final one ppt

8/2/2019 Final One Ppt

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Presented by: Seema Praliya, Pooja Singh, Sunitha Ashok Menon,Shashank Raj Dixit


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Definition

Correlation research is a form of analysis in which you

correlate one variable with another to determine if there is a

relationship between them.


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Goals of Correlation Research:

Helps us to understand related events, conditions, andbehaviors.e.g. Is there a relationship between blood pressure and cholesterol

level?

To make predictions of how one variable might predictanothere.g. Can height of be used to predict the self esteem of people?


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Nature of correlation Research

The relationship between two or more factors are explored,without manipulating any of the variables being investigated.

It seeks to describe the existing relationship betweenvariables.

The degree of relationship is usually expressed using the

correlation coefficient.

Note: With correlation research we can only show the magnitude ordegree of relationship or association between variables, not the cause ofrelationship


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Types of Correlation

Positive Correlation: means that high score in onevariable is associated with high score in another variable

Negative Correlation: means high score in one variableis associated with low score in another variable.


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Correlation Research process

Variables to be studied are identified

Questions and/ or hypotheses are stated

A sample is selected (a minimum of 30 isneeded)

Data are collected

Correlations arecalculated

Results are reported

SurveysObservationMappingSortingArchives

Techniques:

Tactics to analyse the data

Multivariate analysisMultiple regressionFactor analysisMultidimensionalScaling


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General characteristics

Focus on naturally occurring Pattern

Clarify the relationship among a complex set of real world variablesthat vary with the circumstances or setting being studied and are alsolikely to affect the dynamics of socio-physical interaction. By variableswe mean the range of characteristics( of physical features, of people, of

activities, or of meanings)e.g: ways in which two variables A & B may be casually related

A B B A

A

B

C

A causes B ( either directly or indirectly) B causes A ( either directly or indirectly)

C causes both A & B


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The measurement of specific variable:

Data in correlation research is quantified and tallied through thefollowing:1. Categorical measurements: the variable of interest is

sorted into discrete categories, based on verbal and nominal

terms. (e.g. walk, car, bus, metro, train etc are mode oftransportation used to get to work.)

2. Ordinal Scales: Here variables are ordered on some basis(a range from lesser to greater)

3. Interval ratio Scales: Measurement of exact intervals; anyestablished and consistent unit of measurement (dollars,feet, degrees, etc). These should not be used for abstractdata (attitudes, feelings, etc.)


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The researcher develops either :

Descriptive statics : simply presents or describes, important

relationships among variables.

Correlational statistics: measures the strength and direction ofrelationship between two or more variables.

note: all calculated correlation coefficients fall within a rage of -

1.00(negative correlation) to +1.00 (positive correlation).

Correlation coefficient close to 0 indicates no consistent linearrelationship b/w variables, i.e. relationship cannot be graphed asa straight line.

The use of statistics to clarify patterns of

relationship:


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CORRELATIONAL RESEARCH

STRATEGY Relationship studies- how particular events are related

Causal-comparative research- intermediate positionbetween predictive orientation of relationship studies andthe focus on causality that characterizes experimentalresearch.


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RELATIONSHIP STUDIES1) RELATIONSHIP STUDIES:

Although all correlational studies seek to discribe relationships among key variables,relationship studies focus more specifically on the nature and predictive power of suchrelationships.

Eg:Oscar Newmans study of public housing in New York city which produce the theory ofDIFENSIBLE SPACES

a) Records of 169 public housing projects managed by the New York City Housing Authority wereexamined

b) Research team conducted an exhaustive investigation of the complex relationship betweenuser demographics and the physical variables of the housing /site design, and incidence ofcrime

c) The emense research made it possible to determine exactly where is the most dangerous areasof buildings are, as well as to compare crime rates of different building types and projectlayout

Not only this theory of DEFENSIBLE SPACE define a relationship between environmentalvariable and behavioral consequences, but also offers a predictive capacity that can be

articulated as design guidelines, specifically low income housing that incoporates real andsymbolic barriers, defined area of influence, and opportunities for surveillance


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CAUSAL-COMPARATIVE RESEARCH

2) CAUSAL- COMPARATIVE STUDIES:

Causal- comparative studies stake out an intermediate position between the predictiveorientation of relationship studies and focus on casuality that characterizesexperimental research

In causal comparative studies, the researcher select comparable groups of people orcomparable physical environments and then collects data on a variety of relevantvariables.

The causal- comparative design can only ascribe cases in a provisional or hypothetical

way. This is because causal- comparative research relies on natural occurring variable, asdo all correlational studies

To make causal-comparative design persuasive, the researcher must establish theessential comparability of the examples studied. Unfortunately, there are often manyobstacles to establishing the equivalence of the examples/groups in naturally occurringcircumstances .


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TACTICS: COLLECTING DATA Survey

Observation

Mapping Sorting

Archives


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SURVEY METHODSSampling

Stratified random sampling

Sample is a part of parent population.

Sample is any part of a defined population, which isselected or to be selected for questioning, observation andfrom which information can be obtained and statisticalinferences are drawn or prediction made about entirepopulation.

A statistical sample is thus a miniature picture or crosssection of the entire group or aggregate from which thesample is taken.


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POPULATION In a statistical population, every member of a group

possesses the same basic and define characteristics butvarying in amount of quality from one member toanother, for eg. Income tax payers in India.


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METHODS OF DESIGNING SAMPLES Deliberate or purposive or judgment or direct sampling.

Random/ lottery method

Stratified random sampling

Systematic sampling Cluster sampling

Quota sampling- sample fixed, allotted numbers

Note- systematic sampling is one in which every k^th item

is selected in a list representing a population or a stratum.The number k is called sampling intervals.

K= N/n where k= sampling intervals

N= universe size, n= sample size


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DATA

Data sources

Secondary sources

(published and

unpublished literature)

Primary sources

(one to one)


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SURVEY AT GRASS-ROOT LEVEL Reconnaissance survey

House listing survey

Pilot/pre testing survey Household survey

a. Comprehensive household survey

b. Household survey at the selected samples


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TOOLS FOR DATA COLLECTION Schedules

Questionnaire


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Survey questionnaire

Survey is perhaps the most frequently employed tool. It allowsthe researcher to cover an extensive amount of informationacross a large number of people in a limited amount of time

Questionnaire are drafted through the following procedure.

1. Determine main topic to be covered.(orient ques. towardsthat.)

2. Chose either open ended or close ended format(as well ascategorical, ordinal and intervalratio)

Closed and multiple choice question

Q. how often do your parents ask you about your home work?

Never 11-2 times a week 2

3-4 times a week 3

Nearly every day 4


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The main advantage of closed questions are:

The respondent is restricted to a finite (and therefore more

manageable) set of responses, They are easy and quick to answer,

They have response categories that are easy to code,

they permit the inclusion of more variables in a research study

because the format enables the respondent to answer morequestions in the same time required to answer fewer open-ended questions.

Open-ended or free-response questions are not followed byany choices

Q.What are your favourite TV programmes?

(Please specify their titles)

Q.What do you like most about school? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


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The main advantages of open-ended questions are:

They allow respondents to express their ideasspontaneously in their own language,

They are less likely to suggest or guide the answer than

closed questions because they are free from the

format effects associated with closed questions, and

They can add new information when there is very

little existing information available about a topic.


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The main disadvantages of open-ended questionsare:

1. They may be difficult to answer and even moredifficult to analyze,

2. Require time and efforts

3. Require some degree of writing ability.

4. Handwriting can be illegible.


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Phrase question clearly(use short sentences, avoid makingmultiple query in single question)

Q. Do you think it is good idea for children to study geography and

history in primary school? Avoid framing question in negative

Avoid hypothetical question?Q. Would you use the resources in your class if it were available?

Avoid using ambiguous wording, employ nonthreatening

language Use a logical sequence of topic(start with non challenging

and interesting issue but dont place imp. In the end.

Use appealing and simple graphics

Introduce the survey properly(provide a reason for the survey, provide a description of what a researcher is expected

to do. state provisions for keeping individuals responses

confidential


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Observation

Most important aspect figuring out what to look for

Needs to be systematic

As it makes possible to establish justified design objectives

and simplifies complexities

Essential to devise a thorough observation program

To get the direct rather than random approach and not to loseits representativeness

Critical self-questioning is very essential

A great advantage

even a simple numbers count can provide a detailed andpowerful view of the human ecology of a particular setting


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Observation

Range of variables that might be observed Demographic characteristics

study of a day in life of the ledge at Seagram; no. of people intime interval(charted number count in specific timeintervals)

Specific activities Whytes study of urban plazas (used time-lapse camera to

observe behaviour of activity in pedestrian areas)

User reactionsMark Fredericksonss study of design juries, with special focus

on possibility of gender or minority basis(time-frequencymeasures and content process categories)

There can be other variables too.


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Mapping

To get clear and simplified view of distinct features of anarea To determine the imageable features of a place

To help making design guidelines for that place

Sketching and drafting of maps of areas underobservations Sketching through visual or verbal experience, better when

difference is nil

Derivations of certain features that re-occur in all samplemaps; establishing distinguished points of the area

Eg., Kevin Lynchs study image of the city derivations offive major urban features; path, edge, node, landmark anddistrict


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Mapping

Marking out or pointing the different physical featuresover an area layout Eg., Anne Lusks study of greenway bicycle paths as a basis for

formulating design guidelines

Making aesthetically pleasing features on the way prominenton paper by notifying them

Used different stickers as notifications

Measurement of distances between two distinct points byodometer

Was able to determine major destination points alonggreenway occur every two miles


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HABITUAL USER MAPS - Lakefront trail, Chicago


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TRACE OVERLAY MAP - Lakefront trail, Chicago


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Sorting

Highly effective both in research and practicesituations

Sorting of cards having either words or pictures onthem

TypesDirected sort : sorting in categories(eg. , 5- or 7-point

scale from highly preferred to least preferred)

Free sort : sorting in whatever categories suit to

respondent (eg., buildings according to functions, styles,etc.)

Multiple free sort : sorting can be done as many times aspossible


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Sorting

Visual exercise for simplifying complexities anddetermining difference in priorities and therebyapproaches

A creative foundation for dialogue between client andarchitect

Ease of expression of motives


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Archives

Use of existing database and records

Eg., Oscar Newmans study for defensible spaces

He took help of records of New York Housing Authority

and also police force databank for demographic data;and thereby correlating them with physical propertiesof various housing projects

Was able to determine exactly where the most

dangerous areas of buildings are, and comparison ofcrime rates in different building types and projectlayouts


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Tactics : Multivariate analysis

Experienced researcher employ such complex statistical

method to analyze the collected data to come to the conclusion

Multiple Regression

Factor Analysis

Multidimensional Scaling


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Multiple Regression

This is more complex data analysis, used to describe thestrength and direction of variable relationship . Themathematical equations indicate the amount of variance

contributed by each variables.


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Linear Multiple Regression Analysis

Well known statistical technique for fitting mathematicalrelationship b/w independent and dependent variables.

Assumption1. All the variables are independent of each other.2. All the variables are normally distributed.3. All the variables the continuous.4. A linear relationship exist between the dependent and

independent variable.5. The influence of the independent variable is additive.

It is easy adaptability to computer programming whenhandling voluminous data


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Factor Analysis

Depends on interval or ratio data. Instead of using

key variables to predict the outcomes of othervariables, factor analysis aims to articulate anoverall pattern. Variables that share similarpatterns are grouped into clusters known as

factors.


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What is factor analysis ?

Factor analysis is a general name denoting aclass of Procedures primarily used for datareduction and summarization.

Variables are not classified as either dependentor independent. Instead, the whole set of

interdependent relationships among variables isexamined in order to define a set of commondimensions called Factors.


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Purpose of Factor Analysis

To identify underlying dimensions called Factors, thatexplain the correlations among a set of variables.

e.g. lifestyle statements may be used to measure the

psychographic profile of consumers.

To identify a new, smaller set of uncorrelated variables toreplace the original set of correlated variables forsubsequent analysis such as Regression or Discriminant

Analysis.e.g. psychographic factors may be used as independentvariables to explain the difference between loyal and nonloyal customers.


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Multidimensional Scaling Offers more flexibility

Uses both nominal data as well as interval or ration data

Outcome of the analysis is represented graphically.

Its goal is to reveal an underlying pattern or structureamong the variables analyzed, similar to factor analysis, butallowing a greater degree of interpretative flexibility.

Results in a graphical plot that locates relationshipbetween all variables spatially.

Two variables plotted in close proximity means theyrepresent similar patterns of observations and vice versa.


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CORRELATIONAL RESEARCHApplications of the correlational strategy

Prediction Predictor variable (e.g. GPA)

Criterion variable to be predicted (e.g. grad school success) Reliability and validity

Calculating correlations in test-retest reliability andconcurrent validity

Evaluating theories E.g. Heredity and IQ correlations between twins


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STRENGTHS AND WEAKNESSES strengths

nonintrusive natural behaviors Can clarify relationships among two or more naturally

occurring variables.

Well suited to studying the breadth of a setting or aphenomenon Can establish predictive relationships high external validity

weaknesses cannot assess causality (e.g. TV and aggressive behavior) Research can not control levels or degrees of variables directionality problem low internal validity; less suited to exploring the setting or

phenomenon in depth.

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