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John WickLAB B3 Narrative

EDUC 746

The purpose of this study was to predict if skiers are more likely to fall based upon the

season and difficulty level of the ski run. The research question being examined is: Can we

accurately predict a skiers likelihood to fall if we know the season and the difficulty level of the

ski run?

There are three variables in this study: Falling, Difficulty, and Season. The dependent

variable is the probability of falling or not falling. The independent variables are Difficulty and

season. Difficulty is a categorical variable that has been treated as a continuous variable. Season

is a categorical variable that contains three seasons. Two of the seasons Autumn and spring, are

further coded into dummy variables.

The participants for this study are skiers and the sample size is n=15.

The analysis that was performed for this study is Logistical regression. Results of this

analysis indicate that the overall model with predictors doesnt fit well when compared to the

model with no predictors, X2 (3,15) = 2.71, p > .05. Furthermore, the odds ratio is positive,

2.748 > 1 which means that when the difficulty level increases it is more likely that the skier will

fall into the category coded as 1 (falling down), however the p value of the Wald test is .347

indicating that this increase is not statistically significant.

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John WLAB B3 Narra

EDUC

GET

FILE='/Users/John/Documents/APU/EdD/ADV SATS/LAB A/LAB B/LAB B3

Data.sav'.

DATASET NAME DataSet1 WINDOW=FRONT.COMPUTE Season1=0.

EXECUTE.

IF (Season = 1) Season1=1.

EXECUTE.

COMPUTE Season2=0.

EXECUTE.

IF (Season = 2) Season2=1.

EXECUTE.

SAVE OUTFILE='/Users/John/Documents/APU/EdD/ADV SATS/LAB A/LAB B/LA

B3 Data.sav'/COMPRESSED.

LOGISTIC REGRESSION VARIABLES Fall

/METHOD=ENTER Difficulty Season1 Season2

/CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

Logistic Regression

[DataSet1] /Users/John/Documents/APU/EdD/ADV SATS/LAB A/LAB B/LAB B

Data.sav

Case Processing Summary

Unweighted Casesa N Percent

Selected Cases Included in Analysis 15 100.0

Missing Cases 0 .0

Total 15 100.0

Unselected Cases 0 .0

Total 15 100.0

a. If weight is in effect, see classification table for the total

number of cases.

Dependent Variable Encoding

Original Value Internal Value

Not Falling 0

Falling 1

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John WLAB B3 Narra

EDUC

Block 0: Beginning Block

Classification Tablea,b

Observed

Predicted

Fall Percentage

CorrectNot Falling Falling

Step 0 Fall Not Falling 0 6 .0

Falling 0 9 100.0

Overall Percentage 60.0

a. Constant is included in the model.

b. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 0 Constant .405 .527 .592 1 .442 1.500

Variables not in the Equation

Score df Sig.Step 0 Variables Difficulty 1.746 1 .186

Season1 1.250 1 .264

Season2 .417 1 .519

Overall Statistics 2.454 3 .484

Block 1: Method = Enter

Omnibus Tests of Model CoefficientsChi-square df Sig.

Step 1 Step 2.710 3 .439

Block 2.710 3 .439

Model 2.710 3 .439

Model Summary

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John WLAB B3 Narra

EDUC

Step

-2 Log

likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1 17.481a .165 .223

a. Estimation terminated at iteration number 4 because

parameter estimates changed by less than .001.

Classification Tablea

Observed

Predicted

Fall Percentage

CorrectNot Falling Falling

Step 1 Fall Not Falling 4 2 66.7

Falling 1 8 88.9Overall Percentage 80.0

a. The cut value is .500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 1a Difficulty 1.011 .896 1.273 1 .259 2.748

Season1 .928 1.589 .341 1 .560 2.528

Season2 -.418 1.387 .091 1 .763 .658

Constant -1.777 1.890 .884 1 .347 .169

a. Variable(s) entered on step 1: Difficulty, Season1, Season2.