qualitative approach in data analysis (lecture) (2)

8/7/2019 QUALITATIVE APPROACH IN DATA ANALYSIS (lecture) (2)

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QUALITATIVE APPROACH IN DATA ANALYSIS (Sir Dan Fernandez)

Turning tons of data into meaningful interpretation

y There are no universal strategies but there are diff styles to do analysiso Template analysis

o Make template or analysis guideo Analysis is interpretive not statisticalo Common usage in ethnography (culture) ex. Behaviors, events, or linguistic expressions.

o Editing analysiso Read through data and search for meaningful segmentso Develop a category scheme and may use codingo Search for patterns and structureo Common use in grounded-theory and phenomenology

o Immersion analysiso Reflection of text materialso Intuitive crystallization of data

Put meaning in the datao METHOD IN QUALITATIVE ANALYSIS

o Manual methods Manual analysis

y Develop owny Use color codingy The panelist will be very impressed if you use codes

methodologically.

y Use index cardsy Start to classifyy Make conceptual files (read it, read it)

Computer analysisy Use specialized softwarey Use word processors or database

o Ethnographic researcho Analysis begins as the researcher enters the fieldo They continually search for patterns in the behavior and expression of study

participants

o Approaches: Spradleysmethod

y Domain analysisy Taxonomic analysisy Componential analysisy Theme analysis

o Approach in phenomenological data To find patterns of experiences shared by a particular instances To grasp the essential meaning of the experiences being studied.

y Holistic approachy Selective approachy Detailed approach


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o Ethnographic used in investigating cultures by collecting and describing data thatis intended to help in the development of a theory/

o Critical social research used by researcher to understand how peoplecommunicate and develop symbolic meanings

o Ethical inquiry an intellectual analysis of ethical problems. It includes the study ofethics as related to obligation, rights, duty, right and wrong, choice, etc.

Foundational research examines the foundations for a science. Historical research allows one to discuss past and present events in the

context of the present condition, and allows one to reflect and provide

possible answers to current issues and problems.

Grounded theory inductive type of research, based or grounded in theobservations or data from which it was developed

Phenomenology describes the subjective reality of an event, as perceivedby the study population; it is the study of phenomenon.

Philosophical research is conducted by field experts within the boundariesof a specific field of study or profession, the best qualified individual in any

field of study to use an intellectual analyses, in order to clarify definitions,

identify ethics, or make a value judgment concerning an issue in their fieldof study.

o Grounded theoryo It uses the constant comparative method of data analysis.

Glaser and Strauss methody Substantivey Theoretical

Analysis of triangulated datay Construction ofMeta-Matrixy It involves the development of a two or three dimensional matrix

that aligns various types of data

y The construction of such matrix allows researchers to explorewhether statistical conclusions are supported by the qualitative

data for individual study participants.

Assessing data quality in qualitative studies: trustworthiness:y Concerns the truth value of qualitative data and qualitative analysis

o Integrating research evidenceo Meta-analysis

It is a way to combine results of several independent studies To increase statistical power by inc the sample size To resolve uncertainty when reports do not agree To improve estimates of effect size

o Meta-synthesis Deciding on the phenomenon

o It has the same format as research


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DATA ANALYSIS

y Levels of measuremento Ratio absolute zeroo Interval distance is meaningfulo Ordinal attributes can be orderedo Nominal attributes are only named; weakest.

y Types of statisticso Descriptive statisticso Inferential statistics

y Prepare data for analysiso Coding

Binary data (1,2) Nominal data Ordinal data (ranking, order) - higher Interval/ratio data

o TIPS Questionnaire number Respondent number (normally assigned) Check for missing data color coding, or manually check each data Check for correct coding

o Verifying and cleaning data Outliers (very far from reality) Consistency checks come up with alternate questions to verify the answer

o Codebook or coding manualo Data transformations

Item reversals - Recodes

o TIPS Avoid research misconduct

y Fabrication dont invent datay Falsification changing what the person answeredy Poor data managementy Unauthorized use of confidential information

o inspect/ check data for errors use descriptive statistics

y Frequency distributiono frequency tableo histogramo frequency polygon

y Shapes of distributiono symmetric (normally distributed) WOF bimodal, trimodal = extraneous variables

o Asymmetricy Measures of central tendency

o Modeo Mediano Mean the average of the sum of all scores


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o If they are close = normalo If they are far = x

y VARIABILITY OF DISTRIBUTIONSo Homogenous variability limitedo Heterogeneous extremely variedo Range high and low scoreso Standard deviation commonly used variability, it indicates the average amount of

deviation values from the mean.

o Kurtosis Mesokurtic (N) Leptokurtic Platykurtic

y BIVARIATE DESCRIPTIVE STATISTICS (dependent, independent)o Why? Easy o Ways to present: Contingency table or cross tabso Presented as bar graphs

y CORRELATIONo Scatter Plot or Diagramo Correlation coefficient

Pearsons = most commonly usedo Independent/dependent = both must be numerical or else you got problem in analysis,

hard to prove the correlation

y DESCRIBING RISKSo Should be careful in analyzing.

Absolute risk Absolute risk reduction Relative risk Relative risk reduction Odds ratio Number needed to treat

y COMPUTER PROGRAM TO USEo SPSSo EPI infoo SASo MINITABo MATLAB

y INFERENTIAL STATo Estimation

Point estimate C

onfidence intervalso Hypothesis testing

Non directional directional

o Steps in hypothesis testing: Hypothesis Test statistic Level of significance


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Critical value Computations Interpretation

o Normal (Gaussian) Distributiony TYPE OF ERRORS

o Alpha error = you think the result is true Happens because there is a change in alpha

o Beta error Report=no difference Small sample size

y Alpha value vs P-valueo Terms

Critical valuey One tailed testy Two tailed test

y Parametric vs non parametrico Parametric

Based on populationo Non parametric

Advantage: no need of big sample Disad: hard to find instruction to interpret

y Frequently usedo T testo ANOVAo Chi-square test

y MULTIVARIATEANALYSIS

qualitative approach in data analysis (lecture) (2)

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