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Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Testing measurement invariance in longitudinal data with ordered-categorical measures.

Yu Liu1, Roger E Millsap1, Stephen G West1

  • 1Department of Psychology, Arizona State University.

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This study addresses longitudinal measurement invariance for ordered-categorical data in developmental research. It provides methods and tools to ensure measures consistently represent constructs over time, crucial for accurate longitudinal analysis.

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Area of Science:

  • Developmental Psychology
  • Quantitative Psychology
  • Psychometrics

Background:

  • Accurate developmental research relies on longitudinal measurement invariance, ensuring constructs are measured consistently over time.
  • Ordered-categorical indicators (e.g., Likert scales) are common in longitudinal studies but often violate normality assumptions.
  • Existing methods for measurement invariance primarily focus on continuous data, leaving a gap for ordered-categorical longitudinal data.

Purpose of the Study:

  • To extend the concept of measurement invariance to the longitudinal analysis of ordered-categorical indicators.
  • To identify and address common challenges in testing longitudinal measurement invariance with non-normal data.
  • To provide practical tools and procedures for assessing the significance of invariance violations in longitudinal research.

Main Methods:

  • Utilized factor models to examine the relationships between observed indicators and latent constructs over time.
  • Extended measurement invariance testing to ordered-categorical data in a longitudinal context.
  • Developed a procedure and R program for evaluating the practical significance of invariance violations, addressing issues like model identification, sparse/missing data, and estimation.

Main Results:

  • Demonstrated a method for testing longitudinal measurement invariance with ordered-categorical data, overcoming common statistical challenges.
  • Illustrated the application of the developed procedure using an empirical example from the Mexican American Cultural Values scale.
  • Compared the capabilities of major latent variable software (lavaan, Mplus, OpenMx) for handling longitudinal measurement invariance.

Conclusions:

  • The study provides a framework and practical tools for ensuring measurement invariance in longitudinal research with ordered-categorical data.
  • Accurate longitudinal analysis requires careful attention to measurement invariance, especially with non-normally distributed indicators.
  • The developed R program and guidelines aid researchers in conducting more robust and valid developmental studies.