correspondence analysis1

8/3/2019 Correspondence Analysis1

1/35

CORRESPONDENCE ANALYSIS

Presented by

Iqra Siddiqui

Farheen Ahmed QuidwaiM.Shoaib Farooq

Hina Razi

Maria Qayyum


2/35

What is it?

Why use it?

Correspondence

Analysis Overview


3/35

HISTORY OF CORRESPONDENCE

ANALYSIS

These methods were originally developed

primarily in France by Jean-Paul Benzrci in theearly 1960's and 1970's.

similar techniques were developed

independently in several countries, where theywere known as optimal scaling, reciprocalaveraging, optimal scoring, quantificationmethod, or homogeneity analysis).


4/35

WHAT IS CORRESPONDENCE

ANALYSIS? Correspondence Analysis is a technique that

generates graphical representations of theinteractions between object (or "categories") oftwo categorical variables.

Correspondence analysis is a related perceptualmapping technique with similar objectives.

Perceptual mapping: it is a set of techniques thatattempt to identify the perceived relative imageof a set of objects.


5/35

EXAMPLE

INCOMECLASSES

SURFEXCEL

EXPRESSPOWER

BONUS ROWSTOTAL

UPPERCLASS

40 18 2 60

MIDDLECLASS

30 50 10 90

LOWERCLASS

5 10 40 55

COLUMNTOTAL

75 78 52 205

DETERGENT CATEGORY


6/35

EXAMPLE

INCOMECLASSES

SURFEXCEL

EXPRESSPOWER

BONUS ROWSTOTAL

UPPERCLASS

40 18 2 60

MIDDLECLASS

30 50 10 90

LOWERCLASS

5 10 40 55

COLUMNTOTAL

75 78 52 205

DETERGENT CATEGORY


7/35

EXAMPLE


8/35

REASONS FOR USING

CORRESPONDENCE ANALYSIS The primary goal of correspondence analysis is

to transform a table of numerical information

into a graphical display. Form Contingency table.

Perform chi-square test on table.

Create metric distance measure from chi-square association measure.

Place categories on these dimensions so

as to best account for associations.


9/35

ADVANTAGES OF CORRESPONDENCE

ANALYSIS

The simple cross-tabulation if multiple

categorical variables can be represented in aperceptual space.

CA portrays not only the relationships

between the rows and columns, but also the

relationships between the categories of eitherrows or columns.


10/35

DISADVANTAGES OF

CORRESPONDENCE ANALYSIS The technique is descriptive and not at all

appropriate for hypothesis testing.

The technique is quite sensitive to outliers,in terms of either rows or columns.


11/35

DISTINGUISHING CHARACTERISTICS

It is compositional method rather then adecompositional approach.

CA is the basis for developing perceptualmaps.

CA abilities for simultaneously representingrows and columns.


12/35

STAGES IN

CORRESPONDENCE ANALYSIS


13/35

STAGE 1: OBJECTIVES OF CA

Association among only row or column

categories.

Association between both row and column

categories.


14/35

EXAMPLE

INCOMECLASSES

SURFEXCEL

EXPRESSPOWER

BONUS ROWSTOTAL

UPPERCLASS

40 18 2 60

MIDDLECLASS

30 50 10 90

LOWERCLASS

5 10 40 55

COLUMNTOTAL

75 78 52 205

DETERGENT CATEGORY


15/35

EXAMPLE

INCOMECLASSES

SURFEXCEL

EXPRESSPOWER

BONUS ROWSTOTAL

UPPERCLASS

40 18 2 60

MIDDLECLASS

30 50 10 90

LOWERCLASS

5 10 40 55

COLUMNTOTAL

75 78 52 205

DETERGENT CATEGORY


16/35

EXAMPLE

INCOMECLASSES

SURFEXCEL

EXPRESSPOWER

BONUS ROWSTOTAL

UPPERCLASS

40 18 2 60

MIDDLECLASS

30 50 10 90

LOWERCLASS

5 10 40 55

COLUMNTOTAL

75 78 52 205

DETERGENT CATEGORY


17/35

STAGE 2: RESEARCH DESIGN OF

CA Requires only data matrix of non-negative

entries e.g. cross tabulations

Categories in both rows and columns musthave specific meaning for interpretation

purpose.

Objects are rated on a set of characteristics

(e.g: attributes).


18/35

STAGE 3: ASSUMPTIONS OF CA

Homogeneity:

In correspondence analysis, it is assumed thatthere is homogeneity between the columnvariable of the analysis. If homogeneity is notpresent in the analysis, then the result will bemisleading.


19/35


Distributional assumption:Correspondence analysis is a non-parametric technique that assumes

distributional assumptions.

Category assumption:In correspondence

analysis, it is assumed that the discrete datahas many categories.


20/35


Negative values:In correspondence analysis,negative value is not considered.

Continuous data:In correspondenceanalysis, discrete data is used. If we areusing continuous data, then the data mustbe categorized into range.

Correspondence analysis is an exploratorytechnique not a confirmatory technique.


21/35

STAGE 4: DERIVING CA RESULTS

AND ASSESSING FIT

a. Deriving results

Chi-square values obtained for each cell.

Chi-squared values standardized andconverted to distance measure.


22/35

STAGE 4: DERIVING CA RESULTS

AND ASSESSING FIT

b. Assessing fit

Identify appropriate number of dimension.

Assess importance of each dimension bytheir Eigen values.


23/35

STAGE 5: INTERPRETATION OF

THE RESULTS

Identify a categorys association with other

categories.

Determine whether comparisons are to be

made between row or column, row and

column categories.


24/35

STAGE 6: VALIDATION OF THE

RESULTS

Sample

objects


25/35

RESEARCH PAPER

A survey was conducted at the Faculty ofEconomics of the Budapest Tech to measure

and evaluate 17 technical skills required byemployers. The study utilizedquestionnaires torate and rank these skills based on student

assessment. The research was supported by amultivariate statistical method referred to ascorrespondence analysis.

Abstract:


26/35

RESEARCH PAPER

Higher education needs to be aware of thechanging nature of the workplace and of

the requirements ofemployingorganizations.

The main purpose of this study is to findthe technical skills and the major areas ofknowledge that are sought by employersfor our industrial engineering and businessmanagement students entering thejob

market.

Introduction:


27/35

RESEARCH PAPER

A = Word processing (Word, etc.)B = Spreadsheets (Excel, etc.)

C = DatabasesD = Operating systems designE = Project managementF = Computer software and programming

languagesG = Inventory managementH = Logistics (transportation, distribution,

warehousing, suppliers)

Technical skills :


28/35

RESEARCH PAPER

I = Quality management

J = Resource planning & control

K = Web designing, IT, Internet operations

L = Telecommunication

M = Quantitative analiysis (statistics, optimization, etc.)

N = Managerial accounting (budgeting, break-even, costcontrolling)

O = Finance (balance sheet, cost-benefit, cash-flow,investments)

P = Marketing & market research (sales, behaviour, etc.)

Q = Entrepreneurship

Technical skills :


29/35

RESEARCH PAPER

Students were asked to rate from 1 to 7 on aseven point ascending scale these 17

technical skills according to their importance.These competencies are required on enteringemployment and also for their professional

career.

Survey method:


30/35

RESEARCH PAPER

1 Not at all important

2 Scarcely important

3 Slightly important

4 Moderately important

5 Usually important6 Significantly important

7 Extremely important

Survey method:


31/35

RESEARCH PAPER

Data is collected from every type of studentin Faculty of Economics of the Budapestand

thesample size was 242.

Sample size:


32/35

RESEARCH PAPER

Contingency Table :


33/35

RESEARCH PAPER

Significance :In terms of the significance of dependencies thevalue of the chi-square statistic is 2= 925.794,which at a stated level of=0.05 indicates asignificant dependencybetween the rows(attributes) and the columns (contributions) (p=0.000).


34/35

RESEARCH PAPER

Result :


35/35

RESEARCH PAPER

Conclusion : Efforts must be made by the school to

minimize any gaps between our engineeringand economics students perceptions of

marketable skills and actual skills expected byemployers.

The results of this study furnish a good basis

for supervising and further developing thepresent curricula into this direction.

correspondence analysis1

Documents