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Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Variation

Variation

An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...

Multiple Regression

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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A Single-Indicator Factor Approach for Correcting Measurement Error in Time-Varying Predictors in Developmental

¹Department of Education, Pusan National University, Faculty Office Building 2-#403, 2, Busandaehag-ro, 63Beon-gil, Geumjeong-gu, Busan 46241, Republic of Korea.

Behavioral Sciences (Basel, Switzerland)

|June 26, 2026

Summary

Ignoring measurement error in composite predictors within latent growth modeling (LGM) can significantly bias results. Using the single-indicator (SI) factor approach corrects for this error, ensuring more accurate developmental inferences in research.

Keywords:

latent growth modeling simulation study single-indicator factor

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Area of Science:

Psychological Science
Social Science Research
Quantitative Psychology

Background:

Composite scores are common in research but raise measurement error concerns.
Latent growth modeling (LGM) often overlooks measurement error in time-varying predictors.
Time-varying predictors are crucial for occasion-specific influences in LGM.

Purpose of the Study:

Investigate the impact of ignoring measurement error in composite time-varying predictors within LGM.
Evaluate the single-indicator (SI) factor modeling approach for correcting measurement error in time-varying predictors.
Compare traditional LGM with composite predictors to LGM using SI factors.

Main Methods:

Utilized the Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K) dataset.
Employed a Monte Carlo simulation to assess the effects of measurement error.
Compared latent growth models (LGMs) with composite predictors versus SI factor approaches.

Main Results:

Ignoring measurement error in time-varying predictors attenuated occasion-specific effects by up to 30%.
The single-indicator (SI) factor modeling approach effectively accounted for measurement error.
Results highlight significant biases when measurement error is not addressed.

Conclusions:

Correcting for measurement error in time-varying predictors is essential for valid LGM.
The SI factor modeling approach offers a viable solution for measurement error in LGM.
Accurate developmental inferences depend on addressing measurement error in predictors.