week 5, unit 2 quantitative and qualitative data analysis

8/22/2019 Week 5, Unit 2 Quantitative and Qualitative Data Analysis

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Quantitative and

Qualitative Data Analysis

Chapter 15



Introduction

Quantitative or Qualitative?

What is the difference been qualitative and

quantitative?

The distinction between qualitative andquantitative data is not as important as the

distinction between the strategies driving their

collection



Introduction

Quantitative data analysis

Analysis that tends to be based on the

statistical summary of data

Quantitative researchers typically focus on therelationship between or among variables, with

a natural science-like view of social science in

the backs of their minds.



Introduction

Qualitative data analysis

Analysis that tends to results in the

interpretation of action or representations of

meanings in the researcher's own words Empathic understanding or an in-depth, thick

description



Quantitative Data Analysis

Presumes one has collected data about a

reasonably large, and sometimes

representative, group of subjects, whether

these subjects are individuals, groups,organizations, social artifacts, etc.

The data does not always come in the form of

numerical data




Sources of Data for Quantitative Analysis

When data is collected by researcher, codingis an important first step

Coding is the process by which raw data aregiven a standardized form. This meansmaking data computer usable.

For example, if you are coding gender – you mayhave Male = 1 and Female = 2

The assignment of numbers to words isarbitrary




Elementary Quantitative Analyses

Descriptive statistics

Statistics used to describe and interpret sample

data Example

Fifty-five percent of the people sampled were

married.




Elementary Quantitative Analyses

Inferential statistics

Statistics used to make inferences about the

population from which the sample was drawn Example

Men are significantly more likely than women to

have been employed full-time.




Univariate analyses

Analyses that tell us something about one

variable




Bivariate analyses

Analyses that focus on the association

between two variables




Multivariate analyses

Analyses that permit researchers to examine

the relationship between variables while

investigating the role of other variables



Univariate Analysis

Measures of Central Tendency

Mode

The measure of central tendency designed for nominal

level variables. The value or category that occurs mostfrequently. It can be computed for any variable because

all ordinal and interval level variables are also nominal.



Univariate Analysis


Median

The measure of central tendency designed for ordinal

level variables. The middle value when all values arearranged in order. Can also be used for interval variable

because they are also ordinal variables.



Univariate Analysis


Mean

The measure of central tendency designed for interval

level variables. The sum of all values divided by thenumber of values.



Univariate Analysis

How does a researcher know which measure

of central tendency (mode, median, or mean)

to use to describe a given variable?

Do not use a measurement that isinappropriate for a given level of measurement

Example: Mean or Median for a nominal level

variable like gender



Univariate Analysis

Variation

Frequency Distribution

A way of showing that number of times each

category of a variable occurs in a sample Assume we have 20 people in our sample, with

17 females and 3 males



Frequency Distribution

GENDER FREQUENCY %

Female 17 85

Male 3 15

Total N = 20 100



Univariate Analysis

Variation

Examining frequency distribution, and their

percentage distribution is a good way of

understanding variation in nominal or ordinalvariables

Example

If you are looking at gender and discern that

100% of your sample is female and 0% is male,you know that there is no variation in gender in

your sample.



Univariate Analyses

Measures of Dispersion of Variation for

Interval Scale Variables

Measures of dispersion

Measures that provide a sense of how spreadout cases are over categories of a variable



Univariate Analyses

Measures of Dispersion of Variation for

Interval Scale Variables

Range

A measure of dispersion or spread designed for interval-level variables. The difference between

the highest and lowest values.



Univariate Analyses

Standard Deviation

A measure of dispersion designed for interval-

level variables and that accounts for every

value's distance from the sample mean The standard deviation has properties that

make it useful in measuring variation when the

variable is normally distributed



Univariate Analyses

The graph of a normal distribution is bell-

shaped and symmetric

In a normal distribution 68% of cases would

fall between one standard deviation abovethe mean and one standard deviation below

the mean

Standard deviation is not as useful if the

variable is not normally distributed.



Bivariate Analyses

Examining the relationship between variables

Crosstabulation is the process of making a

bivariate table to examine a relationship

between two variables



Bivariate Analyses

Measures of association

Measures that give a sense of the strength of

a relationship between two variable – or how

strongly two variables ―go together‖



Bivariate Analyses

Measures of correlation

Measures that provide a sense not only of the

strength of the relationship between two

variables, but also the direction of theassociation

Pearson’s r is a measure of correlation

designed for examining relationships between

interval level variables.



Stop and Think

Would you expect the association between

education and income for adults in the US to

be positively or negatively correlated?



Bivariate Analyses

Inferential Statistics

P-value

Allows the reader to make an inference about the

relationship between variables. The typical cut off is 0.05, p<.05



Multivariate Analysis and the

Elaboration Model

Why would a researcher want to examine

more than two variables at a time?




Elaboration Model

Elaboration

The process of examining the relationship

between two variables by introducing the

control for another variable or variables




Elaboration Model

Control variable

A variable that is held constant to examine the

relationship between two other variables




Elaboration Model

Partial relationship

The relationship between an independent and

a dependent variable for that part of a sample

defined by one category of a control variable




Elaboration Model

Four kinds of elaboration

1. Replication

2. Explanation

3. Specification4. Interpretation




Elaboration Model

Replication

A kind of elaboration in which the original

relationship is replicated by all of the partial

relationships




Elaboration Model

Explanation

A kind of elaboration in which the original

relationship is explained away as spurious

by a control for an antecedent variable




Elaboration Model

Specification

A kind of elaboration that permits the

researcher to specify conditions under which

the original relationship is particularly strongor weak




Elaboration Model

Interpretation

A kind of elaboration that provides an idea of

the reasons why an original relationship exist

without challenging the belief that theoriginal relationship is causal.




The outputs of qualitative data analyses are

usually words, the inputs are also usually

words – typically in the form of extended texts

Data is almost always derived from what theresearcher has observed, heard in interviews,

or found in documents




Social anthropological versus interpretivist

approaches

Social anthropologists (and others, like

grounded theorists and life historians) believethat there exist behavioral regularities (for

example, rules, rituals, relationships, and so

on) that affect everyday life and that it should

be the goal of researchers to uncover andexplain those regularities.




Social anthropological versus interpretivist

approaches

Interpretivists (including phenomenologists

and symbolic interactionists) believe thatactors, including researchers themselves, are

forever interpreting situations, and that these,

often quite unpredictable, interpretations

largely affect what goes on.




Does qualitative data analysis emerge from

or generate the data collected?

The question of which comes first

Data or ideas about data




The strengths and weaknesses of qualitative

data analysis revisited

Strengths

Can produce theories More likely to be grounded in the immediate

experiences of those participants than in the

speculations of researchers.




The strengths and weaknesses of qualitative

data analysis revisited

Weaknesses

Generalizability




Are there predictable steps in qualitative data

analysis?

First researchers code their own data or

acquire computer-ready data Other steps are much more fluid

Typical flow includes data collection –data

reduction—data displaying—conclusion

drawing and verification




Data Collection and Transcription

Several software packages exist to facilitate

the processing of qualitative data

Qualitative data software packages havemany pros an cons and should be considered

carefully before adopting.




Data Reduction

The various ways in which a researcher orders

collected and transcribed data

Coding and memoing are common datareduction techniques




Coding

The process of assigning observations, or data,

to categories

In qualitative analysis, coding is more open-ended because both the relevant variables and

their significant categories are apt to remain in

question longer




Coding

The goal of coding is to create categories that

can be used to organize information about

different cases Assigning a code to a piece of data is the first

step in coding

The second step is putting the coded data

together with other data coded the same way




Coding

Types of Coding

One purpose of coding is to keep facts straight – called descriptive coding

Coding to advance your analysis is analyticalcoding

The preliminary phase of analytical coding iscalled initial coding

Initial coding eventually becomes focusedcoding, which is concentrating or elaborating oncodes specific to analysis




Coding

Memos

Extended notes that the researcher writes to help

herself or himself understand the meaning of codes




Data displays

Visual images that summarize information



Summary

Quantitative data analyses

Qualitative data analyses



Quiz – Question 1

Measures of central tendency do not include

a. the mode.

b. median.

c. mean.d. standard deviation.



Quiz – Question 2

In a frequency distribution, we are

a. displaying the number of cases that fall in

categories.

b. showing the connections betweendescriptive statistics.

c. examining the central tendencies of

variables.

d. testing out our coding schemes.



Quiz – Question 3

As a measure of dispersion, a _______ tells us

how far the mean is from individual scores.

a. range

b. standard deviationc. mode

d. regular distribution

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