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Analysis of Qualitative and Quantitative Research

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anna arroyo

on 22 June 2014

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Transcript of Analysis of Qualitative and Quantitative Research

Quantitative & Qualitative: The Difference

Characteristics of Inferential Statistics
Sampling Distributions---> random sampling
Quantitative & Qualitative Data: The Differences Cont'd.
Quantitative research values objectivity and emphasizes the rational and scientific. It approaches research with orderly and disciplined procedures and seeks generalization. On the other hand, qualitative research promotes relativism and its findings are the product of the interaction between inquirer and participants. This approach asserts that multiple interpretations exist, and denies a process by which the ultimate truth can be achieved. These two research methods differ completely in their paradigms, and therefore also differ in the way they are analyzed.
Analyzing Qualitative Data
Begins by developing a classification method and indexing data--> often done by developing a
category scheme and coding data according to categories.

Analyzing Quantitative Data
Quantitative Data --> Statistical --> Two types: Descriptive Statistics OR Inferential Statistics
Analysis of Qualitative and Quantitative Data
Anna Arroyo
information is gathered by means of empirical evidence
contains numeric information
data is measured
data can be summarized in a few tables making it easy to read
complexities or context is controlled
data is observed
truth is a composite of realities
grounded in real-life experiences
more difficult to analyze
easier to understand
harder to critique
Descriptive Statistics- used to synthesize and describe data--->Note: This can be univariate (one variable) or bivariate( two variables).

Inferential Statistics- used to make inferences about the population.
Analysis Defined
Transforming the data that has been gathered and producing research results!
Characteristics of Descriptive Statistics
-this equals the sum of all values divided by the number of participants of the sample
-the point in the distribution that divides the scores in half
-the number that occurs most frequently
Frequency distribution
- systemic arrangement of values from lowest to highest
-how spread out the data is
-the highest score minus the lowest score in a distribution
Standard Deviation
-the spread of distribution from the normal distribution
Inferential Statistics consists of two techniques: parameter estimation and hypothesis testing.
Parameter Testing
Hypothesis Testing
Chi-squared test
(estimates a population parameter)
(used to decide hypotheses is aceepted as true or false)
Analysis of variance (ANOVA)
Multiple regression
Analysis of covariance (ANCOVA)
Logistic regression
Multivariate analysis of variance (MANOVA)
Multivariate analysis of covariance (MANCOVA)
Auerbach, C. F., & Silverstein, L. B. (2003). Qualitative Data : An Introduction to Coding and Analysis. New York: New York University Press.

LeCompte, M. D. (2000). Analyzing Qualitative Data. Theory Into Practice, 39(3), 146.

Polit, D. F., & Beck, C. T. (2014). Essentials of nursing research: appraising evidence for nursing practice (8th ed.). Philadelphia: Wolters Kluwer Health /Lippincott Williams & Wilkins

Schultze, R. (2004). Analyzing Quantitative Data -- from Description to Explanation (Book). Statistical Papers, 45(3), 458-459.
Category scheme ---> a system used to sort and organize data (often drafted before the data collection).

Coding---> information is coded for correspondence to the categories. (the central idea of coding is to move from raw text to research concerns in small steps)
Analytic Procedures for Qualitative Data
Content Analysis-analyzes the content or narrative data to identify significant themes and patterns
Ethnographic Analysis- this is done by analyzing various patterns simultaneously, looking for and comparing patterns in the behavior and thoughts of participants
Phenomenological Analysis- three different approaches to this procedure but essentially all describe the essential nature of an experience (often through the identification of vital themes)
Grounded Theory Analysis- also contains three approaches but all involve comparing elements present in one data source.
Rigor and Validity in Quantitative Research
Rigor and validity are essential elements in the interpretation of quantitative data. Allows to assess whether the information is valid. It ensures relevance and reliability of the data.
Trustworthiness and Integrity in Qualitative Research
Trustworthiness and integrity are essential for the interpretation of qualitative data. They aid in assessing the research to be honest and dependable. It allows reader to confide in the accuracy of the data.
Level of measurements:
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