원문: http://people.ysu.edu/~gchang/SPSSE/SPSS_LatinSquare_tire.pdf

원문은 위 링크 자료와 같습니다.

나중에 SPSS에서 Latin Square Design Analysis 할 때 어떻게 하는지 잊어버리고 헤맬 것이 분명하기 때문에 SPSS 스샷 부분만 한글판으로 대체했습니다.

원문 쓰신 분께 허락 안맡음.

 

Latin Square Design Analysis

Goal: Comparing the performance of four different brands of tires (A, B, C, and D).

Background: There are four cars available for this comparative study of tire performance. It is

believed that tires wearing out in a different rate at different location of a car. Tires were installed

in four different locations: Right-Front (RF), Left-Front (LF), Right-Rear (RR) and Left-Rear

(LR). The measurements of the wearing of tires in this investigation are listed in the following

table from a Latin Square Design setting. Three factors are considered in this study. They are tire

position, car and the different tires studied in this investigation.

 

I. Data Entry

Tire wearing measurements variable (tirewear), car ID's variable (car), positions of tires

(position), and the type of tires (tire) can be entered as the way entered in the following tables.

Table on the left shows numbers for categories and table on the right is properly labeled.

<변수 설정>

<숫자 값으로 입력>

<레이블로 입력 – 이때, 변수 유형을 문자로 해준다>

II. Hypothesis

The null hypotheses can be considered are: 1) there is no significant difference in average tire

wearing between different tire positions, 2) there is no significant difference in average tire

wearing between different cars used, 3) no significant difference in average tire wearing between

different brands of tires.

 

III. Analysis

To perform the ANOVA for the Latin-Square design, click through Analyze _ General Linear

Model _ Univariate … and select tirewear for the Dependent Variable box. Select car,

position and tire to the Fixed Factor(s) box. Then, click Model in the upper right hand corner.

In that dialogue box put the circle for Custom and then click car, position and tire over to the

right hand box. In the middle, click the down arrow to Main Effects. Then click off the arrow in

the box labeled Include Intercept in Model. Then hit Continue. For multiple comparisons, click

Post Hoc and select the factors for performing multiple comparison procedure, and check on the

box for selecting the method for comparisons (Tukey), and then click Continue and hit OK.

In the Univariate dialog

box, select tirewear as

the Dependent Variable

and position, tire, and

car as the Fixed Factors.

Click the Model…

button to specify the

Three-way ANOVA

model. Plots… and Post

Hoc … buttons can be

used for multiple

comparisons as in other

ANOVA procedures.

 

In the model specification window, check on Custom bullet and select all three factors in the

model and use Main effects option to build the terms in the model as in the figure below. Then,

click Continue button to complete the model specification.

For Post Hoc analysis, Tukey's-b procedure was chosen and all three factors were selected in the

following figure.

 

P-value는 모두 0.

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