qualitative data analysis

Quantitative and Quantitative and Qualitative Data AnalysisQualitative Data Analysis

Dr. Jagannath.K.DangeDr. Jagannath.K.DangeDepartment of EducationDepartment of Education

Kuvempu UniversityKuvempu UniversityJnanasahyadri, ShankaraghattaJnanasahyadri, Shankaraghatta

Shivamogga-577451Shivamogga-577451KarnatakaKarnataka

Quantitative ResearchQuantitative ResearchQuantity is the unit of analysisQuantity is the unit of analysis– AmountsAmounts– FrequenciesFrequencies– DegreesDegrees– ValuesValues– IntensityIntensity

Uses statistics for greater precision and Uses statistics for greater precision and objectivityobjectivity

Based on the deductive modelBased on the deductive model

Model for Conceptualizing Model for Conceptualizing Quantitative ResearchQuantitative Research

Overall purpose or Overall purpose or objectiveobjective

Research literatureResearch literature

Research questions Research questions and hypothesesand hypotheses

Selecting appropriate Selecting appropriate methodsmethods

Validity and reliability Validity and reliability of the dataof the data

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Creating the Foundation Creating the Foundation for Quantitative Researchfor Quantitative Research

Concept Concept – Abstract thinking to distinguish it from other elementsAbstract thinking to distinguish it from other elements

Construct Construct – Theoretical definition of a concept; must be Theoretical definition of a concept; must be

observable or measurable; linked to other conceptsobservable or measurable; linked to other concepts

Variable Variable – Presented in research questions and hypothesesPresented in research questions and hypotheses

Operationalization Operationalization – Specifically how the variable is observed or measuredSpecifically how the variable is observed or measured

1. Exploratory -- It is a good starting point to get familiarized with some insights and ideas (e.g. identify the dependent and independent variables)

1. Descriptive – “The mapping out of a circumstance, situation, or set of events”

1. Causal—experimenting (statistically speaking) to asses cause and effect. For example, whether or not a Radio program is achieving its objectives. Experiments in the social science take place “naturally” (e.g. The effectiveness of SES on the academic achievement)

Types of Quantitative ResearchTypes of Quantitative Research

Measuring Variables

To establish relationships between variables, researchers must observe the variables and record their observations.

This requires that the variables be measured.

The process of measuring a variable requires a set of categories called a scale

of measurement and a process that classifies each individual into one

category. 77

The Levels of MeasurementThe Levels of Measurement

NominalNominalOrdinalOrdinalIntervalIntervalRatioRatio

Four Basic Types Of Measurement:

• Categorizing -Nominal• Ranking

– Ordinal• Determination of the size interval

– Interval• Determination of the size of ratios

– Ratio

Scales of Scales of Measurement

Nominal

Ordinal

Interval

Ratio

}} Qualitative

}} Quantitative

Nominal Scale Nominal Scale (discrete)(discrete) Simplest scale of measurement Variables which have no numerical value Variables which have categories Count number in each category, calculate

percentage A simple categorical variable A simple categorical variable is binary or is binary or

dichotomous dichotomous (1/0 or yes/no).(1/0 or yes/no). Useful for quantifying qualitative data Examples:

– Gender– Race– Marital status

Ordinal ScaleOrdinal Scale Variables are in categories, but but we can say that but we can say that

some categories are higher than others. some categories are higher than others. Used to arrange data into series Rank-order categories from highest to lowest Intervals may not be equal- Distances between attributes Distances between attributes

do not have any meaning, do not have any meaning,

Count number in each category, calculate percentage Examples: 1st 2nd 3rd 4th 5th

– Likert scale 1.2.3.4.5

the distance from 0 to 1 is not same as 3 to 4the distance from 0 to 1 is not same as 3 to 4

Interval Interval Scale

Quantitative data

Can add & subtract values

Cannot multiply & divide values– No true zero point

Example:– Temperature on a Celsius scale

• 00 indicates point when water will freeze, not an absence of warmth

IntervalIntervalVariables of this type are Variables of this type are called scalar or called scalar or index variablesindex variables in the sense they provide a in the sense they provide a scale or index scale or index that allows us to measure that allows us to measure between levels. between levels.

We can not We can not only measure which is higher or only measure which is higher or lowerlower, but , but how much sohow much so..– Distance is measured between points on a scale with Distance is measured between points on a scale with

even units.even units.– Good example is temperature based on Fahrenheit or Good example is temperature based on Fahrenheit or

Celsius.Celsius.When When distancedistance between attributes has meaning, for between attributes has meaning, for example, temperature (in Fahrenheit) -- example, temperature (in Fahrenheit) -- distance distance from 30-40 is same as distance from 70-80from 30-40 is same as distance from 70-80

Ratio Scale Ratio Scale ((continuous))

Quantitative data with true zero– Can add, subtract, multiply & divide

Examples:– Age– Body weight– Blood pressure– Length of University stay

RatioRatio RatioRatio: : Similar to interval level Similar to interval level variables in that it can variables in that it can measure the measure the distance between two pointsdistance between two points, but , but can do so in absolute terms. can do so in absolute terms. – For example, one can say that For example, one can say that

someone is twice as rich as someone is twice as rich as someone else based on the someone else based on the value of their assets value of their assets since to since to have have no money is based on a no money is based on a starting point of zero.starting point of zero.

Scales of Measurement

• Nominal: classification• Ordinal: ranking• Interval: equal intervals• Ratio: absolute zero

Scale Classification Order Equal Intervals Zero Nominal Yes No No No Ordinal Yes Yes No No Interval Yes Yes Yes No Ratio Yes Yes Yes Yes

Measurement Hierarchy Measurement Hierarchy

NOMINAL

ORDINAL

INTERVAL

RATIO

WEAKEST

STRONGEST

The Hierarchy of LevelsThe Hierarchy of Levels

NominalNominal

IntervalInterval

RatioRatio

Attributes are only named; weakest

Attributes can be ordered

Distance is meaningful

Absolute zero

OrdinalOrdinal

Scales of Scales of Measurement

Nominal

Ordinal

Interval

Ratio

}} Lead to nonparametric statistics

}} Lead to parametric statistics

Two Branches of StatisticsTwo Branches of Statistics

Descriptive– Frequencies & percents– Measures of the middle– Measures of variation

Inferential– Nonparametric statistics– Parametric statistics

Qualitative ResearchQualitative Research

““A A form of social inquiry form of social inquiry that that focuses on the focuses on the way people interpret and make sense of way people interpret and make sense of their experiencestheir experiences and the world in which and the world in which they live.” they live.”

Qualitative ResearchQualitative Research““: Qualitative data analysis is the array of processes and procedures whereby a researcher provides explanations, understanding and interpretations of the phenomenon under study on the basis of meaningful and symbolic content of qualitative data.

It provides ways of discrimnating, examining, comparing and contrasting and interpreting meaningful patterns and themes. It is based on the interpretative philosophy.

Qualitative data are subjective, soft, rich and in-depth descriptions usually presented in the form of words. The most common forms of obtaining qualitative data include semi-structured and unstructured interviews, observations, life histories and documents. The process of analysing is difficult rigorous. .” .”

The Nature of Qualitative Research

• The term qualitative research refers to studies that investigate the quality of relationships, activities, or situations.

• Qualitative data are collected in the form of words or pictures and seldom involve numbers.

What is Qualitative Data Analysis?

• Qualitative Data Analysis (QDA) is the range of processes and procedures whereby we move from the qualitative data that have been collected into some form of explanation, understanding or interpretation of the people and situations we are investigating.

Qualitative/Quantitative Qualitative/Quantitative DifferencesDifferences

The aim is a detailed The aim is a detailed description.description.Researcher may only know Researcher may only know roughly in advance what he/she roughly in advance what he/she is looking for. is looking for. The design emerges as the The design emerges as the study unfolds. study unfolds. Researcher is the data Researcher is the data gathering instrument.gathering instrument.Data is in the form of words, Data is in the form of words, pictures or objects.pictures or objects.SubjectiveSubjective - individuals’ - individuals’ interpretation of events is interpretation of events is important important Qualitative data is more 'rich', Qualitative data is more 'rich', time consuming, and time consuming, and not not generalizable. generalizable. Researcher tends to become Researcher tends to become subjectively immersed in the subjectively immersed in the subject matter.subject matter.

The aim is to classify features, The aim is to classify features, count them, and construct count them, and construct statistical models in an attempt statistical models in an attempt to explain what is observed.to explain what is observed.Researcher knows clearly in Researcher knows clearly in advance what he/she is looking advance what he/she is looking for. for. All aspects of the study are All aspects of the study are carefully designed before data is carefully designed before data is collected. collected. Researcher questionnaires or Researcher questionnaires or equipment to collect numerical equipment to collect numerical data.data.Data is numerical in nature. Data is numerical in nature. ObjectiveObjective – seeks measurement – seeks measurement & analysis of target concepts.& analysis of target concepts.Quantitative data is more Quantitative data is more efficient, able to test hypotheses.efficient, able to test hypotheses.Researcher tends to remain Researcher tends to remain separated from the subject separated from the subject matter. matter.

Qualitative and Quantitative ApproachesQualitative and Quantitative ApproachesQualitative Quantitative

(Usually) Non-probability based sample

Typically a probability-based sample

Non-generalizable Generalizable

Answers Why? How? Answers How many? When? Where?

Formative, earlier phases Tests hypotheses, latter phases

Data are “rich” and time-consuming to analyze

Data are more efficient, but may miss contextual detail

Design may emerge as study unfolds

Design decided in advance

Researcher IS the instrument Various tools, instruments employed

Case StudyCase StudyAttempts to shed light on a phenomena by Attempts to shed light on a phenomena by studying in depth a studying in depth a single case example of the phenomenasingle case example of the phenomena. The case can be an . The case can be an individual person, an event, a group, or an institution. individual person, an event, a group, or an institution.

Grounded Grounded TheoryTheory

Theory Theory is developed inductively from a corpus of datais developed inductively from a corpus of data acquired by a acquired by a participant-observer.participant-observer.

PhenomenologPhenomenologyy

Describes the structures of experience as they present themselves to Describes the structures of experience as they present themselves to consciousness, without recourse to theoryconsciousness, without recourse to theory, deduction, or assumptions , deduction, or assumptions from other disciplinesfrom other disciplines

EthnographyEthnographyFocuses on the sociology of meaning through Focuses on the sociology of meaning through close field observation close field observation of sociocultural phenomenaof sociocultural phenomena. Typically, the ethnographer focuses on a . Typically, the ethnographer focuses on a community.community.

HistoricalHistoricalSystematic collection and objective evaluation of data related to past Systematic collection and objective evaluation of data related to past occurrences in order to test hypotheses concerning causes, effects, occurrences in order to test hypotheses concerning causes, effects, or trends of these events that may help to explain present events or trends of these events that may help to explain present events and anticipate future events.and anticipate future events.

Main Types of Qualitative Main Types of Qualitative ResearchResearch

There are three main methods of data There are three main methods of data collection:collection:

1. Interactive 1. Interactive interviewinginterviewing

People asked to verbally described their experiences of People asked to verbally described their experiences of phenomenon.phenomenon.

2. Written descriptions 2. Written descriptions by participantsby participants

People asked to write descriptions of their People asked to write descriptions of their experiences of phenomenon.experiences of phenomenon.

3. Observation3. Observation Descriptive observations of verbal and non-verbal Descriptive observations of verbal and non-verbal behavior.behavior.

Analysis begins when the data is first collected and is used to guide decisions Analysis begins when the data is first collected and is used to guide decisions related to further data collection.related to further data collection.

"In communicating--or generating--the data, the researcher must make the process "In communicating--or generating--the data, the researcher must make the process of the study accessible and write descriptively so tacit knowledge may best be of the study accessible and write descriptively so tacit knowledge may best be communicated through the use of rich, thick descriptions" (Myers, 2002).communicated through the use of rich, thick descriptions" (Myers, 2002).

Qualitative Data AnalysisQualitative Data AnalysisThe following are The following are the components the components of qualitative of qualitative Data analysis:Data analysis:

A.A.Data Reduction : Data Reduction : "Data reduction refers to the "Data reduction refers to the process of selecting, focusing, simplifying, process of selecting, focusing, simplifying, abstracting, and transforming the data abstracting, and transforming the data that appear that appear in written up field notes or transcriptions." in written up field notes or transcriptions."

The data collected should be The data collected should be reduced in terms of reduced in terms of meaningful termsmeaningful terms. All the information collected . All the information collected should not be presentedshould not be presented

Qualitative Data AnalysisQualitative Data AnalysisB.B. Data Display : Data Display : Data display provides "Data display provides "an organized, an organized,

compressed assembly of information that permits conclusion compressed assembly of information that permits conclusion drawingdrawing..." A display can be an extended piece of text or a ..." A display can be an extended piece of text or a diagram, chart or matrix that provides a new diagram, chart or matrix that provides a new way of arranging way of arranging and thinking about the more textually embedded data.and thinking about the more textually embedded data.

Data display can be extremely helpful in identifying whether a Data display can be extremely helpful in identifying whether a system is working effectively and how to change it. system is working effectively and how to change it.

Data could be Data could be displayed using a series of flow charts that map displayed using a series of flow charts that map out any critical paths, decision pointsout any critical paths, decision points, and supporting evidence , and supporting evidence that emerge from establishing the data for each site. The that emerge from establishing the data for each site. The researcher may (1) use the data from subsequent sites to researcher may (1) use the data from subsequent sites to modify the original flow chart of the first site, (2) prepare an modify the original flow chart of the first site, (2) prepare an independent flow chart for each site; and/or (3) prepare a single independent flow chart for each site; and/or (3) prepare a single flow chart for some events (if most sites adopted a generic flow chart for some events (if most sites adopted a generic approach) and multiple flow charts for others. approach) and multiple flow charts for others.

Qualitative Data AnalysisQualitative Data AnalysisC. C. Conclusion Drawing and Verification : Conclusion Drawing and Verification : Conclusion Conclusion

drawing requires a researcher to begin to decide what drawing requires a researcher to begin to decide what things mean. He does this by noting regularities, patterns things mean. He does this by noting regularities, patterns ((differences/similaritiesdifferences/similarities), ), explanations, possible explanations, possible configurations, causal flows, and propositionsconfigurations, causal flows, and propositions. This . This process involves stepping back to consider what the process involves stepping back to consider what the analysed data mean and to assess their implications for analysed data mean and to assess their implications for the questions at hand. Verification, integrally linked to the questions at hand. Verification, integrally linked to conclusion drawingconclusion drawing, entails revisiting the data as many , entails revisiting the data as many times as necessary to cross-check or verify these times as necessary to cross-check or verify these emergent conclusions. Miles and Huberman assert that emergent conclusions. Miles and Huberman assert that "The meanings emerging from the data have to be tested "The meanings emerging from the data have to be tested for their plausibility, their sturdiness, their ‗confirmability‘ - for their plausibility, their sturdiness, their ‗confirmability‘ - that is, their validity". that is, their validity".

Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysis A. Analytical Induction : A. Analytical Induction : 1.1. Analytic induction is a Analytic induction is a way of building explanations way of building explanations in in

qualitative analysis by qualitative analysis by constructing and testing a set of constructing and testing a set of causal links between events, actions causal links between events, actions etc. etc.

2.2. It is research logic used to collect, develop analysis and It is research logic used to collect, develop analysis and organise the presentation of research findings.organise the presentation of research findings.

3.3. It refers to a systematic and exhaustive examination of a It refers to a systematic and exhaustive examination of a limited number of cases in order to provide limited number of cases in order to provide generalisations and identify similarities between various generalisations and identify similarities between various social phenomena in order to develop contacts or ideas.social phenomena in order to develop contacts or ideas.

4.4. Its formal objective is causal explanation.Its formal objective is causal explanation.

Strategies of Qualitative Data AnalysisStrategies of Qualitative Data AnalysisB. Constant Comparison B. Constant Comparison It includes It includes approaching your approaching your

data with an open mind to identify note worthy patternsdata with an open mind to identify note worthy patterns. This requires that every time you select a passage of text . This requires that every time you select a passage of text

(or its equivalent in video etc.) and code it, you should (or its equivalent in video etc.) and code it, you should compare it with all those passages you have already compare it with all those passages you have already codedcoded that way, perhaps in other cases. that way, perhaps in other cases.

Newly gathered data are continually compared with Newly gathered data are continually compared with previously collected data and their coding in order to previously collected data and their coding in order to refine the development of theoretical categories. refine the development of theoretical categories.

The purpose is to test emerging ideas that might take the The purpose is to test emerging ideas that might take the research in new and fruitful directions. research in new and fruitful directions.

Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysisthese include: these include:

a.a. Word repetitions Word repetitions : Look for commonly used words and words : Look for commonly used words and words whose close whose close repetition may indicated emotions repetition may indicated emotions

b. b. Indigenous categoriesIndigenous categories : It refers to : It refers to terms used by terms used by respondents with a particular meaningrespondents with a particular meaning and significance in their and significance in their setting. setting.

c. c. Key-words-in-context Key-words-in-context : Look for the range : Look for the range of uses of key of uses of key terms terms in the phrases and sentences in which they occur. in the phrases and sentences in which they occur.

d. d. Compare and contrast Compare and contrast : It is essentially the : It is essentially the grounded theory grounded theory idea of constant comparisonidea of constant comparison. Ask, ‗what is this about?‘ and . Ask, ‗what is this about?‘ and ‗how does it differ from the preceding or following statements?‘ ‗how does it differ from the preceding or following statements?‘

e. e. Social science queries Social science queries : Introduce : Introduce social science social science explanations and theoriesexplanations and theories, for example, to explain the , for example, to explain the conditions, actions, interaction and consequences of conditions, actions, interaction and consequences of phenomena. phenomena.

Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysisf f . . Searching for missing information Searching for missing information : It is essential to : It is essential to try to get an idea try to get an idea

of what is not being done or talked outof what is not being done or talked out, but , but which you would have which you would have expected expected to find. to find.

g. g. Metaphors and analogies Metaphors and analogies : People : People often use metaphor to indicate often use metaphor to indicate something something about their key, central beliefs about things and about their key, central beliefs about things and these may these may indicate the way they feel about things indicate the way they feel about things too. too.

h. h. Transitions Transitions : One of the discursive elements in speech which includes : One of the discursive elements in speech which includes turn-taking in conversation as well as the turn-taking in conversation as well as the more poetic and narrative use of more poetic and narrative use of story structuresstory structures. .

i. i. Connectors Connectors : It refers to : It refers to connections between terms connections between terms such as such as causal causal (‗since‘, ‗because‘, ‗as‘ etc) or logical (‗implies‘, ‗means‘, ‗is one of‘ (‗since‘, ‗because‘, ‗as‘ etc) or logical (‗implies‘, ‗means‘, ‗is one of‘ etc.) etc.)

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Strategies of Qualitative Data AnalysisStrategies of Qualitative Data Analysisj.j. Unmarked text Unmarked text : : Examine the text that has not been Examine the text that has not been

codedcoded at a theme or even not at all. at a theme or even not at all. k. k. Pawing (i.e. handling) Pawing (i.e. handling) : It refers to : It refers to marking the text and marking the text and

eyeballing or scanning the text. Circle words, underline, eyeballing or scanning the text. Circle words, underline, use coloured highlighters, run coloured use coloured highlighters, run coloured lines down the lines down the margins to indicate different meanings and coding. Then margins to indicate different meanings and coding. Then look for patterns and significances. look for patterns and significances.

l. l. Cutting and sorting Cutting and sorting : It refers to the traditional technique : It refers to the traditional technique of cutting up transcripts and of cutting up transcripts and collecting all those coded collecting all those coded the same way into piles,the same way into piles, envelopes or folders or pasting envelopes or folders or pasting them onto cards. Laying out all these scraps and re-them onto cards. Laying out all these scraps and re-reading them, together, is an essential part of the reading them, together, is an essential part of the process of analysis. process of analysis.

Strategies of Qualitative Data AnalysisStrategies of Qualitative Data AnalysisC. Triangulation : C. Triangulation : According to Berg and Berg, triangulation is a According to Berg and Berg, triangulation is a

term originally associated with surveying activities, map making, term originally associated with surveying activities, map making, navigation and military practices. navigation and military practices.

The word triangulation was first used in the social sciences as The word triangulation was first used in the social sciences as metaphor describing a form of multiple operationalisation metaphor describing a form of multiple operationalisation or or convergent validation.convergent validation.

Campbell and Fiske were the first to apply the navigational term Campbell and Fiske were the first to apply the navigational term triangulation to research. They used the term triangulation to triangulation to research. They used the term triangulation to describe multiple data collection strategies for measuring a describe multiple data collection strategies for measuring a single conceptsingle concept. This is known as data triangulation. According to . This is known as data triangulation. According to them, them, triangulation is a powerful way of demonstrating triangulation is a powerful way of demonstrating concurrent validityconcurrent validity, particularly in qualitative research. , particularly in qualitative research.

Triangulation is an approach to research that Triangulation is an approach to research that uses a combination of uses a combination of more than one research strategy in a single investigationmore than one research strategy in a single investigation..

Coding• A coding system tells how to distinguish the content from the

medium.• Sections of text transcripts may be marked by the researcher

in various ways (underlining in a colored pen, given a numerical reference, or bracketed with a textual code).

• This section contains data which the researcher is interested in exploring and analysing further.

• In the early stages of analysis, most if not all sections of the text will be marked and given different ‘codes’ depending on their content.

• As the analysis progresses these codes will be refined or combined to form themes or categories of issues.

Develop coding categories

• A major step in analyzing qualitative data is coding speech into meaningful categories, enabling you to organize large amounts of text and discover patterns that would be difficult to detect by just reading observer commentary.

• Always keep the original copy of observer commentary.

Develop coding categories (Conti…)

• Next, conduct initial coding by generating numerous category codes as you read commentary, labeling data that are related without worrying about the variety of categories.

• Write notes to yourself, listing ideas or diagramming relationships you notice. Because codes are not always mutually exclusive, a phrase or section might be assigned several codes.

http://www.utexas.edu/academic/ctl/assessment/iar/glossary.php#initial_coding

Develop coding categories (Conti…)

• Last, use focused coding to eliminate, combine, or subdivide coding categories and look for repeating ideas and larger themes that connect codes.

• Repeating ideas are the same idea expressed by different respondents, while a theme is a larger topic that organizes or connects a group of repeating ideas.

Organizing Data for analysis

Developing your codes• Coding is a process for categorizing your

data. Develop a set of codes using both codes that you predefine and ones that emerge from the data.

• Predefined codes are categories and themes that you expect to see based on your prior knowledge.

Coding your data

• Closely review and code your data. If possible, have more than one person code the data to allow for different perspectives on the data.

• As you proceed you may find that your initial codes are too broad. Create subcategories of your codes as needed. Or you may find that you have created codes that are too detailed and that attempt to capture every possible idea. In that case consider how you can pull categories together into a broader idea.

Coding your data (Conti…)

• Coding is a process of reducing the data into smaller groupings so they are more manageable.

• The process also helps you to begin to see relationships between these categories and patterns of interaction.

Finding themes, patterns, and relationships

• Step back from the detailed work of coding your data and look for the themes, patterns, and relationships that are emerging across your data.

• Look for similarities and differences in different sets of data and see what different groups are saying.

Summarizing your data

• After you have coded a set of data, such as transcripts of interviews with faculty or questionnaire responses, write a summary of what you are learning.

• Similarly, summarize the key themes that emerge across a set of interview transcripts. When available, include quotations that illustrate the themes.

• With your data coded and summarized you are ready to look across the various summaries and synthesize your findings across multiple data sources.

The Coding Process Initially read through text data Divide the text Label the segments

into segments of information Reduce overlap of information with codes and redundancy Collapse codes

into themes

Themes

• A theme is generated when similar issues and ideas expressed by participants within qualitative data are brought together by the researcher into a single category or cluster.

• This ‘theme’ may be labelled by a word or expression taken directly from the data or by one created by the researcher because it seems to best characterise the essence of what is being said.

Thank YouThank YouDr. Jagannath K. DangeDepartment of EducationKuvempu UniversityShankaraghattaDist: ShimogaKarnataka

[email protected]://jkdange.blogspot.com