11. Processing and displaying data

KNOWLEDGE FOR THE BENEFIT OF HUMANITYKNOWLEDGE FOR THE BENEFIT OF HUMANITY
RESEARCH METHODOLOGY (HFS4343)
PROCESSING & DISPLAYING
DATA
Dr.Dr. MohdMohd RazifRazif ShahrilShahril
School of Nutrition & DieteticsSchool of Nutrition & Dietetics
Faculty of Health SciencesFaculty of Health Sciences
UniversitiUniversiti SultanSultan ZainalZainal AbidinAbidin
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S C H O O L O F N U T R I T I O N A N D D I E T E T I C S • U N I V E R S I T I S U L T A N Z A I N A L A B I D I N
Topic Learning Outcomes
At the end of this lecture, students should be able to;
• describe methods for processing data in quantitative
studies
• describe methods for processing data in qualitative
studies
• explain methods of communicating and displaying
analysed data
• demonstrate how to present data in tables
• illustrate different types of graphs to represent data
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Data processing in quantitative studies
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EditingEditing CodingCoding
11stst11stst •• Developing a code bookDeveloping a code book
22ndnd22ndnd •• PrePre--testing the code booktesting the code book
33rdrd33rdrd •• Coding the dataCoding the data
44thth44thth •• Verifying the coded dataVerifying the coded data
STEPS IN CODING

Editing
• Information from data collection is called ‘raw data’ or
‘data’
• Processing data is to ensure that the data is ‘clean’.
– Editing = process of ‘cleaning’ data
• Identify and minimise errors, incompleteness,
misclassification and gaps in the information from
respondents
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Editing (cont)
• Ways of minimising incompleteness
– By inference
– By recall
– By giving back to the respondent
• Two ways of editing data;
– Examine all the answers to one question or variable at a time
– Examine all the responses given to all questions by one
respondent at a time
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Coding
• Method of coding depends on;
– The way a variable has been measured (measurement scale) in
your research instrument (e.g. if a response to a question is
descriptive, categorical or quantitative)
– The way you want to communicate the findings about a variable
to your readers.
• Responses by respondents can be classified into;
– Quantitative response
– Categorical responses (which may be quantitative or qualitative)
– Descriptive responses (which are invariably qualitative)
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Coding (cont)
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Quantitative orQuantitative or
categorical responsecategorical response
CodesCodes
Descriptive responseDescriptive response
Content analysisContent analysis
Identify main themesIdentify main themes
ExamineExamine
verbatimverbatim
response andresponse and
integrate withintegrate with
texttext reportreport
Assign codeAssign code
for eachfor each
theme andtheme and
countcount
frequencyfrequency
11 22

Steps for coding
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11stst11stst ••Developing a code bookDeveloping a code book
22ndnd22ndnd ••PrePre--testing the code booktesting the code book
33rdrd33rdrd ••Coding the dataCoding the data
44thth44thth ••Verifying the coded dataVerifying the coded data

Step 1: Developing a code book
• Code book provides a set of rules for assigning
numerical values to answers obtained from
respondents.
• Structure of code book;
– Column 1: identifies the question and its number in the research
instrument for which the information is being coded
– Column 2: refers to the name of variable in SPSS or MS Excel.
Each variable should have a unique name
– Column 3: lists the responses to the various questions and
actual codes of the code book
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Example of a code book
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Column 1Column 1Column 1Column 1 Column 2Column 2Column 2Column 2 Column 3Column 3Column 3Column 3

Step 2: Pre-testing the code book
• Pre-test involves selecting a few questionnaires and
actually coding the responses
– to ascertain any problems in coding
• If any problem arises, change the code book, after the
pre-test.
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Step 3: Coding the data
• Three ways of coding data
– Coding on the questionnaire itself, if space for coding is
provided at the time of constructing the instrument
– Coding on separate code sheets that are available for purchase
– Coding directly into the computer using a program such as
SPSS.
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Step 4: Verifying the coded data
• Select a few research instruments at random and record
the responses to identify any discrepancies in coding.
• Frame of analysis can also be developed at this point to
identify;
– Which variable are we planning to analyze
– How they should be analysed
– What cross-tabulation you need to work out
– Which variables you need to combine to construct major
concepts or to develop indices
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Data processing in qualitative studies
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Identify the main themesIdentify the main themes
Assign codes to the main themesAssign codes to the main themes
Classify responses under theClassify responses under the
main themesmain themes
Integrate themes and responsesIntegrate themes and responses
into the text of your reportinto the text of your report

Possible results from statistical analysis
1. Significant and predicted results
– Easiest to explain
– Results support logical links developed by the
researcher among the framework, questions,
variables and measurement methods.
2. Significant and not predicted results
– If the results are valid – important addition to the
body of knowledge
– Sometimes the researcher believes so strongly in the
theory that he/she does not believe the results
• Convinced that there was a problem in the methodology
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Possible results from statistical analysis (cont)
3. Non-significant results
– Inconclusive results are the most difficult to explain
– Referred as negative results
– Might be true reflection of reality. New hypotheses?
New body of knowledge?
– Might also due to error due to inappropriate
methodology.
– Do not mean that there are no relationship among
variables or differences between group
• they indicate that the study failed to find any
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Possible results from statistical analysis (cont)
4. Mixed results
– Most common outcome of studies
– Two dependent measures the same variable may
show opposite results
• The difference might be due to methodology problems
– May also indicate a need to modify existing theory
5. Unexpected results
– Relationship found between variables that were not
hypothesized and not predicted from the framework
guiding the study
– Can be used to develop or refine theory and to
formulate later studies
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Methods of displaying data
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TextText TablesTables
GraphsGraphs
StatisticalStatistical
measuremeasure

Structure of a table
• Title – table number and describes the type of data
• Stub – sub-categories of variables in y-axis
• Column heading - sub-categories of variables in x-axis
• Body – cells housing of data
• Supplementary notes or footnotes – source, level of
probability, specific notes, general notes
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11
22
33
44
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Structure of a table (cont.)
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Characteristics Male Female p-value a
Age (years)
Body weight (kg)
Height (m)
BMI (kg/m2)
Body fat (%)
Bone mass (kg)
Table 4.1: Mean difference on anthropometric characteristics
between male (n=60) and female (n=60) college students
Data were presented as mean ± SD
a p-value less than 0.05 is significant
11
55
33
22 44

Percentage (%) in table/ graphs
• We need to calculate measures such as percentages,
cumulative percentages or ratios for accurate and
effective interpretation of data.
• Types of percentage
1. Row percentage
2. Column percentage
3. Total percentage
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Percentage (%) in table/ graphs (cont.)
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RowRow
• Calculated from
the total of all the
subcategories of
one variable that
are displayed
along a row in
different
columns, in
relation to only
one subcategory
of the other
variable.
ColumnColumn
• Calculated from
the total of all the
subcategories of
one variable that
are displayed
along a column in
different rows, in
relation to only
one subcategory
of the other
variable.
TotalTotal
• This standardises
the magnitude of
each cell; that is,
it gives the
percentage of
respondents who
are classified in
the subcategories
of one variable in
relation to the
subcategories of
the other
variable.

Graphs
• A graphic presentation is constructed in relation to two
axes: horizontal (x-axis) and vertical (y-axis).
• If a graph is designed to display only one variable,
represent the subcategories of the variable along the x-
axis and the frequency or count of that subcategory
along the y-axis. The point where the axes intersect is
considered as the zero point for the y-axis.
– When a graph presents two variables, one is displayed on each
axis and the point where they intersect is considered as the
starting or zero point.
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Graphs (cont.)
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Histogram

Graphs (cont.)
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Bar chart

Graphs (cont.)
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Stacked bar chart

Graphs (cont.)
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100% bar chart

Graphs (cont.)
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Box plot

Graphs (cont.)
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Stem-and-leaf display

Graphs (cont.)
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Pie chart

Graphs (cont.)
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Area chart

Graphs (cont.)
32
Trend curve

Graphs (cont.)
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Scatter plot

Strategies for communicating research
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Nutritionist/Nutritionist/
DietitianDietitian
Health careHealth care
professionalsprofessionals
Policy makersPolicy makers
HealthHealth
consumersconsumers

Audience – Nutritionist & Dietitian
• Oral and visual presentations
– Nutrition/ Dietetics research conferences and meetings
– Collaborative nutritionist/ dietitian groups
– Theses and dissertation defenses
– DVD, videotapes and audio taped presentation
– Websites
• Written reports
– Nutrition/ Dietetics-referred journals, books,
monographs, research newsletters, electronic
databases, foundation reports, theses and dissertation
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Audience – Health care professionals
– Professional conferences and meetings
– Interdisciplinary collaboration
– DVD and taped presentation
• Written reports
– Professional journals and books
– Newsletters
– Electronic databases
– Foundation reports
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Audience – Policy makers
– Testifying on health problems to state and federal
legislators
• Written reports
– Research reports to legislators
– Research reports to funding agencies
– Official reports and presentations to policy makers,
practitioners and consumers
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Audience – Health consumers
– Television and radio
– Community meetings
– Patient and family teaching
• Written reports
– Newspaper
– News and popular magazines
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Anatomy of an abstract
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IntroductionIntroduction
ObjectiveObjective
MethodsMethods
ResultsResults
ConclusionConclusion

Abstract – Introduction
• Begin simply, usually with no more than a sentence or
two
• Explain why this research is important
• Call this section ‘introduction’, ‘summary’ or
‘background’
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Abstract – Objective
• Report the primary purpose of the study (can be one or
two sentences)
• Call this section ‘objective’, ‘purpose’ or ‘aims’
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Abstract – Methods
• Describe the design of the study, the methods used to
achieve the purpose, and the procedures applied to
control interval validity
• Include the sampling strategy and the analytic plan
• Identify independent and dependent variable
• Do not overload with details
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Abstract – Results
• Summarize the most important results (whether they
were statistically significant or not)
• Keep in mind that a lack of effect may be as the
presence of one
• Report some statistical results here, but limit these to
test statistics and associated p values
• Do not use this section to comment on the meaning of
the results, but simply report them.
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Abstract – Conclusion
• Focus on the most important implication of the findings
and the usefulness for practice
• Address application issues here
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Poster Presentation
• A research report presented as a visual display
• It can be read and viewed by large groups of people
• Informal setting
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Tips for poster presentation
• Use arrows, flowcharts, diagrams, photos to
demonstrate the research
• Use bullets in the text
• Use a neutral-color background for the poster
• Self-explanatory graphics should dominate the poster
• Text and graphics should be readable from a distance of
4 to 6 feet.
• Vary the font size according to the importance of the
information
• Flow of the poster should be from left to right and top to
bottom
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Oral presentation
• An oral presentation is also called as podium
presentation
• Presentation of key elements of a research study at a
professional conference
• Type of presentation expected;
– Informal roundtable discussion ‘OR’
– Highly formalized keynote speeches
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Suggested format for oral presentation
1. An introduction to the problem or clinical issue
2. The purpose or primary aim of the research and the
research question
3. Design of the study, including a description of the
methods and procedures
4. The findings from the study, including the type of
analysis and the major results
5. A discussion of the results, including major limitations,
as well as the most important findings for clinical
practice
6. Implications for future research and for clinical
practice
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11. Processing and displaying data

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11. Processing and displaying data