Overview Factor Cluster Discriminant Regression SEM …bhermana.staff.gunadarma.ac.id/Downloads/files/39327/Multivariate... · Teknik Multivariate: Praktek & Review Journal Overview

Teknik Multivariate: Praktek & Review Journal

Discriminant Cluster Overview Factor SEM Meta-Analysis Regression

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Multivariate Analysis Praktek & Review Jurnal

Budi Hermana Program Doktor Ilmu Ekonomi

Universitas GUnadarma http://piboonrungroj.files.wordpress.com/2011/07/hypotheses.png



Overview Univariate Analysis

https://rcenterportal.msm.edu/node/63



Overview Bivariate Analysis

https://rcenterportal.msm.edu/node/259



WHY MULTIVARIATE ANALYSIS?

Multivariate analysis consists of a collection of

methods that can be used when several

measurements are made

on each individual or object in one or more

samples

Variable

Units (research units, sampling units,

or experimental units) or observations

Overview



• Dependency

– dependent (criterion) variables and

independent (predictor) variables are

present

• Interdependency

– variables are interrelated without

designating some dependent and others

independent

Selecting a Multivariate Technique

Cooper and Schindler; Business Research Method (8th edition)

Overview



Overview

Dependency Techniques

• Multiple regression

• Discriminant analysis

• Multivariate analysis of variance

• (MANOVA)

• Linear structural relationships (LISREL)

• Conjoint analysis

– Simalto+Plus




Overview

Interdependency Techniques

• Factor analysis

• Cluster analysis

• Multidimensional Scaling (MDS)




Overview


Y X

3 1

5 2

7 3

9 4

11 5

13 6

15 7

17 8

19 9

21 10

Apakah X berhubungan

dengan Y?

Jika:

X adalah jumlah burung camar terbang

di lepas pantai

Y adalah jumlah burung camar terbang

di lepas pantai

Regresi: Y = 1 + 2X r = 1

?



Overview

? Deduksi Hipotesis Verifikasi

Teori

Riset sebelumnya

Data/

Fakta

Pengukuran

Hasil/Diskusi

Research Gap

Research Question

Formulasi

Hipotesis

State of

the Art

Road

Map

Pengujian

Hipotesis

Kontribusi

Premis

Pemilihan

Alat Uji?



Factor Analysis

Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical

Analyses using SPSS.

Factor Analysis



Factor Analysis

Principal component analysis is a multivariate

technique for transforming a set of related (correlated)

variables into a set of unrelated (uncorrelated) variables

that account for decreasing proportions of the variation of

the original observations.

Principal components is essentially a method of data

reduction that aims to produce a small

number of derived variables that can be

used in place of the larger number of original variables to

simplify subsequent analysis of the data





Factor Analysis

Factor analysis, like principal component analysis, is an attempt to

explain a set of data in terms of a smaller number

of dimensions than one begins with, but the procedures used to

achieve this goal are essentially quite different in the two methods.

If the factor model holds but

the variances of the

specific variables are small, we would expect both forms

of analysis to give similar

results.





Factor Analysis

Factor analysis (more properly exploratory factor analysis) is

concerned with whether the covariances or correlations between a

set of observed variables can be explained in terms of a smaller

number of unobservable constructs known

either as latent variables or common factors.



Uji Validitas Konstruk

(Pengujian instrumen/

Kuisener)



Factor Analysis

Application of factor analysis involves

the following two stages:

1 Determining the number of common

factors needed to adequately describe the

correlations between the observed variables,

and estimating how each factor is related to

each observed variable

(i.e., estimating the factor loadings)

2 Trying to simplify the initial solution by the

process known as factor rotation Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical




Factor Analysis

Common factor analysis (CFA)

Exploratory

Factor Analysis

Types of factor analysis

Confirmatory

Factor Analysis

Principal component analysis (PCA)



Factor Analysis

CFA allows the researcher to test the

hypothesis that a relationship between

the observed variables and their

underlying latent construct(s) exists

EFA, traditionally, has been used to

explore the possible underlying

factor structure of a set of

observed variables without

imposing a preconceived structure on

the outcome

Exploratory or Confirmatory Factor Analysis?

Diana D. Suhr, Ph.D Rex Kline (2013) Exploratory and Confi

rmatory Factor Analysis



Factor Analysis

PCA assumes that the common

variance (C) becomes maximized and

there is no unique variance (A and B)

in each variable.

CFA assumes that there is a

substantial amount of unique

variance as well as reliable common

variance.

Hee-Ju Kim (2008)



Factor Analysis

Common Variance yaitu varians yang dibagi dengan varians lainnya;

atau jumlah varians yang dapat diekstrak dengan proses factoring

Unique Variance yaitu varians yang berkaitan dengan variabel tertentu

saja; jenis variabel ini tidak dapat dijelaskan dengan korelasi hingga menjadi

bagian dari variabel lain; namun varians ini masih berkaitan secara unik

dengan satu variabel

Error Variance yaitu varians yang tidak dapat dijelaskan lewat proses

korelasi; jenis varians ini muncul karena proses pengambilan data yang salah;

pengukuran variabel yang tidak tepat, dll

Varians adalah

akar dari

standar devisia



Factor Analysis

Construction of the Correlation Matrix

Method of Factor Analysis

Determination of Number of Factors

Determination of Model Fit

Problem formulation

Calculation of Factor Scores

Interpretation of Factors

Rotation of Factors



Factor Analysis

Statistics Associated with Factor

Analysis

• Bartlett's test of sphericity. Bartlett's test of

sphericity is used to test the hypothesis that the

variables are uncorrelated in the population (i.e.,

the population corr matrix is an identity matrix)

• Correlation matrix. A correlation matrix is a lower

triangle matrix showing the simple correlations, r,

between all possible pairs of variables included in

the analysis. The diagonal elements are all 1.



Factor Analysis

• Communality. Amount of variance a variable

shares with all the other variables. This is the

proportion of variance explained by the common

factors.

• Eigenvalue. Represents the total variance

explained by each factor.

• Factor loadings. Correlations between the

variables and the factors.

• Factor matrix. A factor matrix contains the factor

loadings of all the variables on all the factors



Factor Analysis

• Factor scores. Factor scores are composite scores

estimated for each respondent on the derived factors.

• Kaiser-Meyer-Olkin (KMO) measure of sampling

adequacy. Used to examine the appropriateness of factor

analysis. High values (between 0.5 and 1.0) indicate

appropriateness. Values below 0.5 imply not.

• Percentage of variance. The percentage of the total

variance attributed to each factor.

• Scree plot. A scree plot is a plot of the Eigenvalues against

the number of factors in order of extraction.



Factor Analysis

Praktek Membaca Hasil EFA

1. Pengelompokkan item dan penamaan

faktor

2. Pengujian validitas kontruk pada

kuisener



Factor Analysis

Scree plot

0.5

2 5 4 3 6

Component Number

0.0

2.0

3.0

Eig

envalu

e

1.0

1.5

2.5

1

Fact

or Eigen value % of

variance Cumulat.

% 1 2.731 45.520 45.520 2 2.218 36.969 82.488 3 0.442 7.360 89.848 4 0.341 5.688 95.536 5 0.183 3.044 98.580 6 0.085 1.420 100.000

Cenderung

1 Faktor

Kemiringan/Slope

yang curam

Cenderung

2 Faktor

Faktor yang terbentuk

adalah yang nilai

eigenvalue-nya > 1



Factor Analysis Menu Factor Analysis



Factor Analysis

“Click” untuk memasukkan contoh

butir pertanyan (8 item) yang akan

direduksi/dikelompokkan menjadi

beberapa faktor

“Click” pada

untuk melihat

grafik: “Scree

plot”



Factor Analysis

Ada 2 component/factor

yang nilai eigen value-

nya di atas 1

8 item pertanyaan

mengelompok dalam

2 faktor



Factor Analysis

Butir 1, 2, 3, 4 Factor 1

Butir 5, 6, 7, 8 Factor 2

Penamaan

faktor?

Lihat kemiripan

substansi pertanyaan

dalam satu faktor

Matriks rotasi menunjukan pengelompokkan yang sama.

Matriks ini biasanya digunakan jika ada beberapa butir pada matriks pertama

(component matriks) yang sulit dimasukanan ke faktor satu atau dua karena

nilainya relatif tidak berbeda jauh



Factor Analysis Factor Analysis untuk uji validitas konstruk

(misal pada kuisener)

Contoh:

Menurut model UTAUT dari Venkantesh (2003), contoh konstruk/variabel

yang digunakan yaitu Performance Expectancy yang diukur dengan 4 butir

pertanyaan dan Effort Expectancy yang diukur dengan 4 pertanyaan.

4 Butir pernyataan untuk variabel

“Performance Expectancy”

4 Butir pernyataan untuk variabel “Effort

Expectancy”



Factor Analysis

Masukkan 4 butir

pertanyaan untuk satu

variabel (Performance

Expectancy)

Berdasarkan pertimbangan praktis, perhitungan validitas dengan analisis

faktor ini dilakukan per varibel. Jadi, jika ada 3 variabel maka dilakukan

tiga kali perhitungan

Validitas konstruk dilakukan sekaligus dalam Structural Equation Model

yaitu pada “measurement model”



Factor Analysis

Mengelompok dalam satu faktor, artinya benar 4 pertanyaan tersebut

mengukur satu variabel yang sama yaitu “Performance Expectancy

Uji statistik



Factor Analysis Contoh penyajian hasil uji validitas dan reliabilitas

kuisener



Classification



Classification Cluster Analysis & Discriminant



Classification

Statistical techniques concerned with

classification are essentially of two

types.

Cluster analysis

to uncover groups of observations

from initially unclassified data

Discriminant function analysis

works with data that is already

classified into groups to derive rules

for classifying new (and as

yet unclassified) individuals on the

basis of their observed variable values.





Cluster Analysis Cluster analysis

Agglomerative hierarchical techniques

Distance and similarity measures

Euclidean distance

Euclidean distances are the starting point for many clustering

techniques, but care is needed if the variables are on very

different scales, in which case some form of standardization

will be needed

1

2

k-means clustering

Method of Clustering






Agglomerative hierarchical techniques

clustering techniques that proceed by a

series of steps in which progressively

larger groups are formed by joining

together groups formed earlier in the

process.

to determine the stage at which the

solution provides the best description of

the structure in the data, i.e.,

determine the number of

clusters.

more and more individuals are linked

together to form larger and larger clusters

of increasingly dissimilar elements






Agglomerative Hierarchical Techniques

1 3 2 5 4 60

0.05

0.1

0.15

0.2

1

2

3

4

5

6

1

23 4

5

Dendogram records the

sequences of merges or splits

Dendrogram Nested Clusters



Cluster Analysis

Contoh pengelompokkan 214 negara berdasarkan jumlah penduduk dan

nilai PDB per kapita (sumber: Data World Bank)

Data distandarisasi terlebih dahulu (dikonversi ke nilai Z pada distribusi

normal)



Cluster Analysis

Menu yang digunakan

Nilai Z score (hasil

konversi otomatis)



Cluster Analysis Sebagian tampilan “Dendogram” yang menunjukkan

pengelompokka negara berbentuk diagram pohon

Ada berapa klaster/kelompok negara?

Dapat juga dibuat sub klaster!

Nama Klaster?

Contoh:

• Lower income, lower-middle income, middle income, dst

• Kelompok negara berpenduduk besar dengan

pendapatan tinggi, ……, negara kecil dengan

pendapatan kecil. dst




Method of clustering that produces

a partition of the data into a

particular number of groups set by

the investigator

k-means clustering

To minimize the

variability within clusters

and maximize variability

between clusters.





Cluster Analysis



Cluster Analysis Cluster analysis untuk segmentasi pasar

Tampilan data editor

Sumber data: Santoso (2014)

Latihan ini hanya menggunakan 3 variabel saja yaitu usia, gaji, dan tingkat

konsumsi. Nilai yang dimasukkan dalam analisis klaster adalah nilai yang

sudah dikonversi ke nilai Z

Survey terhadap 50 konsumen (misal yang memilih beberapa

merek produk elektronik).



Cluster Analysis

Jumlah klaster yang

diinginkan ditetapkan

sebanyak 2 klaster saja

“cluster membership”

membuat kolom baru pada

data editor yang

menunjukkan setiap

konsumen (responden)

masuk ke klaster 1 atau 2



Cluster Analysis

Usia klaster 1 = Rata-rata + z x standar deviasi

Usia klaster 1 = 30,12 – 0.6711 x 6,043

Usia klaster 1 = 26,06

Usia klaster 2 = 30,12 + 1.095 x 6,043

Usia klaster 2 = 36,74 tahun

dst untuk variabel gaji dan tingkat konsumsi

Dengan nilai Z (standarisasi) Dengan nilai semula (tanpa standarisasi)

VS

Nama/deskripsi segmen

Segmen 1

Segmen 2

?

?



Discriminant

Discriminant Analysis



Discriminant Discriminant analysis

The lambda coefficient is defined as the proportion of the

total variance in the discriminant scores not explained by differences

among the groups

The canonical correlation is simply the Pearson correlation

between the discriminant function scores and

group membership coded as 0 and 1.

The “Wilk’s Lambda” provides a test for assessing

the null hypothesis that in the population the vectors of means of the

five measurements are the same in the two groups

The eigen value represents the ratio

of the between-group sums of squares to the within-group sum of

squares of the discriminant scores.





Discriminant Inappropriate

application of a statistic

Yes

No Dependent non-metric? Independent variables metric or dichotomous?

Yes

Ratio of cases to independent variables at least 5 to 1?

No Inappropriate application of a statistic

Number of cases in smallest group greater than number of independent variables?

Yes

No Inappropriate application of a statistic

Yes

Sufficient statistically significant functions to distinguish DV groups?

No False

Run discriminant analysis, using method for including variables identified in the research question.

Discriminant analysis



Discriminant

Stepwise method of entry used to include independent variables?

Yes

No

Entry order of variables interpreted correctly?

Yes False

Relationships between individual IVs and DV groups interpreted correctly?

No

Yes

False

No




Discriminant

Yes

Cross-validated accuracy is

25% higher than proportional

by chance accuracy rate?

No False

DV is non-metric level and IVs are interval level or dichotomous (not ordinal)?

Yes

No

True

Satisfies preferred ratio of

cases to IV's of 20 to 1

Yes

No True with caution

Yes

Satisfies preferred DV group

minimum size of 20 cases?

No True with caution

True with caution




Discriminant

Performance Expectancy

Effort Expectancy

Internet Self-Efficacy

Internet Anxiety

Social Influence

Supporting Condition

Jenis Kelamin

Pre

dic

tor

(Me

tric

/Co

ntin

ou

s V

ari

ab

le)

Kategori dengan 2 kelompok:

Pria dan Wanita

Pembuktian isu gender dalam prilaku penggunaan internet atau

adopsi TIK; analisis kesenjangan digital (digital divide) antar

kelompok masyarakat atau antar wilayah/regional



Discriminant



Discriminant

2 Kategori (pria & Wanita)

Untuk menampilkan tabel hasil

klasifikasi (melihat ketepatan/ tingkat

prediksi secara deskriptif)



Discriminant

Statistik Uji

Fungsi/Persamaan Diskriminan

Y = -0.131-0.058-0.202+0.162+0.957–0.212

y = 1 pria, y = 2 Wanita

Struktur Matrix

Urutan variabel berdasarkan

“discriminating power” dari yang

tertinggi ke yang terendah

“Internet self-efficacy merupakan prediktor

yang paling besar kontribusinya dalam

membedakan pria dan wanita berdasarkan

prilaku penggunaan internet”



Discriminant

Fungsi diskriminant dapat memprediski jenis kelamin

dari responden berdasarkan prilaku penggunaan

internet (yang diukur dengan 6 prediktor) dengan

tingkat akurasi 68,8%



Regression

Regression Analysis



Regression Regresi Probit dan Logit

Binary Outcome Dependent variable

bersifat kategori

dengan dua level

Ya/Tidak

Menang/Kalah

Bangkrut/Tidak bangkrut

Sehat/Tidak sehat

Demokratis/Otoriter

Sentralisasi/Desentralisasi

Where τ is the threshold

y* is unobserved, as the underlying latent

propensity that y=1



Regression

The difference between Logistic and Probit

models lies in this assumption about the

distribution of the errors

Logit vs Probit

Standard logistic distribution

of errors

Normal distribution of errors

Park (2010) & Moore (2013) Hasilnya cenderung hampir sama



Regression

Logit = log odds β0+ β1X

When x changes one unit, the logit (log odds)

changes β1 units

When x changes one unit, the odds changes

eβ1 units

Sekretariat pasca melakukan tes masuk program

pasca berdasarkan tidak parameter yaitu test masuk

berbasis komputer, IPK calon pada saat S1, dan

akreditasi program studi dari calon. Hasil seleksi

adalah diterima atau ditolak.

Contoh



Logit Regression



Logit Regression

Dummy variable



Logit Regression

Faktor IPK menunjukkan peluang lebih tinggi untuk diterima

dibandingkan hasil test masuk

Log odd (B=0.804)) IPK > log odd Test (0.02)

Exp(B) untuk IPK (2.235) > Exp(B) untuk test (1.002)

Calon dari program studi terakreditasi A lebih tinggi dibandingkan

dengan calon dari program studi tidak terakreditasi (kategori yang

dijadikan referensi/pembanding)

Peluangnya 4,718 kali dibandingkan calon dari program studi tidak terakreditasi



Logit Regression

Output tabel tersebut mirip dengan tabel

klasifikasi hasil analisis diskriminant

Logit regression menjadi teknik alternatif dengan tujuan

analisisnya yang hampir sama dengan analisis diskriminant.

Perbedaannya, semua prediktor pada analisis diskriminant

harus berskala metrik atau kotinyu (skalanya minimal interval)



Regression Multinomial & Ordinal Regression

Regresi logit bisa diperluas jika variabel respon (dependent

variable) terdiri dari lebih dari 2 tingkat, atau r > 2

r Nominal r Ordinal

Multinomial Logistic

Regression Models

Ordered (ordinal) Logistic

Regression Models



Multinomial Contoh Regresi Multinomial

Lulusan SMA yang akan melanjutkan ke perguruan tinggi

mempunyai tiga pilihan program pendidikan tinggi, yaitu

universitas, sekolah tinggi, dan vokasi. Bagaimana

kecenderungan (peluang) pilihan lulusan SMA tersebut

berdasarkan jenis kelamin, status ekonomi orang tua,

status SMA (negeri atau swasta), serta nilai ujian (misal

nilai UN untuk Matematika, IPS, dan IPA).

Y 3 kategori yang bersifat nomonal

(Universitas, Sekolah Tinggi, Vokasi)

Variabel eksogenus (X) terdiri dari 3 variabel yang bersifat

kategorikal (dummy variable) dan 3 skor ujian yang bersifat

kontinyu/Metrik



Multinomial Contoh Regresi Multinomial



Multinomial

Kategori 2 (Sekolah Tinggi)

Sebagai referensi/pembanding



Multinomial

Exogenous

variable/predictor

yang bersifat

kategorikal (variabel

dummy)

Predictor yang

skalanya kontinyu

ditempatkan

sebagai covariate



Multinomial

“Perempuan dan status ekonomi rendah cenderung memilih program

vokasi, dan yang matematikanya lebih baik cenderung memilih

sekolah tinggi dan universitas”



Panel Data Analysis Multivariate untuk “Panel Data”

Panel data analysis is a method of studying a particular subject within

multiple sites, periodically observed over a defined time frame.

Analisis longitudinal (ada unsur waktu)

Kinerja perusahaan pada sektor manufaktur dalam 5

tahun terakhir

vs cross-sectional ?

Perbandingan daya saing negara di dunia dalam 3

tahun terakhir (cross-country analysis)

Dua

Dimensi

Spatial

Temporal

Cross-sectional unit (perusahaan,

negara, orang, dll)

Periodic observations (Time Span)

Xij

Cross-sectional time-series analysis



Panel Data Analysis Multivariate untuk “Panel Data”

Contoh: sampel perusahaan sebanyak 60 dilihat kinerja keuangannya

selama 3 tahun, misal dengan menggunakan satu dependent variabel dan 4

independent variable

180 data (baris) 60 x 3

Perusahaan Tahun Y X1 X2 X3 X4

P1 P1

P1

2011 2012 2013

10 22 32 15 25 25 15 20 25 30 15 10 22 26 32

P2 P2

P2

2011 2012 2013

10 22 28 15 27

21 19 23 22 18 22 16 19 22 31

…. ……. …. … …. …. ….

P60 P60 P60

2011 2012 2013

21 18 29 17 23

19 21 22 17 26 21 23 18 17 31

Yit = a + bX1it + cX2it + dX3it + eX4it

Long-Form

Data

Format

Perusahaan Y2011 Y2012 Y2013 X12011 X12012

P1 P2 P3 P4 P5 P6 …. ……. …. … …. …. …. .…….. ……

P58 P59 P60

X12013 ………. X42013

Wide-Format



Panel Data Analysis Model – Model Analisis “Panel Data”

Constant Coefficient Model

Fixed Effect Model

Random Effect Model

Dynamic Model

Robust Panel Model

Covariance Structure Model Pro

ble

ms o

f h

ete

rosk

ed

as

tic

ity

an

d a

uto

co

rrela

tio

n

Ordinary least squares (pooled) regression

Least Squares Dummy Variable Model

Robert Yaffee (2003). A Primer for Panel Data Analysis.

Error Component Model

Random Parameter Model

LIMDEP, STATA,

SAS, EViews

SPSS Tricky

(SPPS command;

Wide vs Long-Form Format)

Analysis Generalized Linear Model

Generalized Estimating

Equation



SEM

Structural Equation Model



SEM

SEM is a statistical technique for simultaneously

testing and estimating causal relationships

among multiple independent and dependent constructs

(Gefen et al. 2000)

SEM is a statistical technique for testing and estimating

those causal relationships based on statistical data

and qualitative causal assumptions (Urbach

and Ahlemann, 2010)

SEM A Second Generation of Multivariate Analysis

First Generation

of Multivariate

Analysis

MANOVA. dll

Cluster Analysis

Factor Analysis

Discriminant Analysis

Multiple Regression



SEM

Answers a set of interrelated research

questions in a single, systematic, and

comprehensive analysis

Supports latent variables

Nature

SEM Structural model

Measurement model

Relationship between the empirically

observable indicator variables and

the Latent Variable

Relationships between the Latent

Variable, which has to be derived from

theoretical considerations

common factor underlying factor

Not directly measured



SEM



SEM Measurement Model

~ “Analisis faktor

dengan

menggunakan

SEM”

Korelasi antar PE

dengan EE

Kontribusi (variansi)

indikator PE1 terhadao

Latent Variabel PE

Koefisien regresi untuk

indikator ISE sebagai

independent variable

terhadap latent variabel

ISE



SEM Measurement Model & Structural Model

Koefisien regresi untuk

variabel latent sebagai

exogenous variable

Squared multiple

Correlation antara

Performance dengan

ISE dan Effort

~ padanan r2 pada

analisis regresi

konvensional



SEM

Diagrammatic Syntax

Gefen, D., D.W. Straub, & M.C. Boudreau. 2000. Structural Equation Modeling

and Regression: Guidelines For Research Practice.



SEM

Two general

approaches

Covariance-based structural

equation modeling (CBSEM) -

LISREL, AMOS, EQS, SEPATH,

RAMONA

The component-based approach

PLS

Analy

se

s’ obje

ctives

Sta

tistical a

ssum

ptions

Natu

re o

f t

he f

it s

tatistics

Uses a maximum likelihood (ML)

function to minimize the

difference between the sample

covariance and those

predicted by the theoretical

model

Minimizes the variance of all

the dependent variables instead

of explaining the covariation

1

2



SEM

Urb

ach

and A

hle

mann (

2010).

S

tructu

ral E

quatio

n M

odelin

g in

Info

rmatio

n

Syste

ms R

esearc

h U

sin

g P

art

ial Least

Square

s



SEM



Comparative Analysis between Techniques



SEM



Capabilities by Research Approach



SEM

Framework for applying (PLS) in structural

equation modeling

Urbach and Ahlemann (2010). Structural Equation Modeling in Information

Systems Research Using Partial Least Squares



SEM

Y

X

Z

Persamaan Struktural:

Y = a + bX ….. (1)

Z = c + dY ……(2)

Z = e + f X .…..(3)

Z = c + d Y

Z = c + ad + bd X)

b

d

f

b, d, f

Standardized

Coefficient

(a + bX)

f pengaruh langsung X ke Z bd pengaruh tidak langsung X

ke Z melalui Y

H1

H2

H3



SEM

Perbandingan Hasil Regresi, LISREL, dan PLS

Regresi LISREL PLS





SEM

Latihan SEM

Regressi

Analysis(SPSS)

& AMOS



SEM

Review Journal

Meta-Analysis



Meta-Analysis Peta Konsep



Meta-Analysis

2.1. Regulasi E-Banking

2.2. Teknologi E-Banking

2.3. Dampak E-Banking

2.3.Tipe Produk E-Banking

2.4. Kinerja E-Banking

Bab 2 Tinjauan Pustaka

Keyword

PETA KONSEP E-BANKING



Meta-Analysis

Hasil Penelusuran kata kunci di Google Scholar

untuk 5 tahun terakhir

E-Banking Quality & Adoption

Annotated

Bibliography

Meta-Analysis

Dipilah &

Dipilih

Kutipan/Premis

Mr Z (2000) menyatakan bahwa exploratory empirical

analysis, cross-sectional; Spearman rank order

correlation (karena variable SDM bersifat non-metrik);

cronbach alpha untuk beberapa variable organisasi dan

Mister X (2009) meneliti 273 perusahaan besar, Teori

teknologi informasi dan arsitektur organisasi (disain

organisasi mencakup spesifikasi wewenang pengambilan

keputusan, system evaluasi kinerja, dan system

kompensasi). Exploratory empirical analysis, cross-

sectional; Spearman rank order correlation (karena

variable SDM bersifat non-metrik); cronbach alpha untuk

beberapa variable organisasi.

Menurut Mr T (2010), komputerisasi tidak secara

otomatis meningkatkan produktifitas, tetapi tetapi

merupakan komponen penting dalam system yang lebih

luas mengenai perubahan organisasi yang akan

meningkatkan produktifitas; Jadi perubahan organisasi

merupakan bagian integral dari proses komputerisasi;

Research on E-Banking Service Quality:

State of The Art

Terima kasih,

selamat membuat proposal,

Meneliti, dan publikasi

international



Referensi

Sabine Landau and Brian S. Everitt. 2004. A Handbook of Statistical Analyses using SPSS. Chapman &

Hall/Crc, A Crc Press Company, Washington, D.C.


Diana D. Suhr . Exploratory or Confirmatory Factor Analysis?. Statistics and Data Analysis, University of

Northern Colorado.

Rex Kline. 2013. Exploratory and Confirmatory Factor Analysis

Park. 2010 & Moore. 2013.

Urbach and Ahlemann. 2010. Structural Equation Modeling in Information Systems Research Using Partial

Least Squares. Journal of Information Technology Theory and Application. Volume 11, Issue 2, pp. 5-40,

June 2010.

Gefen, D., D.W. Straub, & M.C. Boudreau. 2000. Structural Equation Modeling and Regression: Guidelines

For Research Practice. Communications of AIS Volume 4, Article 7.

Neil H. Timm. 2002. Applied Multivariate Analysis. Springer-Verlag New York, Inc

Hee-Ju Kim. 2008. Common Factor Analysis Versus Principal Component Analysis: Choice for Symptom

Cluster Research. Asian Nursing Research , March 2008. Vol 2. No 1

Alvin C. Rencher. 2002. Methods of Multivariate Analysis (2nd Edition). A John Wiley & Sons, Inc.

Publication,

Wolfgang Härdle and Léopold Simar. 2007. AppliedMultivariate StatisticalAnalysis (2nd Edition). Springer-

Verlag, Berlin Heidelberg

Robert Yaffee (2003). A Primer for Panel Data Analysis. Connect, Fall 2003 Edition, New York University

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