A lecture to level 7 nursing student, as part of research methodology course

1. Introduction to Biostatistics
Dr. Weam Banjar, DDS., MSc., Comp.Dent Fellowship

2. Population vs. Sample:
Population
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3. Population vs. Sample:
PopulationTarget
population
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4. Population vs. Sample:
PopulationTarget
population
Accessible
population
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5. Population vs. Sample:
PopulationTarget
population
Accessible
populationSample
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6. Population vs. Sample:
PopulationTarget
population
Accessible
populationSampleRepresentative
Sample
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7. Achieving representative sample:
Representative
sample
Probability sampling
technique
Homogenous
population
Large Sample size (power
analysis)
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8. Strata:
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9. Sampling bias:
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10. Sampling Techniques:
Non-probability sampling Probability Sampling
• Does not involve selection of
elements at random
• Rarely representative of the
population
• has an equal, independent
chance of being selected.
• Allows researchers to estimate
the magnitude of sampling
error (difference between
population values and sample
values)
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11. Types of non-probability sampling
technique:
• Convenience sampling: selecting the most
conveniently available people as participants
• Quota sampling: identifying population strata and
figuring out how many people are needed from
each stratum
• Consecutive sampling: recruiting all people from
an accessible population over a specific time
interval
• Purposive sampling: handpicking sample
members
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12. Types of probability sampling
technique:
• Simple random sampling: Completely random method for selecting
subjects
• Stratified random sampling: Involves splitting subjects into
mutually exclusive groups then using use simple random method for
selection
• Systematic sampling: To choose every (nth) participant from a
complete list
• Cluster random sampling: To randomly select participants from a
list that is too large for simple random sampling
• Multistage random sampling: Combination of techniques
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13. Level of variables measurements:
• Nominal: categorical, two or more, no order. Example: race
• Ordinal: can be ranked in order, interval between values can not
interpreted. Example: pain score
• Ratio: numerical, zero is meaningful. Example: age, height
• Intervals: distance between attributes had meaning, interval
between values could be interpreted. Example: age groups
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14. Classification of variables:
• Dependent variable : Outcome of interest (what do you intend to
measure?)
• Independent variable: Exposure of interest (what are you going to
investigate?)
• Confounder: variables that might influence the results other than
independent and dependent variable
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15. Reliability vs validity:
Reliability Validity
• Refers to whether a method or
measurement will repeatedly give
the same result if used by the
same person more than once
• Implies consistency
• Refers to the ability of methodology
to measure what it is supposed to
measure
• Implies accurance
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16. Generalizability:
an important feature of quantitative research because it allows
the researcher to have a fair degree of certainty that the findings
of the research apply to people that have the same, or broadly
similar, characteristics as the people involved in the study.
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17. Basic statistical techniques:
Descriptive statistics Inferential statistics
• Used to describe data
• 1st step in any statistical treatment
• Uses measures of central tendency
and variability (standard deviation)
• Provide an objective way of
quantifying the strength of
evidence for the hypothesis
• To demonstrate that the findings
extend beyond the sample
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18. Descriptive statistics:
Measures of central tendency Measures of dispersion
• Mean= Average
• Median= middle value in an
ordered set of values
• Mode= most frequently occurring
number in a set of observations
• Range: difference between largest
and smallest observations
• Standard deviation (sample):
• Standard error (population)
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19. Measures of central tendencies that could
be used for specific level of measurements :
Mean Median Mode
Nominal
Ordinal X X
Interval/Ratio X X X
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20. Inferential statistics:
• indicate that the statistical findings are subject to some presumption on the part of the
person doing the statistics.
• Presented as:
Probabilities (p values)
Remain open to proved or disproved
• A p value of 0.1 (usually written as p=0.1) would mean that there is a 10% chance that
the findings of the study occurred by chance.
• The lower the number the better (The lower the P value, the higher the power)
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