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A= korelasi
B =  error
C = observe varibles
D= regression coefficient / factor loading
E= construct
F= distrubant / faktor luaran yang mungkin memberi kesan
G=korelasi
H= dependant varible
I = elemen
J=  unobserve varible

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Variable &Symbol

A: Covariens of residual
B : measurement error
C : Observed variables
D : regresi
E  & J : latent variables
F : disturbant
G : korelasi
H : dependant variable
I : Elemen
J : Unobserve variable

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A - Correlation
B - Error
C - Observe variable
D - Regression
E - Latent variable (unobserve variable)
F - Disturbance
G - Impact of one variable on another
H - Endogenous
I - Elements (observe variable)
J - Latent variable

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-Mira Khalisa
-Nur Fadzilah

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Variable & Symbol
A: Covariens of residual
B: Casual relation
C : direct observe variable/dependent variable
represeted as the effect of other variable.
D: Factor loading mean multiple regression.
Akibat dan kesan pada J&E (IV)
E & J: Latent Variable (pembolehubah yang tiada hubungan langsung dengan )
F: error term. iaitu ketidaktentuan hubungan pembolehubah
G: Correation QOL to Funtional
H: Exogenous variable - Dependent variable for Physical, Emotional and functional
I: element

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A=Correlation between error
B=Error
C=Observed variables-Elements
D=Regression
Hubungan antara lebih 2 variables
E=Latent variable/Unobserved variable-Construct-Then akan dapat min
F=Disturbance-faktor-faktor luaran yang memberi impact kepada exogenous
G=Correlation-
H=Endogenous variables
I=Elements
J=Exogenous variables-IV
Q= External variable

Mazliana
Nurhafizah

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Can run regression analyses using SEM software

Mathematics/computer algorithm used by SEM is different, but

Parameter estimates will be identical or very close

Note that fit will be perfect (number of observations and number of parameters are equal)

Running in SEM buysyounothing

»  but, nice analysis to start with (you can check against SPSS or SAS run)

»  SEM allows multiple DVs

»  SEM allows two-group (or multi-group) comparisons

Mazliana
Nurhafizah

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Rather than trying to determine the number of factors, and subsequently, what the factors mean (as in EFA), if you already know (or suspect) the structure of your data, you can use a confirmatory approach

Confirmatory factor analysis (CFA) is a way to specify which variables load onto which factors

The loadings of all variables not related to a given factor are then set to zero

For a reasonable number of parameters, the factor correlation can be estimated directly from the analysis (rotations are not needed)

Mazliana
Nurhafizah

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Exploratory Factor Analysis is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlining theoretical structure of the phenomena.  It is used to identify the structure of the relationship between the variable and the respondent.

-Mira Khalisa
-Nur Fadzilah

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CFA
it is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor). first, resercher develops a hypothesis.
EFA
to identify factors based on data and to maximize the amount of variance explained.[16] The researcher is not required to have any specific hypotheses about how many factors will emerge, and what items or variables these factors will comprise

# data[].attributes.body
A : Covariance of residual
B: Residual/Error
C: Response Item
D: Regression
E: Latent Variable
F: Error in measurement - error depicted from each measuring item of variable
G: Correlation/Factor loading for second order construct
H: Main Construct
I: Endogenous construct - dependent variable
J: Exogenous construct- independent variable

-Mira Khalisa
-Nur Fadzilah

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EFA: “Determine nature and number of latent variables that account for observed variation and covariation among set of observed indicators (≈ items or variables)”

–  In other words, what causes these observed responses?

–  Summarize patterns of correlation among indicators

–  Solution is an end (i.e., is of interest) in and of itself

Mazliana Binti Md Said
Nurhafizah Binti Abdul Musid

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A: Corelations of residuals.
The corelation between 2 errors.

B: Error
-Variance (serakan antara observed value dan predicted value (mean dengan data)) left over after prediction of a measured variable atau outliers (data ekstrem yang melebihi julat data yang didapati)
- Data yang tidak normal - jauh daripada curve.
- error mesti rendah if not, curve akan menjadi lebih besar dan susah untuk mencari tengah.

C: Observed Variables.

D: Regression or Directional Path

E: Latent Variable
A variable in the model that is not measured.  It is also called an unmeasured or unobserved variable or a factor.

F: Disturbance
Variance left over after prediction of a factor.
- Variance bagi unobserved variable.
- adalah something can give impact to the construct faktor luaran yang memberi direct impak kepada exogenous variable. Example: Jantina, Umur, etc.

G: Corelations or Directional Path

H: Endogenous Variables
A variable that is predicted by another variable or dependent variable.

I: Items or Elements bagi construct.

J: Exogenous Variable
Is variable that predicts other variables or Independent variable.

q18- external variables
-Nurshahira Alwani binti Mohd Taufik
-Ummu Taqiah binti Bahari

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CFA is a validating the measurement model of latent (represented by ellipses shape) construct. A latent variable or “hidden” variable is generally thought of as variable that is not directly measurable or observable.

-Mira Khalisa
-Nur Fadzilah

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Meaning of Confirmatory Factor Analysis (CFA)

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Meaning of Confirmatory Factor Analysis (CFA)

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CFA adalah prosedur statistik multivarian yang digunakan untuk menguji sejauh mana pembolehubah diukur mewakili bilangan konstrudan adalah alat yang digunakan untuk mengesahkan atau menolak teori pengukuran.

Multivarian Analisis Data pula  merujuk kepada mana-mana teknik statistik digunakan untuk menganalisis data yang mempunyai lebih daripada satu pembolehubah.

-Nurshahira Alwani binti Mohd Taufik
-Ummu Taqiah binti Bahari

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Meaning of Explatory Factor Analysis (EFA)

# data[].attributes.subject
Meaning of Explatory Factor Analysis (EFA)

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EFA dalah kaedah statistik yang digunakan untuk mendedahkan struktur yang mendasari set yang agak besar pembolehubah dan satu teknik dalam analisis faktor yang matlamat untuk mengukur secara keseluruhannya bagi mengenal pasti hubungan asas antara pembolehubah.

-Nurshahira Alwani binti Mohd Taufik
-Ummu Taqiah binti Bahari