Quantitative and Qualitative Data Analysis Lecture 1: Introduction

Dr. Engr. Sami ur Rahman

Quantitative and Qualitative Data AnalysisLecture 1: Introduction

University Of Malakand | Department of Computer Science | UoMIPS | Dr. Engr. Sami ur Rahman | 2

Statistics (3rd Ed.) by David Freedman, Robert Pisani and Roger Purves. Norton

Doing Data Analysis with SPSS Version 12 by Carver and Nesh.

Qualitative Data Analysis: An Expanded Sourcebook, by Matthew B. Miles and A. Michael Huberman. 2nd Edition. Sage Publications: Thousand Oaks, CA

A Practical Guide to Scientific Data Analysis by David Livingstone ChemQuest, Sandown, Isle of Wight, UK

Reference books


Outline

Motivation

What is Data?

What is Data Analysis

Quantitative Data and Qualitative Data

Quantitative and Qualitative Data Analysis


Things aren’t always what we think!

Blind men and an elephant


Data

Student No Hours Studied Marks1 1 402 4 803 2 504 4 705 5 906 3 607 2 458 1 429 4 8510 3 70

What information do we get from this data??

Data: Values of qualitative or quantitative variables.


Data Analysis

Student No

Hours Studied Marks

1 1 402 4 803 2 504 4 705 5 906 3 607 2 458 1 429 4 8510 3 70

Student No

Hours Studied Marks

1 1 408 1 423 2 507 2 456 3 6010 3 702 4 804 4 709 4 855 5 90

Sorted data


Data Presentation

Student No

Hours Studied Marks

1 1 408 1 423 2 507 2 456 3 6010 3 702 4 804 4 709 4 855 5 90 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

0

10

20

30

40

50

60

70

80

90

100

4042

50

45

60

70

80

70

85

90

Series1Ma

rks

Hours studied


What is data analysis?

Data analysis is the process of turning data into information

An attempt by the researcher to summarize collected data

Data Interpretation is an attempt to find meaning

Good analysis communicates something meaningful about the world


Quantitative Data:

Data that is numerical, counted, or compared on a scale

Qualitative Data:

Textual data

Interview transcripts

Case notes/ clinical notes

Photographs

Video recordings

Types of Data


Quantitative Data Analysis:

Converting quantitative data into information

Qualitative Data Analysis:

Converting qualitative data into information

Types of Data Analysis


Quantitative Analysis


Quantification of Data

Quantification Analysis :

The numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena that those observations reflect.


Quantitative Analysis

Can be used to answer questions like

What is the percent distribution?

How much variability is there in the data?

Are the results statistically significant?


Simple Quantitative Analysis

Averages

Mean: add up values and divide by number of data points

Median: middle value of data when ranked

Mode: figure that appears most often in the data

Percentages


Central Tendency

Central tendency: The way in which quantitative data tend to cluster around some value. A measure of central tendency is any of a number of ways of specifying this "central value"

Median Mode

Central Tendency

Average (Mean)


Mean

Mean (arithmetic mean) of data values

1 1 2

n

ii n

XX X X

Xn n


Mean

The most common measure of central tendency Affected by extreme values (outliers)

0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 12 14

Mean = 5Mean = 6


Median

Median: The “middle” numberNot affected by extreme values

0 1 2 3 4 5 6 7 8 9 10

Median = 5

0 1 2 3 4 5 6 7 8 9 10 12 14

Median = 5


Mode

Mode: Value that occurs most often Not affected by extreme values There may be no mode There may be several modes

Mode = 9

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 1 2 3 4 5 6

No Mode


Simple quantitative analysis

Graphical representations give overview of data

Number of errors made

0

2

4

6

8

10

0 5 10 15 20

User

Nu

mb

er o

f er

rors

mad

e


Simple quantitative analysis

Graphical representations give overview of data

Internet use

< once a day

once a day

once a week

2 or 3 times a week

once a month


Strengths of Quantitative Research

Precise, quantitative, numerical data

Testing hypothesis/confirming theories

Generalizing finding, random samples with sufficient size

Comparatively quick data collection

Less time consuming analysis

May minimize personal bias

.


Weaknesses of Quantitative ResearchOnly applicable for measurable (quantifiable) phenomena

Simplifies and ”compresses” the complex reality, lack of detailed narrative

Theories or categories might not reflect local constituencies’ understandings


Qualitative Analysis


Qualitative Data

Narratives, logs, experience

Interviews

Diaries and journals

Notes from observations

Photographs

Video recordings


What is Qualitative Research?

Research studies that investigate the quality of

Relationships

Activities

Situations

Materials


Qualitative Data Analysis

Used for any non-numerical data collected as part of the evaluation

Unstructured observations

Analysis of written documents

Diaries, observations


Qualitative Data Analysis

Answers questions like:

Is the project being implemented according to plan?

What are some of the difficulties faced by staff?

Why did some participants drop out early?

What is the experience like for participants?


Steps in Qualitative Research

The steps are as follows (in some cases):

Identification of the phenomenon and hypothesis generation

Identification of the participants in the study

Data collection (continual observance)

Data analysis

Interpretation/Conclusions


Generalization in Qualitative Research

A generalization is usually thought of as a statement or claim that applies to more than one individual, group, or situation.

The value of a generalization is that it allows us to have expectations about the future.

A limitation of Qualitative Research is that there is seldom justification for generalizing the findings of a particular study.


Trustworthiness in Qualitative ResearchCheck on the trustworthiness of the researchers:Compare one informant’s description with another informant’s

description of the same thing.Triangulation: Comparing different information on the same

topic. Data triangulation Use of multiple data sources

Students, teachers, administrators, etc. Methods triangulation Interviews, observations, etc.

Researcher triangulation Use a team of researchers.


Criteria for judging research

Quantitative Internal validityDid A cause B?

External ValidityAre these findings generalizable?

ReliabilityAre the measures repeatable?

ObjectivityAre the findings free of researcher bias/values?

QualitativeCredibilityBelievable from participant’s view

TransferabilityCan this finding be transferred to other contexts?

DependabilityWould another researcher come to similar conclusions?


Thanks for your attention

Documents

Quantitative and Qualitative Data Analysis Lecture 1: Introduction