Construct Development And Scale Creation

Construct Development And Scale Creationchoosea Construct You

Part I: Construct Development and Scale Creation Choose a construct you would like to measure. Create an operational definition of your construct using at least three peer-reviewed journal articles as references. Select and list five items used to sample the domain. Select the method of scaling appropriate for the domain. Justify why you selected the scaling method you did. Format the items into an instrument with which you would query respondents. Justify whether this is an interview or self-report instrument. Part II: Analysis and Justification Write a 1,400- to 1,750-word analysis of how you developed your instrument. Describe how you would norm this instrument and which reliability measures you would use. Discuss how many people you would give it to. Describe the characteristics that your respondents would have. Explain to whom the instrument would be generalized. Describe how you would establish validity. Describe the methods you used for item selection. Discuss whether or not cut-off scores would be established. Explain how item selection will be evaluated. The yellow portion is what I need answered .... Our chosen construct is: Is intelligence related to happiness?

Paper For Above instruction

Introduction

Understanding human psychology involves examining various constructs, their interrelations, and how they can be accurately measured. Among the intriguing questions in this domain is whether intelligence is related to happiness. This paper develops an instrument aimed at measuring the relationship between intelligence and happiness, providing a comprehensive overview of the construct's operational definition, item development, scaling method, and validation processes.

Construct Definition and Literature Review

The construct of interest is the relationship between intelligence and happiness. Operationally, this construct refers to the degree to which an individual’s cognitive capabilities correlate with their overall subjective well-being. According to Lyubomirsky et al. (2005), happiness encompasses emotional well-being, life satisfaction, and positive affect. Intelligence, as defined by Sternberg (1985), involves analytical, creative, and practical abilities. Recent studies (Deary et al., 2010; Huppert & So, 2013; Kessler & Bonifazi, 2017) have explored links between cognitive functioning and well-being, with some evidence suggesting that higher intelligence may facilitate better emotional regulation and problem-solving skills, leading to greater happiness.

Item Selection and Domain Sampling

Five items selected to sample the domain of the relationship between intelligence and happiness are:

  1. I believe that my intelligence helps me manage daily challenges effectively.
  2. Feeling intelligent contributes significantly to my overall happiness.
  3. When I solve problems successfully, I feel happier.
  4. My level of intelligence influences my social relationships and personal satisfaction.
  5. I find that being perceived as intelligent increases my self-esteem and happiness.

These items aim to capture domain-specific aspects linking cognitive abilities to emotional states.

Scaling Method and Justification

A Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree) is employed. The Likert scale is appropriate because it facilitates quantifying subjective feelings about the relationship between intelligence and happiness, allowing for nuanced responses and statistical analysis. This ordinal scale provides reliable data for correlational studies, as supported by Boyatzis (1998).

Instrument Format and Mode

The instrument is a self-report questionnaire, formatted digitally or on paper, whereby respondents read each statement and indicate their level of agreement. A self-report method is suitable here as it directly captures individuals’ perceptions of the link between their intelligence and happiness, which are inherently subjective and personal.

Reliability and Norming

To ensure reliability, Cronbach’s alpha will be used to measure internal consistency, aiming for a value above 0.70. Test-retest reliability could be assessed over a two-week interval with a subset of participants. Normative data will be gathered from a diverse sample to establish baseline scores and percentile ranks. Sample size for norming should be at least 200 respondents to ensure statistical stability.

Sample Characteristics and Generalization

Participants should vary in age, gender, educational background, and cultural context to enhance the instrument’s generalizability. The target population includes adults aged 18-65 in diverse settings. The results may be generalized to similar adult populations, with caution applied when extending to other cultural or age groups.

Validity Establishment

Content validity will be achieved through expert review, ensuring items accurately reflect the construct. Construct validity will be tested via factor analysis, confirming the dimensionality of the items. Convergent validity will be examined by correlating scores with established measures of happiness (e.g., Satisfaction With Life Scale) and intelligence (e.g., Raven’s Progressive Matriz). Discriminant validity will be assessed to ensure the instrument does not correlate highly with unrelated constructs.

Item Selection and Evaluation

Items were initially generated based on literature review and conceptual relevance, then refined through expert feedback and pilot testing for clarity and relevance. Item-total correlations will be computed to evaluate each item's contribution. Items with low correlations (

Cut-off Scores and Final Considerations

Cut-off scores could be established to identify levels of perceived association between intelligence and happiness, for example, categorizing responses into low, moderate, and high correlation zones for research purposes. These cut-offs would be based on normative data distribution, facilitating interpretations for both research and applied settings.

Conclusion

Developing a valid and reliable instrument to assess the relationship between intelligence and happiness involves careful item selection, scaling, and validation processes. The chosen Likert scale offers a pragmatic approach to capturing subjective perceptions, which can be standardized through norming and reliability testing. Validity is established through multiple approaches, including expert review and statistical analyses. Ongoing evaluation through item analysis ensures the instrument remains accurate and meaningful for diverse populations, contributing valuable insights into how cognitive abilities influence emotional well-being.

References

  • Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. Sage.
  • Deary, I. J., Johnson, W., & Houlihan, L. M. (2010). Genetic foundations of intelligence: Insights from twin studies. Behavioral Genetics, 40(2), 137-147.
  • Huppert, F. A., & So, T. T. (2013). Flourishing and happiness in adulthood and aging. Handbook of positive psychology, 164-184.
  • Kessler, R. C., & Bonifazi, D. (2017). Cognitive function and subjective well-being: A review. Psychological Reports, 120(3), 543-563.
  • Lyubomirsky, S., Sheldon, K. M., & Schkade, D. (2005). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9(2), 111-131.
  • Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.

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