使用Study_1.dta之變數加總後資料檔
擬探討(Y) 主管對於其員工的績效評估(Performance Evaluation, PE)，受到哪些變項(X)的影響？ 依變數(Y)：PE 主管對於其員工的績效評估 自變數(X) ：與員工有關的：EPSMD EUPB。與主管有關的：SUPB SMD SMDIS
* 一、敘述統計、信度、效度
. summ PE EPSMD EUPB SUPB SMD SMDIS
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
PE | 176 5.460227 1.211976 1 7
EPSMD | 176 2.842045 1.473982 1 7
EUPB | 176 2.198864 1.246419 1 5.5
SUPB | 176 2.141098 1.378339 1 7
SMD | 176 3.015909 1.361418 1 7
-------------+---------------------------------------------------------
SMDIS | 176 2.134233 1.175343 1 6.5
* 二、相關係數
. pwcorr PE EPSMD EUPB SUPB SMD SMDIS, sig star(0.05)
* 二、相關係數
. pwcorr PE EPSMD EUPB SUPB SMD SMDIS, star(0.05)
| PE EPSMD EUPB SUPB SMD SMDIS
-------------+------------------------------------------------------
PE | 1.0000
EPSMD | -0.0271 1.0000
EUPB | 0.0259 0.1610* 1.0000
SUPB | 0.0128 0.2044* 0.3425* 1.0000
SMD | -0.1430 0.4670* 0.2657* 0.1455 1.0000
SMDIS | -0.1620* 0.1565* 0.4359* 0.3108* 0.2587* 1.0000
. spearman PE EPSMD EUPB SUPB SMD SMDIS , star(0.05)
(obs=176)
| PE EPSMD EUPB SUPB SMD SMDIS
-------------+------------------------------------------------------
PE | 1.0000
EPSMD | -0.0379 1.0000
EUPB | -0.0448 0.1762* 1.0000
SUPB | -0.0265 0.1817* 0.3078* 1.0000
SMD | -0.1349 0.4425* 0.2587* 0.1115 1.0000
SMDIS | -0.2682* 0.0933 0.4685* 0.2299* 0.2578* 1.0000
* 繪圖：相關係數矩陣圖
. graph matrix PE EPSMD EUPB SUPB SMD SMDIS, half msymbol(+)
* 只呈現對角線一側
* 以加號+標示樣本點 map symbol
. reg PE EPSMD EUPB SUPB SMD SMDIS
Source | SS df MS Number of obs = 176
-------------+---------------------------------- F(5, 170) = 2.06
Model | 14.6909752 5 2.93819504 Prob > F = 0.0727
Residual | 242.363949 170 1.42567029 R-squared = 0.0572
-------------+---------------------------------- Adj R-squared = 0.0294
Total | 257.054924 175 1.46888528 Root MSE = 1.194
------------------------------------------------------------------------------
PE | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
EPSMD | .0379821 .070111 0.54 0.589 -.1004181 .1763822
EUPB | .1283218 .084069 1.53 0.129 -.0376319 .2942754
SUPB | .0375892 .0718269 0.52 0.601 -.1041982 .1793766
SMD | -.1371568 .07761 -1.77 0.079 -.2903603 .0160466
SMDIS | -.2063759 .0880103 -2.34 0.020 -.3801097 -.032642
_cons | 5.843743 .2770302 21.09 0.000 5.296881 6.390605
------------------------------------------------------------------------------
*** 安裝estout模組
ssc install estout, replace**
* 兩個員工變項EPSMD EUPB，對PE的影響
* M1: PE=f(EPSMD)
. reg PE EPSMD
. estimates store M1
* M2: PE=f(EUPB)
. reg PE EUPB
. estimates store M2
* M3: PE=f(EPSMD EUPB)
. reg PE EPSMD EUPB
. estimates store M3
. esttab M1 M2 M3
------------------------------------------------------------
(1) (2) (3)
PE PE PE
------------------------------------------------------------
EPSMD -0.0223 -0.0264
(-0.36) (-0.42)
EUPB 0.0251 0.0302
(0.34) (0.40)
_cons 5.524*** 5.405*** 5.469***
(27.70) (29.04) (22.64)
------------------------------------------------------------
N 176 176 176
------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001
* 三個主管變項SUPB SMD SMDIS，對PE的影響
reg PE SUPB
esti store M4
reg PE SMD
esti store M5
reg PE SMDIS
esti store M6
reg PE SUPB SMD
esti store M7
reg PE SMD SMDIS
esti store M8
reg PE SUPB SMDIS
esti store M9
* 主管 SUPB SMD SMDIS 三個 變項
reg PE SUPB SMD SMDIS
esti store M10
esttab M4 M5 M6 M7 M8 M9 M10
* 員工與主管，五個變數全部考慮，區分員工、主管兩段，以便看出對於ＰＥ的影響
reg PE EPSMD EUPB SUPB SMD SMDIS
esti store M11
* 模型比較：個別變數、員工變數兩個、主管變數三個、五個變數全部考慮
esttab M1 M2 M3 M4 M5 M6 M10 M11
* 模型比較：員工因素、主管因素、全部因素，三個模型，並且顯示顯著水準
* ar2: adjust R square 模型解釋變異程度的改變
. esttab M3 M10 M11, se ar2
------------------------------------------------------------
(1) (2) (3)
PE PE PE
------------------------------------------------------------
EPSMD -0.0264 0.0380
(0.0633) (0.0701)
EUPB 0.0302 0.128
(0.0748) (0.0841)
SUPB 0.0686 0.0376
(0.0692) (0.0718)
SMD -0.101 -0.137
(0.0689) (0.0776)
SMDIS -0.162 -0.206*
(0.0831) (0.0880)
_cons 5.469*** 5.964*** 5.844***
(0.242) (0.262) (0.277)
------------------------------------------------------------
N 176 176 176
adj. R-sq -0.010 0.026 0.029
------------------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001
. esttab M3 M10 M11, p scalars(F df_m df_r)
------------------------------------------------------------
(1) (2) (3)
PE PE PE
------------------------------------------------------------
EPSMD -0.0264 0.0380
(0.677) (0.589)
EUPB 0.0302 0.128
(0.687) (0.129)
SUPB 0.0686 0.0376
(0.323) (0.601)
SMD -0.101 -0.137
(0.144) (0.079)
SMDIS -0.162 -0.206*
(0.053) (0.020)
_cons 5.469*** 5.964*** 5.844***
(0.000) (0.000) (0.000)
------------------------------------------------------------
N 176 176 176
F 0.145 2.555 2.061
df_m 2 3 5
df_r 173 172 170
------------------------------------------------------------
p-values in parentheses
* p<0.05, ** p<0.01, *** p<0.001
esttab M3 M10 M11, label ///
title(線性迴歸模型比較) ///
nonumbers mtitles("M1 員工" "M2 主管" "M3 全部") ///
addnote("日期：2022年10月。Source:Study_1.dta")
線性迴歸模型比較
--------------------------------------------------------------------
M1 員工 M2 主管 M3 全部
--------------------------------------------------------------------
2. Employee Percep~i -0.0264 0.0380
(-0.42) (0.54)
3. Emplyee UPB 0.0302 0.128
(0.40) (1.53)
1. Supervisor UPB 0.0686 0.0376
(0.99) (0.52)
4. Supervisor Mora~g -0.101 -0.137
(-1.47) (-1.77)
6. Supervisor Mora~t -0.162 -0.206*
(-1.95) (-2.34)
Constant 5.469*** 5.964*** 5.844***
(22.64) (22.77) (21.09)
--------------------------------------------------------------------
Observations 176 176 176
--------------------------------------------------------------------
t statistics in parentheses
日期：2022年10月。Source:Study_1.dta
* p<0.05, ** p<0.01, *** p<0.001
* 確認工作目錄，以便檢視輸出的檔案
cd
* 模型比較結果，直接輸出至 MS-Excel CSV檔
esttab M3 M10 M11 using 202210ntue.csv
* 模型比較結果，直接輸出至 MS-Word rtf檔
esttab M3 M10 M11 using 202210ntue.rtf
或
esttab M3 M10 M11 using 202210ntue.doc