content validity in research methodology

Content Validity

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• Shayna Rusticus 3  

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Content validity refers to the degree to which an assessment instrument is relevant to, and representative of, the targeted construct it is designed to measure.

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Content validation, which plays a primary role in the development of any new instrument, provides evidence about the validity of an instrument by assessing the degree to which the instrument measures the targeted construct (Anastasia, 1988 ). This enables the instrument to be used to make meaningful and appropriate inferences and/or decisions from the instrument scores given the assessment purpose (Messick, 1989 ; Moss, 1995 ). All elements of the instrument (e.g., items, stimuli, codes, instructions, response formats, scoring) that can potentially impact the scores obtained and the interpretations made should be subjected to content validation. There are three key aspects of content validity: domain definition, domain representation, and domain relevance (Sireci, 1998a ). The first aspect, domain...

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Rusticus, S. (2014). Content Validity. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_553

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Design and Implementation Content Validity Study: Development of an instrument for measuring Patient-Centered Communication

Vahid zamanzadeh.

1 Department of Medical-Surgical Nursing, Faculty of Nursing and Midwifery, Tabriz University of Medical Sciences, Tabriz, Iran

Akram Ghahramanian

Maryam rassouli.

2 Department of Pediatrics Nursing, Faculty of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Abbas Abbaszadeh

Hamid alavi-majd.

3 Department of Biostatistics, Faculty of Para Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Ali-Reza Nikanfar

4 Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

Introduction: The importance of content validity in the instrument psychometric and its relevance with reliability, have made it an essential step in the instrument development. This article attempts to give an overview of the content validity process and to explain the complexity of this process by introducing an example.

Methods: We carried out a methodological study conducted to examine the content validity of the patient-centered communication instrument through a two-step process (development and judgment). At the first step, domain determination, sampling (item generation) and instrument formation and at the second step, content validity ratio, content validity index and modified kappa statistic was performed. Suggestions of expert panel and item impact scores are used to examine the instrument face validity.

Results: From a set of 188 items, content validity process identified seven dimensions includes trust building (eight items), informational support (seven items), emotional support (five items), problem solving (seven items), patient activation (10 items), intimacy/friendship (six items) and spirituality strengthening (14 items). Content validity study revealed that this instrument enjoys an appropriate level of content validity. The overall content validity index of the instrument using universal agreement approach was low; however, it can be advocated with respect to the high number of content experts that makes consensus difficult and high value of the S-CVI with the average approach, which was equal to 0.93.

Conclusion: This article illustrates acceptable quantities indices for content validity a new instrument and outlines them during design and psychometrics of patient-centered communication measuring instrument.

Introduction

In most studies, researchers study complex constructs for which valid and reliable instruments are needed. 1 Validity, which is defined as the ability of an instrument to measure the properties of the construct under study, 2 is a vital factor in selecting or applying an instrument. It is determined as its three common forms including content, construct, and criterion-related validity. 3 Since content validity is a prerequisite for other validity, it should receive the highest priority during instrument development. Validity is not the property of an instrument, but the property of the scores achieved by an instrument used for a specific purpose on a special group of respondents. Therefore, validity evidence should be obtained on each study for which an instrument is used. 4

Content validity, also known as definition validity and logical validity, 5 can be defined as the ability of the selected items to reflect the variables of the construct in the measure. This type of validity addresses the degree to which items‏ of‏ an instrument sufficiently represent the content domain. It also answers the question that to what extent the selected sample in an instrument or instrument‏ items‏ is a comprehensive sample of the content. 1 , 6 - 8 This type validity provides the preliminary evidence on construct validity of an instrument‏. 9 In addition, it can provide information on the representativeness and clarity of items and help improve an instrument through achieving recommend- dations from‏ an expert panel‏. 6 , 10 If an instrument lacks content validity, It is impossible to establish reliability for it‏. 11 On the other hand although more resources should be spent for a content validity study initially, it decreases the need for resources in the future reviews of an instrument during psychometric process‏. 1

Despite the fact that in instrument development, content validity is a critical step 12 and a trigger mechanism to link abstract concepts to visible and measurable indices, 7 it is studied superficially and transiently. This problem might be due the fact that the methods used to assess content validity in medical research literature are not referred to profoundly‏ 12 and sufficient details have rarely been provided on content validity process in a single resource. 13 It is possible that students do not realize complexities in this critical process. 12 Meanwhile, a number of experts have questioned historical legitimacy of content validity as a real type of validity. 14 - 16 These challenges about value and merit of content validity have arisen from lack of distinction between content validity and face validity, un-standardized mechanisms to determine content validity and the previously its un-quantified nature. 3 This article aims to discuss on the content validity process, to train quantifying of it with a example instrument. This is designed to measure the patient-centered communication between the patients with cancer and nurses as a key member of the health care providers in oncology wards of Iran.

Nurse-patient communication

For improving patients’ outcomes, nurses cannot perform health services such as physical cares, emotional support and exchanging information with their patients without establishing a relationship with them. 17 During recent decades, patient-centered communication was defined as a communication in which patients’ viewpoints are actively sought by a treatment team, 18 a relationship with patients, based on trust, respect, and reciprocity, and with mutually negotiated goals and expectations that can be an important support and buffer for cancer patients experiencing distress. 19

Communication serves to build and maintain this relationship, to transmit information, to provide support, and to make treatment decisions. Although patient-centered communication between providers and cancer patients can significantly affect clinical outcomes 20 and as an important element improves patient satisfaction, treatment compliance, and health outcomes, 21 , 22 however, recent evidence demonstrates that communication in cancer care may often be suboptimal, particularly with regard to the emotional experience of the patient. 23

Despite the public acceptance, there is little consensus on the meaning and operationalization of the concept of patient-centered communication, 19 , 24 so that a serious limitation is caused by lack of standard instruments to review and promote patient-centeredness in patient-healthcare communication. Part of this issue is related to the extended nature of patient-centeredness construct that has led to creating different and almost dissimilar instruments caused by researchers’ conceptualization and psychometrics. 25 Few‏ instruments can provide a comprehensive definition of this concept in cancer care and in a single tool. 26 Whereas, reviewing the literature in Iran shows that this concept has never been studied in the form of a research study. As one of the research priorities is to conduct research on cancer, 27 no quantitative and qualitative study has been carried out and no instrument has been made yet.

It is obvious that evaluating abilities of nurses in oncology wards to establish a patient-centered communication and its consequences require application of a reliable‏ instrument based on the context and culture of the target group. 26 When a new instrument is designed, measurement and report of its content validity have fundamental importance. 8 Therefore, this study was conducted to design and to examine content validity of the instrument measuring patient-centered communication in oncology wards in northwest of Iran.

Materials and methods

This methodological study is part of a larger study carried out through the exploratory mixed method research (qualitative-quantitative) to design and psychometrics the instrument measuring patient-centered communication in oncology wards in northwest of Iran. Data in the qualitative phase of study with qualitative content analysis approach was collected by semi-structured in-depth interview with 10 patients with cancer, three family members and seven oncology nurses in the Ali-Nasab and Shahid Ayatollah Qazi‏ Tabatabai‏ Hos- pitals of Tabriz and in the quantities phase of study, during a two-step process (design – judgment), the qualitative and quantities viewpoints of 15 experts were collected. 3

Ethical considerations such as approval of the ethic committee of Tabriz University of Medical Sciences, Permissions of administrators of Ali-Nasab and Shahid Ayatollah Qazi‏ Tabatabai‏ Hospitals, anonymity, informed consent, withdrawal from the study, and recording permission was respected.

Stage 1: Instrument Design

Instrument design is performed through three-‏ steps process, including determining content domain, sampling from content (item generation) and instrument construction. 11 , 14 the first step is determining the content domain of a construct that the instrument is made to measure it. Content domain is the content area related to the variables that being‏ measured. 28 It can be identified by literature review on the topic being measured, interviewing with the respondents and focus groups. Through a precise definition on the attributes and characteristics of the desired construct, a clear image of its boundaries, dimensions, and components is obtained. The qualitative research methods can also be applied to determine the variables and concepts of the pertinent construct. 29 The qualitative data collected in the interview with the respondents familiar with concept help enrich and develop what has been identified on concept, and are considered as an invaluable resource to generate instrument items. 30 To determine content domain in emotional instruments and cognitive instruments, we can use literature review and table of specifications, respectively. 3 In practice, table of specifications reviews alignment of a set of items (placed in rows) with the concepts forming the construct under study (placed in columns) through collecting quantitative and qualitative evidence from experts and by analyzing‏ data. 5 Ridenour and Newman also introduced the application of mixed method‏ (deductive- inductive) for conceptualization at the step of content domain determination and items generation. 31 However, generating items requires a preliminary task to determine the content domain an constract. 32 In addition, a useful approach would consists of returning to research questions and ensuring that the instrument items are reflect of and relevant to research questions. 33

Instrument construction is the third step in instrument design in which the items are refined and organized in a suitable format and sequence so that the finalized items are collected in a usable form. 3

Stage2: Judgment

This step entails‏ confirmation‏ by a specific number of experts, indicating that instrument items and the entire instrument have content validity. For this purpose, an expert panel is appointed. Determining the number of experts has always been partly arbitrary. At least five people are recommended to have sufficient control over chance agreement. The maximum number of judges has not been determined yet; however, it is unlikely that more than 10 people are used. Anyway, as the number of experts increases, the probability of chance agreement decreases. After determining an expert panel, we can collect and analyze their quantitative and qualitative viewpoints on the relevancy or representativeness, clarity and comprehend- siveness of the items to measure the construct operationally‏ defined by these items to ensure the content validity of the instrument. 3 , 7 , 8

Quantification of Content Validity

The content validity of instrument can be determined using the viewpoints of the panel of experts. This panel consists of content experts and lay experts. Lay experts are the potential research subjects and content experts are professionals who have research experience or work in the field. 34 Using subjects of the target group as expert ensures that the population for whom the instrument is being developed is represented 1

In qualitative content validity‏ method, content‏ experts and target group’s recommendations are adopted on observing grammar, using appropriate and correct words, applying correct and proper order of words in items and appropriate scoring. 35 However, in‏ the quantitative content validity method, confidence is maintained in selecting the most important and correct content in an‏ instrument, which is quantified by content validity‏ ratio (CVR). In this way, the experts are requested to specify whether an item is necessary for operating a construct in a set of items or not. To this end, they are requested to score each item from 1 to 3 with a three-degree range of “ not necessary,useful but not essential,essential ”‏ respectively. Content validity ratio varies between 1 and -1. The higher score indicates further agreement of members of panel on the necessity of an item in an instrument. The formula of content validity ratio is CVR=(N e - N/2)/(N/2), in which the N e is the number of panelists indicating "essential" and N is the total number of panelists. The numeric value of content validity ratio is determined by Lawshe Table. For example, in our study that is number of panelists 15 members,‏ if‏ CVR is bigger than 0.49, the item in the instrument with an acceptable level of significance‏ will‏ be‏ accepted. 36

In reports of instrument development, the most widely reported approach for content validity is the content validity index. 3 , 34 , 37 Panel members is asked to rate instrument items in terms of clarity and its relevancy to the construct underlying study as per the theoretical definitions of the construct itself and its dimensions on a 4-point ordinal scale (1[not relevant], 2[somewhat relevant], 3[quite relevant], 4[highly relevant]). 34 A table like the one shown below( Table 1 ) was added to the cover letter to guide experts for scoring method.

To obtain content validity index for relevancy and clarity of each item (I-CVIs), the number of those judging the item as relevant or clear (rating 3 or 4) was divided by the number of content experts but for relevancy, content validity index can be calculated both for item level (I-CVIs) and the scale-level (S-CVI). In item level, I-CVI is computed as the number of experts giving a rating 3 or 4 to the relevancy of each item, divided by the total number of experts.

The I-CVI expresses the proportion of agreement on the relevancy of each item, which is between zero and one 3 , 38 and the SCVI is defined as “the proportion of total items judged content valid” 3 or “the proportion of items on an instrument that achieved a rating of 3 or 4 by the content experts”. 28

Although instrument developers almost never give report what method have used to computing the scale-level index of an instrument (S-CVI). 6 There are two methods for calculating it, One method requires u niversal a greement among experts (S-CVI/ UA ), but a less conservative method is ave rages the item-level CVIs (S-CVI/Ave). For calculating them, first, the scale is dichotomized by combining values 3 and 4 together and 2 and 1 together and two dichotomous categories of responses including “ relevant and not relevant ” are formed for each item. 3 , 34 Then, in the universal agreement approach, the number of items considered relevant by all the judges (or number of items with CVI equal to 1) is divided by the total number of items. In the average approach, the sum of I-CVIs is divided by the total number of items. 10 Table 2 provides data for better understanding on calculation CVI and S-CVI by both methods. Data of table has been extracted from judges of our panel about relevancy items of dimension of trust building as a variable (subscale) in measuring construct of patient-centered communication. As the values obtained from both methods might be different, instrument makers should mention the method used for calculating it. 6 Davis proposes that researchers should consider 80 percent agreement or higher among judges for new instruments. 34 Judgment on each item is made as follows: If the I-CVI is higher than 79 percent, the item will be appropriate. If it is between 70 and 79 percent, it needs revision. If it is less than 70 percent, it is eliminated. 39

Number of items considered relevant by all the panelists=3, Number of terms=9, S-CVI/Ave *** or Average of I-CVIs=0.872, S-CVI/UA ** =3/9=.333NOTE: * Item-Content Validity Items, ** Scale-Content Validity Item/Universal agreement, *** Scale-Content Validity Item/Average Number of experts=14, Interpretation of I-CVIs: If the I-CVI is higher than 79 percent, the item will be appropriate. If it is between 70 and 79 percent, it needs revision. If it is less than 70 percent, it is eliminated.

Although content validity index is extensively used to estimate content validity by researchers, this index does not consider the possibility of inflated values because of the chance agreement. Therefore, Wynd‏ et al .,‏ propose both content validity index and multi-rater kappa statistic in content validity study‏ because, unlike the CVI, it adjusts for chance agreement. Chance agreement is an issue of concern while studying agreement indices among assessors, especially when we place four-point scoring within two relevant and not relevant classes. 7 In other words, kappa statistic is a consensus index of inter-rater agreement that adjusts for chance agreement 10 and is an important supplement to CVI because Kappa provides information about degree of agreement beyond chance. 7 Nevertheless, content validity index is mostly used by researchers because it is simple for calculation, easy to understand and provide information about each item, which can be used for modification or deletion of instrument items. 6 , 10

To calculate modified kappa statistic, the probability of chance agreement was first calculated for each item by following formula:

P C = [N! /A! (N -A)!]* . 5 N .

In this formula, N= number of experts in a panel and A= number of panelists who agree that the item is relevant.

After calculating I-CVI for all instrument items, finally, kappa was computed by entering the numerical values of probability of chance agreement (P C ) and content validity index of each item (I-CVI) in following formula:

K= (I-CVI - P C ) / (1- P C ).

Evaluation criteria for kappa is the values above 0.74, between 0.60 and 0.74, and the ones between 0.40 and 0.59 are considered as excellent, good, and fair, respectively. 40

Polit states that after controlling items by calculating adjusted kappa, each item with I-CVI equal or higher than 0.78 would be considered excellent. Researchers should note that, as the number of experts in panel increases, the probability of chance agreement diminishes and values of I-CVI and kappa converge. 10

Requesting panel members to evaluate instrument in terms of comprehensiveness would be the last step of measuring the content validity. The panel members are requested to judge whether instrument items and any of its dimensions are a complete and comprehensive sample of content as far as the theoretical definitions of concepts and its dimensions are concerned. Is it needed to eliminate or add any item? According to members’ judgment, proportion of agreement is calculated for the comprehensiveness of each dimension and the entire instrument. In so doing, the number of experts who have identified instrument comprehensiveness as favorable is divided into the total number of experts. 3 , 37

Determining face validity of an instrument

Face validity answers this question whether an instrument apparently has validity for subjects, patients and/or other participants. Face validity means if the designed instrument is apparently related to the construct underlying study. Do participants agree with items and wording of them in an instrument to realize research objectives? Face validity is related to the appearance and apparent attractiveness of an instrument, which may affect the instrument acceptability by respondents. 11 In principle, face validity is not considered as validity as far as measurement principles are concerned. In fact, it does not consider what to measure, but it focuses on the appearance of instrument. 9 To determine face validity of an instrument, researchers use respondents and experts’ viewpoints. In the qualitative method, face-to-face interviews are carried out with some members of the target groups. Difficulty level of items, desired suitability and relationship between items and the main objective of an instrument, ambiguity and misinterpretations of items, and/or incomprehensibility of the meaning of words are the issues discussed in the interviews. 41

Although content experts play a vital role in content validity, instrument review by a sample of subjects drawn from the target population is another important component of content validation. These individuals are asked to review instrument items because of their familiarity with the construct through direct personal experience. 37 Also they will be asked to identify the items they thought are the most important for them, and grade their importance on a 5-point Likert scale including very important 5 , important 4 , 2 relatively important 3 , slightly important 2 , and unimportant. In quantities method, for calculation item impact score, the first is calculated percent of patients who scored 4 or 5 to item importance (frequency), and the mean importance score of item (importance) and then item impact score of instrument items was calculated by following formula: Item Impact Score= frequency×Importance

If the item impact of an item is equal to or greater than 1.5 (which corresponds to a mean frequency of 50% and an importance mean of 3 on the 5-point Likert scale), it is maintained in the instrument; otherwise it is eliminated. 42

Results of stage1: Designing patient-centered communication measuring instrument

In the one step of our research, which was performed through qualitative content analysis by semi-structured in-depth interview with ten patients with cancer, three family members and seven oncology nurses, the results led to identifying content domain within seven dimensions including trust building, intimacy or friendship, patient activation, problem solving, emotional support, informational support, and spiritual strengthening. Each of these content domains was defined theoretically by combining qualitative study and literature review. In the item generation step, 260 items were generated from these dimensions and they were combined with 32 items obtained from literature and the related instruments. In research group, the items were studied in terms of overlapping and duplication. Finally, 188 items remained for the operational definition of the construct of patient-centered communication, and the preliminary instrument was made by 188 items (pool items) within seven dimensions.

Results of stage 2: Judgment of expert panel on validity of patient-centered communic- ation measuring instrument

In the second step and after selecting fifteen content experts including the instrument developer experts (four people), cancer research experts (four people),nurse-patient communication experts (three people) and four nurses experienced in cancer care, an expert panel was created for making quantitative and qualitative judgments on instrument items. The panel members were requested thrice to judge on content validity ratio, content validity index, and instrument comprehensiveness. In each round, they were requested to judge on face validity of instrument as well. In each round of correspondences via e-mail or in person, a letter of request was presented, which included study objectives and an account on instrument, scoring method, and required instructions on responding. Theoretical definitions of the construct underlying study, its dimensions, and items of each dimension were also mentioned in that letter. In case no reply was received for the reminder e-mail within a week, a telephone conversation would be made or a meeting would be arranged.

In the first round of judgment, 108 items out of 188 instrument items were eliminated. These eliminated items had content validity ratio lower than 0.49, (according to the expert numbers in our study that was 15, numerical values of the Lawshe table was 0.49) or those which combined to remained items based on the opinion of content experts through editing of item. Table 3 shows a sample of instrument items and CVR calculation method for them.

NOTE: * Number of experts evaluated the item essential, ** CVR or Content Validity Ratio = (N e -N/2)/(N/2) with 15 person at the expert panel (N=15), the items with the CVR bigger than 0.49 remained at the instrument and the rest eliminated.

The remaining items were modified according to the recommendations of panel members in the first round of judgment and for a second time to determine content validity index and instrument modification, the panel members were requested to judge by scoring 1 to 4 on the relevancy and clarity of instrument items according to Waltz and Bussel content validity index. 38

In the second round, the proportion of agreement among panel members on the relevancy and clarity of 80 remaining items of the first round of judgment was calculated.

To obtain content validity index for each item, the number of those judging the item as relevant was divided by the number of content experts (N=14). (As one of the 15 members of panel had not scored some items, the analyses were made by 14 judges). This work was also carried out to clarify the items of the instrument. The agreement among the judges for the entire instrument was only calculated for relevancy according to average and universal agreement approach.

In this round, among the 80 instrument items, 4 items with a CVI score lower than 0.70 were eliminated. Eight items with a CVI between 0.70 and 0.79 were modified (Modification of items was performed according to the recommendation of panel members and research group forums). Two items were eliminated despite having favorable CVI scores, one of which was eliminated due to ethical issues (As some content experts believed that the item “ I often think to death but I don’t speak about it with my nurses .” might cause moral harm to a patient, it was eliminated). On another eliminated item, “ Nurses know that how to communicate with me ”, some experts believed that if that item is eliminated, it would not harm the definition of trust building dimension. According to experts suggestions, an item ( Nurses try that I face no problem during care ) was added in this round. After modification, the instrument containing 57 items was sent to the panel members for the third time to judge on the relevancy, clarity and comprehensiveness of the items in each dimension and need for deletion or addition of the items. In this round, four items had a CVI lower than 0.70, which were eliminated.

The proportion of agreement among the experts was also calculated in this round in terms of comprehensiveness for each dimension of the construct underlying study. Table 4 shows the calculation of I-CVI, S-CVI and modified kappa for items in the instrument for 53 remaining items at the end of the third round of judgment. We also used panel members’ judgment on the clarity of items as well as their recommendations on the modification of items.

NOTE : * I-CVI: item-level content validity index, ** p c (probability of a chance occurrence) was computed using the formula: p c = [N! /A! (N -A)!] * .5 N where N= number of experts and A= number of panelists who agree that the item is relevant. Number of experts=14, *** K(Modified Kappa) was computed using the formula: K= (I-CVI- P C )/(1- P C ). Interpretation criteria for Kappa, using guidelines described in Cicchetti and Sparrow (1981): Fair=K of 0.40 to 0.59; Good=K of 0.60 to 0.74; and Excellent=K>0.74

Face validity results of patient-centered communication measuring instrument

A sample of 10 people of patients with cancer who had a long-term history of hospitalization in oncology wards (lay experts) was requested to judge on the importance, simplicity and understandability of items in an interview with one of the members of research team. According to their opinions, to make some items more understandable, objective examples were included in an item. “For instance, the item “N urses try not to cause any problem for me ” was changed into “ During care (e.g. preparation an intravenous line), Nurses try not to cause any problem for me ”. The item “Care decisions are made without paying attention to my needs ” was changed to “ Nurses didn’t ask my opinion about care(e.g. time of care or type of interventions) ”. In addition the quantitative analysis was also performed as calculating impact score of each item. Nine items had item impact score less than 1.5 and they were eliminated from the final instrument for preliminary test. Finally, at the end of the content validity and face validity process, our instrument was prepared with seven dimensions and 44 items for the next steps and doing the rest of psychometric testing.

Present paper demonstrates quantities indices for content validity a new instrument and outlines them during design and psychometrics of patient centered communication measuring instrument. It should be said that validation is a lengthy process, in the first-step of which, the content validity should be studied and the following analyses should be directed include reliability evaluation (through internal consistency and test-retest), construct validity (through factor analysis) and criterion-related validity. 37

Some limitations of content validity studies should be noted, Experts’ feedback is subjective; thus, the study is subjected to bias that may exist among the experts. If content domain is not well identified, this type of study does not necessarily identify content that might have been omitted from the instrument. However, experts are asked to suggest other items for the instrument, which may help minimize this limitation. 11

Content validity study is a systematic, subjective and two-stage process. In the first stage, instrument design is carried out and in the second stage, judgment/quantification on instrument items is performed and content experts study the accordance between theoretical and operational definitions. Such process should be the leading study in the process of making instrument to guarantee instrument reliability and prepare a valid instrument in terms of content for preliminary test phase. Validation is a lengthy process, in the first step of which, the content validity should be studied. The following analyses should be directed include reliability evaluation (through internal consistency and test-retest), construct validity by factor analysis and criterion-related validity. Meanwhile, we showed that although content validity is a subjective process, it is possible to objectify it.

Understanding content validity is important for clinician groups and researchers because they should realize if the instruments they use for their studies are suitable for the construct, population under study, and socio-cultural background in which the study is carried out, or there is a need for new or modified instruments.

Training on content validity study helps students, researchers, and clinical staffs better understand, use and criticize research instruments with a more accurate approach.

In general, content validity study revealed that this instrument enjoys an appropriate level of content validity. The overall content validity index of the instrument using a conservative approach (universal agree- ment approach) was low; however, it can be advocated with respect to the high number of content experts that makes consensus difficult and high value of the S-CVI with the average approach, which was equal to 0.93.

Acknowledgments

The researchers appreciate patients, nurses, managers, and administrators of Ali-Nasab and Shahid Ayatollah Qazi Tabatabaee hospitals. Approval to conduct this research with no. 5/74/474 was granted by the Hematology and Oncology Research Center affiliated to Tabriz University of Medical Sciences.

Ethical issues

None to be declared.

Conflict of interest

The authors declare no conflict of interest in this study.

Statistics Made Easy

What is Content Validity? (Definition & Example)

The term  content validity refers to how well a survey or test measures the construct that it sets out to measure.

For example, suppose a professor wants to test the overall knowledge of his students in the subject of elementary statistics. His test would have content validity if:

• The test covers every topic of elementary statistics that he taught in the class.
• The test does not cover unrelated topics such as history, economics, biology, etc.

A test lacks content validity if it doesn’t cover all aspects of a construct it sets out to measure or if it covers topics that are unrelated to the construct in any way.

When is Content Validity Used?

In practice, content validity is often used to assess the validity of tests that assess content knowledge. Examples include:

Example 1: Statistics Final Exam

A final exam at the end of a semester for a statistics course would have content validity if it covers every topic discussed in the course and excludes all other irrelevant  topics.

Example 2: Pilot’s License

An exam that tests whether or not individuals have enough knowledge to acquire their pilot’s license would have content validity if it includes questions that cover every possible topic discussed in a pilot’s course and exclude all other questions that aren’t relevant for the license.

Example 3: Real Estate License

An exam that tests whether or not individuals possess enough knowledge to get a real estate license would have content validity if it covers every topic that needs to be understood by a real estate agent and excludes all other questions that aren’t relevant.

In each situation, content validity can help determine if a test covers all aspects of the construct that it sets out to measure.

How to Measure Content Validity

In a 1975 paper , C.H. Lawshe developed the following technique to assess content validity:

Step 1: Collect data from subject matter experts.

Lawshe proposed that each subject matter expert (SME) on a judging panel should respond to the question:

“Is the skill or knowledge measured by this item ‘essential,’ ‘useful, but not essential,’ or ‘not necessary’ to the performance of the job?”

Each SME should provide this response to each question on a test.

Step 2: Calculate the content validity ratio.

Next, Lawshe proposed the following formula to quantify the content validity ratio of each question on the test:

Content Validity Ratio = (n e – N/2) / (N/2)

• n e : The number of subject matter experts indicating “essential”
• N: The total number of SME panelists

If the content validity ratio for a given question falls below a certain critical value, it’s likely that the question is not measuring the construct of interest as well as it should.

The following table shows the critical values based on the number of SME panelists:

The content validity index, denoted as CVI, is the mean content validity ratio of all questions on a test. The closer the CVI is to 1, the higher the overall content validity of a test.

The following example shows how to calculate content validity for a certain test.

Example: Measuring Content Validity

Suppose we ask a panel of 10 judges to rate 6 items on a test. The green boxes in the following table shows which judges rated each item as an “essential” item:

The content validity ratio for the first item would be calculated as:

Content Validity Ratio = (n e – N/2) / (N/2) = (9 – 10/2) / (10/2) =  0.8

We could calculate the content validity ratio for each item in a similar manner:

From the critical values table, we can see that an item is considered to have content validity for a panel of 10 judges only if it has a CVR value above 0.62.

For this particular test, only three of the items pass this threshold.

Lastly, we can also calculate the content validity index (CVI) of the entire test as the average of all the CVR values:

CVI = (0.8 -0.2 + 1 + 0.8 + 0.6 + 0) / 6 =  0.5

This CVI value is quite low, which indicates that the test likely doesn’t measure the construct of interest as well as it could.

It would be recommended to remove or modify the items that have low CVR values to improve the overall content validity of the test.

Content Validity vs. Face Validity

Content validity is different from face validity , which is when a survey or test appears valid at face value to both the individuals who take it and the individuals who administer it.

Face validity is a less technical way of assessing the validity of a test and it’s often used just used as a quick way to detect whether or not a test should be modified in some way before being used.

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please were did you get the critical value for each number of panelist from?

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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters  

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles  

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles  

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo:  http://www.qsrinternational.com/products.aspx

Atlas.ti:  http://www.atlasti.com/webinars.html

R- RQDA package:  http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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• The 4 Types of Validity | Types, Definitions & Examples

The 4 Types of Validity | Types, Definitions & Examples

Published on 3 May 2022 by Fiona Middleton . Revised on 10 October 2022.

In quantitative research , you have to consider the reliability and validity of your methods and measurements.

Validity tells you how accurately a method measures something. If a method measures what it claims to measure, and the results closely correspond to real-world values, then it can be considered valid. There are four main types of validity:

• Construct validity : Does the test measure the concept that it’s intended to measure?
• Content validity : Is the test fully representative of what it aims to measure?
• Face validity : Does the content of the test appear to be suitable to its aims?
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Note that this article deals with types of test validity, which determine the accuracy of the actual components of a measure. If you are doing experimental research, you also need to consider internal and external validity , which deal with the experimental design and the generalisability of results.

Table of contents

Construct validity, content validity, face validity, criterion validity.

Construct validity evaluates whether a measurement tool really represents the thing we are interested in measuring. It’s central to establishing the overall validity of a method.

What is a construct?

A construct refers to a concept or characteristic that can’t be directly observed but can be measured by observing other indicators that are associated with it.

Constructs can be characteristics of individuals, such as intelligence, obesity, job satisfaction, or depression; they can also be broader concepts applied to organisations or social groups, such as gender equality, corporate social responsibility, or freedom of speech.

What is construct validity?

Construct validity is about ensuring that the method of measurement matches the construct you want to measure. If you develop a questionnaire to diagnose depression, you need to know: does the questionnaire really measure the construct of depression? Or is it actually measuring the respondent’s mood, self-esteem, or some other construct?

To achieve construct validity, you have to ensure that your indicators and measurements are carefully developed based on relevant existing knowledge. The questionnaire must include only relevant questions that measure known indicators of depression.

The other types of validity described below can all be considered as forms of evidence for construct validity.

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Content validity assesses whether a test is representative of all aspects of the construct.

To produce valid results, the content of a test, survey, or measurement method must cover all relevant parts of the subject it aims to measure. If some aspects are missing from the measurement (or if irrelevant aspects are included), the validity is threatened.

Face validity considers how suitable the content of a test seems to be on the surface. It’s similar to content validity, but face validity is a more informal and subjective assessment.

As face validity is a subjective measure, it’s often considered the weakest form of validity. However, it can be useful in the initial stages of developing a method.

Criterion validity evaluates how well a test can predict a concrete outcome, or how well the results of your test approximate the results of another test.

What is a criterion variable?

A criterion variable is an established and effective measurement that is widely considered valid, sometimes referred to as a ‘gold standard’ measurement. Criterion variables can be very difficult to find.

What is criterion validity?

To evaluate criterion validity, you calculate the correlation between the results of your measurement and the results of the criterion measurement. If there is a high correlation, this gives a good indication that your test is measuring what it intends to measure.

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Research validity in surveys relates to the extent at which the survey measures right elements that need to be measured. In simple terms, validity refers to how well an instrument as measures what it is intended to measure.

Reliability alone is not enough, measures need to be reliable, as well as, valid. For example, if a weight measuring scale is wrong by 4kg (it deducts 4 kg of the actual weight), it can be specified as reliable, because the scale displays the same weight every time we measure a specific item. However, the scale is not valid because it does not display the actual weight of the item.

Research validity can be divided into two groups: internal and external. It can be specified that “internal validity refers to how the research findings match reality, while external validity refers to the extend to which the research findings can be replicated to other environments” (Pelissier, 2008, p.12).

Moreover, validity can also be divided into five types:

1. Face Validity is the most basic type of validity and it is associated with a highest level of subjectivity because it is not based on any scientific approach. In other words, in this case a test may be specified as valid by a researcher because it may seem as valid, without an in-depth scientific justification.

Example: questionnaire design for a study that analyses the issues of employee performance can be assessed as valid because each individual question may seem to be addressing specific and relevant aspects of employee performance.

2. Construct Validity relates to assessment of suitability of measurement tool to measure the phenomenon being studied. Application of construct validity can be effectively facilitated with the involvement of panel of ‘experts’ closely familiar with the measure and the phenomenon.

Example: with the application of construct validity the levels of leadership competency in any given organisation can be effectively assessed by devising questionnaire to be answered by operational level employees and asking questions about the levels of their motivation to do their duties in a daily basis.

3. Criterion-Related Validity involves comparison of tests results with the outcome. This specific type of validity correlates results of assessment with another criterion of assessment.

Example: nature of customer perception of brand image of a specific company can be assessed via organising a focus group. The same issue can also be assessed through devising questionnaire to be answered by current and potential customers of the brand. The higher the level of correlation between focus group and questionnaire findings, the high the level of criterion-related validity.

4. Formative Validity refers to assessment of effectiveness of the measure in terms of providing information that can be used to improve specific aspects of the phenomenon.

Example: when developing initiatives to increase the levels of effectiveness of organisational culture if the measure is able to identify specific weaknesses of organisational culture such as employee-manager communication barriers, then the level of formative validity of the measure can be assessed as adequate.

5. Sampling Validity (similar to content validity) ensures that the area of coverage of the measure within the research area is vast. No measure is able to cover all items and elements within the phenomenon, therefore, important items and elements are selected using a specific pattern of sampling method depending on aims and objectives of the study.

Example: when assessing a leadership style exercised in a specific organisation, assessment of decision-making style would not suffice, and other issues related to leadership style such as organisational culture, personality of leaders, the nature of the industry etc. need to be taken into account as well.

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Conduct Content Analysis: A Comprehensive Guide

Unlock hidden meanings! Learn how to conduct content analysis, determining the presence of words, themes, or concepts in your data.

Content analysis, a diverse research method, provides an organized approach for dissecting and comprehending communication in its multiple forms. Whether evaluating textual documents, visual images, social media content, or audio recordings, content analysis provides researchers with the tools they need to discover hidden meanings, identify common themes, and expose underlying patterns in varied datasets. 

Through this guide, researchers will understand how to conduct content analysis. This guide aims to serve as a beacon for researchers navigating the complicated landscape of content analysis, providing not only a thorough definition and explanation of its significance, but also practical insights into its application across qualitative and quantitative research paradigms. As methods of communication expand and diversify, knowing and mastering content analysis becomes increasingly important for researchers looking to delve deeper into the complexities of human expression and societal dynamics.

Understanding Content Analysis

As previously stated, content analysis is a robust research process used to evaluate and interpret various types of communication, including text and images, with meticulous attention to detail. 

Before understanding how to conduct content analysis, it’s important to recognize the profound significance this methodology holds in both qualitative and quantitative research paradigms, offering unique advantages and insights to researchers across diverse disciplines.

Related article: Research Paradigm: An Introduction with Examples

Content Analysis On Qualitative Research

• Exploration of Complex Phenomena: Qualitative research seeks to understand both the breadth and depth of human experiences, points of view, and behaviors. Content analysis is a systematic method for analyzing textual, visual, or audio data, allowing researchers to identify complex meanings, patterns, and themes in qualitative information.
• Comprehending Context and Culture: A common goal of qualitative research is to comprehend phenomena with regard to their sociocultural environment. Researchers can study how language, symbols, and representations are created and understood within certain social or cultural contexts by using content analysis.
• Theory Building and Grounded Theory: Grounded theory methods, which allow researchers to construct theories based on empirical data, heavily rely on content analysis. Through methodical examination of qualitative data, researchers can identify emerging themes, enhance theoretical frameworks, and formulate theories based on empirical findings.
• Flexibility and Adaptability: Researchers can customize their approach to the details of their research setting by using content analysis, which provides flexibility in data collecting and analysis. Content analysis can be tailored to accommodate a diverse array of qualitative data sources, including but not limited to interview transcripts, social media posts, and historical documents.

Content Analysis On Quantitative Research

• Standardization and Objectivity: When gathering and analyzing data, quantitative research places a strong emphasis on standardization and objectivity. Textual or visual material can be methodically coded and categorized into quantifiable characteristics by researchers using content analysis, which offers an organized framework for quantifying qualitative data.
• Large-Scale Data Analysis: Content analysis can be scaled up to analyze large volumes of data efficiently. Researchers can examine large datasets and reach statistically significant conclusions by using quantitative content analysis, whether the dataset is online forums, news articles, or survey replies.
• Comparative Analysis and Generalizability: Researchers can find trends, patterns, or discrepancies in content across several contexts by using quantitative content analysis to assist comparative study across texts or historical periods. By quantifying textual data, researchers can also assess the generalizability of findings to broader populations or phenomena.
• Integration with Statistical Methods: To improve data analysis and interpretation, quantitative content analysis can be combined with statistical techniques. Techniques such as frequency counts, chi-square tests, or regression analysis can be applied to analyze coded content and test hypotheses derived from theoretical frameworks.

Types Of Content Analysis

• Manifest Content Analysis: Manifest content analysis focuses on analyzing the explicit, surface-level content of communication. It involves identifying and categorizing visible, tangible elements such as words, phrases, or images within the text or other forms of media. The goal is to describe and quantify the visible characteristics of communication without delving into deeper meanings or interpretations.
• Latent Content Analysis: Latent content analysis goes beyond the explicit content to uncover underlying meanings, themes, and interpretations embedded within the communication. It involves interpreting the implicit, hidden messages, symbols, or metaphors conveyed through language, imagery, or other forms of representation. The aim is to uncover deeper insights into the underlying motives, beliefs, or attitudes reflected in the communication.
• Thematic Analysis: Thematic analysis involves identifying, analyzing, and interpreting recurring themes or patterns within the content. It focuses on discovering commonalities, differences, and relationships between concepts or ideas expressed within the communication. The goal is to uncover overarching themes or conceptual categories that capture the essence of the data and provide insights into the underlying phenomena being studied.
• Narrative Analysis: Narrative analysis focuses on analyzing the structure, content, and meaning of narratives or stories within the communication. It involves examining the plot, characters, settings, and other narrative elements to uncover the underlying themes, ideologies, or cultural meanings embedded within the stories. The aim is to understand how narratives shape identity, culture, and social discourse.
• Discourse Analysis: Discourse analysis examines the language, rhetoric, and power dynamics inherent in communication practices. It involves analyzing how language is used to construct social realities, shape identities, and negotiate power relations within specific contexts. The goal is to uncover how language structures and reflects social norms, ideologies, and power dynamics within society.
• Visual Content Analysis: Visual content analysis focuses on analyzing visual elements such as images, symbols, or graphics within communication media. It involves examining the composition, content, and meaning of visual representations to uncover underlying themes, messages, or cultural meanings conveyed through imagery. The aim is to understand how visuals influence perception, cognition, and communication processes.

Preparing For Content Analysis

Before embarking on the journey of content analysis, researchers must lay a solid groundwork by carefully selecting materials for analysis and defining clear categories for coding. This preparatory phase is crucial for ensuring the relevance, reliability, and validity of the content analysis process.

Material Selection

Criteria for choosing materials.

• Relevance to Research Objectives: Select materials that are directly relevant to the research questions or objectives. Ensure that the content aligns with the scope and focus of the study.
• Diversity and Representation: Choose materials that provide a diverse range of perspectives, viewpoints, or contexts relevant to the research topic. Seek to include a variety of sources to capture different dimensions of the phenomenon under study.
• Accessibility and Availability: Prioritize materials that are readily accessible and available for analysis. Consider factors such as copyright restrictions, data availability, and ethical considerations when selecting materials.
• Quality and Authenticity: Verify the credibility and authenticity of the materials to ensure the accuracy and reliability of the data. Use reputable sources and validate the authenticity of primary data sources where applicable.

How To Acquire Materials

• Literature Review: Conduct a comprehensive literature review to identify relevant sources, studies, or datasets related to the research topic. Utilize academic databases, libraries, and online repositories to access scholarly articles, books, reports, and other relevant materials.

Also read: What is a literature review? Get the concept and start using it

• Data Collection: Collect primary data through methods such as interviews, surveys, observations, or document analysis, depending on the research design. Use systematic sampling techniques to ensure representativeness and diversity in the selection of materials.
• Digital Sources: Explore digital sources such as online databases, social media platforms, websites, or multimedia archives to access digital content for analysis. Use web scraping tools, APIs, or data extraction techniques to gather digital data in a structured format.
• Ethical Considerations: Adhere to ethical guidelines and obtain necessary permissions or approvals for accessing and using copyrighted materials or sensitive data. Protect the privacy and confidentiality of participants and respect intellectual property rights when acquiring materials for analysis.

Defining And Identifying Categories

How to define categories.

• Define Research Objectives: Clarify the research questions, objectives, and hypotheses to guide the development of coding categories. Determine the key concepts, themes, or variables of interest that will be coded and analyzed.
• Conduct Preliminary Analysis: Review the selected materials to identify recurring patterns, themes, or topics relevant to the research focus. Use open coding techniques to generate initial categories based on the content of the materials.
• Conceptualize Categories: Organize the initial codes into conceptual categories or thematic domains that encapsulate the main dimensions of the phenomenon under study. Group related codes together and refine the category labels to ensure clarity and coherence.
• Establish Coding Rules: Develop clear and concise coding rules or definitions for each category to guide the coding process. Define inclusion and exclusion criteria, coding criteria, and examples to illustrate the application of each category.
• Pilot Test Categories: Conduct a pilot test or inter-coder reliability assessment to evaluate the clarity, reliability, and validity of the coding categories. Revise and refine the categories based on feedback from pilot testing to improve coding consistency and accuracy.

Best Practices To Identify Categories

• Iterative Process: Approach category development as an iterative process, refining and revising categories based on ongoing analysis and feedback. Continuously review and update categories to capture emerging themes or insights.
• Triangulation: Use multiple sources of data or multiple coders to triangulate findings and ensure the reliability and validity of coding categories. Compare and cross-reference coding results to identify discrepancies or inconsistencies.
• Peer Review: Seek feedback from colleagues, mentors, or experts in the field to validate the relevance and appropriateness of coding categories. Engage in peer review sessions to discuss and refine coding schemes collaboratively.
• Reflexivity: Maintain reflexivity throughout the category development process, critically reflecting on your assumptions, biases, and interpretations. Consider alternative perspectives and interpretations to enhance the richness and depth of coding categories.
• Consult Existing Frameworks: Draw upon existing theoretical frameworks, conceptual models, or coding schemes relevant to the research topic. Adapt and modify existing frameworks to suit the specific context and objectives of the study.

How To Conduct Content Analysis

Mastering content analysis empowers researchers to uncover insights and contribute to scholarly discourse across various disciplines. By following the guidelines outlined in this guide, researchers can conduct meaningful analyses that advance knowledge and inform decision-making processes.

Coding Content

To create an effective coding system, start by identifying the key concepts, themes, or variables you want to analyze within your content. Develop clear and concise code definitions and coding rules to guide the coding process. Ensure that your coding system is comprehensive, covering all relevant aspects of the content you are analyzing. Once your coding system is in place, apply it consistently and systematically to the entire dataset.

Let’s say you’re conducting a content analysis on customer reviews of a product. Your coding system may include categories such as “product quality,” “customer service,” and “value for money.” As you analyze each review, you’ll assign codes to relevant segments of text based on these categories. For example, a positive comment about the product’s durability may be coded under “product quality,” while a complaint about slow shipping may be coded under “customer service.”

Analyzing And Interpreting Results

Once you’ve coded your content, you can begin analyzing it to identify patterns, trends, and insights. Common techniques for analyzing content include frequency analysis, thematic analysis, and comparative analysis. Use these techniques to uncover key themes, relationships between variables, and variations across different segments of your dataset.

When interpreting your content analysis results, consider the context in which the content was produced and the characteristics of your sample. Look for overarching patterns and trends, but also pay attention to outliers or unexpected findings. Consider how your findings relate to existing literature and theories in your field, and be transparent about any limitations or biases in your analysis.

Validating The Results

Validating results in content analysis involves assessing the reliability and validity of your findings to ensure they accurately reflect the underlying content. This may include measures to ensure inter-coder reliability, triangulation with other data sources, and sensitivity analyses to test the robustness of your results.

Common methods used to validate results in content analysis include inter-coder reliability tests, where multiple coders independently code a subset of the data to assess consistency. Triangulation involves comparing findings from content analysis with other methods or sources of data to confirm or refute conclusions. Additionally, sensitivity analyses involve testing the impact of different coding decisions or analytical approaches on the results to assess their robustness.

Reporting Findings

In reporting findings, researchers distill the essence of their content analysis, presenting insights and conclusions clearly and concisely. This section is a very important part of how to conduct content analysis, as it provides guidance on structuring reports, writing effectively, and using visual aids to convey results with clarity and impact.

Writing And Structuring The Report

When writing your content analysis report, start by clearly stating your research objectives and methodology. Present your findings in a logical and organized manner, using descriptive statistics, tables, and visual aids to support your analysis. Discuss the implications of your findings for theory, practice, or policy, and conclude by summarizing the key insights and contributions of your study.

An effective content analysis report should be concise, clear, and well-structured. Use headings and subheadings to guide the reader through the report, and provide sufficient detail to support your conclusions. Be transparent about your methods and any limitations of your analysis, and use language that is accessible to your intended audience.

Organize your report into sections that mirror the steps of your content analysis process, such as coding, analysis, and interpretation. Use descriptive titles and subheadings to clearly delineate each section, and provide ample context and explanation for your findings. Consider including visual aids such as charts or graphs to enhance the clarity and readability of your report.

Visualising Data

Visualizing data is an effective way to communicate your findings and insights to your audience. Common visualizations used in content analysis include bar charts, pie charts, line graphs, and heat maps. Choose the visualization method that best represents the patterns and trends in your data and is most suitable for your audience.

Consider the nature of your data and the preferences of your audience when selecting visualization methods. For example, bar charts are useful for comparing frequencies or proportions across categories, while line graphs are suitable for showing trends over time. Choose visualization methods that are intuitive, informative, and visually appealing to effectively convey your content analysis results.

Related article: Art Of Describing Graphs And Representing Numbers Visually

Tips For A Successful Content Analysis

• Document Your Process: Keeping detailed records of your content analysis process can prove invaluable, aiding in transparency, reproducibility, and troubleshooting. Record decisions made during material selection, category definition, and coding, as well as any challenges encountered and their resolutions. This documentation not only enhances the rigor of your analysis but also facilitates communication with collaborators and reviewers.
• Embrace Iteration: Content analysis is rarely a linear process. Embrace iteration and refinement throughout each stage, from material selection to reporting findings. Regularly revisit and revise coding categories, analytical techniques, and interpretations in response to emerging insights or challenges. Iterative refinement ensures that your analysis remains dynamic and responsive to the complexities of the data.
• Utilize Software Tools: While content analysis can be conducted manually, leveraging software tools can streamline and enhance the process. Explore software options tailored to content analysis tasks, such as qualitative data analysis software (QDAS) or text analysis tools. These tools often offer features for organizing data, coding text, and visualizing results, saving time and enhancing analytical capabilities.
• Prioritize Inter-Coder Reliability: Inter-coder reliability, or the consistency of coding among multiple coders, is crucial for ensuring the validity and reliability of your analysis. Prioritize inter-coder reliability assessments early in the process, involving multiple coders in coding tasks and comparing their results. Establishing clear coding guidelines and conducting regular reliability checks can mitigate discrepancies and enhance the credibility of your findings.
• Consider Cultural Sensitivity: When analyzing content that reflects cultural or linguistic diversity, it’s essential to approach the process with sensitivity and awareness. Consider the cultural context of the content, including language nuances, symbolism, and cultural norms, when interpreting and coding data. Engage with diverse perspectives and seek input from stakeholders to ensure that your analysis accurately reflects the complexity of the cultural landscape.
• Be Mindful of Bias: Conscious and unconscious biases can influence every stage of the content analysis process, from material selection to interpretation of results. Stay vigilant for biases related to personal beliefs, disciplinary perspectives, or preconceived notions about the topic under study. Implement strategies to mitigate bias, such as peer review, reflexivity exercises, and triangulation with multiple data sources.
• Foster Collaboration: Content analysis can benefit from interdisciplinary collaboration and diverse perspectives. Engage with colleagues, mentors, or experts from different fields to enrich your analysis and challenge assumptions. Collaborative approaches can foster creativity, rigor, and innovation, leading to more robust and nuanced findings.
• Stay Open to Serendipity: While content analysis often involves systematic data collection and analysis, don’t overlook the potential for serendipitous discoveries. Remain open to unexpected insights, patterns, or connections that emerge during the analysis process. Serendipity can lead to novel research directions, enriching your understanding of the phenomenon under study.

Science Figures, Graphical Abstracts, And Infographics For Your Research

Mind the Graph is a valuable resource for scientists seeking to enhance the visual communication of their research through science figures, graphical abstracts, and infographics. With its user-friendly interface, extensive template library, and customizable design tools, the platform empowers researchers to create visually compelling and scientifically accurate visualizations that effectively communicate complex ideas and findings to a diverse audience.

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About Jessica Abbadia

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Content tags

Research Methodology

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your research objectives. Methodology is the first step in planning a research project.

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Scientific Method

Qualitative research, experiments.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

Learn More: Steps of the Scientific Method

Variables apply to experimental investigations. The independent variable is the variable the experimenter manipulates or changes. The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable.

Learn More: Independent and Dependent Variables

When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.

Learn More: P-Value and Statistical Significance

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data. Qualitative research can be used to gain a deep contextual understanding of the subjective social reality of individuals.

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups.

Learn More: How the Experimental Method Works in Psychology

Frequent Asked Questions

What does p-value of 0.05 mean?

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the results have occurred by random chance rather than a real effect. Therefore, we reject the null hypothesis and accept the alternative hypothesis.

However, it is important to note that the p-value is not the only factor that should be considered when interpreting the results of a hypothesis test. Other factors, such as effect size, should also be considered.

Learn More: What A p-Value Tells You About Statistical Significance

What does z-score tell you?

A  z-score  describes the position of a raw score in terms of its distance from the mean when measured in standard deviation units. It is also known as a standard score because it allows the comparison of scores on different variables by standardizing the distribution. The z-score is positive if the value lies above the mean and negative if it lies below the mean.

Learn More: Z-Score: Definition, Calculation, Formula, & Interpretation

What is an independent vs dependent variable?

The independent variable is the variable the experimenter manipulates or changes and is assumed to have a direct effect on the dependent variable. For example, allocating participants to either drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

Learn More : What are Independent and Dependent Variables?

What is the difference between qualitative and quantitative?

Quantitative data is numerical information about quantities and qualitative data is descriptive and regards phenomena that can be observed but not measured, such as language.

Learn More: What’s the difference between qualitative and quantitative research?

Explore Research Methodology

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Content Validity in Research: Definition & Examples

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Qualitative Research: Characteristics, Design, Methods & Examples

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• Open access
• Published: 23 May 2024

Translation, validity and reliability of the persian version of the rapid assessment of physical activity questionnaire

• Majid Barati 1 , 2 ,
• Eva Ekvall Hansson 3 ,
• Zahra Taheri-Kharameh 4 , 5 &
• Tari D Topolski 6  

BMC Geriatrics volume  24 , Article number:  452 ( 2024 ) Cite this article

168 Accesses

Metrics details

The purpose of this study was to produce a valid and reliable Persian version of the Rapid Assessment of Physical Activity (RAPA) questionnaire, which previously has been shown to be valid and reliable for assessing physical activity among older adults.

Permission was obtained from the scale developer, who provided a copy of the the Linguistic Validation of the RAPA Qestionnaire, which utilizes a forward-backward translation methodology. Content validity, face validity, and construct validity of the questionnaire were then determined. Comparison of known groups (older adults with more or less than 50% balance confidence) was used to assess construct validity and the Leiden-Padua (LEIPAD) quality of life questionnaire were used to assess convergent validity. Three hundred older adults, who were members of the Qom retirement centers, participated in the study. Thirty participants completed the RAPA twice with a one-week interval to determine test–retest reliability.

Results of comparisons of known groups showed that the mean RAPA score of the older people with greater balance confidence was significantly higher. Significant correlations between most of the scores obtained from both RAPA and the LEIPAD questionnaires confirmed the convergent validity of the questionnaire. Intraclass Correlation Coefficient (ICC) was as high as 0.94 showing that the test–retest reliability was good.

This study showed the Persian RAPA is a reliable and valid instrument for measuring physical activity among older individuals in both research and clinical contexts.

Peer Review reports

Introduction

Effects of age, the progressive declines in physiological function, are associated with mobility impairments and increased dependence. Regular physical activity (PA) can bring significant health benefits to people of all ages, especially those who are aging. Research has increasingly showed that PA can increase active and independent years of life, reduce risk of chronic condition and disability, and improve quality of life [ 1 , 2 ]. The World Health Organization (WHO) defines physical activity as any physical activity produced by the musculoskeletal system that requires energy consumption, and estimates physical inactivity is an independent risk factor for chronic diseases and the cause of 1.9 million deaths worldwide. The recommendations from WHO include both aerobic exercise and strength exercise as well as balance exercises to reduce the risk of falls [ 3 ].

Understanding the amount and type of physical activity of individuals is essential for health promotion planning [ 4 ]. Questionnaires are one of the data collection methods commonly used to evaluate PA in research as well as in clinical practice [ 5 ]. Compared to alternative methods of evaluating PA, questionnaires are short, easy and with minimal financial resources as a screening tool. Standard physical activity assessment tools for the Iranian older people`s use are limited and relatively long [ 6 ].

The Rapid Assessment of Physical Activity (RAPA) is a self-administered questionnaire designed by Topolski et al., in 2006 to examine levels of physical activity. This questionnaire is short and easy to understand and has been widely used in many studies. Another advantage of RAPA is the examination of the strength and flexibility activities that are important to reduce the risk of falls in the older people [ 7 ].

Over the past decade, the RAPA has been utilized in numerous studies as a valid measure of PA [ 12 ]. The translation and validation of this tool also have been extensively studied by researchers in various countries, such as Portugal [ 8 ], Turkey [ 9 ], Spain [ 10 ] and Austria [ 11 ]. These studies have shown that the RAPA can be considered a reliable and valid tool for assessing physical activity. Despite its translation and validation in multiple languages, it is worth noting that a validated version of the RAPA in Persian has not yet been developed in Iran, to the best of our knowledge. The aim of this study was to determine the validity and reliability of the Persian version of RAPA in the older people.

Design and participants

This is a methodological study to determine the psychometric characteristics of the RAPA, as a quickly assessing the level of physical activity in older people in Qom, a provincial city in the central region of Iran. In psychometric studies, it is recommended to have a sample size of at least 300 individuals [ 13 ]. So, a total of 300 older individuals were selected to participate in this study based on specific criteria. The inclusion criteria included people aged 60 years or older, members of Qom’s retirement centers, independently living at home, lack of psychological and cognitive impairment, consent to participate in the study, ability to communicate and respond. The exclusion criterion was as follows: refusal for participation in study. The purpose of the study was explained to the participants.

Translation procedures

After obtaining permission from the scale developer through correspondence, we proceeded to utilize the recommended Forward-Backward method for the translation process. The translation was conducted based on the guidelines provided by the WHO, as part of the International Quality of Life Assessment Project (IQoLAP) [ 14 ]. This approach to translation and validation has been developed for use with the SF-36, but it can also be applied to other translation efforts. The translation process involved two translators: one without medical knowledge and another who was a medical university expert. By comparing the results of both translations, we synthesized a final version, which was then translated back into English by an independent translator. This translated copy was then sent to the original RAPA developer for review. After incorporating necessary modifications based on the developer’s feedback, the final Persian version was approved.

Content validity

In order to assess the content validity of the questionnaire, 10 experts specializing in geriatrics, physical activity, and psychometrics were invited to participate. They were asked to complete the questionnaire and provide feedback based on the content validity index (CVI) and the content validity ratio (CVR). To evaluate the CVR, three items were considered for each question that were: (a) it is necessary; (b) useful but not essential; and (c) not necessary. Panel members selected one of the three options for each item of the scale. Then, the CVR of the RAPA was calculated as follows CVR= (Ne - N/2)/(N/2), in which the Ne is the number of experts evaluating “essential” and N is the total number of experts. The determination of the accepted value was made by referring to Lawshe’s table and considering the number of experts involved. As per Lawshe’s table, an item with a CVR exceeding 0.62 is considered acceptable when there are 10 experts involved [ 15 ].

Then, to assessment the CVI, 3 criteria, simplicity, specificity, and clarity were individually examined in a 4-part Likert scale for each item by 10 experts (e.g. for simplicity = 1, incomprehensible = 4, Quite simple and understandable). For this purpose, in this study, the CVI score will be calculated by summing the percentages of agreement scores for each item that scored 3 and 4 (highest score) on the total number of experts. To determine the suitability of items, we relied on the CVI. Items with a CVI above 0.79 were found to be highly suitable, indicating a strong alignment with the desired criteria. For items falling within the range of 0.70 to 0.79, modifications were deemed necessary to enhance their suitability. Lastly, items with a CVI below 0.70 were deemed unacceptable, as they did not meet the desired criteria [ 16 ].

Face validity

In order to determine face validity, 10 older people with inclusion criteria into research were asked to express for content, clarity, readability, and simplicity and easy to understand tool expressions. Finally, according to patients` feedback and research team, necessary changes were considered; the target population of this study was the older people members of retirement centers in Qom. Sampling method was stratified and inclusion criteria were age equal to and over 60 years, home living, lack of mental and cognitive impairment (score of 6 or above in Farsi version of abbreviated mental test), consent to participate, capable of communicating and responding.

After obtaining permission from the Qom University of Medical Sciences (IR.MUQ.REC.1400.135) and coordinating with retirement centers, Questionnaires were completed within 6 months after the confidentiality of the information was obtained and the participants` consent was obtained.

Instruments

Demographic and medical information, RAPA, Leiden-Padua (LEIPAD) questionnaire and Activities-Specific Balance Confidence scale (ABC) were used to collect data.

RAPA is a self-report physical activity measurement tool originally designed in English in the USA. It contains two sections and 9 items with response yes/no options. The first part of the tool contains 7 items measuring different levels of physical activity. The second part questions the strength and flexibility training. To score the RAPA, the question with the highest score with a “yes” response is chosen from the first items. In addition, an affirmative answer for participating in muscle strengthening activities such as lifting weights or calisthenics gets one point. The older people, who participated in flexibility activities such as stretching or yoga, were awarded two bonus points, leading to a total possible score of 3 points. The reliability and validity of the original version are confirmed [ 7 ].

Quality of life was assessed by using LEIPAD, an Internationally Applicable Instrument to Assess Quality of Life in the older people. This questionnaire was designed by the WHO sponsored by Di Leo et al., and measures the quality of life of the older people in 7 dimensions of physical function (5 questions), self-care (6 questions), depression and anxiety (4 questions), cognitive functioning (5 questions), social function (3 questions), sexual function (2 questions) and life satisfaction (6 questions). It is designed as a Likert and each question has four options ranging from zero (worst case) to three (best case) and has a total of 31 questions with a minimum of 0 and a maximum of 93 [ 17 ]. The validity and reliability of the questionnaire have been confirmed by Maliki et al. [ 18 ].

Balance confidence was measured using the short form of the ABC [ 19 ]. This scale requires participants to rate their confidence in their balance on a scale from 0% (no confidence) to 100% (completely confident) for six different activities of daily living. The overall balance confidence score was calculated as a percentage based on the average of all six items on the ABC-6. Higher scores on the scale indicate greater confidence in one’s balance. A score below 50% on the ABC-6 suggests lower levels of functioning and confidence in maintaining balance. The short version of the scale, known as ABC-6, is a more simplified and concise version compared to the original ABCscale [ 20 ], as suggested by previous studies [ 21 , 22 ]. The Persian version of the ABC-6 scale has been validated and shown to be reliable in measuring balance confidence [ 23 ].

Demographic and disease information questionnaire include age, gender, marital status, residence status, education level, and economic status.

Construct validity

Known group comparison.

Known Group Comparison was used to determine construct validity in this study. This type of validity determines the ability of a tool to differentiate respondents according to a criterion and assumption. In this study, the parameter used was balance confidence in the older people. For this purpose, comparison of RAPA score between two groups of older people with balance confidence was more or less than 50%. We expected people with higher levels of confidence in balance to score better on the RAPA than older people with no confidence in balance [ 24 ]. The Cohen’s d statistic was utilized to compare two groups and assess the magnitude of the effect size.

Convergent validity

In order to investigate convergent validity, the correlation between the RAPA scores and LEIPAD scores were measured. We hypothesized that there would be a positive significant correlation between the RAPA and the LEIPAD. In assessing the strength of the correlation, we utilized a Pearson’s correlation.

• Reliability

One weeks after the initial survey, the RAPA was once again distributed to 30 participants who had previously responded to the first set of questionnaires. These participants had willingly agreed to complete the RAPA twice, with a one-week gap between administrations. The random method was employed to select the participants for this procedure. The purpose of this procedure was to assess the reliability of the questionnaire using the test-retest method [ 25 ].

Data analysis

Data analysis was performed using SPSS16 software at the significant level of 0.05. Sample characteristics and the RAPA score were analysed by using descriptive statistics. As known group comparison, the RAPA score of participants with balance confidence more or less than 50% were evaluated with the independent t-test. To assess the concurrent validity of the RAPA, Pearson’s correlation coefficient between the scores of the RAPA and LEIPAD was computed. A coefficient ranging from 0 to 0.29 indicates a weak correlation. A coefficient between 0.30 and 0.69 suggests a moderate correlation. Finally, a coefficient between 0.70 and 1 indicates a strong correlation [ 26 ]. Test-retest reliability was assessed by computing the intraclass correlation coefficient of each domain. If the index is above 0.80, the stability level is favorable [ 25 ].

Sample characteristics

Mean and standard deviation of the participants’ age was 64.6 ± 5.24 years. Most participants were males (77.7%), married (88.7%), and had low literacy (58.6%). Just 26% of the participants were regularly active according to the RAPA. Most of the participants (71.7%) reported that they did not do strength and/or flexibility training. The demographic characteristics of participants are presented in Table  1 .

Content and face validity

Content validity was assessed for CVI and CVR, and all items achieved satisfactory scores. The overall tool demonstrated a CVI value of 0.96 and a CVR value of 0.94. Furthermore, individual item CVR scores surpassed 0.60, while item CVI scores were above 0.8. The participants approved all 10 items in terms of face validity.

With regard to the known group comparison, shown in Table  2 , the older people with higher confidence exhibited significantly higher RAPA scores than those who had poor confidence.

The correlation between RAPA and LEIPAD is shows in Table  3 , which was used to assess convergent validity. The correlation coefficient of the two questionnaires was positive and significantly in all dimensions except for social function and sexual function.

To assess test-retest reliability, the ICC for the RAPA was calculated 0.94 and interpreted as having very good test-retest reliability. The ICC also exceed 0.95 and 0.91 for aerobic part and strength and flexibility part, respectively (Table  4 ).

The aim of this study was to assess the psychometric properties of the RAPA in Iranian older people. Many studies have been conducted to assess and increase PA in older people. However, an absolute prerequisite of these studies is the availability of a short, valid and reliable instrument. In the current study, the questionnaire was translated by experienced and skilled experts who followed the principles of translation and ensured accuracy in cultural adaptation. The study strictly adhered to the recommended steps for instrument translation and cultural adaptation of the translated version. Content validity was assessed using CVI and CVR, and all items received satisfactory scores. Face validity was qualitatively confirmed through feedback from 10 older adults.

In this study, to evaluate the construct validity of the questionnaire, we used the method of known groups comparison for balance confidence parameter. The results showed that RAPA score was significantly lower in older people with confidence less than 50% as expected. This assumption in the study was confirmed. Another study that showed balance confidence is a main determinant of physical activity levels in the older people with diabetes [ 27 ]. These results are consistent with those of the Spanish version of RAPA, which also showed that physical activity was significantly and inversely correlated with BMI and waist circumference [ 28 ].

The correlation between the scores obtained from the RAPA and LEIPAD questionnaires varied from low to moderate, suggesting a positive correlation of the same magnitude. This finding supports the concurrent validity of the questionnaire, aligning with the results of previous studies conducted in this field. CHAMPS was used to assess the validity of the original version of RAPA and the results showed there is a significant correlation between RAPA and CHAMPS ( r  = 0.54). Results of the Portuguese version of RAPA showed that lower levels of physical activity were associated with worse self-report disability and slower speed [ 8 ]. Validity of the Mexican-American-Spanish version of RAPA was determined by assessing the correlation between RAPA data and the accelerometer as a direct measure of physical activity level. There was a significant relationship between RAPA and moderate and vigorous minutes of physical activity, indicating RAPA validity [ 10 ]. The Turkish version of the RAPA showed acceptable concurrent validity, because there were positive correlations between the RAPA, International Physical Activity Questionnaire- Short Form and Physical Activity Scale for the older people [ 9 ]. Although we did not use the direct physical activity tools mentioned in other studies, the present results also showed that RAPA was significantly associated with health outcomes.

In this study, the test-retest reliability of the RAPA was assessed. ICC for the RAPA was calculated to be 0.94, indicating very good test-retest reliability. In the original study of the RAPA, the test–retest was not evaluated. But in Turkish version, the weighted kappa coefficients exceeded 0.81 for both parts of RAPA, the aerobic score and strength and flexibility score, showing that the test–retest reliability was very good [ 9 ]. In contrast, the Chilian version, was not an authorized translation, and did not follow the prescribed translation and validation process required by the developer, exhibited an ICC that was lower than the favorable stability. As did the Portuguese version, which showed a moderate test–retest reliability with a weighted κ = 0.67 [ 8 ].

Based on the findings of this study, RAPA is a reliable and valid instrument that features a short completion time, capability of use in different settings, simple scoring, and suitable reliability and validity, and is a useful tool.

This study had limitations. First, all of the tools used to assess the validity were self-report.

Although these questionnaires were recognized as valid and standard tools, objective tools such as accelerometers or pedometers may provide more accurate information on the validity of the Persian version of RAPA. Second, sampling carried out only in the retirement centers in this study reduces the generalizability of the findings. In future studies, it is also recommended that researchers focus on the sensitivity and specificity of the questionnaire.

Conclusions

The findings of this study suggest that the RAPA questionnaire has good psychometric properties for use with older Iranian adults. The RAPA was originally developed for use by genentologist to prompt talking with their patients about the need to engage in physical activity, however, it has been shown to be is appropriate for use to measure amount and type of physical activity as well as health outcomes in both research and clinical settings.

Data availability

The datasets utilized and/or analyzed during the present study are available from the corresponding author upon reasonable request.

Abbreviations

Iterative reliability coefficient

Rapid Assessment of Physical Activity

Activities-Specific Balance Confidence scale

Leiden-Padua

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Acknowledgements

This study was part of a project at Qom University of Medical Sciences (IR.MUQ.REC.1400.135). The researchers hereby express their gratitude to all those who assisted in the research, as well as the older people participating in the study and the research deputy of Qom University of Medical Sciences.

This research was supported by the Qom University of Medical Science.

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Autism Spectrum Disorders Research Center, Hamadan University of Medical Sciences, Hamadan, 6517838695, Iran

Majid Barati

Department of Public Health, Asadabad School of Medical Sciences, Asadabad, Iran

Department of Health Sciences, Lund University, Lund, Sweden

Eva Ekvall Hansson

Spiritual Health Research Center, School of religion and health, Qom University of Medical Sciences, Qom, Iran

Zahra Taheri-Kharameh

Department of Public Health, School of Health, Qom University of Medical Sciences, Qom, Iran

Department of Health Services, University of Washington, Seattle, WC, 89101, Iran

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Contributions

Conceptualization: ZTK. Methodology: ZTK, MB. Investigation: ZTK. Data Analysis: ZTK, MB. Manuscript Writing: MB, TDT, ZTK, EEH. Manuscript Revision and Editing: EEH, TDT, ZTK, MB. All authors have thoroughly reviewed and endorsed the final manuscript.

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Correspondence to Zahra Taheri-Kharameh .

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The research adhered to the principles outlined in the Declaration of Helsinki, and we obtained approval from the Medical Ethics Committee at Qom University of Medical Sciences (registration number: IR.MUQ.REC.1400.135) to conduct the study. We provided a comprehensive explanation of the study to potential participants who met the eligibility criteria. Prior to their inclusion in the study, we obtained written informed consent from all participants. It is important to note that participants had the freedom to withdraw from the study at any time.

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Barati, M., Hansson, E.E., Taheri-Kharameh, Z. et al. Translation, validity and reliability of the persian version of the rapid assessment of physical activity questionnaire. BMC Geriatr 24 , 452 (2024). https://doi.org/10.1186/s12877-024-05065-3

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Received : 03 November 2023

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Published : 23 May 2024

DOI : https://doi.org/10.1186/s12877-024-05065-3

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ORIGINAL RESEARCH article

Analyzing student response processes to refine and validate a competency model and competency-based assessment task types provisionally accepted.

• 1 University of Paderborn, Germany

The final, formatted version of the article will be published soon.

Regarding competency-oriented teaching in higher education, lecturers face the challenge of employing aligned task material to develop the intended competencies. What is lacking in many disciplines are well-founded guidelines on what competencies to develop and what tasks to use to purposefully promote and assess competency development. Our work aims to create an empirically validated framework for competency-oriented assessment in the area of graphical modeling in computer science. This article reports on the use of the think-aloud method to validate a competency model and a competency-oriented task classification. For this purpose, the response processes of 15 students during the processing of different task types were evaluated with qualitative content analysis. The analysis shed light on the construct of graphical modeling competency and the cognitive demand of the task types. Evidence was found for the content and substantive aspect of construct validity but also for the need to refine the competency model and task classification.

Keywords: competency-oriented assessment, Task Types, Validation, think-aloud, Competency model, Graphical modeling, conceptual modeling, computer science

Received: 06 Mar 2024; Accepted: 03 Jun 2024.

Copyright: © 2024 Soyka and Schaper. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Chantal Soyka, University of Paderborn, Paderborn, Germany

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