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HLM階層線性模型演講
講師 : 中興大學 許健將顧問
2013/03/29
關鍵字請搜尋 : 三星課程網
A simple question
How much of the variability in pupil attainment is attributable to
schools level factors and how much to pupil level factors?
First of all we have to define what we mean by “variability”
A toy example – first we calculate the mean
Overall mean(0)
Two schools each with two pupils.
Overall mean= (3+2+(-1)+(-4))/4=0
3
2
-1
-4
attainment
School 2School 1
Calculating the “variance”
Overall mean(0)
3
2
-1
-4
attainment
School 2School 1
The total variance is the sum of the squares of the departures of the
observations around mean divided by the sample size(4) =
(9+4+1+16)/4=7.5
The variance of the school means around the overall mean
3
2
-1
-4
attainment
School 2School 1
Overall mean(0)
2.5
-2.5
The variance of the school means around the overall mean=
Total variance =7.5
(2.52+(-2.5)2)/2=6.25
The variance of the pupils scores around their school’s
mean
3
2
-1
-4
attainment
School 2School 1
2.5
-2.5
The variance of the pupils scores around their school’s mean=
The variance of the school means around overall mean = (2.52+(-
2.5)2)/2=6.25
Total variance =7.5=6.25+1.25
((3-2.5)2 + (2-2.5)2 + (-1-(-2.5))2 + (-4-(-2.5))2 )/4 =1.25
Returning to our question
How much of the variability in pupil attainment is attributable to
schools level factors and how much to pupil level factors?
In terms of our toy example we can now say
6.25/7.5= 82% of the total variation of pupils attainment is
attributable to school level factors
1.25/7.5= 18% of the total variation of pupils attainment is
attributable to pupil level factors
常數項
常數項
ijX
ijYjW ijr
ju0
ju1
j1
j0
00
10
01
11
ijijjj
ijjijjij
rXuu
XWXWY


10
11100100 
高階變項是對低
階迴歸係數做解
釋,而不是對依
變項本身做解釋
模型建構歷程
Snijders and Bosker(2002):
1.變項必要性與精簡性的取捨
2.統計顯著性與理論性的取捨
3.固定效果須有強力理論支持
4.由資料內容決定隨機效果
5.若交互作用達顯著,即使造成交互作用的變項之主要效果
未達顯著,則該變項亦需被保留。
6.若隨機效果達顯著,則其固定效果須被保留
7.聚合(Aggregate)後的階層變項可能是個重要變項
8.若變項間產生交互作用,則交互作用不具隨機效果
模型建構歷程
Hox(2002): (針對探索性研究)
1.先用隨機效果變異數模型並記錄模型適配度
2.將所有最低階之變項投入隨機效果共變數模型,決定需要
被保留之變項
3.檢視所有第二階層變項之固定效果,並比較模式間之適配
度
4.檢視第3步驟的變項中具有隨機效果者
5.將第4步驟中具有顯著解釋力的變項,依其隨機性之有無,
決定模型
6.將資料折半,投入相同模型中,確認其顯著性是否與完整
資料之模型相同
重新檢驗資料 檢驗ICC
一般迴歸分析 共變數模型
零模型
檢驗截距信度
不可接受 可接受
不顯著 顯著
步驟一:零模型
步驟二:共變數模型
共變數模型
階層一固定效果檢驗截距信度
逐一剔除不顯著者
檢驗離異數重新檢驗模型
保留顯著者
不顯著 顯著
步驟三:固定效果模型
不進一步分析 檢驗截距信度
保留顯著者逐一剔除不顯著者
重新檢驗模型 檢驗離異數
不顯著 顯著
檢驗離異數
階層二固定效果
係數低
係數高
步驟四:隨機係數模型
保留顯著者逐一剔除不顯著者
重新檢驗模型
不顯著 顯著
檢驗離異數
階層二隨機效果
檢驗離異數
步驟五:完整模型
保留顯著者逐一剔除不顯著者
重新檢驗模型 檢驗離異數
不顯著 顯著
檢驗離異數
階層二隨機效果
步驟六:複核效化 複核效化
ICC=26.12/26.12+81.24=0.24ICC=26.12/26.12+81.24=0.24
‘MathAch’ ≈50.76
(1.15)
‘MathAch’ ≈50.76
(1.15)
‘MathAch’ ≈ 46.36 + 2.40 x Homework 
(1.15)     (0.28)
‘MathAch’ ≈ 46.36 + 2.40 x Homework 
(1.15)     (0.28)
∆(Deviance)=3798.68 – 3727.48=71.20∆(Deviance)=3798.68 – 3727.48=71.20
18.012.26/34.2112.26)(
12.024.81/28.7124.81)(
22
22


B
w
RR
RR
學校
個體
null model
‘MathAch’ ≈ 46.32 + 1.98 x Homework
(1.76) (0.93)
‘MathAch’ ≈ 46.32 + 1.98 x Homework
(1.76) (0.93)
‘MathAch’ ≈ 49.05 + 1.98 x Homework ‐4.06 x 
Public
(2.18)   (0.92)                        (1.98)
‘MathAch’ ≈ 49.05 + 1.98 x Homework ‐4.06 x 
Public
(2.18)   (0.92)                        (1.98)
‘MathAch’ ≈ 48.52 -3.26 x Public + 2.30 x
Homework - 0.50 x HomePublic
(1.88) (3.02) (3.71) (1.59)
‘MathAch’ ≈ 48.52 -3.26 x Public + 2.30 x
Homework - 0.50 x HomePublic
(1.88) (3.02) (3.71) (1.59)
‘MathAch’ ≈ 52.72 -6.05 x Public + 0.92 x
Homework - 0.68 x HomePublic
(0.12) (0.38) (0.42) (0.10)
課程訊息
HLM階層線性模式基礎班台北場
日期:2013年4月27日(六)至4月28日(日)
時間:每日9:00-12:00與13:30-17:00
地點:台北商業技術學院
課程講師:中興大學 許健將顧問
詳請請關鍵字搜索 : 三星課程網

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