Group+E



**Ani Ausar Seker Ba **

**Rebekah Ruth Harrison **

**Brittany Lee Hilton-Cramer **

**Dawn Bridge Price **

=  Group E Wiki Terms:   =


 * Equal ratio scale **

An equal ratio scale is a scale of equals. Bracey used an example of temperature in his book. He explains the difference, in degrees, of Celsius and Fahrenheit; where 0 degrees Celsius and 32 degrees Fahrenheit are the same temperature (Bracey, 2004). This is a useful in data and in the classroom, because having an equal ratio scale allows the everyone looking at the data to know where the fixed number and allows to be used in contrast with other statistical information.


 * Implication **

An implication is defined as something that is implied as a consequence of something else (Vocabulary Building Dictionary, 2007). In one case defining what constitutes a “rural” school “has tangible implications for public policies and practices in education” (Arnold et al., 2007). In this case the meaning of rural will cause implication to be slightly different with each definition of rural. Being clear on what you are implying, as well as stating your research clearly, is important, because your data or information may be taken in the wrong context.


 * Measures of dispersion **

A measure of dispersion is a real number that is zero if all the data are identical, and increases as the data become more diverse. It cannot be less than zero. (Wikipedia 2011). One measure of dispersion is the range. For example, to say people varied from wealth from $10,000 to $80,000 is to almost calculate the range (Bracey 2006). To complete the calculation you subtract and get a range of $70,000. Other measures of dispersion are mean difference, median absolute deviation, and average deviation. I would use one measure of dispersion, the range, to calculate the differences of my students' initial scores and their improvement or decline.


 * Normal Curve Equivalent (NCE) **

Normal curve equivalents were originally created in an attempt to amalgamate students' performances from different standardized tests and it represents an attempt to use a student's raw score to calculate a percentile and use the distribution of scores on the test as a bell-shaped normal curve (Popham 2011). NCE would have been helpful when I was teaching GED because many of my students had taken different preliminary assessments (CASAS, TABE, DC CAS, etc.). These tests are often based upon a grade-level equivalent but sometimes this is still not an accurate representation of the student's progress. NCE gives you another way to report and analyze a student's raw score and to compare them with other students.


 * Percentile **

A true definition of percentile is very hard to pinpoint. One definition is “A percentile is a measure that tells us what percent of the total frequency scored at or below that measure” (Schultzkie, 2011). The percentile is “most often used for determining the relative standing of an individual in a population or the rank position of the individual” (Schultzkie, 2011). Determining percentiles could be very beneficial in trying to determine where a student stands within your class and amongst their peers. In this way a teacher could see which students are consistently excelling in class and those that may need additional assistance.


 * Percentile Ranks **

The percentile Rank is defined as “the percentange of students in the norm group performing below a particular students score” (Boudett, City & Murnane, 2010, p.37).

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Schultzkie gives some very specific points about percentile Ranks. They include

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">• <span style="color: #000000; font-family: 'Times New Roman','serif'; font-size: 16px;">percentile rank is a number between 0 and 100 indicating the percent of cases falling at or below that score.

<span style="color: #000000; font-family: 'Times New Roman','serif'; font-size: 16px;"> • percentile ranks are usually written to the nearest whole percent: 74.5% = 75% = 75th percentile

<span style="color: #000000; font-family: 'Times New Roman','serif'; font-size: 16px;"> • <span style="font-family: 'Times New Roman','serif'; font-size: 16px;">scores are divided into 100 equally sized groups

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> • scores are arranged in rank order from lowest to highest

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> • there is no 0 percentile rank - the lowest score is at the first percentile

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> • there is no 100th percentile - the highest score is at the 99th percentile.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> • you cannot perform the same mathematical operations on percentiles that you can on raw scores. You cannot, for example, compute the mean of percentile scores, as the results may be misleading. (2011)

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">As with determining percentiles, the percentile rank is beneficial in making comparisons among students. In addition, it allows a teacher to see how their students compare to other norm referenced groups.

In statistics reliability refers to the quality and consistency of measurement. Reliability is the degree of consistency and validity to which measurements repeatedly provide the same score (Gaziano & Raulin, 2004).
 * Reliability**

“The concept of reliability of measures is critical in research because, if the measures are not reliable, the study cannot produce useful information” (Gaziano & Raulin, 2004). In education it is important to use accurate measurements when conducting research in order to produce reliable results to be used for educational improvement.

Capable of replication. The ability to duplicate or copy a study to see if the same results are obtained if repeated exactly in another setting or instance. If a study has high replicability then the results of the study can be duplicated reliably when tested again using the same procedures. If a research finding cannot be replicated, then considerable doubt is cast on the genuineness of that finding” (Graziano & Raulin, 2004).
 * Replicability**

Replicability is important in the field of education if specific results are to be beneficial and applied to larger populations. If significant results are found in an action research study in a classroom, it is important to check the reliability of these findings and determine if the study can be replicated using another classroom population to produce the same results.


 * <span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Standard Score **

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">A standard score can be helpful when looking at data. It may also be known as a z-score (Lund Research, 2010). Bracey states that a “standard score is the most technical of test scores and the most widely used as well” (Bracey, 2010). Bracey explains that a standard score is created by “converting raw scores into scores in terms of how big they are in standard deviation units” (Bracey, 2010). Bahar explains that this is done by taking the raw scores of the data analyzed by using an equation much like this one Z= X-mean/SD (Bahar, 2011). Standards Score’s are useful because it allows the scores to be in a normal distribution, which makes the data easier to view and understand.


 * <span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Stanine **

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">During World War II, US Air Force psychologists developed the standard nine point scale; they used the name "stanine" as an abbreviation (Biehler et al 2009). Stanines are especially useful in education as a means of grouping students and implementing tracking (Biehler et al 2009). Stanines are less precise than a percentile rank score, but organizes students' scores into equal-sized subgroups (Calstat.org 2011). The chart below shows how students could be grouped according to stanine scores.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">% of normal curve 4 7 12 17 20 17 12 7 4

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Stanine Score 1 2 3 4 5 6 7 8 9

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">* Stanine scores 1,2,3 are below average; scores 4,5,6 are average; and 7,8,9 are above average.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Converting Percents to Stanine

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Stanine 1 is between 0 to 4%

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 2 is between 4.1% to 11%.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 3 is between 11.1% to 23%.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 4 is between 23.1% to 40%.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 5 is between 40.1% to 60%.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 6 is between 60.1% to 77%.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 7 is between 77.1% to 89%.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 8 is between 89.1% to 96%.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> Stanine 9 is between 96.1 % to 100% ( www.readingstats.com 2011)

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">In my opinion, stanines are a viable option to assist in grouping and tracking students.. This system can be used in your classroom because it provides a system of analyzing student performance in order to group them for differentiation strategies. It also can provide proof that the teacher is monitoring and recording student progress which is imperative in today's schools. Another advantage of using stanines is that they are approximate scale scores, and their very inexact nature conveys more clearly to people that educational measurement is not a super precise enterprise (Popham 2011).


 * <span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Statistical Significance **

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">“The statistical significance of a result is the probability that the observed relationship (e.g., between variables) or a difference (e.g., between means) in a sample occurred by pure chance ("luck of the draw"), and that in the population from which the sample was drawn, no such relationship or differences exist” (Statsoft, 2011). It may be easier to “say that the statistical significance of a result tells us something about the degree to which the result is ‘true’ “ (Statsoft, 2011). Joseph Eisenhauer feels that “statistical significance alone does not imply that test results are of much practical importance” (2009, p.42). In general, statistical significance helps to define whether or not a relationship exists between two variables. In the classroom, it may be important to determine if something that we are doing as a teacher is affecting the outcomes of our students. By changing the variables, for example, length of time given for in class studying for a test, a teacher can determine whether or not this significantly affects the grades that students receive on the test.

Derived from the combination of the terms “data, and subjective”. **Data**- Is a Latin word plural for datum, a single unit of information pertaining to facts, figures, and numerical values obtained from experiments, and surveys for making calculations and drawing conclusions. Subjective data is a form of data which is based on a person’s personal perceptions, feelings, and opinions and is therefore subjective to past experiences and personal preferences. It is not directly observable and exists only in the mind of an individual. Unlike Objective data it is not based on facts, and is not free of bias, or prejudice. As the authors Cushman & Rosenberg, 1991 state the benefits for including subjective data in a study; “Collecting subjective data will add little to the cost of the study, but may provide significant insights not obtainable by objective methods. In addition, subjective data may be particularly useful if objective measurements fail to detect any differences between conditions” (Cushman & Rosenberg, 1991). The use of subjective data in conducting research in the classroom is important for understanding student perceptions and opinions. It is important to not only analyze the statistical conclusions drawn from research in the classroom but also from the personal experiences of the individual students in order to improve educational planning for the future.
 * Subjective data**

References:

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Arnold, M.L, Biscoe, B., Farmer, T. W., Dylan, L, & Shapley, K. L., (2007). How the

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">government defines “rural” has implications for educational policies and practices, //Regional Educational Laboratory Southwest// []

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Bahar, M. (2011). The difference and relationship between the ssee and uee-1 scores of antolian

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">vocational high schools, //Educational Sciences: Theory and Practice.// 11 (2) []

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Biehler, McCown, & Snowman (2009) //Psychology applied to teaching//. Boston, MA: Hoffman

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Mifflin

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Bracey,G.W. (2006). //Reading educational research how to avoid getting statistically snookered//.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Portsmouth, NH: Heinemann

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Boudett, K.P., City, E.A. & Murnane, R.J. (2010). //Data wise: a step-by-step guide to using//

//<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">assessment results to improve teaching and learning //<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">. Cambridge, Massachusetts:

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Harvard Education Press

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Calstat.org (2011) How test results are interpreted. Retrieved on August 2, 2011 from

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<span style="color: #000000; font-family: 'Times New Roman','serif'; font-size: 16px;">Cushman, W. H., & Rosenberg, D. J. (1991). //Human factors in product design//. Amsterdam: Elsevier.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Eisenhauer, J.G. (2009). Explanatory power and statistical significance. //Teaching Statistics//,

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">31(2), 42-46. doi: 10.1111/j.1467-9639.2009.00364.x

Graziano, A.M., & Raulin, M.L. (2004) //Research methods: A process of inquiry (5th ed.)// Boston: Pearson Education Group, Inc.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Lund Research Ltd. (2010). //Standard Score//. Retrieved August 1, 1001, from,

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<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Measures of dispersion (2011) Retrieved on August 2, 2011 from []

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Popham, W.J. (2011). //Classroom assessment: What teachers need to know//. Boston, MA:

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Pearson Education.

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Schultzkie, L. (2011). Percentiles and more quartiles. //Oswego City School District Regents Exam//

//<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Prep Center //<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">. Retrieved from

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Shingler, J., Van Loon, M. E., Alter, T. R., & Bridger, J. C. (2008). The Importance of Subjective Data for Public Agency Performance Evaluation. //Public Administration Review//, 68(6), 1101-1111. doi:10.1111/j.1540-6210.2008.00958.x

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Snowman, J., McCown, R., Biehler, R. (2009) //Psychology applied to teaching//. Boston, MA: Hoffman Mifflin

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Statsoft Electronic Statistics Textbook (2011). //Elementary concepts in statistics//. Retrieved from

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<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">Vocabulary Building Dictonary. (2007). //Implication//. Retrieved August 1, 2011, from

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<span style="font-family: 'Times New Roman','serif'; font-size: 16px;"> www.readingstats.com (2011) Stanine scores. Retrieved on August 2, 2011 from

<span style="font-family: 'Times New Roman','serif'; font-size: 16px;">[]