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Related Concept Videos

Variation01:19

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.
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Rate-Determining Steps03:08

Rate-Determining Steps

Relating Reaction Mechanisms
In a multistep reaction mechanism, one of the elementary steps progresses significantly slower than the others. This slowest step is called the rate-limiting step (or rate-determining step). A reaction cannot proceed faster than its slowest step, and hence, the rate-determining step limits the overall reaction rate.
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Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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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...
Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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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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Related Experiment Video

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Operant Procedures for Assessing Behavioral Flexibility in Rats
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Published on: February 15, 2015

Rater variables associated with ITER ratings.

Michael Paget1, Caren Wu, Joann McIlwrick

  • 1Office of Undergraduate Medical Education, Health Sciences Centre, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.

Advances in Health Sciences Education : Theory and Practice
|July 11, 2012
PubMed
Summary
This summary is machine-generated.

Holistic assessment, like the ITER, shows leniency bias. Delayed completion and higher preceptor workload can reduce this bias in performance ratings.

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Area of Science:

  • Medical Education
  • Assessment in Healthcare

Background:

  • Holistic assessment tools like the ITER are favored for authentic performance evaluation.
  • However, the subjective nature of these assessments introduces susceptibility to rater bias.

Purpose of the Study:

  • To investigate the association between specific rater variables and In-Training Evaluation Report (ITER) ratings.
  • To identify factors influencing leniency bias in performance assessments.

Main Methods:

  • An observational study analyzed 1050 online ITERs completed by preceptors for clerks.
  • Generalized estimating equation models identified variables associated with global ITER ratings (1-5).

Main Results:

  • Two-thirds of students received ratings of 'above expected level' or 'outstanding', indicating leniency bias.
  • Increased time to complete an ITER and a higher number of completed ITERs per preceptor were associated with lower ratings.

Conclusions:

  • Leniency bias is evident in ITER ratings, with approximately 66.4% of students rated highly.
  • Delayed ITER completion and increased preceptor workload may mitigate this bias.
  • Further research is essential to confirm and address sources of rater bias in performance assessments.