Data Management for Basic Reserach

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6. Basic Medical Research Course

7. Answers of 3 Questions From 37 Participants
1.Name of variable and measurement scale of one
qualitative data and one quantitative data which
you want to be analysed ?
2. What is the most difficulty /challenge to analyse
data ?
3. What is your expectation on data management
lecture and discussion ?

10. Qualitative Vs Quantitative
Qualitative Quantitative Mixed
Design Design emerges as
the study unfolds.
(Flexible to allow for
unseen problems
that may arise
during research
process)
Fixed
All aspects of study
are carefully
designed before
data is collected
Variables variables that are
not measurement in
number
Variables
Whose values result
from counting or
measuring

11. Qualitative Vs Quantitative
Qualitative Quantitative Mixed
Data In form of “words,
pictures or objects”
More 'rich', time
consuming, and
less able to be
generalized.
(Subjective)
In form of ”numbers” and
statistics
is more efficient, able to
test hypotheses, but may
miss contextual detail.
(Objective)
Measurement Nominal
(Name , no
ordering )
Ordinal (order
implied in Level)
Measured on a numeric
or quantitative scale
(ordinal, interval and
ratio scales )
-Continuous numerical
-Discrete numerical

12. Qualitative Vs Quantitative
Qualitative Quantitative Mixed
Descriptive Graph, Pie, bar Chart
Frequency tables
Histogram/stem & leaf
Box-whisker plot
Mean graphs
Means (SD)
Median (IQR)
Inferential Ground theory
Triangulation
X2 tests
T test /paired t
/independent “t” test
ANOVA /MANOVA
Correlation & Regression
Multiple Regression

13. Data
Categorical
Ordinal
e.g <good,
poor, fair>
Nominal
e.g. Gender
Numerical
Continuous
<Age/Height of
students >
Discrete
< number of
pregnant women)
 

14. “Measurement Scale in SPSS :
Country of the Participants”
Normal Data Vs Numerical Data (Coding)

21. • Questionnaires, Format, Checklist
• Instruments and Materials
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Validity &
Reliability of
Questionnaires
Standardization of Instruments & Materials
Validity
-FaceValidity
-Content validity
-Criterion validity
Reliability
-Test-Retest Reliability
(Kappa Statistics)
-Internal Consistency
(Cronbach's’ α)
-Alternate forms
reliability:
Pilot test !

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“***Just in case syndrome” in making questionnaires

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 
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Manually/software
(Electronically)
Statistical Software
Descriptive Data Analysis
Distribution of data
Outliers/ extreme value

27. “ Data Entry : Country of the Participants”
Normal Data Vs Numerical Data (Coding)
(Using Excel format)

28. “ Data Entry : Country of the Participants”
Normal Data Vs Numerical Data (Coding)

29. Data Checking Using Frequency Tables:
“Country data of 37 Participants”

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Manually/software
(Electronically)
Statistical Software
Descriptive Data Analysis
Distribution of data (Normal /abnormal )
Outliers/ extreme value

37. •

40. Dependent & Independent variables
Independent variable is exposure
Dependent variable is outcome variable
Exposure
Outcome

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IV
DV

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
 µ σ
 µ σ

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“Outcome variable”
(Continuous Numerical )
One- “Exposure variable”(IV)
(Continuous Numerical data )
Simple linear correlation “r”
More than ONE
“ Exposure variable” (IV)
(Continuous Numerical data )
Multiple Regression

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How about categorical / ordinal data & Continuous
numerical data?
? ?Logistic regression Spearman rank correlation

46. Types of
Data Analysis
( tests)
No of
Variable
No of IV
( Sub groups )*
No of DV
(sub-
groups)*
Remark /Type of data
Univariate One Descriptive summary statistics
Bivariate Two One One Association /Relation
b/t IV & DV
X2 test* Two One
(≥ 2 )*
One
(≥ 2 )*
Both IV & DV are
Categorical/nominal/ordinal
“t” tests** Two One
(2)**
One IV- Cat/ Nominal/ordinal
(dichotomous)
DV- continuous numerical
ANOVA”F” Test
(One way)***
Two One
(≥ 3)***
One IV- Cat/ Nominal/ordinal
DV- continuous numerical
Simple linear
correlation
Two One One IV- continuous numerical
DV- continuous numerical
Multivariate > Two >one >one IVs- continuous numerical
DVs- continuous numerical

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48. Absolute Risk
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Risk Measurement/Estimation of Risk

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50. Research Topic
External Validity
Internal Validity
Writing up Report with Valid Results & Conclusion

53. Type- I(α ) andType II (β) Error
Decision based on
Statistical test of
Sample data
H0 true HA true
Accept H0
(Cannot reject
H0)
OK
(H0 vs H0)
Type II error ()
(HA true But
Cannot reject H0)
Reject H0 &
Accept HA
Type I error ()
(H0 but Accept HA
OK
(HA true &
Accept HA)
(p=1-)
 - level of significance( 0.05) in 95% CI 1- “ power of the test”
No study is perfect, there is always the chance for error
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True Results

54. Type I andType II Errors
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55. CATEGORICAL VARIABLES