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Provide a response that continue discussion and provide more insight on topic. APA Format required and provide a doctoral student response. Must be at least 200 words per response.

1st. Post: I can use statistics, precisely central tendencies, in multiple job areas. Clinical researchers used those concepts to identify typical laboratory values. For example, to determine average hemoglobin values in males and females, researchers collected thousands of blood samples from healthy subjects and then determined which values were typical. 

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I need to know how to interpret central tendencies in clinical research articles. For example, a research article could provide a median age for developing the disease and age ranges (Suljic, 2021). Knowing that information helps target specific age groups to prevent and treat certain conditions. 

In addition, as a leader, I can use those statistics for service planning, resource allocation, and bed utilization (Singh, 2022). Hospitals could compare heart attack modes over the years to evaluate treatment effectiveness. The average hospital stay (how many days) helps determine the necessary equipment and resources. The median of the entire population can be used to determine the age to offer vaccines. Hospitals could use measures of central tendencies for quality improvement by analyzing customer behaviors, results of interventions, and purchase patterns. Central tendency measures could be used to compare patient satisfaction across hospital units. Central tendency measures could be used to create benchmarks and desired outcomes. 

2nd Post: Tests could be administered for various reasons and scored in multiple ways. Standardized tests are administered, scored, and interpreted in a standardized manner (Mertler, 2007). Standardized test score classification is based on their purpose (achievement, diagnostic, aptitude, and state-mandated) or the type of scores (criterion-references or norm-referenced).   Figure 1 shows the scoring classification for both types of tests.

Figure 1

Scoring of Criterion-Based and Norm-Based Standardized Tests 

A diagram of a testDescription automatically generated with medium confidence

Note. Reprinted from “Interpreting Standardized Test Scores: Strategies for Data-Driven

Instructional Decision Making
,” by C. A. Mertler, 2007, SAGE. Copyright 2007 by SAGE. 



Criterion-based scores compare students’ scores to some pre-established criteria. The scores are always reported as “raw,” and they only depend on the student’s performance without any relationship to the performance of other students. On the other hand, norm-referenced scores compare student’s performance to the performance of other students who already took the test (the norm group). Norm-referenced scoring is more rigorous and based on national norms, which must be representative and current. Norm-referenced test results often fit in a normal distribution curve. 

In criterion-based scoring, educators could use two grading approaches: pass/fail or discretionary grading (Melrose, 2017). Pass/fail or satisfactory/unsatisfactory approach evaluates overall understanding and competence. For example, to pass the national nursing NCLEX exam, nursing students must score 80% or above. In the second approach, called discretionary grading, students get grades with assigned letters from F- to A+ or numerical values from 0% to 100%. 

Melrose (2017) noted that pass/fail grading stimulates intrinsic motivation and self-direction but does not recognize student performance differences. Discretionary grading increases extrinsic motivation and self-improvement but also could promote unhealthy competition. 

            Norm-references test scores include percentile ranks, linear standard scores (T-scores and z-scores), normalized standard scores (such as stanines), and developmental and growth scales (such as grade-equivalent and age-equivalent scores) ) (Mertler, 2007). Percentile ranks indicate how well a student does compared to other students who also took the test (Salkind, 2008). The score of 87% indicates that the student performed better than 87% of other students from the same his/her norm group. 

            Linear standard scores are obtained by converting raw scores into standard form (Brock, n.d). Z-scores are obtained by converting raw scores using the sample’s mean and standard deviation. Z-scores have a mean of 0 and a standard deviation 1 (See Figure 2). The score one standard deviation below the mean will have a minus sign (-1). Z-scores are often converted into another type of score because the minus sign could be easily forgotten or omitted. T-scores have a mean of 50 and a standard deviation 10 (See Figure 2). The formula for converting T-scores into z-scores is T = 100 + 15(z). If a variable is normally distributed, two-thirds of the population would have T-scores between 40 and 60. 

Figure 2           

Norm-Referenced Scores in Relation to Normal Distribution Curve


A diagram of a normal distributionDescription automatically generated

Note. Reprinted from “Descriptive Statistics and Psychological Testing,” by S. E. Brock, n.d., 

California State University. (