data, including the four variables that may affect generalisability and risk of bias. The COSMIN quality ratings were mostly single coded, where 18 % of the included studies were double coded.

Data synthesis

All extracted data (Additional file 3) were sorted on disorder or symptom and on instrument name, thereby creating a table of instruments for each disorder (Tables 1, 2, 3 and 4). When a study investigated multiple instruments for multiple disorders (e.g. one instrument that measures depression and one that measures anxiety), we reported the instruments in the table for the appropriate disorder. We created a separate table for instruments that measured multiple disorders or general symptoms. It was not possible to synthesise the data in a quantitative analysis, such as a meta-analysis, because the included studies investigated a variety of instruments, applying various methods to obtain psychometric data and reporting various outcome measures.

Results

Study selection

The PubMed search yielded 2644 results and the PsycINFO search added 370 unique studies (Fig. 1). After excluding studies that were not in English and search results that were irrelevant studies, we assessed 2839 studies for eligibility (Fig. 1). Step one of the inclusion, i.e. assessment was conducted using online self-report instruments, left 1159 studies. Of these, 194 investigated and reported psychometric data (step 2). Next, we included 62 studies that investigated instruments for assessing common mental health disorders (step 3). Finally, we excluded 6 studies that did not report psychometric data that were relevant for our overview and synthesis, so we included 56 studies in our review. See Fig. 1 for a flow chart.

Study characteristics

The details of the 56 included studies and their results are presented in Additional file 3. Combined, these studies described psychometric data for 62 different instruments. These studies and instruments are presented in Additional file 3. The data are summarised in Tables 1, 2, 3 and 4. The samples of most studies (48 of 56) contained a larger percentage of women (range 0 % to 100 %; Additional file 3). Seven studies included a sample with an average age below 20. Most studies recruited their samples from the general population using advertisements or links on websites (i.e. self-referral). Also common were studies among university students. Patient populations were less common, as 14 of the 62 instruments were investigated among patient populations. See Tables 1, 2, 3 and 4 and Additional file 3. All 56 of the included studies investigated internet-based instruments that were completed on a desktop, laptop or tablet computer, while none of the studies reported that their instruments were completed on cellular phones or smartphones.

Outcomes

We found instruments for all of the included mental health disorders. An average of 2.5 psychometric characteristics were reported for each instrument. None of the studies reported measurement error or responsiveness of instruments. We left the empty columns of these two outcomes in Additional file 3, but omitted them in Tables 1, 2, 3 and 4. Of the 62 investigated instruments, 29 assessed depressive symptoms. Of these, the CES-D and the Montgomery–Åsberg Depression Rating Scale Self Report (MADRS-S) were most frequently studied (6 studies each). Least studied were instruments for measuring suicidal ideation (1 study on 2 single items), self-harm (1 study) and stress (1 study).

Transdiagnostic online instruments

Seven instruments assessed both depressive and anxiety symptoms, or anxiety symptoms that apply to several disorders, such as the Beck Anxiety Inventory (BAI). These can be roughly divided in short instruments that screen for disorders, e.g. the Web Screening Questionnaire (WSQ) [18] and the Web-Based Depression and Anxiety Test (WB-DAT) [8], and scales that assess symptom severity, e.g. the Hospital Anxiety and Depression Scale (HADS) [19] and the Depression Anxiety Stress Scales (DASS) [20]. The short screening questionnaires had poor to adequate criterion validity for screening individual disorders [8, 18, 21]. Of the symptom severity scales, the HADS was investigated in 5 studies [19, 22–25]. These 5 studies showed a fair to good internal consistency. The online HADS is the only instrument we found that was investigated among several patient populations [19, 23, 24]. Although the factor structure may be different from how the measure was designed [19, 23], there is mounting evidence that support adequate validity of the online HADS.

Online assessment of depression

Our review includes 29 instruments that measure depressive symptoms. These consist of 22 instruments that measure depression alone and 7 transdiagnostic instruments. The 22 studies on instruments for depression generally reported recruiting their samples from the general population. Five studies investigated instruments for depression among patient populations [3, 6, 26–28], each investigating a different instrument.

The full version of the CES-D has been evaluated in 6 studies [5, 29–33], and 5 characteristics were each reported by at least 2 studies (Table 2). Moreover, all 6 studies recruited their samples among non-patients, so the results can be considered complementary. The internal consistency was investigated in 5 of these studies, reporting a Cronbach’s alpha of .89 to .93. Factor analysis showed that the CES-D consists of 2, 3 or 4 factors [32, 33]. The 2-factor solution was among an English speaking population, the 3-factor solution among a Spanish speaking and the 4-factor solution among a Chinese speaking population [32, 33]. Adequate psychometric characteristics were found for the CES-D regarding equivalence of mean scores with the paper version [31, 33], convergent validity [5, 30] and criterion validity [5, 30]. One study [33] conducted a full measurement invariance analysis using confirmatory factor analysis, comparing paper and online formats, and found only a negligible difference in the latent mean score of one factor. Overall, it can be concluded that the online CES-D has good psychometric characteristics among non-patient populations, and that a start has been made to investigate its intercultural validity.

Another commonly investigated instrument was the MADRS-S [3, 4, 34–37]. Five of these studies reported Cronbach’s alpha, which is adequate to excellent (.73 to .90, Table 2) [3, 4, 34–36]. Thorndike and colleagues [37] found that the scale consists of 3 factors. Four studies found that the mean score of the MADRS-S does not differ significantly between the online and the paper version [3, 4, 35, 36].

Online assessment of GAD

The GAD-7 and two shorter versions were studied among a sample recruited from the general population [38]. The scale showed promising internal consistency, convergent validity and predictive validity. The psychometrics of the GAD-7 were similar among a population of people with hearing loss [6].

Online assessment of panic disorder and agoraphobia

Internet interventions for PD/A, such as self-help courses, have been relatively extensively researched. Therefore, Austin and colleagues [39] and Carlbring and colleagues [4] studied the online questionnaires usually employed for such research. They focussed on equivalence of mean scores with paper versions of the same instruments. This equivalence could generally be assumed due to high correlations, but the study of Carlbring [4] found that online versions yield significantly lower mean scores for the Body Sensations Questionnaire (BSQ) and Agoraphobic Cognitions Questionnaire (ACQ) and higher scores for the Mobility Inventory (MI) subscale Alone. Finally, an agoraphobia screening item augmented with images was found to have adequate criterion validity (AUC .73) [7]. All these studies recruited their samples from the general population.

Online assessment of social phobia

Two studies [3, 40] independently investigated the equivalence between online and paper versions of the online versions of the Social Interaction Anxiety Scale (SIAS) and Social Phobia Scale (SPS). Both did not find a difference between formats in mean score, but the factor structure did differ between formats [40], indicating that scores cannot be compared across formats. Adequate to good internal consistency of these scales has also been found in three studies [3, 40, 41], and adequate convergent validity of the SIAS in two [40, 41]. Lindner and colleagues revised item 14 of the SIAS, because the original item only applied to heterosexual people. This change did not alter the internal consistency or convergent validity of the scale [41]. The study of Hedman and colleagues [3] recruited people classified with social phobia, but more research among patient groups is recommended.

Online assessment of specific phobia

Two of the transdiagnostic screening measures [18, 21] included specific phobia. These showed poor criterion validity for specific phobia. One instrument, the Flight Anxiety Situations Questionnaire (FAS), has been studied for aviophobia [42]. This study showed near perfect criterion validity (AUC .99). Considering aviophobia is only one of many different specific phobias, much more development is needed in this area.

Online assessment of OCD

Four instruments for OCD have been studied, all in the US and among the general population [43–45]. Each instrument was studied only once. Williams and colleagues [45] investigated differential item functioning between black and white Americans, finding significant differences for the Padua Inventory (PI). Next to these 4 instruments, the WSQ [18] and the CIDI-SF [21] also screen for OCD.

Online assessment of PTSD

Like instruments for OCD, 4 instruments for PTSD have been studied, all in the US and among the general population [31, 46–48]. The transdiagnostic WSQ [18] also screens for PTSD. One additional study investigated an instrument for perinatal PTSD [49]. Miller and colleagues [47] checked the factor structure of their measure for PTSD (National Stressful Events Survey) using item-response theory. The factor structure was confirmed, but the items of the instrument may cover too narrow a range of the latent factors.

Online assessment of worry and stress

The PSWQ, assessing worry, was studied twice [20, 50]. These studies found slightly differing values for internal consistency (.73 and .88). We found one study on an instrument that assesses stress [51].

Online assessment of suicidal ideation and self-harm

We found one study on an instrument that assesses self-harm. [52] This study used Rasch analysis to further confirm the factors of the Inventory of Statements About Self-injury (ISAS), obtained by factor analysis, and their unidimensionality. Furthermore, we found two single-item measures for suicidal ideation, being item 9 of the BDI-II and item 9 of the MADRS-S [36]. Item 9 of the online BDI-II yielded lower scores than item 9 of the paper version of the BDI-II [36]. The WSQ [18] also contains an item that screens for suicidal ideation, but the validity of this item was not investigated (also see [53]).

Generalisability and risk of bias

The sample sizes of the included studies were generally adequate for analysing psychometric properties. Nine studies contained over 1000 participants. The other studies in the tables (n = 46) had an average sample size of 261 participants. A sample size below 100 was found in 10 studies, which generally gives too little statistical power for psychometric analyses [54]. It should be noted that required sample sizes differ per number of items and type of analysis. Most results could be biased due to selectively missing data. Two studies reported missing data and included numbers. In 33 studies, the amount of missing data was not specifically reported, but could be deduced or estimated. Missing data were not reported by or could not be deduced in 21 studies (see Additional file 3). Overall, COSMIN quality ratings of ‘Excellent’ were rare and ‘Poor’, ‘Fair’ and ‘Good’ ratings were equally common. Instead of adding the COSMIN ratings to the tables and Additional file 3, we decided to report the characteristics the ratings are based on, because the ratings do not always do justice to a study’s quality. The study characteristics give an objective and interpretable indication of the robustness and generalisability of a study’s findings. Lastly, 47 of the 62 instruments were investigated in only one study (Tables 1, 2, 3 and 4), so the robustness of the psychometric properties of these instruments relies heavily on the aspects of the individual studies and cannot be easily generalised to other populations or settings.

Discussion

This review systematically studied the scientific literature on the psychometrics of online instruments that measure common mental health disorders. We report characteristics of 62 instruments. Most of these instruments were investigated among samples recruited from the general population. We found at least one online measure for each of the included mental health disorders and symptoms. The results are scattered over different instruments and different characteristics and, therefore, can be synthesised for only a few instruments. We found few instruments that measure specific phobia, stress, worry self-harm and suicidal ideation. There were no studies that reported that the questionnaires were completed on cellular phones or smartphones.

The CES-D is the most well-studied online instrument and there is evidence for adequate psychometric properties among samples recruited from the general population. The MADRS-S has been well-studied as well, mostly showing mean score equivalence between online and paper versions. Finally, the HADS is the only instrument that was investigated among both the general population and two patient populations, showing adequate psychometric properties.

Ideally, two or more online instruments would be available for each disorder, with all of their characteristics examined in several studies, among various populations. There are clear gaps in the tables presented in this study, which warrant further research and development. The psychometric properties measurement error and responsiveness were not reported for any instrument. Furthermore, while there is an abundance of online instruments for depressive symptoms, there is a shortage of instruments for other disorders. Although a few new instruments have been developed in the meantime, e.g. for suicidal ideation [55], more instruments are needed.

Equivalence between paper and online versions of an instrument has mostly been studied in the form of equivalence of mean scores by correlations and t-tests. We can conclude that correlations are high and differences are small. However, mean score equivalence is only one aspect of measurement invariance. Two studies conducted a broad range of measurement invariance tests [33, 40]. While Yu and Yu [33] found only a negligible difference in the mean score of the somatic factor of the CES-D, Hirai and colleagues [40] found that factor structures of the SIDAS and SPS differ between formats. Differing factor structures indicate that different constructs are assessed and scores cannot be compared across formats. It is important to note that possibly not only the format differs between paper and online versions, but the setting as well. Online questionnaires can be completed at the participant’s home on a device (s) he is familiar with. In the study of Yu and Yu [33], participants completed the paper questionnaires at home, while in the study of Hirai and colleagues [40], participants completed the paper questionnaires in a lab. It is recommended to study inter-format equivalence in one setting, and to use a broad range of measurement invariance aspects, e.g. using multiple-group confirmatory factor analysis [56].

This systematic review has some limitations. Firstly, we may not have included all studies on psychometrics of online instruments for common mental health disorders, because there may be studies that applied online assessment without mentioning it in the title or abstract. Online assessment is increasingly common and increasingly less important to mention. Secondly, we decided not to label the quality of the included studies, even though a quality assessment is common practice in systematic reviews. Because psychometric properties are dependent on study characteristics, it is more insightful to inspect these characteristics in order to decide whether an instrument has been investigated well enough for the purpose, population and setting one wants to use it for. Thirdly, our search has been updated up to January 1st 2014 and several psychometric studies on online instruments have been published since. Finally, our search strings (Additional file 2) can be made more comprehensive by adding ‘distress’, ‘mhealth’ and ‘response processes’. The omission of these terms have not impacted our results, however.

Future psychometric studies are encouraged to investigate and explore different devices, formats and media. Only one study in our review [37] investigated the effects of different formats of online questionnaires and the preferences of the participants. An instrument’s format, e.g. the layout, design, font type and the number of items per page, interacts with its content and with the characteristics of the individual who completes the items. [57] Different formats could also include other media than text, such as audio, images and video, see e.g. [6] and [7]. Another area to explore is measurement by smartphones, which we did not encounter in the included studies. The validity of measurement by smartphone applications has been studied in other fields, such as psychotic symptoms. [58] An advantage of measurement by smartphones is that it enables momentary assessment, opposed to retrospective assessment, because an individual can have access to his/her smartphone all day long.

Conclusions

We found at least one online measure for each of the included mental health disorders and symptoms, and there is mounting evidence for adequate psychometric properties of common instruments such as the CES-D, MADRS-S and HADS. Overall, the results are scattered over different instruments and different characteristics, and much work still has to be done in this field. With this systematic review we provide a framework for future research into psychometrics of online instruments. Furthermore, our overview of instruments can guide professionals when choosing an instrument for assessing common mental health disorders online. The tables (Additional file 3) provided with this systematic review are free to use and expand. We encourage researchers to fill in the missing data and to add innovative instruments.

References

  1. 1.

    National Institute for Health and Clinical Excellence (NICE). Common mental health disorders: Identification and pathways to care. London: NICE; 2011.

    Google Scholar 

  2. 2.

    Alfonsson S, Maathz P, Hursti T. Interformat reliability of digital psychiatric self-report questionnaires: a systematic review. J Med Internet Res. 2014;16(12), e268. doi:10.2196/jmir.3395.

    PubMed Central  Article  PubMed  Google Scholar 

  3. 3.

    Hedman E, Ljotsson B, Ruck C, Furmark T, Carlbring P, Lindefors N, et al. Internet administration of self-report measures commonly used in research on social anxiety disorder: A psychometric evaluation. Comput Hum Behav. 2010;26(4):736–40.

    Article  Google Scholar 

  4. 4.

    Carlbring P, Brunt S, Bohman S, Austin D, Richards J, Ost LG, et al. Internet vs. paper and pencil administration of questionnaires commonly used in panic/agoraphobia research. Comput Hum Behav. 2007;23(3):1421–34.

    Article  Google Scholar 

  5. 5.

    Cuijpers P, Boluijt P, Van SA. Screening of depression in adolescents through the Internet : sensitivity and specificity of two screening questionnaires. Eur Child AdolescPsychiatry. 2008;17(1):32–8. doi:10.1007/s00787-007-0631-2.

    Article  Google Scholar 

  6. 6.

    Rogers KD, Young A, Lovell K, Campbell M, Scott PR, Kendal S. The British Sign Language versions of the Patient Health Questionnaire, the Generalized Anxiety Disorder 7-item Scale, and the Work and Social Adjustment Scale. J Deaf Stud Deaf Educ. 2013;18(1):110–22. doi:10.1093/deafed/ens040.

    PubMed Central  Article  PubMed  Google Scholar 

  7. 7.

    van Ballegooijen W, Riper H, Donker T, Martin Abello K, Marks I, Cuijpers P. Single-item screening for agoraphobic symptoms: validation of a web-based audiovisual screening instrument. PLoS One. 2012;7(7):e38480. doi:10.1371/journal.pone.0038480.

    PubMed Central  Article  PubMed  Google Scholar 

  8. 8.

    Farvolden P, McBride C, Bagby RM, Ravitz P. A Web-based screening instrument for depression and anxiety disorders in primary care. J Med Internet Res. 2003;5(3), e23. doi:10.2196/jmir.5.3.e23.

    PubMed Central  Article  PubMed  Google Scholar 

  9. 9.

    Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535. doi:10.1136/bmj.b2535.

    PubMed Central  Article  PubMed  Google Scholar 

  10. 10.

    Terwee CB, Mokkink LB, Knol DL, Ostelo RW, Bouter LM, de Vet HC. Rating the methodological quality in systematic reviews of studies on measurement properties: a scoring system for the COSMIN checklist. Qual Life Res. 2012;21(4):651–7. doi:10.1007/s11136-011-9960-1.

    PubMed Central  Article  PubMed  Google Scholar 

  11. 11.

    Terwee CB, Jansma EP, Riphagen II, de Vet HC. Development of a methodological PubMed search filter for finding studies on measurement properties of measurement instruments. Qual Life Res. 2009;18(8):1115–23. doi:10.1007/s11136-009-9528-5.

    PubMed Central  Article  PubMed  Google Scholar 

  12. 12.

    World Health Organization. ICD-10 Version:2015. 2015. http://apps.who.int/classifications/icd10/browse/2015/en. Accessed January 2015.

  13. 13.

    American Psychiatric Association. Diagnostic and statistical manual of mental disorders, (DSM-5®). American Psychiatric Pub.; 2013.

  14. 14.

    Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res. 2010;19(4):539–49. doi:10.1007/s11136-010-9606-8.

    PubMed Central  Article  PubMed  Google Scholar 

  15. 15.

    Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63(7):737–45. doi:10.1016/j.jclinepi.2010.02.006.

    Article  PubMed  Google Scholar 

  16. 16.

    Mokkink LB, Terwee CB, Knol DL, Stratford PW, Alonso J, Patrick DL, et al. The COSMIN checklist for evaluating the methodological quality of studies on measurement properties: a clarification of its content. BMC Med Res Methodol. 2010;10:22. doi:10.1186/1471-2288-10-22.

    PubMed Central  Article  PubMed  Google Scholar 

  17. 17.

    Mokkink LB, Terwee CB, Gibbons E, Stratford PW, Alonso J, Patrick DL, et al. Inter-rater agreement and reliability of the COSMIN (COnsensus-based Standards for the selection of health status Measurement Instruments) checklist. BMC Med Res Methodol. 2010;10:82. doi:10.1186/1471-2288-10-82.

    PubMed Central  Article  PubMed  Google Scholar 

  18. 18.

    Donker T, Van SA, Marks I, Cuijpers P. A brief Web-based screening questionnaire for common mental disorders: development and validation. J Med Internet Res. 2009;11(3):e19. doi:10.2196/jmir.1134.

    PubMed Central  Article  PubMed  Google Scholar 

  19. 19.

    McCue P, Martin C, Buchanan T, Rodgers J, Scholey A. An investigation into the psychometric properties of the Hospital Anxiety and Depression Scale in individuals with chronic fatigue syndrome. Psychol Health Med. 2003;8(4):425–39. doi:10.1080/1354850310001604568.

    Article  CAS  PubMed  Google Scholar 

  20. 20.

    Zlomke KR. Psychometric properties of internet administered versions of Penn State Worry Questionnaire (PSWQ) and Depression, Anxiety, and Stress Scale (DASS). Comput Hum Behav. 2009;25(4):841–3.

    Article  Google Scholar 

  21. 21.

    Carlbring P, Forslin P, Ljungstrand P, Willebrand M, Strandlund C, Ekselius L, et al. Is the Internet-administered CIDI-SF equivalent to a clinician-administered SCID interview? Cogn Behav Ther. 2002;31(4):183–9.

    Article  Google Scholar 

  22. 22.

    Andersson G, Kaldo-Sandstrom V, Strom L, Stromgren T. Internet administration of the Hospital Anxiety and Depression Scale in a sample of tinnitus patients. J Psychosom Res. 2003;55(3):259–62.

    Article  PubMed  Google Scholar 

  23. 23.

    McCue P, Buchanan T, Martin CR. Screening for psychological distress using internet administration of the Hospital Anxiety and Depression Scale (HADS) in individuals with chronic fatigue syndrome. Br J Clin Psychol. 2006;45(Pt 4):483–98. doi:10.1348/014466505X82379.

    Article  PubMed  Google Scholar 

  24. 24.

    Thoren ES, Andersson G, Lunner T. The use of research questionnaires with hearing impaired adults: online vs. paper-and-pencil administration. BMC Ear Nose Throat Disord. 2012;12:12. doi:10.1186/1472-6815-12-12.

    PubMed Central  Article  PubMed  Google Scholar 

  25. 25.

    Whitehead L. Methodological issues in Internet-mediated research: a randomized comparison of internet versus mailed questionnaires. J Med Internet Res. 2011;13(4):e109. doi:10.2196/jmir.1593.

    PubMed Central  Article  PubMed  Google Scholar 

  26. 26.

    Drake G, Csipke E, Wykes T. Assessing your mood online: acceptability and use of Moodscope. Psychol Med. 2013;43(7):1455–64. doi:10.1017/S0033291712002280.

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  27. 27.

    Faries DE, Houston JP, Sulcs EM, Swindle RW. A cross-validation of the provisional diagnostic instrument (PDI-4). BMC Fam Pract. 2012;13:104. doi:10.1186/1471-2296-13-104.

    PubMed Central  Article  PubMed  Google Scholar 

  28. 28.

    Zimmerman M, Martinez JH. Web-based assessment of depression in patients treated in clinical practice: reliability, validity, and patient acceptance. J Clin Psychiatry. 2012;73(3):333–8. doi:10.4088/JCP.10m06519.

    Article  PubMed  Google Scholar 

  29. 29.

    Birnholz JL, Young MA. Differential item functioning for lesbians, bisexual, and heterosexual women in the center for epidemiological studies depression scale. Assessment. 2012;19(4):502–5. doi:10.1177/1073191112440032.

    Article  PubMed  Google Scholar 

  30. 30.

    Donker T, Van SA, Marks I, Cuijpers P. Brief self-rated screening for depression on the Internet. J AffectDisord. 2010;122(3):253–9. doi:10.1016/j.jad.2009.07.013.

    Google Scholar 

  31. 31.

    Fortson BL, Scotti JR, Del Ben KS, Chen YC. Reliability and validity of an Internet traumatic stress survey with a college student sample. J Trauma Stress. 2006;19(5):709–20. doi:10.1002/jts.20165.

    Article  PubMed  Google Scholar 

  32. 32.

    Leykin Y, Torres LD, Aguilera A, Munoz RF. Factor structure of the CES-D in a sample of Spanish- and English-speaking smokers on the Internet. Psychiatry Res. 2011;185(1–2):269–74. doi:10.1016/j.psychres.2010.04.056.

    PubMed Central  Article  PubMed  Google Scholar 

  33. 33.

    Yu SC, Yu MN. Comparison of Internet-based and paper-based questionnaires in Taiwan using multisample invariance approach. CyberpsycholBehav. 2007;10(4):501–7. doi:10.1089/cpb.2007.9998.

    Article  Google Scholar 

  34. 34.

    Hedman E, Ljotsson B, Blom K, E-A S, Kraepelien M, Ruck C, et al. Telephone versus internet administration of self-report measures of social anxiety, depressive symptoms, and insomnia: psychometric evaluation of a method to reduce the impact of missing data. J Med Internet Res. 2013;15(10):e229. doi:10.2196/jmir.2818.

    PubMed Central  Article  PubMed  Google Scholar 

  35. 35.

    Holländare F, Askerlund AM, Nieminen A, Engstrom I. Can BDI-II and MADRS-S be transferred to online use without affecting their psychometric properties? E-J Appl Psychol. 2008;4(2):63–5.

    Google Scholar 

  36. 36.

    Holländare F, Andersson G, Engstrom I. A comparison of psychometric properties between internet and paper versions of two depression instruments (BDI-II and MADRS-S) administered to clinic patients. J Med Internet Res. 2010;12(5):e49. doi:10.2196/jmir.1392.

    PubMed Central  Article  PubMed  Google Scholar 

  37. 37.

    Thorndike FP, Carlbring P, Smyth FL, Magee JC, Gonder-Frederick L, Ost LG, et al. Web-based measurement: Effect of completing single or multiple items per webpage. Comput Hum Behav. 2009;25(2):393–401.

    Article  Google Scholar 

  38. 38.

    Donker T, Van SA, Marks I, Cuijpers P. Quick and easy self-rating of Generalized Anxiety Disorder: validity of the Dutch web-based GAD-7, GAD-2 and GAD-SI. Psychiatry Res. 2011;188(1):58–64. doi:10.1016/j.psychres.2011.01.016.

    Article  PubMed  Google Scholar 

  39. 39.

    Austin DW, Carlbring P, Richards JC, Andersson G. Internet administration of three commonly used questionnaires in panic research: Equivalence to paper administration in Australian and Swedish samples of people with panic disorder. Int J Test. 2006;6(1):25–39.

    Article  Google Scholar 

  40. 40.

    Hirai M, Vernon LL, Clum GA, Skidmore ST. Psychometric properties and administration measurement invariance of social phobia symptom measures: Paper-pencil vs. Internet administrations. J Psychopathol Behav Assessment. 2011;33(4):470–9.

    Article  Google Scholar 

  41. 41.

    Lindner P, Martell C, Bergstrom J, Andersson G, Carlbring P. Clinical validation of a non-heteronormative version of the Social Interaction Anxiety Scale (SIAS). Health Qual Life Outcomes. 2013;11:209. doi:10.1186/1477-7525-11-209.

    PubMed Central  Article  PubMed  Google Scholar 

  42. 42.

    Skolnick RB, Schare ML, Wyatt KP, Tillman MA. Aviophobia assessment: validating the Flight Anxiety Situations Questionnaire as a clinical identification measure. J Anxiety Disord. 2012;26(8):779–84. doi:10.1016/j.janxdis.2012.07.006.

    Article  PubMed  Google Scholar 

  43. 43.

    Coles ME, Cook LM, Blake TR. Assessing obsessive compulsive symptoms and cognitions on the internet: evidence for the comparability of paper and Internet administration. Behav Res Ther. 2007;45(9):2232–40. doi:10.1016/j.brat.2006.12.009.

    Article  PubMed  Google Scholar 

  44. 44.

    Storch EA, Khanna M, Merlo LJ, Loew BA, Franklin M, Reid JM, et al. Children’s Florida Obsessive Compulsive Inventory: psychometric properties and feasibility of a self-report measure of obsessive-compulsive symptoms in youth. Child Psychiatry HumDev. 2009;40(3):467–83. doi:10.1007/s10578-009-0138-9.

    Article  Google Scholar 

  45. 45.

    Williams MT, Turkheimer E, Schmidt KM, Oltmanns TF. Ethnic identification biases responses to the Padua Inventory for obsessive-compulsive disorder. Assessment. 2005;12(2):174–85. doi:10.1177/1073191105275620.

    Article  PubMed  Google Scholar 

  46. 46.

    Elhai JD, Miller ME, Ford JD, Biehn TL, Palmieri PA, Frueh BC. Posttraumatic stress disorder in DSM-5: estimates of prevalence and symptom structure in a nonclinical sample of college students. J Anxiety Disord. 2012;26(1):58–64. doi:10.1016/j.janxdis.2011.08.013.

    Article  PubMed  Google Scholar 

  47. 47.

    Miller MW, Wolf EJ, Kilpatrick D, Resnick H, Marx BP, Holowka DW et al. The Prevalence and Latent Structure of Proposed DSM-5 Posttraumatic Stress Disorder Symptoms in U.S. National and Veteran Samples. Psychological Trauma: Theory, Research, Practice, and Policy. 2012.

  48. 48.

    Read JP, Farrow SM, Jaanimagi U, Ouimette P. Assessing trauma and traumatic stress via the Internet: Measurement equivalence and participant reactions. Traumatology. 2009;15(1):94–102.

    Article  Google Scholar 

  49. 49.

    Callahan JL, Borja SE, Hynan MT. Modification of the Perinatal PTSD Questionnaire to enhance clinical utility. J Perinatol. 2006;26(9):533–9. doi:10.1038/sj.jp.7211562.

    Article  CAS  PubMed  Google Scholar 

  50. 50.

    Verkuil B, Brosschot JF. The online version of the Dutch Penn State Worry Questionnaire: factor structure, predictive validity and reliability. J Anxiety Disord. 2012;26(8):844–8. doi:10.1016/j.janxdis.2012.08.002.

    Article  PubMed  Google Scholar 

  51. 51.

    Herrero J, Meneses J. Short Web-based versions of the perceived stress (PSS) and Center for Epidemiological Studies-Depression (CESD) Scales: A comparison to pencil and paper responses among Internet users. Comput Hum Behav. 2006;22(5):830–46.

    Article  Google Scholar 

  52. 52.

    Kortge R, Meade T, Tennant A. Interpersonal and intrapersonal functions of deliberate self-harm (DSH): A psychometric examination of the Inventory of Statements About Self-Injury (ISAS) scale. Behaviour Change. 2013;30(1):24–35.

    Article  Google Scholar 

  53. 53.

    Hemelrijk E, van Ballegooijen W, Donker T, van Straten A, Kerkhof A. Internet-based screening for suicidal ideation in common mental disorders. Crisis. 2012;33(4):215–21. doi:10.1027/0227-5910/a000142.

    Article  PubMed  Google Scholar 

  54. 54.

    Kline P. Handbook of psychological testing. London: Routledge; 1993.

    Google Scholar 

  55. 55.

    van Spijker BA, Batterham PJ, Calear AL, Farrer L, Christensen H, Reynolds J, et al. The suicidal ideation attributes scale (SIDAS): Community-based validation study of a new scale for the measurement of suicidal ideation. Suicide Life Threat Behav. 2014;44(4):408–19. doi:10.1111/sltb.12084.

    Article  PubMed  Google Scholar 

  56. 56.

    Hirschfeld G, von Brachel R. Multiple-Group confirmatory factor analysis in R–A tutorial in measurement invariance with continuous and ordinal indicators. Pract Assessment, Res Eval. 2014;19(7):2.

    Google Scholar 

  57. 57.

    Groves RM, Fowler Jr FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R. Survey methodology (Vol. 561). Hoboken, NJ: John Wiley & Sons; 2011.

  58. 58.

    Palmier-Claus JE, Ainsworth J, Machin M, Barrowclough C, Dunn G, Barkus E, et al. he feasibility and validity of ambulatory self-report of psychotic symptoms using a smartphone software application. BMC Psychiatry. 2012;12:172. doi:10.1186/1471-244X-12-172.

    PubMed Central  Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the research assistants who helped with study inclusion and data extraction: Sumeyye Pinar, Chrysanthi Karampetsi, Lisa Hegelmaier and Stefania Vacaru. No external funding sources were involved in this study.

Author information

Affiliations

  1. Department of Psychiatry, VU Medical Centre / GGZ inGeest, Amsterdam, Netherlands

    Wouter van Ballegooijen, Patricia van Oppen & Johannes H. Smit

  2. Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

    Wouter van Ballegooijen, Heleen Riper & Pim Cuijpers

  3. EMGO Institute for Health and Care Research, VU Medical Centre, Amsterdam, Netherlands

    Wouter van Ballegooijen, Heleen Riper, Pim Cuijpers, Patricia van Oppen & Johannes H. Smit

Authors
  1. Wouter van Ballegooijen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heleen Riper
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pim Cuijpers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patricia van Oppen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Johannes H. Smit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wouter van Ballegooijen.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

WvB, HR and JHS designed the study. WvB conducted the literature searches. WvB, JHS and PvO extracted the data. WvB drafted the manuscript. WvB, HR, PC, PvO and JHS critically revised the manuscript and approved the final version.

Additional files

Additional file 1:

PRISMA Checklist. (DOC 60 kb)

Additional file 2:

Search strings. (DOCX 18 kb)

Additional file 3:

Included studies and extracted data. (XLS 117 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

van Ballegooijen, W., Riper, H., Cuijpers, P. et al. Validation of online psychometric instruments for common mental health disorders: a systematic review. BMC Psychiatry 16, 45 (2016). https://doi.org/10.1186/s12888-016-0735-7

Download citation

Keywords

  • Internet
  • Depression
  • Anxiety
  • Measurement
  • Psychometrics
  • Systematic review

BMC Psychiatry

ISSN: 1471-244X

Contact us