Nursing Data Analysis.

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Data Analysis
Prof.Dr.Chinna Chadayan.N
RN.RM., B.Sc (N)., M.Sc (N)., Ph.D (N).,
Professor
Enam Nursing College
M.Sc (N) I year
Unit 17b
Process of Data Analysis
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2. Meaning of Data Analysis
Data analysis summarizes collected data.
It involves the interpretation of data gathered through the use of
analytical and logical reasoning to determine patterns, relationships
or trends.
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3. Process of Data Analysis
Data Analysis is a process of collecting, transforming, cleaning,
and modeling data with the goal of discovering the required
information. The results so obtained are communicated,
suggesting conclusions, and supporting decision-making. Data
visualization is at times used to portray the data for the ease of
discovering the useful patterns in the data. The terms Data
Modeling and Data Analysis mean the same.
Data Analysis Process consists of the following phases that are
iterative in nature −
•Data Requirements Specification
•Data Collection
•Data Processing
•Data Cleaning
•Data Analysis
•Communication
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4. 1.Data Requirements Specification
Based on the requirements of those directing the analysis, the data necessary as inputs to the analysis is identified (e.g., Population of
people). Specific variables regarding a population (e.g., Age and Income) may be specified and obtained. Data may be numerical or
categorical.
2.Data Collection
Data Collection is the process of gathering information on targeted variables identified as data requirements. The emphasis is on
ensuring accurate and honest collection of data. Data Collection ensures that data gathered is accurate such that the related decisions are
valid. Data Collection provides both a baseline to measure and a target to improve.
3.Data Processing
The data that is collected must be processed or organized for analysis. This includes structuring the data as required for the relevant
Analysis Tools. For example, the data might have to be placed into rows and columns in a table within a Spreadsheet or Statistical
Application. A Data Model might have to be created.
4.Data Cleaning
The processed and organized data may be incomplete, contain duplicates, or contain errors. Data Cleaning is the process of preventing
and correcting these errors. There are several types of Data Cleaning that depend on the type of data.
5.Data Analysis
Data that is processed, organized and cleaned would be ready for the analysis. Various data analysis techniques are available to
understand, interpret, and derive conclusions based on the requirements. Data Visualization may also be used to examine the data in
graphical format, to obtain additional insight regarding the messages within the data.
Statistical Data Models such as Correlation, Regression Analysis can be used to identify the relations among the data variables. These
models that are descriptive of the data are helpful in simplifying analysis and communicate results.
6.Communication / Publication
The results of the data analysis are to be reported / Published in the prescribed format in the Journals
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23. Findings of the
Study
• The findings of the study are the presentation of the results in the
form of data or facts. The reporting of the data is an objective
process – no opinions. (Data are plural. Datum is singular.)
• Findings are written in the past tense and are the results of data
analysis. They also include a description of the study sample and
whether any subjects have dropped out.
• Descriptive statistics are always used, but inferential statistics are only
used where hypotheses are tested or research questions are posed.
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24. Findings of the
Study
• Presentation of Findings
• Narrative presentation –
• The findings should be clearly and concisely presented in the
text. As much attention should be given to data that fail to
support as to those that do support.
• The statistical tests, the test results, degrees of freedom and
the probability values (in two decimal places) should be listed.
• In qualitative research, the narrative presentation will have
many direct quotes, then a summary of patterns and themes
found in the data.
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25. Findings of the
Study
• Tables – means of organizing data so that they may be more easily
understood and interpreted.
• Information presented in tables should be discussed in the text
• Tables should appear as soon as possible after they have been referred to in the text
• Titles should be clear, concise and contain the variables that are presented
• All data entries should be rounded to the same number of decimal places – decimal
points should line up
• Where data are not available “–” should be used
• Figures – any visual presentation other than a table – graphs, diagrams,
drawings, etc.
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26. Discussion of
Findings
• Explain the meaning of the information in easily understood terms
• Discuss how reliability and validity were maintained
• Discuss the results in terms of whether they were:
• significant and in keeping with those predicted
• non-significant – explain
• significant but opposite to those predicted
• mixed results
• Compare results to previous studies
• Discuss statistical and clinical significance
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28. Conclusions
• The researcher attempts to show what knowledge has been gained by
the study and also tries to generalize that knowledge considering the
population and the sample. Must address:
• Was the study problem answered?
• Was the research purpose met?
• Was the research hypothesis supported?
• Was the theoretical framework supported?
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29. Implications
• This gives the researcher the opportunity to be creative – give the
meaning of the conclusions for the body of knowledge, for theory,
and for practice. It contains suggestions for making changes, for
implementing findings, for further studies, and for incorporation into
the body of knowledge of nursing and other disciplines.
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31. Recommendations
• Recommendations for further research
• Logical extensions of the study – answers the question “What comes next?”
• Replication of the study – maybe a different sampling or setting. If these are
not done, implementation of research findings are seriously hampered.
• Correction of the study limitations – sample, instrument, control of variables,
change in methodology
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32. Thank u
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