analyzing & interpreting data handout

At JMU, all students are required to complete a general education course regarding health and wellness issues

Analyzing and Interpreting Data Handout

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Analyzing and Interpreting Data HandoutAssessment Institute Summer 2005This handout includes three sections:

Section 1 Descriptive Statistics Typically Used in Assessment

Section 2 Inferential Statistics Typically Used in Assessment

Section 3 Confidence Intervals and Effect Sizes

Section 4 Wellness Domain Example InformationSection 5: SPSS Comparing Means for Different Groups Section 6: SPSS Comparing Means on Different OccasionsSection 7: SPSS - Correlation

Section 8: SPSS Comparing Means on Different Occasions for Different GroupsSection 9: SPSS Scoring from a Key, Creating a Total Score and Item AnalysisSection 1 Descriptive Statistics Typically Used in Assessment

There are two descriptive statistics that are used quite often for assessment purposes:

1. The average is perhaps the most commonly computed statistic and we typically compare the averages of groups comprised of different people (e.g., the average Spring 2005 KWH score for students who completed GHTH100 versus the average Spring 2005 KWH score for students who completed GKIN100). We also compare averages of groups comprised of the same students. This is often the case when students have completed the same assessment on different occasions. For instance, because the same students completed the KWH in Fall 2003 and Spring 2005, we can compare their average on the test prior to exposure to any JMU coursework (Fall 2003) to their average after having completed 45 70 hours of coursework (Spring 2005). Keep in mind that averages are only appropriate if the variable is continuous and is not too terribly skewed. Examples of variables that are continuous are GPA, test scores and number of credit hours. Examples of variables that are NOT continuous include a students major, enrollment status and gender. It is always a good idea to look at a histogram of the variable to have a sense of the shape of the distribution and to look for outliers. 2. Correlations are also typically computed. Correlations capture the extent to which two variables are linearly related to one another. Correlations range from -1 to 1 with values closer to |1| being indicative of a stronger relationship between the two variables. Positive correlations imply that high values on one variable are associated with high values on the other variable; low values on one variable are associated with low values on the other variable. Negative correlations imply that high values on one variable are associated with low values on the other variable; low values on one variable are associated with high values on the other variable. In order to compute a correlation between two variables, a sample must be used that has data on each of the variables. A correlation would be an appropriate statistic to use if one were interested in the relationship between: a. scores from different tests taken by the same students

b. scores from the same test taken by the same students on different occasions

c. scores on a test and final grade in a course

d. scores on a test and GPA

When using a correlation, keep in mind that both variables used in a correlation need to be continuous. Also, if the range of a variable is restricted or if the relationship between the two variables is not linear, be aware that the value of the correlation will be deflated and/or it may be inappropriate to a correlation. Always look at a scatterplot to ensure linearity and to identify potential outliers.3. If a cutoff score or standard is being used, then you will often see the % of students scoring above or below the standard reported. ___________________________________________________________________________

Section 2 Inferential Statistics Typically Used in Assessment Inferential statistics are used when we are interested in knowing the answer to the following question: How likely is it to have found results such as mine in a population where the null hypothesis is true? Inferential statistics are used when one wants to not only describe what is happening in their sample, but generalize their findings to the population. There are three commonly used inferential tests in assessment:

1. Independent Samples t-test: When the averages of two groups are being compared and the groups consist of different students, the independent samples t-test is used. The null hypothesis is that there is no difference between the population means of the two groups. A low p-value associated with a t-statistic indicates that it is unlikely to find that value of t in a population where the null hypothesis is true. When p-values are less than .05 (< .05) we reject the null hypothesis and claim that there are statistically significant differences between the averages of the two groups. (If you have more than two groups, you would use a between-subjects ANOVA.)2. Paired Samples t-test: When the two groups consist of the same students, a paired samples t-test is used. In assessment, this test is typically used to compare averages from the same students who took the test on two different occasions. Again, the null hypothesis is that there is no difference between the population means and a p < .05 implies that the two means are significantly different from one another. (If you have more than two groups or occasions, you would use a within-subjects ANOVA.)3. Correlation (r): Correlations can be used as a descriptive statistic and as an inferential statistic. When used inferentially, the null hypothesis is commonly that the population correlation is equal to zero, meaning that there is no relationship between the two variables in the population. A low p-value associated with r implies that it is unlikely for that value of r to occur in a population where there is no relationship between the two variables. If p < .05, one concludes that there is a statistically significant relationship between the two variables. A note about the use of inferential statistics in assessment: Remember that although you might like statistics, the people who you will be reporting the statistics to may not! You always want to keep the statistics simple to ease interpretation. Also, always include graphical representations of the results if possible. If you decide to use inferential statistics, keep in mind that the significance of the test (whether the p-value is < .05) is a function not only of the magnitude of the effect (i.e., difference in means), but also a function of the sample size. You might obtain statistical significance with a small, negligible effect simply due to use of a large sample or conversely, you may fail to find statistical significance for a large effect due to the use of a small sample. ___________________________________________________________________________

Section 3 Confidence Intervals and Effect SizesAlthough it is not typical practice in assessment, I recommend that instead of or in addition to using inferential tests, that confidence intervals and/or effect sizes be used to interpret the practical importance of results. Rather than assuming that the population mean is the same as your sample mean, a 95% confidence interval lets your reader know that you are being cautious. A 95% confidence interval allows you to say that you are 95% confident that the population mean is contained within the interval. Ill show you how to obtain the 95% confidence interval for the mean in SPSS. As well, confidence intervals will be reported in SPSS for the differences between means. An effect size captures the magnitude or practical importance of your effect. For instance, a commonly used effect size to capture the practical significance of a mean difference is Cohens d. Cohens d represents the standardized mean difference, which is simply the number of standard deviation units by which means differ. The practical significance of a mean difference is typically considered as small, medium or large for Cohens d values of .3, .5 and .8, respectively. A correlation is not only a descriptive and inferential statistic, it is also an effect size. Values of r considered to be small, medium or large are .1, .3 and .5, respectively. To calculate an effect size I suggest using a spreadsheet that will calculate a variety of different effect sizes that was created by David Wilson and available for download from the internet: http://mason.gmu.edu/~dwilsonb/ma.html.

___________________________________________________________________________

Section 4 - Wellness Domain Example Information

Note: All data is actual data from real JMU students with identifying information omitted to protect the students identities. Some of the details below, such as the specific goals and objectives being assessed and what data is actually used for assessment purposes by the Wellness faculty were formulated for instructional purposes.We will be calculating the statistics described above using data from JMU students collected for assessment purposes in the wellness domain.

A. What kind of program is the wellness domain?At JMU, all students are required to complete a general education course covering health and wellness issues. The majority of students take one of two courses within their first two years to fulfill this requirement, either GHTH 100: Personal Wellness or GKIN 100: Lifetime Fitness and Wellness. At JMU, this general education program is referred to as the Wellness Domain of Cluster 5.B. What are the programs goals & objectives?

The faculty teaching these courses created the goals and objectives shown in Table 1 in response to the question, What should a student know and be able to do as a result of completing their general education wellness course? As you can see, the majority of the objectives are cognitive in nature, although there is one that is behavioral (4c).C. What measures does the program use to assess their goals and objectives?

The primary assessment method used by the program in order to determine the extent to which students are attaining the objectives is the KWH. The Knowledge of Health and Wellness (KWH) is a 35-item multiple-choice test created by faculty to assess the majority of their objectives. You can see from the Table of Specifications (Test Blueprint) which items were created to measure which objectives. Note that there are more items associated with Goal 2 than with any other goal. The faculty intended this to be the case since the material associated with Goal 2 is emphasized most heavily in the wellness courses. Items on the KWH are scored right or wrong, with the number of correct items serving as the total score. The range of scores that can be obtained on this scale is 0 to 35. (A copy of the KWH has been supplied to you by the doctoral assistant and is on LIGHT BLUE paper)

D. What is the programs data collection scheme?

In order to determine what incoming freshmen know and dont know about health and wellness upon arriving at JMU, a random sample of students were administered the KWH and during a campus-wide assessment day in August of Fall 2003 (F03). This same instrument was administered to these same students (pre/post) during assessment day in February of Spring 2005 (S05), which is typically the second semester of the students sophomore year. Because students typically complete their health and wellness course during the first two years of college, testing students in February of their sophomore year yielded a sample consisting of students who have not yet completed their requirement as well as students who have completed their requirement.

E. What is the programs data management plan?

Once the data have been scanned, scored and merged from the various different sources (be sure that all assessment data contains an identification # for the student so merging information is possible), it is a good idea to create what we call a data management plan, which essentially is just a spreadsheet containing information about the variables in the data set. The data management plan for the data we will be using in this workshop is in Table 2. Note how the spreadsheet contains the names for each variable, a more descriptive label for each variable, the possible range of values for a variable (if numeric), when the data for the variable was collected and whether or not the variable is numeric (values represented as a number in the data set) or character (values represented by a letter or a word). It is important to note this since many statistical operations are only possible with numeric variables (e.g., you cannot calculate an average for a character variable). You should also indicate how missing data is represented in the data set (we suggest the use of a period, .) and if applicable, specific information about what the values of a variable represent. For instance, if you use numbers to indicate whether or not a student took a particular course, be sure to indicate in your data management plan what those numbers represent, (e.g., 0 = Did NOT take course, 1 = Did take course).

Table 1.

Table 2

___________________________________________________________________________Section 5: SPSS Comparing Means for Different Groups

Data should be saved on your desktop and is called Wellness_Data.sav.

To open data set in SPSS, either:1) Start (Programs(SPSS for Windows (then File(Open(Data)2) Double-click on the SPSS shortcut on the desktop (then File(Open(Data)3) Double-click directly on the SPSS data set file icon (Wellness_data.sav) in the explorer window.

We will be using syntax to run our analyses rather than drop-down menus so that you can have a record of the analyses you completed. To open the syntax window that will be used for our analyses, open the Wellness_Syntax.sps directly from the desktop or go to File(Open(Syntax.Our main assessment question is whether or not there are differences on the KWH test administered in Spring 2005 for students who have completed their wellness domain course requirement and those who have not.

Before comparing the means of different groups, first youll want to obtain descriptive statistics and a histogram for the variable kwhtotF05. Use the following syntax to obtain descriptive statistics. Note that you can include comments in the syntax by starting the comment with an asterisk and ending it with a period.*Descriptive statistics for KWH Spring 2005 Administration.

Examine

Variables = kwhtot05

/Plot histogram /Statistics Descriptives

/Cinterval 95.To run the commands, simply highlight them and select Run ( Selection. A new window will open with your output. You will need to save this output file. The output of any additional analyses you run will be located below your prior analyses.

Let us now compare the kwhtots05 score for students who have completed their course requirement vs. those who havent. The syntax for this comparison is below. Means Tables = kwhtot05 by numwell

/cells count mean stddev min max skew kurt semean.

Now we will conduct an independent samples t-test to determine if the means are significantly different from one another in the population. T-test

Groups=numwell(0 1)

/Missing=Analysis

/Variables=kwhtot05

/Criteria=CIN(.95).

The F in the table above is a test of whether or not the population variances of the two groups are equal. You want the p-value (Sig.) associated with that F to be > .05 to conclude that the population variances are indeed equal. If you have satisfied that assumption, interpret the results in the column labeled Equal variances assumed. If you have not satisfied that assumption (p < .05), then interpret the results in the column labeled Equal variances not assumed. Because we have satisfied that assumption, we interpret the results in the left column. Because the p-value (Sig. 2-tailed) is > .05, we conclude that the difference in test performance for students who have and have not completed their wellness course requirement is not statistically significant. To obtain the effect size, open the ES_calculator.xls, enable macros and go to the Menu spreadsheet. Select t-test (independent) and simply enter the t and sample sizes for your two groups to obtain your effect size (d). Our effect size is -.05, implying that the results are also not practically significant. Section 6: SPSS Comparing Means on Different Occasions

Now we will compare the performance of students on the KWH in fall 2003, when they were incoming freshmen, versus the spring of 2005, when they were second-semester sophomores. This analysis will be different from before because groups are not indicated by a variable. Instead, we have different variables for each occasion.I would first recommend obtaining the descriptive statistics and histogram as you did above for the variable kwhtots03. After you have done that, you can calculate the means for each occasion. Means Tables = kwhtot03 kwhtot05


To determine if the difference is statistically significant, we can run a paired samples t-test.T-test

Pairs = kwhtot03 with kwhtot05 (paired)

/Criteria = CIN(.95)

/Missing = analysis.

___________________________________________________________________________________________Section 7: SPSS - Correlation

Suppose we are interested in the relationship between course grade in GHTH 100 and students KWH test score as sophomores. First, I would look at the descriptive statistics for GHTH 100 course grade the numeric version of the variable (hth100). Then I would ask for a scatterplot of the two variables.

Graph

/Scatterplot(bivar)=kwhtot05 with hth100

/Missing=listwise.

Below are the commands to obtain a correlation between the two variables. Correlations

/Variables=kwhtot05 hth100

/Print=twotail nosig

/Statistics descriptives

/Missing=listwise.

Section 8: SPSS Comparing Means on Different Occasions for Different GroupsNext we will compare the KWH averages in fall 2003 and spring 2005 for students who have and have not completed their course requirement.Means Tables = kwhtot03 kwhtot05 by numwell


To create the graph in Excel, open pre post graph.xls from Desktop. Note how I only entered in the information in the yellow cells.

Students who have completed the requirement by Spring 2005 score slightly high as incoming freshmen than students who have not completed the requirement. Both students who have and have not completed the requirement are scoring higher on the test as second-semester sophomores compared to when they were freshmen. To obtain effect sizes for the differences in Fall 2003 and Spring 2005 averages within each group (Requirement completed vs. Requirement NOT completed) you need to obtain the correlation between the scores within each group.

sort cases by numwell.

temporary.

split file by numwell.

correlations variables = kwhtot03 kwhtot05.

In ES_calculator, select Mean Gain Scores. You will calculate the effect size for each group separately. This results in an effect size of d = .74 for the students who have not completed the requirement and d = .61 for student who have. Both effects are considered medium to large. It would be anticipated that the students who have not taken the course would make lesser gains in knowledge than those students who have taken the course, but this is not what our results indicate. Of course, we have to keep in mind that the sample size in this example is extremely low for students who have taken the course. We also need to acknowledge the outliers in our data perhaps results would like quite different if they were removed. However, if the results we obtained are a true indication of the state of affairs, it would indicate that students are gaining knowledge about health and wellness during their first year and a half in college due to maturation alone. The wellness course requirement does not seem to add significantly to the knowledge they are picking up from other sources.Section 9: SPSS Scoring from a Key, Creating a Total Score and Item AnalysisTo score multiple-choice or true-false items, you typically begin with persons response to their items in raw form (1 indicated they selected option A, 2=B, 3=C, etc.).

To obtain coefficient alpha, item difficulty and item discrimination in SPSS for such items you must create new variables for each item representing whether or not the person obtained the correct (1) or incorrect (0) response. Im making the distinction here between raw item responses (1,2,3,4) and scored item responses (0,1).

Consider the data set prelim IA kin test3.sav that contains the responses to 35 items from 271 different students. In this particular data set, a response of 9 indicates that the data for that item is missing.

Rather than using pull-down menus, I am going to use syntax to do my analyses. Make sure you have you data file of raw item responses open in SPSS and then go to File( New ( Syntax.

1. First thing you want to do let SPSS know that values of 9 for your raw item response variables, rr1 rr35, are to be treated as missing. Below is the syntax I used to do this, with the first line being a comment not an actual command used by SPSS. Comments begin with an asterisk and end with a period. After an SPSS command, put the command Execute.

* Here I am telling SPSS to treat values of 9 for rr1 thru rr35 as missing.

Missing values rr1 to rr35 (9).

2. Now I will create 35 new variables which are going to be the keyed response for each item.

Compute key1=4.

Compute key2=2.

Compute key3=1.

Compute key4=3.

Compute key5=4.

Compute key6=2.

Compute key7=3.

Compute key8=1.

Compute key9=4.

Compute key10=2.

.

.

.

Compute key31=2.

Compute key32=3.

Compute key33=3.

Compute key34=1.

Compute key35=4.

execute.

3. Now I am going to create a variable that will represent the total score for each person (called total). Im initially going to set it equal to 0 for each person. (This is called initializing a variable).

* Start everyone off as if they didn't have any correct answers.

Compute total = 0.

execute.

4. I am also going to create 35 new variables that represent the item scores ( 0 = wrong, 1 = right) or the scored item responses for each person.

*Create 35 new variables that will eventually be item scores or

scored item responses for each person , item1 thru item35.

Vector item(35).5. Now you will use a do loop to: a) create values of 0 or 1 for item1 thru item35 (by comparing rr1 thru rr35 to key1 thru hey35) and b) compute the total score by adding the # of items a person got correct.

* Create scored item responses (35 of them called item1 - item35) and create a total score.

Vector raw = rr1 to rr35.

Vector key = key1 to key35.

Loop #i = 1 to 35.

if raw(#i) = key(#i) item(#i) = 1.

if raw(#i) ne key(#i) item(#i) = 0.

if raw(#i) = key(#i) total = total + 1.

end loop.

execute.

6. Obtain item analysis information using scored item responses.

*Obtain item analysis information such as item difficulty, item discrimination and coefficient alpha.

*Make sure to use scored item responses, not raw item responses.

Reliability

/Variables = item1 to item35

/FORMAT = nolabels

/Scale(alpha)=all/model=alpha

/Statistics=descriptive scale

/summary=total.

execute.7. Prepare the data so you can know which response is the correct answer when doing you item distractor analysis. Youll do this by creating value labels for the raw item responses such that the correct answer has an asterisk following it.*Add labels to raw item responses so that the correct answer is signified with an asterisk.

Add value labels rr3 rr8rr11rr14rr22rr29rr341 "1*".

Add value labels rr2rr6 rr10 rr12 rr13 rr16 rr18 rr20rr25 rr28 rr312 "2*".

Add value labels rr4rr7rr15rr17rr19rr23rr26rr30rr32rr333 "3*".

Add value labels rr1rr5rr9rr21rr24rr27rr354 "4*".

execute.8. Ask for the statistics that you need to look at the percentage of students choosing each distractor and their average total score.*Distractor analysis commands.

*Make sure to use raw item responses, not scored item responses.

MEANS

TABLES = total by rr1 to rr35

/CELLS NPCT MEAN.

execute.I usually like to take the output from the above syntax for items that may be problematic and copy it into Excel so I can make some nice looking charts. Shown below is an example of how I extracted that information from SPSS to use it in Excel.

Open the Excel file called: preliminary IA GKIN100 test3 Spr05.xls. In the data sheet, you should paste the information from SPSS in the highlighted portions below in the worksheet entitled data for prob items:

This graph is then formed on another sheet in Excel, called item 22.

The average score for the KWH Spring 2005 administration is 21.31 and we are 95% confident that the population mean is between 20.71 and 21.92. Scores are varying from the mean by about 3.13 points. The lowest score in our data set is 5, the highest is 28. These values fall in the range of possible values that could be obtained from this test (0 to 35).

The histogram shows that the scores are negatively skewed due to a small number of values below 10. It is quite possible that these scores are outliers (students may not have been motivated to put forth effort on the test). I would probably drop these students from the data set, but we will leave them in for the workshop.

There are 11 students who have not completed their wellness course compared to 94 who have completed their wellness course. The average for those who have not completed their course is somewhat lower (M = 21.18) than the average for those who have completed their course (M = 21.33). If the outliers were removed, the latter mean would increase (outliers are in latter group can tell by looking at the minimum)

Students scores as sophomores as higher (M = 21.31) then their scores as freshmen (M = 18.82), indicating a gain in health and wellness knowledge.

There is a statistically significant difference between students scores as sophomores versus when they were freshmen. Using the ES_calculator, select t-test(dependent), and enter in the sample size, t and correlation reported in the SPSS output. The resulting d indicates a large effect (d = .80) implying practical significance.

The scatterplot indicates a positive linear relationship between test score and course grade. There does seem to be a restriction of range issue with the KWH variable (most values between 17 and 28), which may cause our correlation to look low. Might also consider the student obtaining a low KWH score and low grade as an outlier.

There is a positive relationship between the two variables that is considered to be a large effect. The correlation is also statistically significant meaning that there is a significant relationship between the two variables in the population. The positive correlation is supporting evidence for the validity of the KWH scores and is of the magnitude typical for general education assessments.

Lots of information here! I usually take some of the statistics and create a graph in excel.

Double-click on the output for this item and highlight the information shown, then hit Copy (ctrl C).

Paste information here.

These two columns should adjust after information is pasted.

_1178773794.xlsTable 1. Goals & Objectives

Wellness Domain Learning Objectives & Test Specification Table for KWH

GoalsObjectivesAssessment Items Spring 2005# KWH Items/ Objective% of KWH

1Students should be able to understand the dimensions of wellness, the various factors affecting each dimension, and how dimensions are interrelated.aIdentify the dimensions of wellness.30, 1625.71

bIdentify factors that influence each dimension of wellness.6, 2425.71

cRecognize how dimensions of wellness are interrelated.5,8,10,13411.43

Total # Items for Goal 1:822.86

2Students should be able to understand the relationship between personal behaviors and lifelong health and wellness.aRecognize the importance of lifestyle in disease prevention14,20, 3538.57

bRecognize the relationship between health behaviors and wellness.22, 23, 18, 3411.43

cIdentify and apply the theories of health behavior change.25, 11, 2638.57

dExamine the role of consumer health issues related to overall wellness2, 12, 27, 19411.43


3Students will recognize an individuals level of health and wellness and understand how these levels impact quality of lifeaAssess ones levels of health and wellness17, 2825.71

bEvaluate how ones levels of health and wellness compare to recommended levels9, 29, 7, 4, 31514.29

cRecognize how genetics, environment and lifestyle behaviors influence health and wellness levels.112.86


4Students will identify and implement strategies to improve their wellnessaIdentify a realistic and adjustable personal wellness plan.32, 33, 1538.57

bRecognize how to use self-management skills relating to healthy lifestyle behaviors.21, 3425.71


Total # Items35100.00

cParticipate in a greater number of healthy wellness-related activities.Assessed via HWQ-------

* Not an actual goal/objective; created only for Assessment Institute instructional purposes.

&LLearning ObjectivesKWH Test Blueprint&CWellness Domain&R&D

Table 2. Data Management Plan

Information Regarding "Wellness Data.sav"

N = 106

Variable InformationCourse Grades

NameLabelPossible ValuesWhen Data CollectedTypeCharacterNumeric

idNumericA+4

kwhtot03KWH Total Fall 20030 - 35Fall 2003NumericA4

kwhtot05KWH Total Spring 20050 - 35Spring 2005NumericA-3.7

ghth100Personal Wellness Course GradeLetter GradeFall 2003 thru Fall 2004CharacterB+3.3

gkin100Lifetime Fitness & Wellness Course GradeLetter GradeFall 2003 thru Fall 2004CharacterB3

hth100Personal Wellness Course GradeNumeric GradeFall 2003 thru Fall 2004NumericB-2.7

kin100Lifetime Fitness & Wellness Course GradeNumeric GradeFall 2003 thru Fall 2004NumericC+2.3

q1 - q35KWH Raw Item Responses - Spring 20051 - 4Spring 2005NumericC2

C-1.7

Missing data indicated for all variables by "."D+1.3

D1

Note. Variable names in SPSS can be no longer than 8 characters and must begin with a letter.D-0.7

F0

&LData Management Plan&CWellness Data &R&D

Sheet3

2776.14

0.9612159329

_1178774299.xlsTable 2. Data Management Plan

Data Management Plan: "Wellness Data.sav"

N = 105

Variable InformationCourse Grades

NameLabelPossible ValuesWhen Data CollectedTypeCharacterNumeric

idNumericA+4

kwhtot03KWH Total Fall 20030 - 35Fall 2003NumericA4

kwhtot05KWH Total Spring 20050 - 35Spring 2005NumericA-3.7

ghth100Personal Wellness Course GradeLetter GradeFall 2003 thru Fall 2004CharacterB+3.3

gkin100Lifetime Fitness & Wellness Course GradeLetter GradeFall 2003 thru Fall 2004CharacterB3

hth100Personal Wellness Course GradeNumeric GradeFall 2003 thru Fall 2004NumericB-2.7

kin100Lifetime Fitness & Wellness Course GradeNumeric GradeFall 2003 thru Fall 2004NumericC+2.3

took_hthDid the student take GHTH100?0 = "Did NOT take GHTH100" 1 = "Did take GHTH100"C2

took_kinDid the student take GKIN100?0 = "Did NOT take GKIN100" 1 = "Did take GKIN100"C-1.7

numwellHas the student completed their wellness domain requirement?0 = "Requirement NOT completed" 1 = "Requirement Completed"D+1.3

D1

Missing data indicated for all variables by "."D-0.7

F0

&LData Management Plan&CWellness Data &R&D

Table 1. Goals & Objectives

Test Specification Table for KWH

GoalsObjectivesAssessment Items Spring 2005# KWH Items/ Objective% of KWH

1a30, 1625.71

b6, 2425.71

c5,8,10,13411.43


2a14,20, 3538.57

b22, 23, 18, 3411.43

c25, 11, 2638.57

d2, 12, 27, 19411.43


3a17, 2825.71

b9, 29, 7, 4, 31514.29

c112.86


4a32, 33, 1538.57

b21, 3425.71


Total # Items35100.00

cAssessed via HWQ-------

* Not an actual goal/objective; created only for Assessment Institute instructional purposes.

&LLearning ObjectivesKWH Test Blueprint&CWellness Domain&R&D

analyzing & interpreting data handout

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