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A Community-Based Validation Study of the Short-Form 36 Version 2 Philippines (Tagalog) in Two Cities in the Philippines

Nina t. castillo-carandang.

1 Department of Clinical Epidemiology, College of Medicine, and Institute of Clinical Epidemiology, National Institutes of Health, University of the Philippines (U.P.) Manila, Manila, Philippines

2 LIFEcourse study in CARdiovascular disease Epidemiology (LIFECARE) Philippines Study Group, Lipid Research Unit, U.P.-Philippine General Hospital, U.P. Manila, Manila, Philippines

Olivia T. Sison

Mary lenore grefal.

3 Cardinal Santos Medical Center, San Juan City, Metro Manila, Philippines,  

Oliver C. Alix

Elmer jasper b. llanes, paul ferdinand m. reganit, allan wilbert g. gumatay, felix eduardo r. punzalan, felicidad v. velandria, e. shyong tai.

4 Department of Medicine, Yong Loo Lin School of Medicine, National University Health System, Singapore, Singapore

Hwee-Lin Wee

5 Department of Pharmacy, National University of Singapore and Department of Rheumatology & Immunology, Singapore General Hospital, Singapore, Singapore

Conceived and designed the experiments: NTCC RGS EST HLW. Performed the experiments: NTCC OTS RGS EJBL PFMR AWGG FERP FVV MLG OCA. Analyzed the data: NTCC OTS EST HLW. Wrote the manuscript: NTCC OTS RGS EJBL PFMR AWGG FERP FVV EST HLW. Final approval of the version to be published: NTCC OTS RGS EJBL PFMR AWGG FERP FVV EST HLW.

Associated Data

To evaluate the validity and reliability of the Philippines (Tagalog) Short Form 36 Health Survey version 2 (SF-36v2 ® ) standard questionnaire among Filipinos residing in two cities.

Study Design and Setting

The official Philippines (Tagalog) SF-36v2 standard (4-week recall) version was pretested on 30 participants followed by formal and informal cognitive debriefing. To obtain the feedback on translation by bilingual respondents, each SF-36v2 question was stated first in English followed by Tagalog. No revisions to the original questionnaire were needed except that participants thought it was appropriate to incorporate " po " in the instructions to make it more polite. Face-to-face interviews of 562 participants aged 20-50 years living in two barangays (villages) in the highly urbanized city of Makati City (Metro Manila) and in urban and rural barangays in Tanauan City (province of Batangas) were subsequently conducted. Content validity, item level validity, reliability and factor structure of the SF-36v2 (Tagalog) were examined.

Content validity of the SF-36v2 was assessed to be adequate for assessing health status among Filipinos. Item means of Philippines (Tagalog) SF-36v2 were similar with comparable scales in the US English, Singapore (English and Chinese) and Thai SF-36 version 1. Item-scale correlation exceeded 0.4 for all items except the bathing item in PF (correlation: 0.31). In exploratory factor analysis, the US two-component model was supported. However, in confirmatory factor analysis, the Japanese three-component model fit the Tagalog data better than the US two-component model.

Conclusions

The Philippines (Tagalog) SF-36v2 is a valid and reliable instrument for measuring health status among residents of Makati City (Metro Manila) and Tanauan City (Province of Batangas).

Introduction

The Short Form 36 (SF-36) Health Survey is a generic instrument which assesses “functional health and well-being from the patient’s perspective”.[ 1 ] It is a 36-item questionnaire which has been translated to over 140 languages and is used globally to assess changes in health status as well as comparing the burden of illness in a population. The eight areas of perceived health in SF-36 include: Physical Functioning (PF), Role Physical (RP), Bodily Pain (BP), General Health (GH), Vitality (VT), Social Functioning (SF), Role Emotional (RE) and Mental Health (MH). The scores range from zero (0) to one hundred (100) with higher score representing better health status.

Previous clinical trials and research studies in the Philippines have used version one of the SF-36[ 2 - 4 ]. However, there is an improved version known as the SF-36 version 2 (SF-36v2) where the instructions and questionnaires items were revised to improve their clarity. In SF-36v2, the following revisions were made: (1) simpler instructions and improved layout for questions and answers to improve clarity, (2) improved phrasing of some items to provide greater comparability with translations and cultural adaptations widely-used in the U.S. and in other countries and (3) revision of response options from dichotomous to five-level response choices for the role physical and role emotional items to improve sensitivity and from six-level to five-level response choices for the vitality and mental health items for simplification[ 5 ]. As any revisions to the SF-36v1 may be expected to alter its psychometric properties, we seek to perform a socio-cultural validation of the Philippines (Tagalog) SF-36v2 among urban and rural adults living in two cities in the Philippines.

Study Design and Study Participants

This was a cross-sectional community survey of adults aged 20-50 years old. The study was done in 3 phases: pre-testing, informal and formal cognitive debriefing/interviews and a community survey. Ethics approvals were obtained from the Cardinal Santos Medical Center and from the University of the Philippines Manila. Pretesting of SF-36 was conducted among thirty (30) participants of varied backgrounds (i.e. tricycle drivers, factory workers, students, housewife, midwife and clinical researchers) in purposively chosen communities (one rural and two urban communities). The main purpose of pre-testing was to identify difficult words and phrases. Informal cognitive interviews (n= 24, all self-administered Tagalog questionnaire) were conducted during which participants were asked about their overall feedback, feelings and perceptions about the Philippines (Tagalog) SF-36v2. Formal cognitive interviews (n=36, all interviewer-administered Tagalog and US (English) questionnaire) during which respondents assessed each individual item in the questionnaire were then conducted. During formal cognitive debriefing the following were asked for each question and its associated response options: (1) Did you have difficulty understanding this question/response choice? (2) What does this question/response choice mean to you? (3) Is the question/response choice relevant to your condition? (4) How would you have worded this question/response option? (5) Is the response option consistent with the question? The following were asked for the instructions on how to answer the questionnaire: (1) Did you have difficulty understanding this instruction? (2) What does this instruction mean to you? (3) How would you have worded this instruction?

The community survey was conducted in 2 cities: (1) Makati which is the central business district of the Philippines, located 10 kilometers from the capital city of Manila; and (2) Tanauan (province of Batangas) which is 70 kilometers south of Manila. The selection of specific barangays was based on purposive sampling. Barangays were chosen with the assistance of the City Health Officers, and Planning and Development Officers of the local government units. The presence of local collaborators, accessibility, safety and security were primary considerations for choice of barangays. To the extent possible – some attempt was also made to reflect the diversity of barangays (e.g., rural and urban, major sources of household income, geographic location – lowland, coastal, upland, etc.). Makati is a 1 st class city (based on average annual income of 400 million pesos or more, approx. USD 9.1 million) with 33 barangays (113,418 households, average household size 4.5 adults or children)[ 6 ]. Tanauan, on the other hand, is a 2 nd class city (average annual income between 320 to 400 million pesos, approx. USD 7.3 million to USD 9.1 million) with 47 barangays (31,268- households, average household size of 5 adults or children). Two barangays or villages (with an estimated 11,000 households) in Makati and 8 barangays (2 in the city center and 6 in outlying areas; with approximately 10,000 households) in the city of Tanauan[ 7 ] were included in the study. Residents of Makati as well as those from the more highly urbanized barangays of Tanauan (as in other urban areas in the Philippines) mainly earned their income from employment in offices and factories and from business. However, as is typical in the Philippines, many rural residents of Tanauan derived their income from agriculture. Both cities were accessible, relatively safe and secure; and the local government unit (LGU) and local collaborators agreed to participate in the study. The participants were fluent in Filipino (Tagalog) or English and gave written informed consent. As part of a longitudinal study to evaluate the risk of developing cardiovascular diseases in the Philippines and three other Southeast Asian countries (Indonesia, Malaysia, Thailand)[ 8 ], participants who had existing cardiovascular disease as determined by participant’s medical history (previous myocardial infarction, stroke, peripheral arterial disease); had a history of malignancies (treated or otherwise); and had plans to migrate outside their community within the next 5 years were excluded.

The names of household heads were obtained from the LGUs and entered into Microsoft Excel 2007 ® and duplicates were removed. Random sampling was performed using a random number table. A total of 2,160 households were selected with 300 households for each of the 2 barangays in Makati City and 120 households for each of the 8 barangays in Tanauan City. As it was more difficult to locate potential study participants in the highly urbanized barangays of Makati, we pre-selected a larger number of households in Makati. A list of household members was generated and the Kish method was used to randomly select only one person per household[ 9 ]. Five hundred sixty-two persons (180 in Makati and 382 in Tanauan; total response rate: 35.8% among those successfully contacted, Figure 1 ) agreed to participate in the study.

The team with the assistance of the barangay health worker or any knowledgeable local resident then located the selected households to ensure that the household member was indeed living in that household and that inclusion criteria were met. If the said household member was not present during the initial visit, an appointment was made for a face-to-face interview at their convenience. A maximum of 3 visits including weeknights and weekends was done before the selected member was considered unavailable. Written informed consent was usually obtained during the initial visit while most interviews were conducted during the third visit.

The Questionnaire

In the questionnaire, each question and its corresponding response options were first presented in English and followed immediately by its Filipino (Tagalog) translation. Participants could choose between the English or Tagalog versions of the questionnaire. During the formal cognitive interview which is interviewer-administered, once the participant had decided on the language version, the interviewer would read the questionnaire in one language only. For bilingual respondents, they were asked to evaluate both English and Tagalog versions. Nonetheless, all of them chose to complete the questionnaire in Tagalog. We are aware that this is not the typical presentation of different language versions of SF-36v2 questionnaires. However, this was considered a pragmatic compromise to reduce the number of survey questionnaires that an interviewer needs to carry around. Unlike in other major cities, transportation may be challenging in certain parts of Makati and Tanauan, particularly during monsoon season when floods are common. Although the SF-36v2 is designed for self-administration, prior experience in other research projects informed us that face-to-face interviews with the use of cue cards is more appropriate due to the limitations posed by the literacy level of some respondents.

Data Confidentiality

When data was collected, each participant was identified using only a code. The files linking the code to the subject identifiers were kept in a separate file from the data. Only the Principal Investigator and the data manager have access to this linking file. All data were kept on a computer that is password protected. All other study investigators (both within and without the Philippines) were allowed access to de-identified data only, and only after written permission from the Principal Investigator was obtained.

Content Validity

Three aspects of validity were assessed and these were content validity, item level validity and construct validity. Item level validity and construct validity are described in the statistical analyses section. Content validity, also known as “content relevance” or “content coverage,” evaluates whether the questions (content domain) in a measurement tool are appropriate relative to its intended use. Clarity, comprehensiveness and redundancy of items and scales of an instrument are evaluated. There are usually no standards against which it can be measured statistically as it is based more on previous research and on lay and expert opinion.

The ultimate aim of the scale is that we can infer from the final scores and draw valid conclusions about the population that had been studied. The higher the content validity of a measure, the broader the inferences we can draw. The questions that make up the SF-36v1 have been validated in many languages and socio-cultural contexts and are deemed suitable for assessing health status. Content validity was assessed through eliciting expert opinions of the Philippine LIFECARE research team (5 cardiologists, 1 health social scientist, 1 nutritionist, 1 medical technologist) as well as feedback from lay persons during the informal and formal cognitive interviews.

Statistical Analyses

Ceiling and floor effects.

The percentage of respondents achieving maximum and minimum scores were examined as this has impact on the sensitivity and responsiveness of an instrument. For example, if a respondent has achieved the maximum possible score of 100 (ceiling), then any improvement in his/her health status cannot be picked up by the instrument. Similarly, if a respondent has achieved the lowest possible score of 0 (floor), then further deterioration of his/her health status cannot be picked up by the instrument. When the floor and ceiling effects are high, the instrument has limited value for measuring changes in health status or discriminating between respondents with small differences in health status. Based on published Singapore SF-36v1 data[ 10 ], it was hypothesized that ceiling effects would be 30-40% for PF, BP and SF and less than 5% for GH, VT and MH. It was also hypothesized that with the increase in response options from 2 to 5 on RP and RE, the observed ceiling effects of approximately 70% in the Singapore SF-36v1 data will be reduced to 30-40%, in line with other scales using the 5-level response option. Similarly, based on published Singapore English SF-36v1 data[ 10 ], it was hypothesized that floor effects would be less than 1% on all scales. In the Singapore English SF-36v1, floor effects for RP and RE were approximately 10%. The authors believed that this will reduce to approximately 1% with the revised number of response options.

Item level validity of the SF-36v2 would be supported if Likert scale scoring assumptions were fulfilled: (1) Item means and standard deviations being similar within each scale, (2) Item – scale correlations > 0.4 and of similar magnitude within each scale and (3) Successful tests of item discriminant validity (correlation between an item and its hypothesized scale being higher than the correlations between that item and other scales). Item means and standard deviation of the Philippines (Tagalog) SF-36v2 were compared with published data on four scales (PF, BP, GH and SF) from the Singapore (English)[ 10 ], Thailand[ 11 ], Japan[ 12 ] and US (English) SF-36v1[ 13 ]. The remaining four scales of SF-36v1 (RP, RE, MH and VT) were excluded from the comparison because the number of response choices increased from 2 to 5 in RP and RE while the number of response choices decreased from 6 to 5 in MH and VT in SF-36v2.

Construct validity

Both exploratory (EFA) and confirmatory factor analyses (CFA) were performed to evaluate the factor structure of SF-36v2 (Tagalog). As the names suggest, EFA is generally employed when the factor structure is unknown whereas CFA is used to confirm an a priori factor structure[ 14 ]. In EFA, principal component analysis with varimax rotation was performed and it was specified that two factors be extracted. This is in accordance with the US two-component model. In CFA, two separate models were evaluated. The first CFA model was based on the published US two-component model for the SF-36v2[ 5 ] while the second CFA was based on the published Japanese three-component model [ 15 ]. Given that the sociocultural context of the Philippines is likely to be more similar to Japan than the US, we hypothesized that the three-component model would fit the Philippines data better than the US two-component model. The following goodness-of-fit statistics were used to compare the two CFA models: Akaike information criterion (AIC), Bayesian information criterion (BIC), likelihood ratio (LR), root mean square error of approximation (RMSEA), adjusted Satorra-Bentler variance estimates, comparative fit index (CFI) and Tucker-Lewis index (TLI). With AIC, BIC, LR, RMSEA and adjusted Satorra-Bentler variance, smaller values indicate better model fit. With CFI and TLI, values closer to 0.95 or more indicate better model fit[ 16 ].

Reliability

This study measured reliability by measuring internal consistency or homogeneity (i.e., the degree to which a group of items in a domain or scale measure the same characteristic). High internal consistency connotes greater reliability of the score. It is deemed optimal for the items to be moderately correlated with each other and that each item should correlate with the total scale score. An instrument is considered reliable for measurements at the group level if Cronbach’s alpha exceeded 0.7 and reliable for measurements at the individual level if Cronbach’s alpha exceeded 0.9.

Pre-testing, Cognitive Debriefing/Interviews and Content Validity

The key take home message from pre-testing was that the SF-36v2, which was designed to be self-administered, should be interviewer-administered. There were no changes to the contents of SF-36v2. The experts and lay persons who participated in either the pre-testing or cognitive interviews all agreed that the items in the Philippines (Tagalog) SF-36v2 were mostly understandable and were relevant to a general assessment of a Filipino’s health status. In the informal Cognitive Debriefing (n=24), 42% were males and 58% were females. Most of the respondents were blue collar workers. In the formal Cognitive Debriefing (n=36), 28% were males and 72% were females. Mean (SD) age was 34 years. More than half (56%) were unemployed, 39% had some college education (but were not college graduates) and 36% were high school graduates. Sixteen of the 36 respondents (44%) felt that the questionnaire was too long.

Most of the participants in both formal and informal cognitive interviews said they were comfortable answering the SF-36v2 and gave no suggestions for re-wording them. Bilingual respondents found the translation adequate. Interestingly, many respondents expressed difficulty with the PF items. The authors’ general experience with the SF-36v2 in other countries is that respondents would have problems with RE and MH items. Nonetheless in general the Philippines respondents found the SF-36v2 items easy to understand. Respondents found it easier if the tool was interviewer-administered vs. self-administered as is the usual method of administration of SF-36v2. The questions and the responses were deemed to be easy to understand and there were no changes made except for the need to make the introduction to the questionnaire and the initial instructions sound “polite” by adding the word “ po ” so as to be culturally apt in the course of the face-to-face interview. Average time for the total interview (including demographic questions and time taken to clarify if respondents understood the questions) was 36 minutes.

Community Survey for Validation of SF-36v2 ®

Five hundred sixty-two respondents participated in the cross-sectional community survey ( Table 1 ). Majority (62%) were females; mean age for both sexes was 36 years; 64% were married; and 41% had at least college education. Thirty-eight percent were unemployed, 35% employed, 24% self-employed and the rest were retirees and students.

Ceiling and Floor Effects

As hypothesized, ceiling effects were moderate for SF (31.5%), RP (32.7%) and RE (39.2%). The remaining scales had minimal ceiling effects ranging from 2 to 19 percent, which was better than hypothesized. On the other hand, floor effects were absent in all scales except for one respondent each in SF and RE ( Table 2 ).

Item level validity

Item means and standard deviations were similar within each scale in the Philippines (Tagalog) SF-36v2 ( Table S1 ). In addition, item-scale correlation exceeded 0.4 for all except one PF item (bathing) where item-scale correlation was 0.31 ( Table S2 ). Furthermore, item-other scale correlations were poor, thus providing evidence for item discriminant validity. For example, item-other scale correlations between PF items and other scales ranged from 0.08 to 0.38 ( Table 2 ). Hence, item level validity of the Philippines (Tagalog) SF-36v2 was supported.

At the scale level, mean scores ranged from 68.09 (BP) to 83.07 (PF) ( Table 2 ). Philippine norm-based scores were not computed as this sample is not representative of the Philippines Tagalog-speaking general population. However, if the US norms[ 5 ] were applied, then this study sample has scores that are very similar to the US general population ( Table 3 ).

Ref. Ware JE. SF-36® Health Survey Update. Retrieved from http://www.sf-36.org/tools/SF36.shtml/

Item level factor analysis of the 36 items extracted eight factors, which explained 57 percent of the total variance ( Table S3 ). The first factor explained 25 percent of the total variance, while the other factors were less significant, each explaining only less than 10 percent of the total variance. In the Singapore (English) SF-36v1, item level factor analysis yielded seven factors instead of eight.

In EFA, the Philippines (Tagalog) factor structure is very similar to the Singapore (English) and Japan SF-36v1 and rather distinct from the US (English) SF-36v1 ( Table 4 ). This provided further support that the Japanese 3-component model may be a better fit to this dataset than the US two-component model. Indeed, in CFA, the Japanese 3-component model was superior with goodness of fit index being 0.933 and 0.833 for the Japanese and US models, respectively ( Table 5 ). With regards to reliability, the SF-36v2 Filipino version exhibited good internal consistency with Cronbach’s alpha coefficient exceeding the recommended value of 0.70 for all scales except GH, VT and SF ( Table 6 ).

Principal components extraction with varimax rotation. Factor loadings ≥ 0.40 were considered significant and are in bold.

h 2 = proportion of total variance of each scale explained by the two extracted components

●: Strong association (r ≥ 0.70)

◑: Moderate to substantial association (0.30 < r < 0.70)

O: Weak association (r ≤ 0.30)

† presented US SF-36v1 data as the authors did not have access to US SF-36v2 data.

Abbreviations – AIC: Akaike information criteria; BIC: Schwarz Bayesian information criteria; CFI: comparative fit index; CI: confidence interval; LR: Log likelihood ratio; RMSEA: root mean squared error of approximation; SBadj: Adjusted Satorra-Bentler variance estimates; TLI: Tucker-Lewis index

To the best of the authors’ knowledge, this is the first paper to report the reliability and validity of the Philippines (Tagalog) SF-36v2. Ceiling effect was within expected level while floor effect was almost non-existent. Content, item level and construct validity were supported. For example, item means and standard deviations of Philippines (Tagalog) SF-36v2 for PF, BP, GH and SF were highly similar to the item means and standard deviations of corresponding SF-36v1 scales from three other countries. Similar to the Singapore (English) SF-36v1, VT and MH items overlapped on two factors instead of all VT items loading onto one factor and all MH items loading onto another factor. Unlike the Singapore English SF-36v1, the Philippines (Tagalog) SF-36v2 PF items loaded onto two factors rather than one, thus generating one more factor compared to the Singapore (English) SF-36v1. In addition, it was confirmed that the Japanese three-component model better described the Philippines data compared to the US two-component model, lending further support to the need for a different model in Asia. Although the internal consistency of three Philippines (Tagalog) SF-36v2 scales were below threshold, it was noted that similar observations were made with the Singapore (English) SF-36v1[ 10 ] where Cronbach’s alpha were 0.67 and 0.58 for VT and SF, respectively. In the US English SF-36v2[ 5 ], internal consistency of SF was borderline at 0.68. This is probably because the SF scale comprises only two items. Due to insufficiently high Cronbach’s alpha, the authors cautioned on the use of GH, VT and SF scales independently but supported their use as part of Physical Component Summary Score (PCS) and Mental Component Summary Score (MCS). PCS and MCS are two summary scales derived from the eight scales of SF-36v2.

This paper is important in several ways. First, the psychometric properties of the Philippines (Tagalog) SF-36v2 have not been previously evaluated. Given that 96% of people living in the Philippines can speak Tagalog[ 17 ], this study has provided important information for a questionnaire that is likely to be used widely in the Philippines. Second, the data in this study were compared with those from the region (Singapore, Thailand and Japan) and found that the Philippines (Tagalog) SF-36v2 performed similarly with these countries. This is important for multinational clinical trials in Asia as it implies that meaningful cross-country comparisons may be made since the same concept of health status is being measured across various countries.

A potential limitation of this study is that the sample is not representative of the Tagalog-speaking general population in the Philippines. Our subjects were aged 20-50 years old. When compared to the respective population of the same age band, our sample reflects the population with regards to the highest education attained but had an over-representation of female participants and slight under-representation of married participants. However, based on the authors’ experience in conducting this study in two cities, it is likely that conducting such a population-based study will be a mammoth task requiring huge amount of resources, particularly as the Philippines is made up of more than 7,100 islands. The authors have tried to mitigate this by sampling from both rural and urban populations. In addition, the response rate may limit the authors’ ability to generalize the findings. There were two major reasons for the apparently low response rate. First, response rate was computed as a percentage of those who were successfully located. Many of those who were successfully located were at work even though the interviewers tried to visit on different times of the day or both weekdays and weekends. If the response rate was computed as a percentage of those who were successfully located and eligible, then it will be a high response rate of 70.8%. It should be pointed out that upfront refusal was only 8.4% among those successfully located. The second reason was that the eventual sample size was based on a combination of willingness to be interviewed as well as availability for physical examination. 100 of those who were willing to be interviewed were not available for physical examination due to their work schedule. However, the authors believe that the response rate is comparable to or even better than other similar door-to-door surveys. In the Global Study of Sexual Attitudes and Behaviors which includes the Philippines as a study site, the mean overall response rate was 19%, and ranged from 8–55% in the various countries[ 18 ].

In conclusion, the results of this study support the reliability and validity of the SF-36v2 Philippines (Tagalog) for assessing health status among Tagalog-speaking urban and rural adults aged 20-50 years old in Makati and Tanauan.

Supporting Information

Item means and standard deviations (SD).

Spearman item-scale correlations of the Philippines (Tagalog) SF-36v2.

Item level factor analysis of the Philippines (Tagalog) SF-36v2.

Acknowledgments

We would like to thank Alice Sun-Cua, MD, MSc for her assistance in preparing the initial draft of the article; Tina Saban, MD; Anna Kristina M. Gutierrez, MD; Christine Gisel W. Chu, MD; Daphne Joyce R. Demetria, MD; and the staff of LIFECARE Philippines for their help in conducting the field work for the validation study.

Funding Statement

The authors would like to acknowledge seed funding from Pfizer Pte Ltd Investigator Initiated Grant for the LIFECARE study which began in 2007. Pfizer Pte Ltd and Pfizer Philippine had no input on study design, execution, analysis, interpretation and writing of the manuscript. The authors would also like to acknowledge their academic sponsors. The LIFECARE Philippines study team raised additional funds from government agencies (Department of Health, Philippine Council for Health Research and Development) and professional medical associations in the Philippines (Diabetes Philippines, Philippine Society of Hypertension, and the Philippine Lipid and Atherosclerosis Society). The authors would also like to acknowledge the in-kind contribution of University of the Philippines Manila for providing office space and utilities.

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Home » Validity – Types, Examples and Guide

Validity – Types, Examples and Guide

Table of Contents

Definition:

Validity refers to the extent to which a concept, measure, or study accurately represents the intended meaning or reality it is intended to capture. It is a fundamental concept in research and assessment that assesses the soundness and appropriateness of the conclusions, inferences, or interpretations made based on the data or evidence collected.

Research Validity

Research validity refers to the degree to which a study accurately measures or reflects what it claims to measure. In other words, research validity concerns whether the conclusions drawn from a study are based on accurate, reliable and relevant data.

Validity is a concept used in logic and research methodology to assess the strength of an argument or the quality of a research study. It refers to the extent to which a conclusion or result is supported by evidence and reasoning.

How to Ensure Validity in Research

Ensuring validity in research involves several steps and considerations throughout the research process. Here are some key strategies to help maintain research validity:

Clearly Define Research Objectives and Questions

Start by clearly defining your research objectives and formulating specific research questions. This helps focus your study and ensures that you are addressing relevant and meaningful research topics.

Use appropriate research design

Select a research design that aligns with your research objectives and questions. Different types of studies, such as experimental, observational, qualitative, or quantitative, have specific strengths and limitations. Choose the design that best suits your research goals.

Use reliable and valid measurement instruments

If you are measuring variables or constructs, ensure that the measurement instruments you use are reliable and valid. This involves using established and well-tested tools or developing your own instruments through rigorous validation processes.

Ensure a representative sample

When selecting participants or subjects for your study, aim for a sample that is representative of the population you want to generalize to. Consider factors such as age, gender, socioeconomic status, and other relevant demographics to ensure your findings can be generalized appropriately.

Address potential confounding factors

Identify potential confounding variables or biases that could impact your results. Implement strategies such as randomization, matching, or statistical control to minimize the influence of confounding factors and increase internal validity.

Minimize measurement and response biases

Be aware of measurement biases and response biases that can occur during data collection. Use standardized protocols, clear instructions, and trained data collectors to minimize these biases. Employ techniques like blinding or double-blinding in experimental studies to reduce bias.

Conduct appropriate statistical analyses

Ensure that the statistical analyses you employ are appropriate for your research design and data type. Select statistical tests that are relevant to your research questions and use robust analytical techniques to draw accurate conclusions from your data.

Consider external validity

While it may not always be possible to achieve high external validity, be mindful of the generalizability of your findings. Clearly describe your sample and study context to help readers understand the scope and limitations of your research.

Peer review and replication

Submit your research for peer review by experts in your field. Peer review helps identify potential flaws, biases, or methodological issues that can impact validity. Additionally, encourage replication studies by other researchers to validate your findings and enhance the overall reliability of the research.

Transparent reporting

Clearly and transparently report your research methods, procedures, data collection, and analysis techniques. Provide sufficient details for others to evaluate the validity of your study and replicate your work if needed.

Types of Validity

There are several types of validity that researchers consider when designing and evaluating studies. Here are some common types of validity:

Internal Validity

Internal validity relates to the degree to which a study accurately identifies causal relationships between variables. It addresses whether the observed effects can be attributed to the manipulated independent variable rather than confounding factors. Threats to internal validity include selection bias, history effects, maturation of participants, and instrumentation issues.

External Validity

External validity concerns the generalizability of research findings to the broader population or real-world settings. It assesses the extent to which the results can be applied to other individuals, contexts, or timeframes. Factors that can limit external validity include sample characteristics, research settings, and the specific conditions under which the study was conducted.

Construct Validity

Construct validity examines whether a study adequately measures the intended theoretical constructs or concepts. It focuses on the alignment between the operational definitions used in the study and the underlying theoretical constructs. Construct validity can be threatened by issues such as poor measurement tools, inadequate operational definitions, or a lack of clarity in the conceptual framework.

Content Validity

Content validity refers to the degree to which a measurement instrument or test adequately covers the entire range of the construct being measured. It assesses whether the items or questions included in the measurement tool represent the full scope of the construct. Content validity is often evaluated through expert judgment, reviewing the relevance and representativeness of the items.

Criterion Validity

Criterion validity determines the extent to which a measure or test is related to an external criterion or standard. It assesses whether the results obtained from a measurement instrument align with other established measures or outcomes. Criterion validity can be divided into two subtypes: concurrent validity, which examines the relationship between the measure and the criterion at the same time, and predictive validity, which investigates the measure’s ability to predict future outcomes.

Face Validity

Face validity refers to the degree to which a measurement or test appears, on the surface, to measure what it intends to measure. It is a subjective assessment based on whether the items seem relevant and appropriate to the construct being measured. Face validity is often used as an initial evaluation before conducting more rigorous validity assessments.

Importance of Validity

Validity is crucial in research for several reasons:

• Accurate Measurement: Validity ensures that the measurements or observations in a study accurately represent the intended constructs or variables. Without validity, researchers cannot be confident that their results truly reflect the phenomena they are studying. Validity allows researchers to draw accurate conclusions and make meaningful inferences based on their findings.
• Credibility and Trustworthiness: Validity enhances the credibility and trustworthiness of research. When a study demonstrates high validity, it indicates that the researchers have taken appropriate measures to ensure the accuracy and integrity of their work. This strengthens the confidence of other researchers, peers, and the wider scientific community in the study’s results and conclusions.
• Generalizability: Validity helps determine the extent to which research findings can be generalized beyond the specific sample and context of the study. By addressing external validity, researchers can assess whether their results can be applied to other populations, settings, or situations. This information is valuable for making informed decisions, implementing interventions, or developing policies based on research findings.
• Sound Decision-Making: Validity supports informed decision-making in various fields, such as medicine, psychology, education, and social sciences. When validity is established, policymakers, practitioners, and professionals can rely on research findings to guide their actions and interventions. Validity ensures that decisions are based on accurate and trustworthy information, which can lead to better outcomes and more effective practices.
• Avoiding Errors and Bias: Validity helps researchers identify and mitigate potential errors and biases in their studies. By addressing internal validity, researchers can minimize confounding factors and alternative explanations, ensuring that the observed effects are genuinely attributable to the manipulated variables. Validity assessments also highlight measurement errors or shortcomings, enabling researchers to improve their measurement tools and procedures.
• Progress of Scientific Knowledge: Validity is essential for the advancement of scientific knowledge. Valid research contributes to the accumulation of reliable and valid evidence, which forms the foundation for building theories, developing models, and refining existing knowledge. Validity allows researchers to build upon previous findings, replicate studies, and establish a cumulative body of knowledge in various disciplines. Without validity, the scientific community would struggle to make meaningful progress and establish a solid understanding of the phenomena under investigation.
• Ethical Considerations: Validity is closely linked to ethical considerations in research. Conducting valid research ensures that participants’ time, effort, and data are not wasted on flawed or invalid studies. It upholds the principle of respect for participants’ autonomy and promotes responsible research practices. Validity is also important when making claims or drawing conclusions that may have real-world implications, as misleading or invalid findings can have adverse effects on individuals, organizations, or society as a whole.

Examples of Validity

Here are some examples of validity in different contexts:

• Example 1: All men are mortal. John is a man. Therefore, John is mortal. This argument is logically valid because the conclusion follows logically from the premises.
• Example 2: If it is raining, then the ground is wet. The ground is wet. Therefore, it is raining. This argument is not logically valid because there could be other reasons for the ground being wet, such as watering the plants.
• Example 1: In a study examining the relationship between caffeine consumption and alertness, the researchers use established measures of both variables, ensuring that they are accurately capturing the concepts they intend to measure. This demonstrates construct validity.
• Example 2: A researcher develops a new questionnaire to measure anxiety levels. They administer the questionnaire to a group of participants and find that it correlates highly with other established anxiety measures. This indicates good construct validity for the new questionnaire.
• Example 1: A study on the effects of a particular teaching method is conducted in a controlled laboratory setting. The findings of the study may lack external validity because the conditions in the lab may not accurately reflect real-world classroom settings.
• Example 2: A research study on the effects of a new medication includes participants from diverse backgrounds and age groups, increasing the external validity of the findings to a broader population.
• Example 1: In an experiment, a researcher manipulates the independent variable (e.g., a new drug) and controls for other variables to ensure that any observed effects on the dependent variable (e.g., symptom reduction) are indeed due to the manipulation. This establishes internal validity.
• Example 2: A researcher conducts a study examining the relationship between exercise and mood by administering questionnaires to participants. However, the study lacks internal validity because it does not control for other potential factors that could influence mood, such as diet or stress levels.
• Example 1: A teacher develops a new test to assess students’ knowledge of a particular subject. The items on the test appear to be relevant to the topic at hand and align with what one would expect to find on such a test. This suggests face validity, as the test appears to measure what it intends to measure.
• Example 2: A company develops a new customer satisfaction survey. The questions included in the survey seem to address key aspects of the customer experience and capture the relevant information. This indicates face validity, as the survey seems appropriate for assessing customer satisfaction.
• Example 1: A team of experts reviews a comprehensive curriculum for a high school biology course. They evaluate the curriculum to ensure that it covers all the essential topics and concepts necessary for students to gain a thorough understanding of biology. This demonstrates content validity, as the curriculum is representative of the domain it intends to cover.
• Example 2: A researcher develops a questionnaire to assess career satisfaction. The questions in the questionnaire encompass various dimensions of job satisfaction, such as salary, work-life balance, and career growth. This indicates content validity, as the questionnaire adequately represents the different aspects of career satisfaction.
• Example 1: A company wants to evaluate the effectiveness of a new employee selection test. They administer the test to a group of job applicants and later assess the job performance of those who were hired. If there is a strong correlation between the test scores and subsequent job performance, it suggests criterion validity, indicating that the test is predictive of job success.
• Example 2: A researcher wants to determine if a new medical diagnostic tool accurately identifies a specific disease. They compare the results of the diagnostic tool with the gold standard diagnostic method and find a high level of agreement. This demonstrates criterion validity, indicating that the new tool is valid in accurately diagnosing the disease.

Where to Write About Validity in A Thesis

In a thesis, discussions related to validity are typically included in the methodology and results sections. Here are some specific places where you can address validity within your thesis:

Research Design and Methodology

In the methodology section, provide a clear and detailed description of the measures, instruments, or data collection methods used in your study. Discuss the steps taken to establish or assess the validity of these measures. Explain the rationale behind the selection of specific validity types relevant to your study, such as content validity, criterion validity, or construct validity. Discuss any modifications or adaptations made to existing measures and their potential impact on validity.

Measurement Procedures

In the methodology section, elaborate on the procedures implemented to ensure the validity of measurements. Describe how potential biases or confounding factors were addressed, controlled, or accounted for to enhance internal validity. Provide details on how you ensured that the measurement process accurately captures the intended constructs or variables of interest.

Data Collection

In the methodology section, discuss the steps taken to collect data and ensure data validity. Explain any measures implemented to minimize errors or biases during data collection, such as training of data collectors, standardized protocols, or quality control procedures. Address any potential limitations or threats to validity related to the data collection process.

Data Analysis and Results

In the results section, present the analysis and findings related to validity. Report any statistical tests, correlations, or other measures used to assess validity. Provide interpretations and explanations of the results obtained. Discuss the implications of the validity findings for the overall reliability and credibility of your study.

Limitations and Future Directions

In the discussion or conclusion section, reflect on the limitations of your study, including limitations related to validity. Acknowledge any potential threats or weaknesses to validity that you encountered during your research. Discuss how these limitations may have influenced the interpretation of your findings and suggest avenues for future research that could address these validity concerns.

Applications of Validity

Validity is applicable in various areas and contexts where research and measurement play a role. Here are some common applications of validity:

Psychological and Behavioral Research

Validity is crucial in psychology and behavioral research to ensure that measurement instruments accurately capture constructs such as personality traits, intelligence, attitudes, emotions, or psychological disorders. Validity assessments help researchers determine if their measures are truly measuring the intended psychological constructs and if the results can be generalized to broader populations or real-world settings.

Educational Assessment

Validity is essential in educational assessment to determine if tests, exams, or assessments accurately measure students’ knowledge, skills, or abilities. It ensures that the assessment aligns with the educational objectives and provides reliable information about student performance. Validity assessments help identify if the assessment is valid for all students, regardless of their demographic characteristics, language proficiency, or cultural background.

Program Evaluation

Validity plays a crucial role in program evaluation, where researchers assess the effectiveness and impact of interventions, policies, or programs. By establishing validity, evaluators can determine if the observed outcomes are genuinely attributable to the program being evaluated rather than extraneous factors. Validity assessments also help ensure that the evaluation findings are applicable to different populations, contexts, or timeframes.

Medical and Health Research

Validity is essential in medical and health research to ensure the accuracy and reliability of diagnostic tools, measurement instruments, and clinical assessments. Validity assessments help determine if a measurement accurately identifies the presence or absence of a medical condition, measures the effectiveness of a treatment, or predicts patient outcomes. Validity is crucial for establishing evidence-based medicine and informing medical decision-making.

Social Science Research

Validity is relevant in various social science disciplines, including sociology, anthropology, economics, and political science. Researchers use validity to ensure that their measures and methods accurately capture social phenomena, such as social attitudes, behaviors, social structures, or economic indicators. Validity assessments support the reliability and credibility of social science research findings.

Market Research and Surveys

Validity is important in market research and survey studies to ensure that the survey questions effectively measure consumer preferences, buying behaviors, or attitudes towards products or services. Validity assessments help researchers determine if the survey instrument is accurately capturing the desired information and if the results can be generalized to the target population.

Limitations of Validity

Here are some limitations of validity:

• Construct Validity: Limitations of construct validity include the potential for measurement error, inadequate operational definitions of constructs, or the failure to capture all aspects of a complex construct.
• Internal Validity: Limitations of internal validity may arise from confounding variables, selection bias, or the presence of extraneous factors that could influence the study outcomes, making it difficult to attribute causality accurately.
• External Validity: Limitations of external validity can occur when the study sample does not represent the broader population, when the research setting differs significantly from real-world conditions, or when the study lacks ecological validity, i.e., the findings do not reflect real-world complexities.
• Measurement Validity: Limitations of measurement validity can arise from measurement error, inadequately designed or flawed measurement scales, or limitations inherent in self-report measures, such as social desirability bias or recall bias.
• Statistical Conclusion Validity: Limitations in statistical conclusion validity can occur due to sampling errors, inadequate sample sizes, or improper statistical analysis techniques, leading to incorrect conclusions or generalizations.
• Temporal Validity: Limitations of temporal validity arise when the study results become outdated due to changes in the studied phenomena, interventions, or contextual factors.
• Researcher Bias: Researcher bias can affect the validity of a study. Biases can emerge through the researcher’s subjective interpretation, influence of personal beliefs, or preconceived notions, leading to unintentional distortion of findings or failure to consider alternative explanations.
• Ethical Validity: Limitations can arise if the study design or methods involve ethical concerns, such as the use of deceptive practices, inadequate informed consent, or potential harm to participants.

Also see  Reliability Vs Validity

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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