🎯 Key Takeaways for quick navigation: The video discusses quantitative research and focuses on measuring variables as scales for hypothesis testing. Variables can be manifest (directly measurable) or latent (hidden and multidimensional), and latent variables often require the use of measurement scales. Measurement scales are sets of criteria or questions used to measure latent variables, and they can be adapted from existing scales in the literature. Translation of measurement scales should involve a two-way translation process to ensure fidelity and objectivity. The video provides an example of a model with manifest and latent variables, including moderating variables that influence the relationship between other variables. It emphasizes the importance of justifying the links between variables with hypotheses in quantitative research. 17:47 When creating a measurement scale, it's important to adapt it to your specific context and research, not just accept a scale as-is. 25:35 To create a measurement scale, follow four key steps: literature review, qualitative exploratory study, quantitative exploratory study, and quantitative confirmatory study. 30:28 During the quantitative exploratory study, gather responses from a representative sample to identify common criteria and group them into dimensions. 32:17 Once you've developed your measurement scale, it becomes your instrument for assessing a specific aspect in your research. 34:20 If existing measurement scales don't fit your context, consider contextualizing them by blending elements from various scales or creating a new one for your specific needs. 35:25 When developing a measurement scale, you may need three initial phases: qualitative exploratory, interactive exploratory, and confirmatory quantitative. 35:54 For scale validation, you typically need four phases: three for validation and the final phase for large-scale data collection. 36:38 Developing your measurement scale requires prior research experience, dedication, and time commitment. 37:22 Validity and reliability are key aspects when assessing measurement scales. 38:42 Consider the nature of variables, including explanatory, mediating, moderating, and control variables. 39:18 Qualitative data categorization may precede scale validation, but ensure you have a qualitative foundation for your scale. 40:15 Adapt existing measurement scales if they are suitable for your context; no need to start from scratch. 43:11 Choose between factor analysis and confirmatory factor analysis based on the conditions and normality of data. 44:25 When using an existing scale from literature, ensure it comes from a reputable source and check the credentials of the author and jury. 48:28 Saturation in qualitative data collection is reached when new information ceases to emerge, typically around 30-50 respondents, but it can vary based on the complexity of the subject. Made with HARPA AI
Bonjour Monsieur Au début, merci pour votre explication c est tellement riche et explicatif, je suis une étudiante master business administration, dans mon enquête vers la recherche j ai besoin de votre aide pourriez vous s il vous plaît me communiquer votre email, cela sera tellement gentil de votre part.
🎯 Key Takeaways for quick navigation:
The video discusses quantitative research and focuses on measuring variables as scales for hypothesis testing.
Variables can be manifest (directly measurable) or latent (hidden and multidimensional), and latent variables often require the use of measurement scales.
Measurement scales are sets of criteria or questions used to measure latent variables, and they can be adapted from existing scales in the literature.
Translation of measurement scales should involve a two-way translation process to ensure fidelity and objectivity.
The video provides an example of a model with manifest and latent variables, including moderating variables that influence the relationship between other variables.
It emphasizes the importance of justifying the links between variables with hypotheses in quantitative research.
17:47 When creating a measurement scale, it's important to adapt it to your specific context and research, not just accept a scale as-is.
25:35 To create a measurement scale, follow four key steps: literature review, qualitative exploratory study, quantitative exploratory study, and quantitative confirmatory study.
30:28 During the quantitative exploratory study, gather responses from a representative sample to identify common criteria and group them into dimensions.
32:17 Once you've developed your measurement scale, it becomes your instrument for assessing a specific aspect in your research.
34:20 If existing measurement scales don't fit your context, consider contextualizing them by blending elements from various scales or creating a new one for your specific needs.
35:25 When developing a measurement scale, you may need three initial phases: qualitative exploratory, interactive exploratory, and confirmatory quantitative.
35:54 For scale validation, you typically need four phases: three for validation and the final phase for large-scale data collection.
36:38 Developing your measurement scale requires prior research experience, dedication, and time commitment.
37:22 Validity and reliability are key aspects when assessing measurement scales.
38:42 Consider the nature of variables, including explanatory, mediating, moderating, and control variables.
39:18 Qualitative data categorization may precede scale validation, but ensure you have a qualitative foundation for your scale.
40:15 Adapt existing measurement scales if they are suitable for your context; no need to start from scratch.
43:11 Choose between factor analysis and confirmatory factor analysis based on the conditions and normality of data.
44:25 When using an existing scale from literature, ensure it comes from a reputable source and check the credentials of the author and jury.
48:28 Saturation in qualitative data collection is reached when new information ceases to emerge, typically around 30-50 respondents, but it can vary based on the complexity of the subject.
Made with HARPA AI
Je vous remercie pour cette explication bien détaillée. C'est très enrichissant pour tout doctorant.
Merci beaucoup
Bravo Ssi Smail. C'est très intéressant et très enrichissant.
merci pour cette modeste présentation très constructive .
Merci c'est tres généreux de votre part
Merci pour votre explication 🙏
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Bonjour Monsieur
Au début, merci pour votre explication c est tellement riche et explicatif, je suis une étudiante master business administration, dans mon enquête vers la recherche j ai besoin de votre aide pourriez vous s il vous plaît me communiquer votre email, cela sera tellement gentil de votre part.