Post on 13-Dec-2015
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Overview Univariate Analysis
https://rcenterportal.msm.edu/node/63
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Overview Bivariate Analysis
https://rcenterportal.msm.edu/node/259
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
• 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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Overview
Dependency Techniques
• Multiple regression
• Discriminant analysis
• Multivariate analysis of variance
• (MANOVA)
• Linear structural relationships (LISREL)
• Conjoint analysis
– Simalto+Plus
Cooper and Schindler; Business Research Method (8th edition)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Overview
Interdependency Techniques
• Factor analysis
• Cluster analysis
• Multidimensional Scaling (MDS)
Cooper and Schindler; Business Research Method (8th edition)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Overview
Cooper and Schindler; Business Research Method (8th edition)
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
?
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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?
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Factor Analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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.
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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.
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Uji Validitas Konstruk
(Pengujian instrumen/
Kuisener)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis
Common factor analysis (CFA)
Exploratory
Factor Analysis
Types of factor analysis
Confirmatory
Factor Analysis
Principal component analysis (PCA)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis
Praktek Membaca Hasil EFA
1. Pengelompokkan item dan penamaan
faktor
2. Pengujian validitas kontruk pada
kuisener
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis Menu Factor Analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis
“Click” untuk memasukkan contoh
butir pertanyan (8 item) yang akan
direduksi/dikelompokkan menjadi
beberapa faktor
“Click” pada
untuk melihat
grafik: “Scree
plot”
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis
Ada 2 component/factor
yang nilai eigen value-
nya di atas 1
8 item pertanyaan
mengelompok dalam
2 faktor
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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”
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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”
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis
Mengelompok dalam satu faktor, artinya benar 4 pertanyaan tersebut
mengukur satu variabel yang sama yaitu “Performance Expectancy
Uji statistik
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Factor Analysis Contoh penyajian hasil uji validitas dan reliabilitas
kuisener
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Classification
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Classification Cluster Analysis & Discriminant
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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.
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Cluster Analysis Cluster analysis
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
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Cluster Analysis Cluster analysis
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Cluster Analysis
Menu yang digunakan
Nilai Z score (hasil
konversi otomatis)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Cluster Analysis Cluster analysis
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.
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Cluster Analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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).
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
?
?
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Discriminant
Discriminant Analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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.
Sabine Landau and Brian S. Everitt (2004), A Handbook of Statistical
Analyses using SPSS.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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 analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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 analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Discriminant
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Discriminant
2 Kategori (pria & Wanita)
Untuk menampilkan tabel hasil
klasifikasi (melihat ketepatan/ tingkat
prediksi secara deskriptif)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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”
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Discriminant
Fungsi diskriminant dapat memprediski jenis kelamin
dari responden berdasarkan prilaku penggunaan
internet (yang diukur dengan 6 prediktor) dengan
tingkat akurasi 68,8%
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Regression
Regression Analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
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Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Logit Regression
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Logit Regression
Dummy variable
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
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Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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)
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
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Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Multinomial Contoh Regresi Multinomial
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Multinomial
Kategori 2 (Sekolah Tinggi)
Sebagai referensi/pembanding
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Multinomial
Exogenous
variable/predictor
yang bersifat
kategorikal (variabel
dummy)
Predictor yang
skalanya kontinyu
ditempatkan
sebagai covariate
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Multinomial
“Perempuan dan status ekonomi rendah cenderung memilih program
vokasi, dan yang matematikanya lebih baik cenderung memilih
sekolah tinggi dan universitas”
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Structural Equation Model
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis 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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Diagrammatic Syntax
Gefen, D., D.W. Straub, & M.C. Boudreau. 2000. Structural Equation Modeling
and Regression: Guidelines For Research Practice.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Gefen, D., D.W. Straub, & M.C. Boudreau. 2000. Structural Equation Modeling
and Regression: Guidelines For Research Practice.
Comparative Analysis between Techniques
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Gefen, D., D.W. Straub, & M.C. Boudreau. 2000. Structural Equation Modeling
and Regression: Guidelines For Research Practice.
Capabilities by Research Approach
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Framework for applying (PLS) in structural
equation modeling
Urbach and Ahlemann (2010). Structural Equation Modeling in Information
Systems Research Using Partial Least Squares
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Perbandingan Hasil Regresi, LISREL, dan PLS
Regresi LISREL PLS
Gefen, D., D.W. Straub, & M.C. Boudreau. 2000. Structural Equation Modeling
and Regression: Guidelines For Research Practice.
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Latihan SEM
Regressi
Analysis(SPSS)
& AMOS
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
SEM
Review Journal
Meta-Analysis
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
Meta-Analysis Peta Konsep
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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
Teknik Multivariate: Praktek & Review Journal
Discriminant Cluster Overview Factor SEM Meta-Analysis Regression
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.
Cooper and Schindler; Business Research Method (8th edition)
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