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Dear Sir/Ma'am,
Greetings!
I developed a psychometric tool consisting of 95 items. I had shared with 5 experts for content validation. 51 items are removed after the content validation process. According to Polit & beck (2006) and Polit et al., (2007) the acceptable CVI values should be 1. The 51 items did not get the required value of CVI for acceptance thus, the 44-items are retained.
My question is 'Should I circulate the questionnaire (with 44-items) to students for criterian-releated validity or 'Should I go with 95 original items?'
If I go with 95 items then what is the use of doing content validation or computing CVI. Am I following the right path if I decide to go with 44 retained items? please say
Big Thank you,
Narottam Kumar
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I don't understand why you would want to include the items that were rejected by the experts, especially given the issues with CVI. Can you please explain why you are considering this option.
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Hello,
Using factor analysis, I recently created a social questionnaire with four factors, each containing four items. My next step is validating the questionnaire.
I want to show that this questionnaire can differentiate between 3 product categories that have different social characteristics.
- What statistical method should I use to prove the differences?
- Should I ask the same respondents to evaluate all 3 products? or it is ok to have separate respondents for each product category.
Thank you
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As long as you have complete data (i.e., responses on all four scales and evaluations of all three products), then your statistical approach will be identical. So, if you use the same requirements for both samples, then it should not matter which one you use.
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Hi,
I have turned up a questionnaire prepared in English. I want to convert it into Turkish than practice it. But I cannot be sure whether I should analyze its validity and reliability because it is not a scale. I hope I can have a chance to pick your brains. Thank you in advance.
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Thank you for the interesting question. In my opinion, content validity and the instrument's reliability are proven by experts to be useful and important even if you modified the questions or not. Also, in the case of a different language.
Kind Regards,
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Hi everyone,
I am looking for book recommendations on how to construct a scale, preferably with a focus on (and examples from) social sciences. Any suggestions?
Thanks in advance!
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Hi,
This is one of the most widely used books on scale development
DeVellis RF. Scale Development: Theory and Application. Los Angeles, CA: Sage Publications (2012).
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Hi,
Anybody come across scales that measure uniqueness and self image of individuals as a result of acquiring a certain product or brand. I found number of scales that measures the perception of the brand itself or the perception of a person about himself; but could not find anything that measures the perception of a person about him/herself as a result of a product or a brand possession.
If anyone can help, this would be grateful.
Thanks
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The Rosenberg self-esteem scale (RSES), designed by sociologist Morris Rosenberg, is a commonly used self-esteem measure in social science research. It employs a scale of 0–30, with a score of less than 15 indicating an issue with low self-esteem. The RSES is constructed in the same manner as social-survey questions.
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What should be done if items did not load in EFA?
should they be discarded? Or that means that they are unique and do not have partners?
What if these items are important or represent our dependent variables ? Can we include them as single items?
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Faten Amer, Usually items not loading on their respective constructs are discarded. However, at times discarded items can be combined to make a new construct.
Robert has already given you advice regarding single items.
Can you share a little more information regarding the EFA, so that we could together see what you have done and how could we help you further? Information such as:
- Which extraction method are you using?
- How the number of factors was identified?
- and, which rotation method was employed?
There is a common misconception between PCA and EFA, and at times right parameters (above) not selected. Consider reading the following article, if not already done:
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical assessment, research, and evaluation, 10(1), 7.
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I want to read more than one load cell using a single Raspberry Pi.
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Yes you can use more than one loadcells with raspberry pi... check the raspberry pi GPIO
You may share the same HX711 for several loadcells but cannot measure readings simultaneously...
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What are the programs which can be used for structural modeling?
I have used an AMOS previously but the trial period had run off, so are there any other available programs for the use which are free of charge?
thanks
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JASP is one of the latest software and it is free. If you need any assistance you can contact
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How can I validate a questionnaire for a small sample of hospitals' senior executive managers?
Hello everyone
-I performed a systematic review for the strategic KPIs that are most used and important worldwide.
-Then, I developed a questionnaire in which I asked the senior managers at 15 hospitals to rate these items based on their importance and their performance at that hospital on a scale of 0-10 (Quantitative data).
-The sample size is 30 because the population is small (however, it is an important one to my research).
-How can I perform construct validation for the items which are 46 items, especially that EFA and CFA will not be suitable for such a small sample.
-These 45 items can be classified into 6 components based on literature (such as the financial, the managerial, the customer, etc..)
-Bootstrapping in validation was not recommended.
-I found a good article with a close idea but they only performed face and content validity:
Ravaghi H, Heidarpour P, Mohseni M, Rafiei S. Senior managers’ viewpoints toward challenges of implementing clinical governance: a national study in Iran. International Journal of Health Policy and Management 2013; 1: 295–299.
-Do you recommend using EFA for each component separately which will contain around 5- 9 items to consider each as a separate scale and to define its sub-components (i tried this option and it gave good results and sample adequacy), but am not sure if this is acceptable to do. If you can think of other options I will be thankful if you can enlighten me.
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Faten Amer , sample size it is not a problem at al in Bayesian Factor Analysis, see for example:
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How can i validate a questionnaire for hospitals' senior managers?
Hello everyone
-I performed a systematic review for the strategic KPIs that are most used and important worldwide.
-Then, I developed a questionnaire in which I asked the senior managers at 15 hospitals to rate these items based on their importance and their performance at that hospital on a scale of 0-10 (Quantitative data).
-The sample size is 30 because the population is small (however, it is an important one to my research).
-How can I perform construct validation for the items which are 46 items, especially that EFA and CFA will not be suitable for such a small sample.
-These 45 items can be classified into 6 components based on literature (such as the financial, the managerial, the customer, etc..)
-Bootstrapping in validation was not recommended.
-I found a good article with a close idea but they only performed face and content validity:
Ravaghi H, Heidarpour P, Mohseni M, Rafiei S. Senior managers’ viewpoints toward challenges of implementing clinical governance: a national study in Iran. International Journal of Health Policy and Management 2013; 1: 295–299.
-Do you recommend using EFA for each component separately which will contain around 5- 9 items to consider each as a separate scale and to define its sub-components (i tried this option and it gave good results and sample adequacy), but am not sure if this is acceptable to do. If you can think of other options I will be thankful if you can enlighten me.
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After the survey is completed..but it is better to increase the number of studied samples..so that the result will be bette
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executive functioning = working memory, attention shifting and inhibition if task.
Please suggest me scale who asses all three of them or even i am ready to go with different scales. kindly mention authors also.
I want to use these tests for my m.phil thesis which is non funded and i am unable to bear expenses of publishers. 
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Yes Talha, you get free access to the Moca test at > https://www.mocatest.org
You will have to demonstrate your competence qualifications to get the required badge.
A useful example of a Functional backup is to ask the client to carry out a simple task AND report back to you through her/his nurse (or email /phone message, etc). Simple tasks like "Please send me your contact information tomorrow", or "Please draw a copy of the shape on this page, then give it to your nurse tomorrow morning", require INITIATIVE, a major executive function skill not tested by simple tests like MOCA where initiative is provided by the tester.
Note that the second task mentioned above requires LESS initiative than the first, since you leave behind a tangible cue for compliance.
Good fortune, Paul
xPsychologist Paul McGaffey, PhD(ABD)
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Dear researchers,
I am trying to develop two scales for a project I'm working on
there are no specific scales in the literature so we decided we could try to create the scales we needed.
I used cronbach's alpha to determine reliability, and i used principle component analysis. But I'm having some difficulites interpreting the results and knowing what I need exactly from PCA
For the first scale
it consists of 11 items
cronbach's alpha is about 0.85 and mean inter item correlation was 0.35
I used PCA and after parallel analysis i ended with one component/factor
based on the component matrix all variables loaded strongly on the component (0.5+)
Since all items loaded on one component, is that considered good for a scale?
As for the second scale
cronbach's alpha was about 0.83 and inter item correlation was about 0.26
I also used PCA and after parallel analysis I ended up with two factors
the first factor had a big eigenvalue while the second factor was just barely above "significance"
Based on the component matrix, all variables loaded pretty well on the first component (0.3+) but some variables loaded on both components, either positively or negatively
I removed some variables that loaded on two components,
cronbachs alpha became a bit lower but mean inter item correlation increased to 0.29
after PCA and parallel analysis only one factor was retained and all variables loaded nicely on that component
To recap, my questions are:
for the first scale, is having one component a sign of a good scale?
and for the second scale, how do interpret negative loadings on one component and positive loadings on the other, and how do i interpret loadings on two components
last but not least, is what i did for the second scale (removing some variables) a good or bad decision?
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Dear Dr. Jawad thank you very much indeed for the question.
I am taking information from Alyami, M., Henning, M., Krägeloh, C. U., & Alyami, H. (2020). Psychometric Evaluation of the Arabic Version of the Fear of COVID-19 Scale. International journal of mental health and addiction, 1–14. Advance online publication. https://doi.org/10.1007/s11469-020-00316-x
to answer your question.
I quote directly from the statistical section of this paper
"1. They assessed nternal consistency using Cronbach alpha coefficients (α), inter-item correlations as well as corrected item-total correlations.
2. According to DeVellis (2016); Nunnally and Bernstein (1994) a Cronbach’s α of .70 or higher indicates acceptable reliability
3. According to Ferketich (1991).” Inter-item correlations and corrected item-total correlations between .30 and .70 indicate medium to strong associations between items."
If all the components are having strong factor loadings this is an indication that the items are good.
For the second scale if you have 2 components you look at the rotated component matrix
.795 -.178
.843 -.202
If you have 2 components and one is positive and one is negative you go for the one which is positive and having higher value.
I hope this really helps. All the very best
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I'm researching on burnout in counseling psychologists and we're trying to look into which source of social support could be most helpful to buffering the effects of burnout. Would anyone happen to know if there's a scale that measures social support from the organization, social support from supervisors, and social support from colleagues altogether?
Any help would be appreciated.
Thank you!
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Have you found an answer?
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I'm proceeding to adapt a scale in English into my own language, and also wanna make some small changes not related to cultural problems. The original scale is designed to measure a general concept, now I want to change the items a little bit so it can measure that concept in a context-specific way.
So I wanna ask what is the right procedure to do it? Should I run a translation-adaptation process first (translate, make some cultural changes if needed and run a pilot study to check the reliability & validity of the adapted scale) then make the context-specificity changes that I want for the research topic;
or translate and make the changes at the same time, then run the pilot study to check the reliability & validity of the adapted scale?
Thank you in advance.
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This would be helpful... Good luck
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I am a student who needs to do some research for my master thesis about the expected product care of a phone in the future. To be precise: I’m doing research on the impact that a Country of Origin label has on the usage of a mobile phone. In this research, I’ll give different information to different respondent groups (the “made-in-label” will differ). Now I want to develop or use an existing scale that measures how well the respondent thinks he’ll take care of the product. For instance: “I will use this product longer than a year”, “I will take care of this product” etc.
Does anyone know a reliable existing scale or how to develop a new one?
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What about trying the satisfactory scales since they are related to the consumer behavior and atitudes towards a product and the consumer decision making (taking care of a product or not)?
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In order to measure research engagement of academicians, will it be wise to use the JES (Job engagement scale) by Rich et al. (2010) with modifications in words like (Research activities instead of Job)?
Adding a file for reference.
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Thank you very much.
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I am trying to obtain permission to use Muller/McCloskey scale on job satisfaction among nurses and in a meantime would love to see how it is scored.
I found references for factor loadings, correlations of the subscales etc. but need to look at scoring which is I think building into a continuous variable.
Thanking in advance to anybody who can help.
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What is McCloskey/Mueller satisfaction scale?
· Learn more Originally developed to rank rewards that nurses value and that encourage them to remain in their jobs, the McCloskey/Mueller Satisfaction Scale (MMSS) is being used extensively in research and practice to measure nurse job satisfaction.
Background:The eight-factor, 31-item McCloskey/Mueller Satisfaction Scale is one of the most widely used scales for measuring job satisfaction among nurses. However, this scale was developed in 1990 for the American nursing context, and its psychometric validity and utility for use with non-American nurse
Mueller and McCloskey Satisfaction Scale (MMSS) Original Citation – Mueller CW, McCloskey JC. Nurses' job satisfaction: a proposed measure. Nurs Res. 1990 Mar-Apr;39(2):113-7. View in PubMed Contact Information Sharon Sweeney Center for Nursing Classification & Clinical Effectiveness College of Nursing 407 NB University of Iowa Iowa City, Iowa 52242 sharon-sweeney@uiowa.edu Price & Availability – Contact Sharon Sweeney for application. Available for use with payment. Brief Description of Instrument – Measures nurses' job satisfaction in 8 domains: satisfaction with extrinsic rewards, scheduling, family/work balance, co-workers, interaction, professional opportunities, praise/recognition, and control/responsibility. Originally developed for use with hospital staff nurses. Scale Format – 5-point Likert (5=very satisfied, 3=neither satisfied nor dissatisfied, 1=very dissatisfied). 31 items. Administration Technique – Self-administered questionnaire.
Scoring and Interpretation – Sum items of subscale.
Test-retest Reliability – Global scale = 0.64, six month interval. For subscale information, please see original citation. 1 / 2
Mueller and McCloskey Satisfaction Scale (MMSS) Internal Consistency – Global Scale = 0.89. For subscale information, please see original citation. Construct Validity – Correlated with characteristics from the Job Characteristics Inventory. Correlations found in areas of autonomy, friendship opportunities, feedback, variety and task identity. Correlations with intent to stay on job also found. Please see original citation for details. Criterion-Related Validity – Correlated with Brayfield-Rothe General Satisfaction Scale, Hackman and Oldham's Job Diagnostic Survey. Correlations ranging from 0.53 to 0.75 were found. Please see original citation for details. Strengths – Reliable and valid measure. Limitations – Assess different job facets, but not how important that is to the individual nurse. Notes for Consideration – Can use as a global scale or as subscales as required. Published APN Studies using instrument – Koelbel P, Fuller S & Misener T (1991). An explanatory model of nurse practitioners job satisfaction. Journal of American Academy of Nurse Practitioners, 3(1), 17-24. View in PubMed Koelbel P, Fuller S & Misener T (1991b). Job satisfaction of nurse practitioners: An analysis using Herzberg’s theory. Nurse Practitioner, 16(4), 43, 46-52, 55-56. View in PubMed Wild P, Parsons V, Dietz E (2006). Nurse practitioner's characteristics and job satisfaction. Journal of the American Academy of Nurse Practitioners, 18(11), 544-549. View in PubMed Flannery J, Van Gaasbeek DE. Factors in job satisfaction of the psychiatric clinical nurse specialist. Nursingconnections. 1998 Winter;11(4):27-36. View in PubMed
Reference:
Muller McCloskey Job Satisfaction Scale MMSS is being scoring
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Which is a lucid standard comprehensive book to guide on psychological scale construction and its standardization?
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Shweta Singh, here are some additional texts, though I'm not sure whether there are later editions for some of them, and some might be a bit too focused on health for your specific needs:
Aday LA, Cornelius L J: Designing and Conducting Health Surveys: A Comprehensive Guide, 3rd Ed, Jossey Bass, San Fransisco, 2006.
Kaplan RM, Saccuzzo DP: Psychological Testing: Principles, Applications, and Issues, 5th Ed, Wadsworth, Belmont, CA, 2001.
McDowell I, Newell C: Measuring Health: A Guide to Rating Scales and Questionnaires (2nd ed.), Oxford University Press, New York, 1996.
Murphy KR, Davidshofer CO: Psychological Testing: Principles and Applications, 4th Ed, Prentice Hall, Upper Saddle River, NJ, 1998.
Nunnally JC, Bernstein IH: Psychometric Theory, 3rd Ed, McGraw Hill, New York, 1994.
Streiner DL, Norman GR: Health Measurement Scales: A Practical Guide to their Development and Use, 2nd Ed, Oxford University Press, New York, 1995.
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I am developing a scale to investigate self-understanding in relation to cultural diversity among university students. I will be using this scale to investigate criterion validity for the scale I developing. 
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موضوع جميل جدا
أحتاج إلى مثل هذا ربما نستفيد من دراستكم أيها الدكتور العزيز
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Dear All,
I am working with environmental scales to analyze the influence of individual's attitudes on their pro-environmental behavior performance. I am guided by the theory of planned behavior proposed by Icek Ajzen, besides some environmental concern scales. However, I am figuring out how to create correctly the composite variables for the attitudes, subjective norms and behavioral control. From a paper (Meijer et al 2016) I found that the author multiply each belief with their respective outcome. This score will be sum up with the rest statements of the same category e.g. attitudes and that would be then added in to the model. Apparently this is not too common and so, I would like to hear from you an advice on how to build them?
Thank you,
Miriam
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Dear Mariam,
Please, how many items did you use to capture attitude? Did you also capture the two dimension of attitude - experiential and instrumental? What semantic differential scales did you use 5 point or 7 point? For example, if you use 5 point scale for semantic differential, say useless/useful, useless: 1,2,3,4,5: useful. Please, recode this as -2,-1,0,1,2. Then you generate the attitudinal score by summing all the items. So positive average attitudinal score represents positive or favorable attitude towards the behaviour whereas average negative attitudinal score indicates negative or unfavorable attitude towards the behaviour.
This how you compute behavioral belief: First recode the point scale as I have indicated previously. Afterwards, just multiple the belief strength by the corresponding outcome evaluation for each items. Then, sum up all to get behavioural belief index.
This computation is based Fishbein and Ajzen 2010 (The reasoned action approach book).
I hope this explanation helps with your analysis.
All the best
Emmanuel
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For example: Would using a 7 point scale of 0 - 3 in steps of 0.5 give you different results to using a 7 point scale of 0 - 6 in steps of 1? I'm aware that verbal labels are likely to be better but I'm interested in the possible differences between purely numerical scales that use 0.5 or 1 increments.
Thanks.
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Hello David,
As the 0-6 scale your describe is a simple, linear transformation of the 0-3 scale (by doubling the scores), there is no statistical or measurement advantage to either. Whether respondents react similarly to the different response scales is an empirical question.
Good luck with your work.
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Hello
I want to build a questioner about emotional and cognitive Strategies . my question is:
how should I Formulate sentences or items?
they should began mostly with " I thing " or "I feel" to represent the emotional and cognitive side of the subject ?
or
they should began with a verb to present a behavior or an act "I do" to represent the Strategies?
Tank you
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The sun was just an example. I would make the statement and then using likert scales have 1 as never, going up the scale to 5 always. Sorry, I should have put that.
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Hi everyone! I am trying to find the "how to" on item difficulty and discrimination on SPSS and its interpretation. I have read about and performed the command analyze> scale> reliability analysis, to get the corrected item total correlations (which I believe can be interpreted as a dicrimination analysis...?). I also watched videos that teach that you could calculate dificulty index by calculating means and sums in the analyze> descriptive statistics> frecuencies and interpreting the means. But this is a different method to the one I read about in books and articles (rbis).
I am using this article as reference:
It describes item correlation, discrimination index and difficulty index as different methods for item reduction. Is it adecuate to use just one or two of these analysis? Must we use all at the same scale construction proyect? According to the item correlation method, I only keep 4 of 30 initial items from the scale so far.
Proyect Description: Scale construction with 30 initial dichotomous items (True/False)
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Maria Acevedo Velazquez, just quickly in case it helps, I think that obtaining item-total correlations (through the reliability section of SPSS) is not going to give you the kind of answers that you want.
I think you also need to be clear about what is mean by discrimination. In some contexts, it means that scores on one construct are different from scores on another construct so that researchers are assured they're not just measuring the same construct in two different ways. In your context (from what I can pick up from your question), you want to be able to discriminate participants one from the other. That's a different kettle of fish.
I hope my pointing out those different things (both of which are dealt with in the Boateng et al. article that you mention) helps you set your sights a bit more clearly.
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I am currently trying to create a scale to measure a multi-dimensional parenting construct. There is currently no strongly established theory about the construct and I am investigating it in an age group that has not typically been the focus of parenting researchers. I created a list of 26 items based on a qualitative study and have done an EFA on the data. Almost half of the items are skewed and some are quite kurtotic due to low base rates of those parenting behaviours. However, I believe that these items are theoretically relevant to the construct of interest. Due to high skew/kurtosis/presence of non-normality, I used polychoric correlations for the EFA. A 3 factor solution was recommended.
My questions are:
1) The determinant of the matrix is less than .00001 but Bartlett's and KMO are good (fit indices are generally good as well). I have read in previous discussions online that <.00001 determinants may arise due to high kurtosis in items. Does anyone know of a reference/resource that explains this in more detail and/or has recommendations that it's not the end of the world?
2) A number of the skewed/kurtotic items have low communalities (<.40) even though they have factor loadings of over >.40. What are the best practices or existing rules of thumb on how to proceed with elimination of items to refine the scale? Should I delete the items with low communalities (despite the sufficient factor loadings), and then re-run EFA? Or should I delete items based on low factor loadings (<.40), then re-run EFA? If the latter, would it be necessary to do anything with (i.e., elimiate) the items that have low communality? Or just leave them?
Thanks very much in advance.
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Vivien So... One of the very important aspects of scale development is the sample size. You need to have to adequate sample size to perform EFA analysis, otherwise, problems like skewness and kurtosis will appear. Further, in your study (as it is exploratory in nature), you can initially retain items with low commonalities (<.40), but check whether items these are cross-loaded on some other factors. If so, delete them. These are the following articles that you may find useful:
  • Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: a primer. Frontiers in Public Health, 6, 149.
  • Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967-988.
  • Morgado, F. F., Meireles, J. F., Neves, C. M., Amaral, A., & Ferreira, M. E. (2017). Scale development: ten main limitations and recommendations to improve future research practices. Psicologia: Reflexão e Crítica, 30 (1), 1-20.
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Hi everyone! I am currently working on constructing a true/false binary response questionnaire of parenting knowledge. I found out that factor analysis is not appropriate for binary variables. Also I have read articles about principal component analysis, and polychoric and tetrachoric correlations, but found no examples on how to use these concepts for scale construction. Anyone knows about specific SPSS steps, other programs or plug-ins and interpretation for scale constructing for these analyses? Is there any other appropriate analysis? I would be thankful of any help!
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Hello Maria,
If you use the R plug-in for spss, then yes, there are a lot of things (including IRT) that you can do (see this link for information: https://developer.ibm.com/technologies/analytics/tutorials/ba-call-r-spss/).
On the other hand, the free, stand-alone program, FACTOR, handles the polychoric correlations quite nicely and offers many features not readily available in spss (such as parallel analysis) for factor analytic studies:
Good luck with your work.
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I have administered a 15statement attitude questionnaire with agree/ disagree response codes. The scale was developed as per method of Thurstone scale construction. I am finding mixed approaches to interpret the data:
1. one approach says just count the number of agree statements and that is a person's attitude score or take their percent
2. second approach says that the score for each statement is the median for that statement at the time of scale construction
Can anyone please guide on what is the right appraoch?
Many thanks!
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Thank you so much Dr.
Jimmy Y. Zhong
. Sincerely appreciate your prompt response.
Warm regards,
Vibha
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Many researches have Exploratory Factor Analysis followed by Confirmatory factor Analysis when theory is available. My research has not established model can I apply this Exploratory Factor Analysis and the result cab be taken as a scale developed
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Generally speaking, the EFA does not have to be followed by CFA, since the two analysis seeks different targets. EFA is mainly used for scale reduction and CFA is mainly used when the aim is to validate a measurement model in the structural equation context.
Now regarding the part that there is no theory available and that your research has not a proposed research model yet. However, I would expect that your questionnaire has a set of pre-defined (factors/constructs) that the (items/questions) are trying to measure.
Therefore, If you are developing a new scale then you definitely should apply EFA and ensure that the new (items/questions) are properly loaded on the targeted (factors/constructs). Later, once your new scale validated with EFA of identified (factors/constructs), you can run CFA, to validate the measurement model.
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Hi everyone,
I've conducted an EFA and ended up with 5 factors. A few of the items are cross-loading over 2-3 factors. I have already removed 10 items that either do not correlate or cross-load significantly.
I am fairly happy with the factors, however, the cross-loading items are confusing me and I have a few questions.
1. When calculating the total scores, means and Cronbach's alphas for each factor, do I include the items which cross load with other items?
2. When I present the final scale/solution, how do I present the cross-loading items?
3. There is one factor which is negatively predicted ('Lack of Support' [all items have a negative value]), however, I have changed the scoring so it positively predicts the factor (Support). There is one item in this subscale which cross-loads with another. How does this impact the scoring? Should I try to remove this item?
4. I started with a 37-item scale and I now have 27 items. How many items are too many to delete? At what point should I just accept it as an overall scale with a good Cronbach's alpha (.921) and say further research into factors and subscales is needed?
I am reluctant to delete the few cross-loading items I have remaining, as when they are removed from the analysis, the reliability score decreases for the individual factors and the overall scale.
This is my first time doing an EFA and so I would be very grateful for any advice or recommendations you may have.
Thank you.
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Hello Jessica,
Factor solutions can include variables that show salient affiliation with more than one factor (some personality measures are notorious for this type of structure). However, the concerns associated with cross-loading are usually: (a) the structure is more complex than Thurstone's idealized concept of "simple structure"; (b) it may make the task of characterizing what a factor represents more challenging; and (c) perhaps the variable isn't as well measured or defined as it could have been.
The answer to your question depends on your specific research aims as concerns this set of variables (and how these do or don't reflect the concerns listed above). If your goal is to derive the "cleanest" possible structure, then throwing out variables/items may be the way to go. Do recognize that the possible concern here is that you end up defining very restricted factors that may not fully represent the target constructs. As well, depending on your sample, it's possible that the resultant structure to choose may be overfitted to the sample and not generalize as well to other data sets.
In any event, if you elect to retain cross-loading items, then:
1. Yes, they are included in any representation of a factor (e.g., an estimated factor score, or just a summated score), or for score reliability estimates;
2. You present a factor structure matrix/table that shows all variables that you deem salient with each factor (factor pattern and factor inter-correlation matrices as well, if you used an oblique rotation);
3. If all loadings on a factor are negative, then you may reverse the sign and characterize the resultant variate as a reversed polarity construct estimate. (You may do the same with mixed sign loadings, as long as you reverse each variable's sign accordingly.)
4. The facetious answer is, if you get down to two items or fewer, you've likely gone too far! The more serious answer is, there is no way to predict in advance how many "keepers" there are from a preliminary set of variables that were constructed, identified, or adapted to tap one or more constructs. That's why people engage in EFA or CFA in the first place; to help identify what structures are supported by data and what structures are not.
The final note I would make is that Cronbach's alpha for an "overall" scale score might not be the best indicator, especially when you have identified multiple factors for that batch of variables. For individual factor scales, sure.
Good luck with your work.
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What are the general suggestions regarding dealing with cross loadings in exploratory factor analysis? Do I have to eliminate those items that load above 0.3 with more than 1 factor? 
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All of the responses above and others out there on the internet seem not backed by any scientific references. For that reason, this response aims to equip readers with proper knowledge from a book of a guru in Statistics, Joseph F. Hair, Jr.
First, it must be noted that the term cross-loading stemmed from the idea that one variable has moderate-size loadings on several factors, all of which are significant, which makes the interpretation job more arduous.
A loading is considered significant (over a certain threshold) depending on the sample size needed for significance [1], which can be seen as follow:
Factor loading - Sample size needed for significance
-----------------------------
.30 - 350
.35 - 250
.40 - 200
.45 - 150
.50 - 120
.55 - 100
.60 - 85
.65 - 70
.70 - 60
.75 - 50
-----------------------------
When a variable is found to have more than one significant loading (depending on the sample size) it is termed a cross-loading, which makes it troublesome to label all the factors which are sharing the same variable and thus hard to make those factors be distinct and represent separate concepts. The ultimate goal is to reduce the number of significant loadings on each row of the factor matrix (i.e. make each variable associate with only one factor). The solution is to try different rotation methods to eliminate any cross-loadings and thus define a simpler structure. If the cross-loadings persist, it becomes a candidate for deletion. Another approach is to examine each variable's communality to assess whether the variables meet acceptable levels of explanation. All variables with communalities less than .50 are viewed insufficient.
RESPECIFY THE MODEL IF NEEDED
What if we should not eliminate the variable base on rigid statistics because of the true meaning that a variable is carrying? Problems include (1) a variable has no significant loadings, (2) even with a significant loading, a variable's communality is deemed too low, (3) a variable has a cross-loading. In these cases, researchers can take any combination of the following remedies:
+ Ignore those problematic variables and interpret the solution as is but the researcher must note that the variables in question are poorly presented in the factor solution
+ Consider possible deletion: depending on the variable's overall contribution to the research as well as its communality index. If the variable is of minor importance to the study's objective and also has unacceptable communality value, then delete it and derive new factors solutions with those variables omitted.
+ Employ alternative rotation method: could be oblique method if only orthogonal had been used.
+ Decrease/increase the number of factors retained: to see whether a smaller/larger factor structure will solve the problem.
+ Modify the type of factor model used (component versus common factor): to assess whether varying the type of variance considered affects the factor structure.
Note:
No matter which options are chosen, the ultimate objective is to obtain a factor structure with both empirical and conceptual support. As we can see, many tricks can be used to improve upon the structure, but the ultimate responsibility rests with the researcher and the conceptual foundation underlying the analysis. Indeed, some empirical researches chose to preserve the cross-loadings to support their story-telling that a certain variable has indeed double effects on various factors [2]. So, ultimately, it's your call whether or not to remove a variable base on your empirical and conceptual knowledge/experience.
Reference:
[1] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis 7th Edition Pearson Prentice Hall
[2] Le, T. C., & Cheong, F. (2010). Perceptions of risk and risk management in Vietnamese Catfish farming: An empirical study. Aquaculture Economics & Management, 14(4), 282-314. https://doi.org/10.1080/13657305.2010.526019
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I would like to model Knowledge as a formative composite measure using PLS. I understand that formative measures alone are unidentifiable and unfortunately, I don't have any outcome variables to include in the model. I'm wondering if I could use the repeated indicator approach to model only the measurement of a construct by turning indicators into single-item first-order constructs and the composite a second order construct?
Also, I'm wondering if anyone is familiar with Adanco? Adanco is able to estimate item loadings of a composite construct, although the weights are all equal when using Mode B. Would modeling the variable this way be any more useful than simply calculating item-total correlations?
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I order to test if the formative indicators result in substantially high R-Squared (also known as redundancy analysis), you can define one single indicator of the tentative outcome of all the formative indicators. If you are taking the composite of the multiple dimensions, you must understand that the dimensions should have substantial correlation amongst them, and secondly, the development of a composite should justify theoretically.
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Hi, there.
A few days ago, I posed a quesiton about factor analysis of survey results, but I left many important details unspecfied. HERE is the more detailed one.
I develop a theoretical model of language assessment literacy (LAL) based on literature. In this model, LAL is captured in seven dimensions. A survey of 56 items with a 5-point Likert scale (unknowledgable, slightly knowledgable, moderately knowledgable, rather knowledgable, very knowledgable) is designed to measure primary EFL teachers' LAL. This survey is first piloted with 71 target teachers, and 65 cases are valid. Many of the respondents have strings of similar responses to successive different items. An exploratory factor analysis (SPSS) with generalised least squares and direct oblimin methods shows that nine factors have Eigenvalues greater than 1, but the first item has excessively large loadings (about 60%) on the first factor. The correlation matrix shows significant correlations between every two items (ranging from .22 to .92), suggesting multicollinearity. The corrected item-total correlations range from .58 to. 85, indicating good discrimination among respondents.
How should I revise the survey items to make the survey work so that I can confirm my model?
THANK YOU VERY MUCH.
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Manzu, I would capture some of the advice above with regard to your small sample size, but, much more pessimistically, I would say that you are largely wasting your time and effort to conduct any kind of factor analysis with as few participants as you have. Your results are almost inevitably going to be wonky.
Just as one example, one rule of thumb for exploratory factor analysis recommends 10 times the number of participants as there are items. Although there are other ways of determining an appropriate number of participants, you really have approximately only one participant for each item.
I'm sure you don't have anywhere near enough participants for conducting confirmatory factor analysis, either.
If you want to try to do something valid with your data, I'd look carefully at the interitem correlations and wording of your items. Perhaps they would give you a sense of which items might be redundant.
I don't know why you claim that corrected item-total correlations are able to indicate "discrimination among respondents", but I certainly think that those correlations would be meaningless if you have seven domains and are using the total score across them all as a basis for the total score in the corrected item-total correlations.
For what it's worth, I'd not use coefficient alpha to help sort things out. In my view, that metric is far less useful than is generally believed. There is substantial and authoritative information about that - information that many researchers seem either to be unaware of or to ignore.
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I am writing a paper in which I am calculating/estimating how many students are socially marginalized in Denmark. I have applied Exploratory Factor Analysis (EFA) and determined that seven items correlate strongly and are likely connected to a single underlying factor. However, three of these items are measured on a Likert scale (1-4) without a neutral category, while the others are measured on a Likert scale (1-5) with a neutral category. Is it possible to weight each item so that they contribute equally to the composite measure and, if so, how should I proceed? Is there any literature that you can recommend on creating composite measures using items with different scales or ranges? Any help would be appreciated, thank you.
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You can re-score each variable, x, as its z-score, where z = [x - (mean of x)]/(sd of x). That will give every z-scored variable a mean of zero and a std. dev. of 1.0. With equal dispersions, all variables will have the same weight in an additive composite scale calculated from the z-scores. This approach is covered in most standard textbooks on forming composite scales, but I don't have access just now to my usual resources so can't give you a citation; sorry.
Z-scores treat the underlying variables as interval/ratio in measurement, whereas Likert items are actually just ordinal. Doing EFA on those items makes the same assumption about level of measurement, so you may not be bothered by that aspect of the z-score approach. But if that is a concern, you can accomplish approximately equal weights by just re-scaling your variables to give them all the same range. You could re-code your 4-point items as follows: 1=1, 2=2.33, 3=3.66, 4=5. That would (approximately) preserve the equal-appearing intervals of your original 4-point items, while giving all items the same range, therefore approximately the same dispersion, therefore approximately equal weight in a composite, additive scale.
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HI Researachers,
do we have a scale for COVID 19 for economic impact?
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COVID-19 has triggered wartime effects. Thus, I'd recommend measuring it using a single-item scale. See examples below.
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We can say that these factorial analysis approach are generally used for two main purposes:
1) a more purely psychometric approach in which the objectives tend to verify the plausibility of a specific measurement model; and,
2) with a more ambitious aim in speculative terms, applied to represent the functioning of a psychological construct or domain, which is supposed to be reflected in the measurement model.
What do you think of these generals' use?
The opinion given for Wes Bonifay and colleagues can be useful for the present discussion:
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Hi! Factor analysis help to identify the underlying dimensions. Sometimes the measurement items varies based on various contexts. Besides if the measurement is not well established conducting factor analysis can produce clear dimensions that can be used for the particular research model. Thanks
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I am working on academic anxiety of undergraduate students. Is there any scale constructed/available to measure academic anxiety of undergraduate students?
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The Kuwait University Anxiety Scale is one of them.
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I'm an undergraduate student who has a course in test construction as well as research. The construct we are studying is mental toughness, and we plan on using the MTQ-48 to assess it. I am aware that there is a technical manual for the MTQ-48 available online, unfortunately, it does not contain a scoring procedure, and does not have information about which items belong to what subscale. Using other references, what we got is that the MTQ-48 is scored on a 5-point likert scale; with 1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree. We are also aware of what items fall under each subscale, such as items 4,6,14,23,30,40,44,and 48 which fall under the challenge subscale. However, we were not able to find a reference stating which items are negatively scored. While we could make an educated guess, it is required that we have a source for the scoring procedure. If anyone here has such a reference, or knows the items, it would be highly appreciated. Thanks!
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If you want a short unidimensional measure of mental toughness that has demonstrated promising invariance across sports and general samples let me know.
Happy to provide items (NO COSTS) and assist.
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Is there anyone out there who's done research on Transformational Leadership (TL) without using the standardized questionaires like the Multifactor Leadership Questionaire (MLQ)?
I'm doing a study on the effect of leadership on employee behaviors (OCB in specific) and my set of data is an employee survey of motivation and the psychological working environment (based on QPS Nordic). The data was thus not gathered for my specific study, which leads to some extra challenges. One is determining measures of perceived transformational leadership. My intention is to point out questions that seem to map the four i-s of such leadership and then do some validation testing on them.
Anyone who's done this before, or who has done some sort of validation testing of such surveys against the established ones when it comes til TL?
I'm grateful for all suggestions, experiences and tips!
Kind regards,
Christian Otto Ruge
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Thanks a lot, Gayan. Do you also happen to know any scales for mapping the self determination theory?
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I would be thankful if someone could give me a short breakdown on how to test unidimensionality of polytomous data (likert-scale) using IRT-models in R.
As it is my understanding that the ltm-package is obsolete, I'm looking for an equivalent to ltm::unidimTest() (perhaps within the mirt-package?).
Thank you
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you can use sirt package for this purpose. DETECT and Noharm tests can be conducted with sirt package. You can use expl.detect() function for DETECT method and noharm.sirt() function for NOHARM method.
Hope helps.
AFK
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The existing question is no for deletion.
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If you get positive correlation, respondents with higher time spent are disagree more( 5: disagree )
Positive correlation means high value with high value ( coding value)
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I am looking for a concise scale that would place a person on the continuum between "When someone needs help, I am usually the one who steps up" and "Oh, surely someone else will do something".
I have found some related scales/constructs, but none is what I am looking for:
-Locuf of Control is focused on oneself - "how much I am in control of what is happening to ME." I am interested in one taking control over helping someone else.
-Bystander attitude - I even found a "Bystander Attitude Scale", but it relates to sexual violence. I am interested in helping behaviors (from charitable giving to stepping up to defend someone in dangerous situation)
-Altruism related more to "wanting to help" rather than "taking the (uncomfortable) step and helping"
-Proactiveness - the scales I found all relate to one's own career development
Thank you, I appreciate all suggestions very much!
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Thanks Matt!
Communalism seems a good proxy for what I'm after.
We're also considering what you mentioned: developing a scale/construct to address this. The proactive end of the scale would probably match closely what Zimbardo describes as "small everyday heroes", that is people who (1) care for others and (2) do step up even if it comes with a risk/cost.
Best,
Michal
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Hello,
I am a master student in nursing at the University of Antwerp. I'm doing my thesis about courageous leadership. For this a made a conceptual model and a new questionnaire.
The aim of my research now is to develop a valid measurment. For this a developed a questionnaire with 41 questions about taking action, honesty, ... Now a need to do a factor analyse. But my questionnaire has 2types of likertscales is this a problem?
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Dear @Kenneth Philips if you have collected your data you have very few options:
-Keep all your data and make all item scores as comparable by making them even with a similar method to what I described.
-Split the scale into two if it makes sense.
-Redo the data collection phase.
As you know already, it is a must your advisor agree to your solution. "Making even procedure" is easily trackable through desriptives such as mean and median, so you have to write what you have done in your thesis.
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I am in the middle of questionnaire development and validation processes. I would like to get expert opinion on these processes whether the steps are adequately and correctly done.
1. Items generation
Items were generated through literature review, expert opinion and target population input. The items were listed exhaustively till saturation.
2. Contents validation
Initial items pool were then pre-tested with 10-20 target population to ensure comprehensibility. The items were then reworded based on feedback.
3. Construct validity
a) Bivariate correlation matrix to ensure no items correlation >0.8
b) Principal Axis Loading with Varimax Rotation. KMO statistic >0.5. Barttlets Test of Sphericity significant. Communalities less than 0.2 were then removed one-by-one in turn. Items with factor loading high cross-loading were removed one-by-one in turn. Then, item with factor loading <0.5 were removed one-by-one in turn. This eventually yielded 17 variables with 6 factors, but 4 factors have only 2 items. So I try to run 1, 2, 3, 4, 5 and 6 factor models, and found that 4-factor model is the most stable (each factor had at least 3 items with factor loadings >0.4). Next analysis is only on 4-factor model.
c) Next, i run Principal Component Analysis without rotation on each factor (there are 4 factors altogether), and each resulted in correlation matrix determinant >0.01, KMO >0.5, Bartlett significant, total variance >50%, and no factor loading <0.5.
d) I run reliability analysis on each factor (there are 4 factors altogether) and found cronbach alpha >0.7, while overall realibility is 0.8.
e) I run bivariate correlation matrix and found no pairs correlation >0.5.
f) Finally, i am satisfied and decided to choose four-factor model with 17 variables and 4 factors (each factor has 5,4,4,4 items), and each factor had at least 3 items with loadings >0.5. Realibilility for each factor >0.7 while overall is 0.8.
.
My question is, am i doing it right and adequate?
Your response is highly appreciated.
Thanks.
Regards,
Fadhli
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attached file may help....
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Hi!
I'm looking for a software that will gave me the possibility to ask questions with standard Likert scale 1-5 AND in the same instruction/question I will have space to ask about for example 'On what level/extent this question refers to your job responsibilities?' I have in mind the Side-By-Side Matrix Questions.
Example:
Question 1
Instruction 1 Agree 1:2:3:4:5 Disagree || Low 1:2:3:4:5 High
Instruction 2 Agree 1:2:3:4:5 Disagree || Low 1:2:3:4:5 High
Instruction 3 Agree 1:2:3:4:5 Disagree || Low 1:2:3:4:5 High
Thanks in advance!
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Hello Marek,
Qualtrics (www.qualtrics.com) software offers side-by-side questions with a large range of options. It is, however, commercial software.
Good luck with your work!
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I am trying to analysis the effect of various independent variable like service quality, ISP commitments,corporate image, product attributes, trustworthiness, service value, switching cost over the dependent variable brand loyalty for ISP customers. I have created questionnaire that consist ordinal scale (from very poor to very good) for some independent variables and interval scale(Strongly disagree to strongly agree) for other independent variables. How can I merge the interval and ordinal scale to analyse and develop relationship with the interval scaled loyalty.
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It isn't clear why you consider rating from poor to good to be ordinal, and consider rating disagree to agree to be interval. They are both the same. Most people would treat them as continuous and use correlation and other methods based on correlation (e.g., regression, SEM). Some people, however, are concerned that intervals might be unequal and prefer to use methods designed for categorical variables (logistic regression, log-linear analysis).
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I'm writing my paper on problems and challenges faced by school counsellors :development of a scale .please help me with some good reference
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Agree with Peter Samuels ...
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The Cronbach alpha for two factors are 0.5 and 0.4 respectively. What does this signify? Is there a justification/support to retain the factor or eliminate the factor?
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Interesting..
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I am currently doing a research proposal surrounded perceptions and influences of revealing attire and sexual assault. The research questions are as follows:
1. Is there a difference between how men Perceive the intent of women’s Sexual attire and the motivations identified by women?
Hypothesis - it is hypothesised there will be a difference between how men perceive the intentions of a revealingly dressed woman compared to females.
2. In cases of sexual, does the victim hold more responsibility if they are wearing sexual attire?
hypothesis - there will be a gender difference in the amount of responsibility placed on a victim in a sexual violence case if she is wearing sexual attire. Precisely, women will place less responsibility compared to men.
3. Are men more likely to approach women in revealing clothing?
- it is predicted that participants believe that men will be more likely to approach a woman if she is in revealing clothing.
For this study a psychometric scale construction method will be used. The questionnaire will be split into 3 sections:
1. assessment of motivation (participants shown image of a model in revealing clothing then asked to rate her motivations for wearing such clothes by indicating how much they agree to give statements such as “she wishes to feel attractive” “she intends To convey sexual interests“ on a 5-point likert scale from 1(strongly disagree) to 5 (strongly agree).
2. Direct assessment of motivation. (Women complete this section only and instructed to respond to 5 questions on a 3-point likert scale yes, sometimes, no, with questions such as do you dress revealingly to feel attractive...)
3 assessment of victim blame and reactions. (how much responsibility subjects place on this model if she was a victim of a rape scenario. participants will provide an answer using a 5-point likert scale from 1(not much) to 5(a lot). Men will also be asked if they saw her on a night out how likely would they be to approach her, 1(extremely unlikely) 5 (extremely likely).
I am struggling on on the data analysis section as I am supposed to include how I would analyse the data my study would produce. I also need to explain why.
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Hi everyone. I am sorry to interrupt this discussion but I was wondering if i could get some expert advice on how to analyse my data.
I am trying to run the statistical analysis for a cross cultural study comprising of two country samples. I have 5 variables, one mediator(perceived value) and 3 moderators (subjective wellbeing, healthy lifestyle, ethical identity), IV (conspicuous consumption), DV(customer loyalty) all measured on a 7 point likert scale. I was wondering if 3 moderators is too many and what would be the statistical tests I should perform for my study.
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Hi Researchers, I have examined several studies to see what is the most common methodology in scale measurement (Including Churchill's paradigm), But there are still some confusions in my mind. Please help me to distinguish between steps in developing and validating a scale.
1/When we say "developing" (not validating), does it mean that using one set of data would be enough?
3/Exploratory=development? Vs confirmatory =validating ??
4/ What are the statistical methods usually used in the developing step (again not validation): EFA with factorisation tests alone (Bartlett, KMO...)?
5/Is Cronbach's alpha used in the exploratory step? Confirmatory step? Both of them?
6/Is Amos a compulsory step in validating or we could just use another factor analysis in a second set of data with additional statistical tests (e g Joreskog?)
7/Is developing a scale what we call "measurement model"? Which means no independant, moderating or mediating variables are involved, right?
I am looking forward to read your expert answers. Thank you very much indeed.
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How to normalize numeric ordinal data from [1, Infinity) to [1,5]? I want to normalize relative scores which is ordinal by nature. But range of score can be from [1, Infinity). So, I need to bring it on scale of [1,5]. Can anybody help me figure it out?
Data values are double (float) type values.
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We had a similar problem in the context of educational assessment where very often people work with "judgement scales" of both types, i.e. two-sided bounded (like your [1,5]) as well as one-sided bounded (like your [1,inf] or [0, inf]), and they have to merge those distinct scales in some way. Fortunately, there are not so many ways to do such mergings, if you want to keep things simple and choose the right mathematical framework which suggests you the appropriate transformation. Just picking the one or another transformation to fit your data e.g. to a normal (or some other) distribution is IMHO a brute statistical approach which doesn't do justice - plausibly - to the inherent structure of your data. For instance, in our case, it was plausible to assume a sub-additive operation on scores, something of the form a + b - a*b, and derive a structure which turns out to be a semi-module. From here, everything else could be derived - without guesswork. The 1-1 transformation from a bounded to a unbounded interval we used had the relatively simple form: 2-log ( score / (1 - score) ), where 2-log is the binary log function, and scores are numbers between 0 and 1. The resulting numbers are between -inf and +inf, but of course we can adapt it such that the resulting numbers start from 0 onwards. Because the transformation is 1-1, it has an inverse which goes the other way back. Works great! Probably there are statistical techniques for doing tests with your data, but that is none of our concern in our context.
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Dear researchers,
A student of mine is currently preparing her thesis on a specific construct that has never been measured. She's intending to apply Churchill Paradigm to developp a scale. Would that be sufficient as a research problem for her thesis and for building a model (knowing that she will use both exploratory and confirmatory factor analysis)?My question: Can she settle for the developpement of a scale as her main research problem? Should she not look for other relevant constructs to be mainstreamed into the final confirmatory model? Or is it not necessarily a requirement? I am looking forward to read your answers. Thank you in advance for helping me.
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I agree with Danial Goldman that it is up to your university to make the decisions, but I can say that at my university, this would certainly be adequate for a masters thesis but not necessarily for a PhD.
One possible way to resolve this would be at the level of validation, since that typically requires collecting other measures to demonstrate that the proposed measures has the predicted relationship to other concepts. So, a detailed pretest of the measure might be sufficient.
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In the model in my thesis, I draw a direct link between the two constructs organizational constraints and workload to the construct Abusive Supervision. Studies have shown that constraints and workload are stressors and negatively impact performance, motivation and well-being. I justify an increase in Abusive Supervision that way, that there were studies that have shown that supervisors e.g. treat the employees hostilely, because they perform poorly and thus throw a bad light on the supervisor. Can I put my questionnaire under these assumptions? And just assume from previous works, without interrogating the performance separately, that the two stressors have a negative impact on performance and thus favor hostile supervisor behaviour?
Like this: Organisational Constraints ++----> Abusive Supervision
Or do i Need to put it this way: Organisational Constraints- - ----> Perfomance ++-----> Abusive Supervision
I hope my Question is understandable. Thanks in Advance!
Best Regards
Kim
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You can develop your questionnaire, based on these assumptions, and then use the results to possibly falsify those assumptions, which would be great, as that's the goal of science anyway. But you need to word the questionnaire in a way that does not produce a bias towards or away from your assumption.
Is our questionnaire going to be a large scale survey or an interview? If it's an interview especially, you're going to want the questions to be open ended so as not the lead the interviewee. So you wouldn't want a question like "do you agree that when your work is of poor quality, your supervisor acts abusively?" And instead perhaps ask "how does your supervisor respond to the quality of your work?"
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Hello, I want to use the Job related affective well being scale from 1999, Paul T. Van Katwyk, Suzy Fox, Paul E. Spector, and E. Kevin Kelloway, http://shell.cas.usf.edu/~pspector/scales/jawspage.html for my master Thesis. I Need a german Version of it. On the page is the german Version from a master Thesis. Is it okay to use this items? Or am i just allowed to translate englisch items to german to use them for my work?
Kind Regards
Kim
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Make sure you get an independent "back translation" into English to make sure that the content does indeed match the original.
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I'm developing a questionnaire to assess observational data in an area of research which is notoriously prone to low inter-rater reliability. Hence, I'm looking for factors in questionnaire design that are generally known to show positive impact on inter-rater agreement. Unfortunately, any means of physically and/or verbally interacting with the raters prior to data collection is not an option, so methods such as Frame of Reference (FOR) training are not an option in my case.
My initial choice regarding the response format would be a behavioral anchored scale, but previous research have shown insufficient improvements in agreement of raters. I don't wish to open up a discussion on the topic on idiosyncratic variation - which will remain a problem - but instead focus on possible improvements regarding response format / scale construction. Perhaps someone can supply me with interesting research regarding this topic?
Many thanks!
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More items, less answer choices, more rating training.
Hope this helps,
Matt
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Specialists in testing and scales construction believe that the value of the reliability coefficient found by test-retest method is usually noticed to be higher than the value of that found by Alpha cronbach method. Is there a scientific justification for this differenc?
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If the assumptions underlying both reliability measures were met, i.e. essential tau-equivalence for alpha and parallel measures for retest reliability, both measures would be identical. However, the assumptions are seldom tested, and violating the assumptions leads to different values. One reason for a low rest-reliability is the time-lag: With increasing time interval between the measurement time-points the correlation will decrease because of occasion-specific influences. This correlation is often treated as a reliability coefficient, which is generally not correct because the assumptions for this coefficient were not met.
HTH, Karin
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I'm doing a research about the impact of sensory factors on tourist experience in hotels (sensory branding of hotels and its impact on tourist experience).
Are there some scales that you can offer me about this topic?
Do I need to combine the sensory dimensions and tourist experience scales? or can I reach a combined one?
Thank you in advance for taking the time to answer my question.
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I have seen this work, the authors have similar studies, it helped me quite. Thank you very much for your support.
Best wishes..
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Can anyone suggest a link between these two factors? How will you justify an effort to find a relation between the two?
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Matthew Kerry Thank you so much. That was extremely helpful.
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Aloha,
I am a atudent (business psychology) and I prepare my bachelor thesis. The main aim of the thesis is the validation of two translated scales.
For the translation I adhered closely to WHO Guideline "Process of translation and adaptation of instruments":
  • Forward translation
  • Expert panel Back-translation
  • Pre-testing and cognitive interviewing
  • Final version
How would you validate statistically the scales?
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Leon Vahlkamp, you are also suggested to collect data to examine factor structure of the translated scale (especially if the scale is not unidimensional) using exploratory and confirmatory factor analysis.
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This particular scale is needed for my thesis. Thank you very much.
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Most studies use either the Bennett & Robinson deviance scale that assesses deviance directed at organizations or people, or the Spector et al. Counterproductive Work Behavior Checklist (CWB-C) that in addition to CWB-Organization and CWB-People, assesses five dimensions of Abuse, Production Deviance, Sabotage, Theft, and Withdrawal:
You can find the Bennett & Robinson scale in their 2000 paper, and you can find the CWB-C scale here: http://shell.cas.usf.edu/~pspector/scalepage.html
Bennett, R. J., & Robinson, S. L. (2000). Development of a measure of workplace deviance. Journal of Applied Psychology, 85(3), 349-360. doi:http://dx.doi.org/10.1037/0021-9010.85.3.349
Spector, P. E., Fox, S., Penney, L. M., Bruursema, K., Goh, A., & Kessler, S. (2006). The dimensionality of counterproductivity: Are all counterproductive behaviors created equal? Journal of Vocational Behavior, 68(3), 446-460. doi:http://dx.doi.org/10.1016/j.jvb.2005.10.005
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A semi-serious discussion on manners in respect of adults, children, infants and animals led to these questions:
1. On what type of scale would you measure "manners"? From 'no manners' to 'very good manners' (e.g., 0 - 4); or 'very bad manners .. no manners ... very good manners' (e.g., -4 ... 0 ... +4)?
2. Having in mind your choice of scale, how would you operationalize the construct 'manners', in order to measure it?
Enjoy, and thank you in advance for your ideas.
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Hi Leopold,
in measurement questions like this one I think the first issue to attend to is whether you conceive of the measurand (the thing being measured) as a latent trait/variable or not. Measurands that we believe are latent traits are (real but un-observable) things that exist inside people, like intelligence or extroversion. In that case you assume that the latent variable has an underlying continuous structure... this implies a couple of things: 1) that you attempt to scale the responses so that individuals are reporting about themselves ("I always greet the older people first"), 2) as you choose items (indicators of the latent variable) you attempt to scale the response options - even when they are likert scaled - in such a way that you can add them up so that higher scores indicate more of the thing being measured. So this would imply choosing a number of items that all point at 'manners' and that are all scaled positively. Lower scores will indicate the absence of the thing being measured.
On the other hand you may conceive of 'manners' as a constructed entity and would be creating therefore an index variable. Index variables do not need to have indicators that are all on the same scale and responses to items can be combined in any way (for example you might combine items so that they maximize the prediction of some outcome).
I recommend the very good article "Three Cs in measurement models: causal indicators, composite indicators, and covariates"by Bollen & Bauldry (Psychol Methods. 2011 Sep;16(3):265-84. doi: 10.1037/a0024448.)
Hope that is helpful to you in this very interesting conversation!
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I want to measure success in creative/cultural entrepreneurship. Besides economic growth, subjective perceived success and survival, "NON-TECHNICAL INNOVATION" and "ARTISTC MERIT" are my success/performance indicators. Does anyone know scales and constructs to measure them via questionaire? Thank you in advance.
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Appraisal theory / contextualised factor (sometimes called affective decision ecology) might be relevant. But you will need to bring these perspectives into your discourse.
Affective
Attitude
Emotion
and some other related concepts are used in art works. Movie industry uses this and observes what elicits response...e.g. music.
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I'm exploring the concept that scales can be ordered and that certain items should carry more weight in a scale. I came across guttman scalograms and Mokken scaling techniques. Creating the initial Mokken scale makes sense. What I don’t get is that after I get my H coefficients and AISP to run with Mokken, how do I evaluate the data in a meaningful way?
If I use a mokken analysis on a 10 item likert survey, it wouldn't make sense to get an overall composite mean score to represent the latent ability since I established that items are ordered on difficulty. Do the H coefficients determine item weights? How can I sum a participants score on my newly created Mokken scale?
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In regards to IRT, I would suggest you to start non-parametric IRT (KernsmoothIRT) in R to evaluate/describe the use of response options for all items. May want to drop some if they do not work well with overall scores. If response options look OK, move to a parametric graded response model (ltm package in R) for item parameter estimation, identification of patterns of responding, and constructing of a standard error of measurement that accounts for where on the continuum of scale performs best/worst. It might be meaningful to start with the theory behind the scale development to help.
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I am currently preparing a thesis proposal for a study of the effect(s) of gender stereotypes and discrimination on aspects of employee well-being.  I intend to survey employees on their experiences of gender stereotyping/discrimination and the effect of these experiences on (a) job satisfaction, (b) organizational commitment, (c) turnover intentions, and (d) withdrawal behaviors.  Thus, I am interested in investigating the relationship between gender discrimination and the aforementioned occupational outcomes. I have been researching appropriate measures for these constructs but have yet to reach a consensus on what to use.  
Through my research I have found many scales that measure sexism, although the majority are mostly-if not entirely-attitudinal measures.  Below is the current list of my top choices to measure gender stereotypes, sexism and discrimination (in descending order):
(1) Schedule of Sexist Events
(2) Stigma Consciousness Questionnaire
(3) Ambivalent Sexism Inventory
The Schedule of Sexist Events scale is my top choice currently as it most closely measures actual behavioral instances of experienced discrimination (SSE-Lifetime and SSE-Recent).  A limitation of this scale, however, is that it samples women exclusively.  If at all possible, I would like to use a measure that allows for a heterogeneous gender sample.  Based on others' experience, does anyone have any recommendations for measures to use?  Does anyone have any experience with the SSE or SCQ and ASI?  Any and all recommendations would be greatly appreciated.  I am not trying to determine whether an individual holds sexist beliefs, but rather if and how much an individual has experienced gender discrimination.
Thus far I have chosen the following measures for the remaining variables (but I am open to change):
Job Satisfaction: Job Satisfaction Survey (Spector, 1985).
Organizational Commitment: Organizational Commitment Questionnaire (Mowday, 1979)
Turnover Intentions: Turnover Intention Scale (TIS-6)
Withdrawal Behaviors: N/A 
I am comfortable with my choices of the JSS and OCQ.  I am, however, uncertain of the TIS-6 and have not been able to find a verified and valid measure for withdrawal behavior.  Any suggestions on scales/surveys/questionnaires that would fit these variables would be greatly appreciated.  
Thank you for your time and consideration!
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Three-Component Model file attached. In the same file, you also have the Organizational Questionnaire.  Hope it will help you. 
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Hi,
Can anyone please share some recent and good research papers which lay the foundation and explicitly explain/describe the ground work for psychometric scale construction.
I shall be highly grateful for the help.
Regards to all
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The purpose of scale construction is to design a questionnaire that provides a quantitative measurement of an abstract theoretical variable.
Scale construction can be seen as a specialized area of survey design. Not all surveys are scales, however. Before trying to apply the procedures presented here to the construction of your questionnaire you should decide whether it really is a scale. The most obvious key is that a scale uses a moderately large number of items to measure a single construct. The items within a scale are typically
interchangeable , meaning that the response to one item has exactly the same theoretical meaning as the response to any other item within the scale. Each
item is designed to be a different way to “ask” about the same theoretical variable. Some questions may written so that more positive responses indicate less of the variable in question , but the expectation is that the magnitude of the correlations between items (whether positive or negative) should be relatively high throughout a scale. Additionally, after a participant completes a scale the responses are aggregated in some way, such as by taking the average, to produce the actual measurement. The specific response provided to any individual question is usually not of interest.
• Good scales possess both validity and reliability
. A scale has validity if it properly represents the theoretical construct it is meant to measure. A scale has reliability if repeated measurements under the same circumstances tend to produce the same results. These two concepts are very important to scale construction.
• Sometimes a single questionnaire contains items from several different scales mixed together. This is perfectly legitimate. In this case your items will not all be interchangeable - items making up different subscales will be slightly different. The items should, however, be interchangeable within each subscale.
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Dear all,
I would like to use a system of scoring to rank some statements regarding risk management application in a project. I have seen the system of scaling used by the INK model (Dutch version of EFQM model) which uses a four scale rating using 10, 7, 3, and 0 which respectively means 'totally applied', ' to a large extent applied', ' limited applied' and ' not applied'. I would like to know whether there are specific scientific reasons behind these numbers or any similar scales. Why, for example, this model uses 10, 7, 3, 0 and not 10, 7, 5, 0? I am looking for a scientific article that explains the reason behind this scale or any other scales. Does anyone has the experience of using the similar scale? Does anybody know how a scale should be defined and what are the reasons that should be considered?
I appreciate any answer/ comment on this question.
regards,
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These two standards can be useful as a reference: 
1. Risk management: Risk assessment techniques BS EN 31010:2010
I used semi-quantitative analysis for my PhD using rating matrices. 
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Dear all,
my dependent and independent variables are 7-Point Likert scale and measure perception toward effective leadership, therefore I assume my data is of ordinal nature.
The independent variables are six constructs with 5 questionnaire items each, which measure cultural values.
The dependent variables are 2 constructs with 19 items each, which measure the perception of effective leader attributes.
So I hypothesize that each of the culture dimensions are associated with perceived effective leader attributes. I have collected the data and intend to do the statistical analysis as follows:
1. Reliability Analysis - Cronbach's Alpha
2. Factor Analysis
But then I'm struggling between ANCOVA or Spearman's Rho to test association. I understand that ANCOVA is used for interval and Spearman for ordinal data.
Could you please advise on an appropriate method?
Many thanks,
Ahmad
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If you have categorical variables, then the analysis of covariance would be suitable. But you mention Likert scale, so we assume you have rating 1-7; in which case the ANCOVA may not be most optimal
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Dear all,
I'm investigating perceptions of effective leadership of Germans and Indians (I want to know which leader attributes are mutually endorsed and which are not). Part of the questionnaire assessed the attitude towards cultural values of the respondent, the other part attitudes toward effective leader attributes.
I have run a questionnaire and gained N=209 responses. The questions were asked on a 7-point Likert scale and the precise questionnaire can be found here: http://globeproject.com/data/GLOBE-Phase-2-Beta-Questionnaire-2006.pdf (I only used part of the questionnaire and not all questions, as I only investigate perceptions). I hypothesize that certain elements of culture are predictors of certain perceived effective leader attributes.
Now I find it a bit difficult to assess which statistical tests make sense. I've done some research online and the information is quite ambiguous and sometimes conflicting.
Therefore, I'd be really grateful if anyone here could advise.
Thank you,
Ahmad
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There is a difference between working with individual items that used Likert-style scoring, versus combining those items into a scale that would have interval-level measurement properties.
Because there have been so many questions about this issue, I have compiled a set of resources on it.:
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I am doing a study on determinants of pro-environmental behavior, where I propose values orientation (egoistic, altruistic and biospheric) and awareness of environmental threats as possible determinants. all of the IVs that I use for my treatment are continuous in nature (scale). The DV is a set of scores as well.  I initially ran a multiple regression to test my hypothesis, but I am told to use N-factorial/ way ANOVA instead, so that I can see the interactions among my variables. I am attaching my concept for reference. 
Any suggestions/ advise would be much appreciated.
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I don't recommend cutting your variables up into categories for two reasons. First, you lose information. Second, trying to examine interaction effects when both your variables include multiple categories can be a real headache.
Hence, you should follow the advice of others and test multiplicative effects between your independent variables in the regressions equation.
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The Likert scale items are intended to answer a of whether communication between hospital staff is effective or not .
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There is a lot of debate whether parametric tests or non-parametric tests can be used. But in summary if you are prepared to assume your data represents interval data then parametric tests are usually not inappropriate.
Some useful references in this article: http://eprints.uwe.ac.uk/30814/
With regards the neutral response, these references may be of interest:
Armstrong, R. L. (1987). The midpoint on a five-point Likert-type scale. Perceptual and Motor Skills, 64(2), 359-362.
Bishop, P. A., & Herron, R. L. (2015). Use and misuse of the Likert item responses and other ordinal measures. International journal of exercise science, 8(3), 297.
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Dear everyone, 
I am in quite some distress regarding my thesis.
I have set up a survey, consisting out of 21 questions. All 'based' upon the Likert scale fundaments, yet not completely like it.
Some questions I've made can be answered with 5 questions, ranging from: completely agree, to completely disagree. However, I also have some questions (i.g. What source sounds most trustworthy? and then 5 possible answers).
Basically my thesis is about' what can stimulate the consumer acceptance of Halal food'. This is my DV(consumer acceptance) and out of my 21 questions I have constructed 17 IVs, that form(in groups) 6 main constructs(Health, CSR, Price, etc) that possibly influence the consumer acceptance.
I hope until here everything is quite clear. 
So what I want to do is to check if there are any correlated answers(let's say I see someone answered question 1 with the first option and then it maybe correlates with him answering question 14 in the same way). Plus I would like to see which questions are significantly answered in the same way(to see what the majority of people deem important).
I have no clue why my supervisor told me this survey was okay, as now I have no clue how to do this. I don't even know where to start(reliability checks), as I have no clue how to combine these Likert-scale question and the non-traditional Likert-scale questions..
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P.s. my sample size is 104 respondents
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Can anyone please suggest appropriate scales for measuring university graduates' voluntary and involuntary unemployment, subjective and objective employability and visible and invisible underemployment? 
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Very helpful..thanks a lot, you