Quantitative and Qualitative Data Analyses and Presentation Prof - - PowerPoint PPT Presentation

Quantitative and Qualitative Data Analyses and Presentation

Prof Lester M. Davids (lesterdavids@gmail.com)

There are two important days in one’s LIFE… …the DAY that you are BORN, and the DAY you find out WHY! CHANGE will not come if we wait for some other person or some other time. We are the ones we’ve been waiting for. WE are the change that we seek.

Barack Obama

WHAT IS RESEARCH?

What is your question? What is novel in your research ? What is your hypothesis ?

What is your question ? (Statement of Purpose) What is novel in your research ? (Why am I doing this?) What is your hypothesis ? (What am I hoping to find?)

What is Research Design and Why is it Important ?

• Helps to organize your thoughts.
• Sets the boundaries of your study.
• Maximizes the reliability of your findings
• Avoids misleading or incomplete conclusions.
• Will affect the quality and reliability of your final

results and the overall value of your study

Problems to Avoid when Designing a Research Study



Lack of Specificity -- needs to describe the process of investigation in clear and concise terms.

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Poorly Defined Research Problem -- formulate a well thought out problem statement OR hypothesis to test [identifying the research problem always precedes choice of design].

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Lack of Theoretical Framework -- i.e. the conceptual foundation of your study. Your research design should include an explicit set of logically derived hypotheses, basic postulates, or assumptions that can be tested in relation to the research problem.

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Significance -- the research design must include a clear answer to the "So What?" question. Why is your study important and how does it contribute to the larger body of literature ?

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Relationship between Past Research and Your Study -- Your literature review should include an explicit statement linking the results of prior research to your research you are about to undertake.

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Techniques or Instruments -- be clear in describing the techniques [e.g., semi- structured interviews] or instruments [e.g., questionnaire] used to gather data. Your research design should note how the technique or instrument will provide reasonably reliable data to answer the questions associated with the research problem.

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Limitations of Study -- all studies have limitations, explain them. It is important to include a statement concerning what impact these limitations may have on the validity of your results and what you recommend to improve future studies

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Proximity/Provincialism/Internationalisation -- this refers to designing a narrowly applied scope, geographical area, sampling, or method of analysis that restricts your ability to create meaningful outcomes; results may not be relevant in other settings.

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Objectives, Hypotheses, or Questions -- your research design should include one or more questions or hypotheses that you are attempting to answer about the research problem underpinning your study. They should be clearly articulated and closely tied to the overall aims of your paper.

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Statistical Treatment -- in quantitative studies, you must give a complete description of how you will organize the raw data for analysis. Statistically, this involves describing the data through the measures of central tendencies like mean, median, and mode that help lead to meaningful interpretations of key trends or patterns found within the data.

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Vocabulary -- research often contains jargon and specialized language that the reader is presumably familiar with. However, avoid overuse of technical or pseudo-technical terminology.

WHAT ARE STATISTICS?

Definition : Collecting, analysing and interpreting data Descriptive Statistics – describe the sample or summarise information about the sample (N) vs Inferential Statistics – used to make inferences or generalizations about the broader population Parametric – any numerical quantity that characterises a population vs Non - Parametric – any non- numerical quantity that arises within a population

• Eg. Quantitative - Means, median,

mode etc.

• Eg. Qualitative – t-test, ANOVA,

correlations

QUALITATIVE RESEARCH

Nature of Qualitative Research (Bryman, 2008)

• 1. General Research Questions
• 2. Collection of Information
• selection of relevant cases
• definition of the kind of instruments
• collecting information

“must explore a qualitative argument in a qualitative way. The question has to be grounded in a qualitative argument.” (Crescentini 2009, p.432)

• 3. Transformation in data
• converting information into data

“Data do not exist in nature, rather it emerges from the interaction of researchers with the field.” (Crescentini 2009, p.434)

• 4. Interpretation of data
• BE EXPLICIT!
• 5. Conceptual and Theoretical Framework
• specification of research questions
• collection of further data/information
• 6. Writing up finding/conclusions

Qualitative Research Study Design Naturalistic Emergent Purposeful Real-world situations Non-manipulative; Non- controlling Researcher avoids rigid designs so as to respond to opportunities to pursue new discoveries Case studies; information rich and illuminative Data Collection Data Observations yield in depth understanding; interviews; personal perspectives Personal experience and engagement Researcher’s personal experience ; researcher has direct contact with people Empathic neutrality Researcher seeks information without judgement (neutrality); show openness, sensitivity, respect Dynamic systems Attention to process, focus on individual, organisation, community or entire culture

Analysis Unique case orientation Each case is special and unique; cross-case analysis follows from and depends upon the quality of individual case studies Inductive analysis Emersion in the details and specifics of the data to discover important patterns, themes Guided by analytical principles rather than rules Holistic perspective Complex system is more than the sum of its parts; Focus is on complex interdependencies and system dynamics Context sensitive Places findings in a social, historical and temporal context Careful comparative case analyses

Methods

• Comparability of Data across sources

Variance questions Questions that deal with differences between phenomena and the explanation for these differences Dependent Variable This variable depends on other factors that are

• measured. It is the presumed effect.

Independent Variable This variable that is stable and unaffected by other

• variables. It is the presumed cause.

Sentence : “The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]. Sentence : “[Global Warming] causes a change in [Arctic temps] and it is not possible that [Arctic temperatures] could cause a change in [global warming]. CAUSE and EFFECT

Validity “Qualitative researchers must attempt to rule out most threats to validity after the research has begun by relying on evidence collected during the research process itself in order to effectively argue that any alternative explanations for a phenomenon are implausible.” (USC Edu, p. 5) “the characteristic of being founded on truth, accuracy, fact or law. The degree of which a test or measurement accurately measures or reflects what it purports to measure” (Vandenbos, 2007, p.975) “the extent to which an account accurately represents the social phenomena to which it refers” (Hammersley 1990, p. 57)

V.R.R.

Usefulness of the study… Interpretations are related to the research question…

Rigour “locating situatedness, trustworthiness and authenticity” (Tobin and Begley, 2004, p390) Reliability Reproducibility of results. “In depth-ness” explanation of the study methodology…

• Eg. We used participant observations…

Did the researcher follow all the steps of the process leading to a clear understanding of the study ? Is an interview the same if you did it in person, via telephone or via a computer ?

Qualitative (Categorical) Variables SUMMARY Frequency table Bar Charts Pie Charts – numbers and % of cases in each group Mode

QUANTITATIVE RESEARCH

Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon. Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Before designing a QUANTITATIVE RESEARCH STUDY, you must decide whether it will be DESCRIPTIVE or EXPERIMENTAL because this will dictate how you gather, analyze, and interpret the results.

A descriptive study is governed by the following rules:

• subjects are generally measured
• nce;
• the intention is to only establish

associations between variables;

• the study may include a sample

population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner]. Its main characteristics are:



The data is usually gathered using structured, existing research instruments.



The results are based on larger sample sizes that are representative of the population.



The research study can usually be replicated or repeated, given its high reliability.



Researcher has a clearly defined research question to which objective answers are sought.

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All aspects of the study are carefully designed before data is collected.

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Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or

• ther non-textual forms.

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Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.

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Researcher uses tools, such as questionnaires or computer software, to collect numerical data. The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

METHODS GLOSSARY

RESEARCH FLOWCHART

Hypothesis

Null (H0; Common view) vs. Alternative (H1)

Sample Size

Power Analyses – Determines how many samples are needed to have an acceptable p-value in order to reject a null hypothesis

Collect Raw Data Output Data Data Set

Data Set Statistical Methods

• Analyse
• Understand the data

better - Descriptive

Central Tendency

(Mean, Median and Mode) Measures of central tendency = i) the arithmetic mean - used in scientific experimentation, ii) the geometric mean - used in finance to calculate compounding quantities, iii) the median is used as a robust mean in case of skewed data with many

• utliers and,

iv) the mode is frequently used in determining the most frequently occurring data. Assumption : All data from this world has a relatively NORMAL distribution

How is the data distributed around the mean ? Degrees of Freedom – number of independent values or quantities which can be assigned to a statistical distribution (Variance) Standard deviation - how the data is distributed about the mean value (1% vs 5%) Statistical variance - a measure of how the data distributes itself about the mean

• r expected value

Type I error- “false positive” = incorrect rejection of the true null hypothesis in favour of the alternative Type II error- “false negative” = false acceptance of a null hypothesis that is not actually true (HIV eg. telling the patient they are free of HIV, when they not!!).

Standard error of the mean (SEM) - also called the standard deviation (SD) of the mean, is a method used to estimate the standard deviation of a sampling

• distribution. Shows us how the mean varies from experiment to experiment using

the same quantities. If the variation is high, i.e. external variables are different, the SEM will be high, if the experiment is repeatable without lots of variations, SEM will be low tending towards zero. Students T-test - assumes that the data is more or less normally distributed and that the variance is equal; indicates whether the null hypothesis is correct or not; generally used to test differences between two groups (experimental vs control/placebo) Independent One-Sample T-Test - used to test whether the average of a sample differ significantly from a population mean. Independent Two-Sample T-Test – If you are comparing two samples not strictly related to each other

Correlation - is a way to express relationship between two data sets or between two variables; the strength between two variables. A Chi-Square test can be used if the data is qualitative rather than quantitative. Pearson correlation coefficient (or Pearson Product-Moment Correlation) will

• nly express the linear relationship between two variables.

Linear Regression – best fit line in a graph; predicting the unknown value of one variable (dependent) from the known value (independent) of another

• Example. Is there a difference between online education and traditional

pedagogies at a Grade 11 education level ? Parametric Between S (Inter) Within S (Intra) 2 Groups >2 Groups Sample t-test One-Way ANOVA 2 Groups >2 Groups Paired Sample t- test One-Way Repeated Measures ANOVA

Qualitative (Categorical) Variables Quantitative SUMMARY Frequency table Bar Charts Pie Charts – numbers and % of cases in each group Mode Frequency table Histogram Mean, Median, Mode Variance, Std Deviation Min, Max, Range