10 Bivariate Analysis V2.1

8/2/2019 10 Bivariate Analysis V2.1

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Research Methods: Level 6Final Year Project Toolkit


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The story so far...

Developing research questions

Critical thinking and literature reviews

Research design Data collection methods

Resourcing

Collecting and coding data

Now...analysis!

The ResearchThe Research

ToolkitToolkit


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Bivariate analysis techniques for describingand exploring relationships between twovariables.

Explore everything? No! Choice over what todescribe and explore should be theory driven.

When examining causality:

Independent and dependent variables

Bivariate analysisBivariate analysis

Independentvariable (X)

DependentVariable(Y)


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Common technique for categorical variables(nominal & ordinal) is contingency tables or crosstabulation

Exploring variable responses by differentrespondent groups or exploring hypotheses aboutrelationships between two variables

Cross tabulations placing one variable in thecolumn and one in the row.

Convention is usually:

Column independent Row -de endent

variables:variables:

contingencycontingency

tablestables


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Cross tabulationsCross tabulations

How often do you go food shopping?

Gender

Count Male Female Total

Daily 40 55 95

Several times a week 56 58 114

Several times a month 20 36 56

Several times a year or less 5 2 7

Total 121 151 272

Example of a cross tabulation frequency of foodshopping by gender, count (n=272)

Column totals

(marginals)

Row totals(marginals)

Grand total

C b l i


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Cross tabulations:Cross tabulations:

columncolumnExample of a cross tabulation food shopping by gender,percentage (n=272)

Gender

Percentage Male Female

Daily 33.1 36.4Several times a week 46.3 38.4

Several times a month 16.5 23.8

Several times a year or less 4.1 1.3

Total 100.0 100.0

n = 121 151

C b l i


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rowrowExample of a cross tabulation food shopping by gender,percentage (n=272)

Gender Total n


Daily 42.1 57.9 100.0 95

Several times a week 49.1 50.9 100.0 114

Several times a month 35.7 64.3 100.0 56

Several times a year or less 71.4 28.6 100.0 7

Total 44.5 55.5 100.0 272

b l iC t b l ti


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totaltotalExample of a cross tabulation food shopping by gender,percentage (n=272)

Gender


Daily 14.7 20.2

Several times a week 20.6 21.3

Several times a month 7.4 13.2

Several times a year or less 1.8 0.7

Total 44.5 55.5


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Presenting crossPresenting cross

tabulationstabulations


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Further analysis with categorical variables:

Why does the relationship exist?

How are the variables associated?

Are there any other variables thatimpact on the relationship?

More analysis withMore analysis with

categorical datacategorical data


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Elaboration and spurious relationship analysis...

the extent to which a relationship is affected bythe introduction of other variable.

Is the relationship still exist and to what extentwhen another variable is introduced?

In example is the frequency of food shoppingaffected by the same degree when anothervariable such as employment status isintroduced?

ElaborationElaboration

analysisanalysis


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Mindful of spurious false relationships

Spurious when an association made betweentwo variables is not due to a direct cause-and-effect relationship due to a third known orunknown variable.

Elaboration analysis adding a 3rd variable :

allows more building of a more complexpicture of the data

allows consideration of possible spurious

relationships

SpuriousSpurious

relationshipsrelationships


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Example crossExample cross

tabulationtabulation Gender

Male Female Total

Employed full-time Daily count 10 20 30

% within gender 15.4 22.2 19.4

Several times a week count 36.0 40.0 76.0


Several times a month count 15 30 45


Several times a year or less count 4 0 4


Total count 65 90 155

% within gender 100.0 100.0 100.0

Employed part-time Daily count 30 35 65


Several times a week count 20 18 38


Several times a month count 5 6 11


Several times a year or less count 1 2 3


Total count 56 61 117


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Bryman, A. (2008) Social Research Methods. 3rd Ed.Oxford: Oxford University Press.

David, M. and Sutton, C. (2011) Social Research : An

Introduction. 2nd ed. London: Sage.

ReferencesReferences


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This resource was created by the University of Plymouth, Learning from WOeRk project. Thisproject is funded by HEFCE as part of the HEA/JISC OER release programme.

This resource is licensed under the terms of the Attribution-Non-Commercial-ShareAlike 2.0 UK: England & Wales license (http://creativecommons.org/licenses/by-nc-sa/2.0/uk/).

The resource, where specified below, contains other 3rd party materials under

their own licenses. The licenses and attributions are outlined below:

1. The name of the University of Plymouth and its logos are unregistered trade marks of the University. TheUniversity reserves all rights to these items beyond their inclusion in these CC resources.

2. The JISC logo, the and the logo of the Higher Education Academy are licensed under the terms of the CreativeCommons Attribution -non-commercial-No Derivative Works 2.0 UK England & Wales license. All reproductions

must comply with the terms of that license.

Author Laura Lake

Institute University of Plymouth

Title Research Methods: Level 6Final Year Project Toolkit

Description Bivariate AnalysisDate Created May 2011

Educational Level Undergraduate

Keywords Independent variable, dependent variable, causation, crosstabulation, elaboration analysis, UKOER, LFWOER, CPD,Learning from WOeRK, UOPCPDRM, Continuous professionaldevelopment, Quantitative , Qualitative, HEA, JISC, HEFCE

Back page originally developed by the OER phase 1 C-Change project

University of Plymouth, 2010, some rights reserved
http://cpdoer.net/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://creativecommons.org/licenses/by-nc-sa/2.0/uk/http://cpdoer.net/

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10 Bivariate Analysis V2.1