Analyzing Assessment Data

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Leena Kuruvila

ANALYZING ASSESSMENT DATA 4.docx

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Analyzing Assessment Data

Reliability is “the capability of an analysis to give to administer steady and persistent

scores” (Billing & Halstead, 2015, p. 436). It is troubled with the test’s ability to yield accurate  accurate: accurate  right

Analyzing Assessment Data

Leena Kuruvila

Grand Canyon University NUR: 648E

February 22nd 2021

results. Test-retest is the most basic clarification of reliability, well-defined by the applicability

of results. Reliability is measured on a scale of 0 to 1 where a reliability coefficient of 1 indicates

that there is a 100% communication between two methods or tests. Greater than 0.80 should well

test reliability be, whilst 0.70 to 0.80 is the acceptable test reliability. Test reliability is

considered poor when it is less than 0.70. The test reliability is 0.844 in the sample test statistics

in Chapter 24 of Teaching in Nursing. implying that the test has a good interior consistency.

Raw score is the amount of test questions which were responded correctly. The raw

scores in the test statistics in Chapter 24, shows that a majority of students score between 70.0

and 74.4 with the mean score being 72.69. The raw score’s trend also shows that most of the

students are collected at scores of between 65.6 and 83.2. Only a few students score below 65.6

and above 83.2. To instructors, this evidence is of great importance as it benefits them to make

assumptions about teaching or instructing and to measure or attempt to describe the performance

of students (Casabianca et al., 2015). Nonetheless, raw scores have petite meaning by themselves

and need to be converted to other types of scores in order to manage crucial evidence on process

and relation with other students taking exact tests among others.

The meekest measure of variability strongminded by removing lowest score from the

highest score is where the range is predictable. The sample test statistic’s range is 44 (92 – 48).

The range is a measure of variability, which in this case indicates high variability from the

average value (Billing & Halstead, 2015).

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A standard error of quantity provides an approximation of how the difference among the

observed score and the true score (Billing & Halstead, 2015). It measures the exactness of a

sample distribution’s depiction of a population using the standard abnormality. For a given

sample, the standard error can be computed by dividing the standard abnormality by the square

root of the sample size (Ilola, 2018). In the case of the provided sample test statistics, the

standard error is 1.822 (9.813÷√29). This is a small standard error that indicates that the sample

is more representative of the overall student population. To check student’s true score from the

observed score, teachers can use standard error (Frey, 2018). According to Billing and Halstead

(2015), instructors and faculty members can give a student the help of doubt, adding the standard

fault to each raw before assigning the grades.

Beginning with, testing the essence of a test, it is indispensable to look at analyzing each

of the fragments. Analytical analysis of each test item comprises the point biserial index (PBI)

the p-value (item difficulty index) for the accurate answers and the ploys to each item

(McDonald, 2018). Nevertheless, an upgrading can be made to individual item analysis multiple

choice classroom tests and statistical evidence administered can be used to expand the items and

illuminate test results for future use. Assessing the p-value of each item and testing the PBI of

each item is encompassed in the process of analyzing individual items. The right answers of the

PBI and p-value correspond to the p-value and PBI of an item. Recognize that correct options

have a positive PBI and identify whether any distractors have a positive PBI to ensure that the

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eliminating them or to ensure that their difficulty level is desirable. Likewise, it is of great

essence to consider gain or abandon items that come out to be shocked. Revise the items based

on the data and the study evaluation before considering entering them in the item bank.

When determining the benefit of items, that p-value that is less than 0.30 is measured

very difficult items whilst those whose p-value is greater than 0.90 are considered easy items

(McDonald, 2018). Very easy items cannot be detached from a test but those with very high p

ethics should be examined carefully and revised before being entered into the item bank.

Nonetheless, difficult questions with a p-value less than 0.30 can be excepted from a test or

maintained if their difficulty level is treated as needed. From these, we find that a p-value of 076

is neither very high nor very low, therefore, there is no course to eliminate the item.

An essential stage in the individual item is watching thoroughly at the PBI of each option

analysis. One or more ploy having a positive PBI and another a negative PBI implies that

students who reached low on the test selected the correct option more time and again than those

that talented high on the test (McDonald, 2018). It illustrates an aspect of item uncertainty that

wronged or deceived the high-achieving students. It may also be an indication that all the

students had experienced confusion on the item that they were guessing. A teacher should check

these items judiciously to either eradicate them from the test or revise them for future use.

To know that learning has taken place we custom tools like tests by computing the

performance of the students (Center for Teaching Innovation, n.d.). We can say that learning has

taken place based on the model test statistics, admitting partly, given most scores of the

checking are near to the mean score, assuming that the passing score is 75%. The mean score, in

this case, is 75.4 and the median is 77 viewing that both are close demonstrating that scores are

distributed around the mean and as such, most students have performed well.

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options do not complicate the students. Furthermore, it is of great kernel to identify the ploy that

was not chosen since this means they did not work as prepared. Likewise, review the items that

have intruded minimum standards where items with a p-value of less than 0.30 normally

illustrate that they were too problematic for the group and it may be necessary to consider

While the model statistics show that examinees performed well on the test, more can be

done to close the gap among the highest score of 93 and the lowest score of 52, by confirming

that more students perform high scores. These steps include denoting to individual learner

assessment data and using recall question and answer sessions to check prior learning.

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References

Billings, D. M. G., & Halstead, J. A. (2015). Teaching in nursing: A guide for faculty (5th ed.).

St. Louis, MO: Saunders. ISBN-13: 9780323290548

Casabianca, J. M., Lockwood, J. R., & McCaffrey, D. F. (2015). Trends in Classroom

Observation Scores. Educational and psychological measurement, 75(2), 311–337.

https://doi.org/10.1177/0013164414539163

Center for Teaching Innovation (n.d.). Measuring Student Learning.

https://teaching.cornell.edu/teaching-resources/assessment-evaluation/measuring-student-

learning

Frey, B. (2018). The SAGE encyclopedia of educational research, measurement, and evaluation

(Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781506326139

Ilola, E. (2018, Sept 26). A beginner’s guide to standard deviation and standard error.

https://s4be.cochrane.org/blog/2018/09/26/a-beginners-guide-to-standard-deviation-and-

standard-error/

McDonald, M. E. (2018). The nurse educator's guide to assessing learning outcomes.