The Yellow Portion Would Be My Portion The Last Time I Had T

The Yellow Portion Would Be My Portion The Last Time I Had Two Peo

The yellow portion would be my portion... the last time I had two people say they would do it, only to look later and say they, cant do it......Please look over the assignment.... The yellow portion is what I need completed. No word count, just answered properly Our chosen construct is: Is intelligence related to happiness? 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. Format your paper according to APA guidelines.

Paper For Above instruction

The measurement of complex psychological constructs such as the relationship between intelligence and happiness requires a systematic approach to instrument development, encompassing defining the construct, generating appropriate items, selecting measurement scales, and establishing reliability and validity. This essay details the development of an instrument designed to explore the association between intelligence and happiness, including operational definitions, item selection, scaling method, and validation procedures, aligned with APA scholarly standards.

Construct Development and Operational Definition

The construct selected for measurement is the relationship between intelligence and happiness. Operationally, intelligence can be defined as "an individual's capacity for reasoning, problem-solving, and understanding complex ideas," referencing Sternberg (2012), who conceptualizes intelligence as a multifaceted ability comprising analytical, creative, and practical components. Happiness, on the other hand, is operationalized as "an individual's overall sense of subjective well-being," as defined by Lyubomirsky et al. (2005). This construct can be quantitatively assessed through the respondent's self-reported levels of life satisfaction, positive affect, and absence of negative affect. The relationship between these two constructs is hypothesized to be positive, indicating that higher intelligence might be associated with greater happiness, although empirical testing is necessary.

Item Generation and Sampling Domain

To accurately sample the domain of the construct, five items are generated to capture various facets of intelligence and happiness. These items include:

1. "I solve problems efficiently in my daily life."
2. "I often find creative solutions to challenges I face."
3. "I feel satisfied with my life overall."
4. "I experience frequent positive emotions."
5. "I find it easy to understand complex ideas."

These items are designed to reflect practical and cognitive aspects of intelligence along with subjective well-being indicators.

Scaling Method and Justification

A 5-point Likert scale ranging from "Strongly Disagree" to "Strongly Agree" is selected as the measurement method. This scale allows respondents to express varying degrees of agreement or disagreement with each statement. The Likert scale is justified due to its widespread use in psychological assessments, ease of administration, and ability to capture nuanced responses (Likert, 1932). It facilitates quantitative analysis and comparison across respondents, making it suitable for self-report measures of subjective constructs such as happiness and perceived intelligence.

Instrument Format and Data Collection Method

The instrument would be in a self-report questionnaire format, given the subjective nature of the constructs measured. This allows respondents to reflect on their own feelings and abilities confidentially and efficiently. Self-report instruments are often preferred for constructs like happiness and perceived intelligence, as they directly tap into personal perceptions and experiences, which may not be observable externally.

Norming and Reliability Measures

To norm this instrument, a large and diverse sample representing the target population—adults aged 18-65—would be recruited. The sample should encompass varied socio-economic, educational, and cultural backgrounds to enhance generalizability. The sample size should be at least 300 participants to ensure stable norms and adequate statistical power. Reliability would be assessed through internal consistency measures, with Cronbach’s alpha coefficients being calculated for the entire instrument and subscales. Test-retest reliability would also be established by administering the instrument to a subset of participants twice, with a 2-4 week interval.

Sample Characteristics and Generalization

Respondents should ideally be adults of diverse demographic backgrounds to maximize the generalizability of findings. Characteristics such as age, gender, educational attainment, and cultural background will influence responses and should be documented for subgroup analyses. The instrument aims to be generalizable to the adult population in various settings, including academic, clinical, and community environments—where understanding the relationship between intelligence and happiness can have practical implications.

Validity Establishment and Item Selection

Establishing validity involves multiple approaches. Content validity will be ensured through expert review and alignment with theoretical definitions outlined by Sternberg (2012) and Lyubomirsky et al. (2005). Construct validity will be assessed via factor analysis to confirm the underlying structure of the instrument, ensuring that items load appropriately onto relevant factors. Convergent validity will be evaluated by correlating scores with established measures of intelligence (e.g., the WAIS) and happiness (e.g., the Subjective Happiness Scale). Discriminant validity will also be examined to ensure the instrument does not measure unrelated constructs.

Item Evaluation and Cut-off Scores

Item evaluation involves statistical analysis, including item-total correlations and factor loadings, to identify poorly performing items. Items showing low correlations or cross-loading on multiple factors will be revised or removed. Cut-off scores could be established to identify individuals with notably high or low levels of the constructs, which can be useful in clinical settings. These cut-offs will be based on normative data and percentile ranks but will require further validation through additional samples.

Conclusion

Developing a reliable and valid instrument to measure the relationship between intelligence and happiness is a multi-step process involving precise operational definitions, careful item generation, appropriate scaling, and rigorous validation. Ensuring reliability through internal consistency and test-retest assessments, along with establishing validity via multiple methods, will provide a robust tool for researchers and practitioners. This instrument can shed light on how cognitive abilities correlate with subjective well-being, informing psychological theory and intervention strategies.

References

• 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. (2012). Successful intelligence. Cambridge University Press.
• Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1-55.
• Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201-211.
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• Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.
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