Download - PPT - Data Analysis and Interpretation
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Analyzing and
Interpreting the DataAll meaningsdepend on the key of
interpretation.
-George Eliot
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Analysis and Interpretation
ANALYSIS clos e exam inat ion: the examination of something
in detail in order to understand it better or draw
conclusions from it
assessment: description or explanation of data,which should be based on careful consideration or
investigation
INTERPRETATION
establ ishm ent o f meaning: an explanation of the
significance of research data gathered
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Effective Data AnalysisData ANALYSIS and INTERPRETATION is theprocess of: assigning meaning to the collected
information, as well as determining the conclusions, significance,
and implications of the findings.
REMEMBER:
The steps involved in data analysis are a
function of the type of information collected. However, returning to the purpose of the
assessment and the assessment questionswill provide a structure for the organization of
the data and a focus for the analysis.
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The Body of your Paper
should be composed of the
following:
1. verbal meaning of the numerical or
raw data,
2. analysis, and
3. interpretation
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Effective Data Analysis
Effective data analysis involves:
keeping your eye on the main
research goal,
managing the data you gathered,
engaging in the actual process of
quantitative &/or qualitative analysis,
presenting your data, and
drawing meaningful & logical
conclusions .
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Always refer back to the Big
PictureAnalysis should be approached as a
critical,
reflective, and
iterative process
that cycles between data and an
overarching research framework that
keeps the big picture in mind.
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The analysis of NUMERICAL
(QUANTITATIVE) DATAis represented in mathematical terms. Themost common statistical terms include: the mean, which represents a numerical
average for a set of responses.
standard deviation, which represents thedistribution of the responses around themean. It indicates the degree of consistencyamong the responses.
frequency distribution, which indicates thefrequency of each response. For example, ifrespondents answer a question using anagree/disagree scale, the percentage ofrespondents who selected each response on
the scale would be indicated.
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The analysis of NARRATIVE
(QUALITATIVE) DATA
is conducted by organizing the data into
common themes or categories.
It is often more difficult to interpret
narrative data since it lacks the built-instructure found in numerical data.
Initially, the narrative data appears to be a
collection of random, unconnectedstatements.
The assessment purpose and questions
can help direct the focus of the dataor anization.
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Strategies in
analyzing NARRATIVE
(QUALITATIVE) DATAFocus groups and Interviews: Read and organize the data from each
question separately. This approach permits
focusing on one question at a time (e.g.,experiences with tour guides, characteristics
of tourists, tourism board responsibilities).
Group the comments by themes, topics, or
categories. This approach allows for focusingon one area at a time (e.g., characteristics of
tour guideslevel of preparation, knowledge
of the destination, availability).
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Strategies in
analyzing NARRATIVE
(QUALITATIVE) DATADocuments Code content and characteristics of
documents into various categories (e.g.,training manual policies and procedures,communication, responsibilities).
Observations
Code patterns from the focus of theobservation (e.g., behavioral patterns amount of time engaged/not engaged inactivity, type of engagement, communication,
interpersonal skills).
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Managing Data
Regardless of data type, managingyour data involves
familiarizing yourself with appropriate
software developing a data management system
systematically organizing and screening
your data
entering the data into a program
and finally weeding out the unnecessary
from the necessary data
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Data Types
Different data types demand discretetreatment, so itsimportant to be able to
distinguish variables by
cause and effect (dependent orindependent)
measurement scales (nominal, ordinal,
interval, and ratio)
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Presenting Quantitative Data
Presenting quantitative data ofteninvolves the production of graphs andtables
These need to be1. selectively generated so that they make
relevant arguments
2. informative yet simple, so that they aidreadersunderstanding
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Uncovering and Discovering
ThemesThe methods and logic of qualitative dataanalysis involve:
uncovering and discovering themes
that run through raw data, and interpreting the implication of those
themes for research questions