Measurement Involves Assigning Numbers To Objects To Represe ✓ Solved

Measurement involves assigning numbers to objects to represe

Question #1: Tell whether the following statement is true or false: Measurement involves assigning numbers to objects to represent the amount of an attribute.

Answer: True. Measurement involves assigning numbers to objects to represent the amount of an attribute, using a specified set of rules. Researchers strive to develop or use measurements whose rules are isomorphic with reality.

Measurement is the assignment of numbers to represent the amount of an attribute present in an object or person, using specific rules.

Rules are necessary to:

  • Promote consistency
  • Ensure interpretability

Advantages of Measurement:

  • Removes guesswork
  • Provides precise information
  • Less vague than words

Theories of Measurement

Psychometrics is a branch of psychology concerned with the theory and methods of psychological measurement. There are two main theories of measurement:

  • Classical test theory (CTT)
  • Item response theory (IRT)

Errors of Measurement

The obtained score can be defined as:

Obtained score = True score + Error

Definitions:

  • Obtained score: an actual data value for a participant.
  • True score: value that would be obtained for a hypothetical perfect measure of the attribute.
  • Error of measurement: represents measurement inaccuracies.

Factors That Contribute to Errors of Measurement

  • Situational contaminants
  • Transitory personal factors
  • Response-set biases
  • Administration variations
  • Instrument clarity
  • Item sampling

Major Types of Measures

  • Generic
  • Specific
  • Static
  • Adaptive
  • Reflective scales
  • Formative indexes

Measurement Taxonomy

Four measurement property domains:

  • Cross-sectional domains
  • Reliability
  • Validity
  • Longitudinal measurement domains

Reliability

Reliability can be defined as:

  • Consistency: the absence of variation in measuring a stable attribute for an individual.
  • Reliability assessments involve computing a reliability coefficient, most of which are based on correlation coefficients.

Question #2: Tell whether the following statement is true or false: Reliability coefficients usually range from .00 to 1.00, with higher values reflecting less reliability.

Answer: False. Reliability coefficients usually range from .00 to 1.00, with higher values reflecting greater reliability, not less reliability.

Reliability Assessment Types

  • Test–retest reliability
  • Interrater reliability
  • Parallel test reliability

Reliability Coefficient (R)

The reliability coefficient (R) represents the proportion of true variability to obtained variability:

R = VT / Vo

Reliable measures should ideally reach at least .70, with .80 being preferable. Reliability can be improved by increasing the length of the instrument and is generally lower in homogeneous samples compared to heterogeneous ones.

Internal Consistency

This refers to how consistently all of the instrument’s items measure the same attribute, and is evaluated by administering the instrument on one occasion. Most widely used evaluation method is the coefficient alpha.

Measurement Error

Measurement error is present unless a reliability coefficient is 1.0, which is virtually never the case. It is used to estimate the range within which the true score lies, often expressed through the standard error of measurement (SEM).

Validity

Validity indicates the degree to which an instrument measures what it is supposed to measure and comprises four aspects:

  • Face validity
  • Content validity
  • Criterion-related validity
  • Construct validity

Construct validity concerns whether an instrument adequately measures the construct of interest and is tested through various methods including hypothesis-testing and convergent validity.

Critiquing Data Quality in Quantitative Studies

When evaluating data quality, key questions to consider include:

  • Can I trust the data in this study?
  • Are the measurements of key constructs reliable and valid?
  • Are change scores reliable and responsive?

Conclusion

In summation, the understanding of measurement, reliability, validity, and data quality is crucial for researchers in ensuring effective data collection and interpretation. Accurate measurements lead to insightful research outcomes and ultimately contribute to the advancement of knowledge across various fields.

References

  • Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2004). Theoretical Expansions of the Classical Test Theory. Psychological Methods, 9(4), 297-320.
  • DeVellis, R. F. (2016). Scale Development: Theory and Applications. Sage Publications.
  • Furr, M. R., & Bacharach, V. R. (2013). Psychometrics: An Introduction. Sage Publications.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Messick, S. (1989). Validity. In Educational Measurement (3rd ed., pp. 13-104). American Council on Education.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill.
  • Rosenberg, M. (2016). Understanding Reliability and Validity in Qualitative Research. Houghton Mifflin Harcourt.
  • Schmitt, N. (1996). Uses and Abuses of Coefficient Alpha. Psychological Assessment, 8(4), 350-353.
  • Streiner, D. L., & Norman, G. R. (2008). Health Measurement Scales: A Practical Guide to Their Development and Use. Oxford University Press.
  • Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing. Academic Press.

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