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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

Ratio Level of Measurement

Ratio Level of Measurement

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.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

Measures of Intelligence

Measures of Intelligence

Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this; it...

Wechsler's Contribution to Measures of Intelligence

Wechsler's Contribution to Measures of Intelligence

David Wechsler, a psychologist who worked with World War I veterans, developed a significant IQ test in 1939 called the Wechsler-Bellevue Intelligence Scale. This test was innovative because it combined several subtests that measured both verbal and nonverbal skills, reflecting Wechsler's belief that intelligence is a global capacity involving purposeful action, rational thinking, and effective interaction with the environment. This test later evolved into the Wechsler Adult Intelligence Scale...

Self-Evaluation Maintenance Model

Self-Evaluation Maintenance Model

The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...

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How to Measure and Explain Achievement Change in Large-Scale Assessments: A Rejoinder.

Marian Hickendorff, Willem J Heiser, Cornelis M van Putten

|December 29, 2009

Summary

This rejoinder examines validity challenges in large-scale student achievement assessments. It addresses measurement issues, trend stability, and the reception of research findings on educational trends.

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Published on: September 11, 2021

Area of Science:

Educational Measurement and Assessment
Quantitative Research Methods
Educational Policy Analysis

Background:

Large-scale assessments are crucial for monitoring student achievement trends.
Methodological rigor is essential for the validity of these assessments.
Previous research has highlighted challenges in interpreting achievement trends.

Purpose of the Study:

To address substantive and methodological validity issues in large-scale student achievement assessments.
To comment on the discussion paper by Van den Heuvel-Panhuizen et al. (2009).
To discuss the implications of identified trends and their reception.

Main Methods:

Rejoinder and critical analysis of existing assessment methodologies.
Focus on challenges in defining and measuring student achievement.
Examination of strategies for ensuring assessment stability over time.

Main Results:

Identified significant methodological challenges in large-scale assessments.
Highlighted issues in determining what and how to measure student progress.
Discussed the complexities of interpreting and utilizing trend data.

Conclusions:

Emphasizes the need for robust methodological frameworks in educational assessment.
Stresses the importance of careful consideration of validity in trend analysis.
Suggests critical reflection on the dissemination and impact of assessment findings.