INTRO TO BIOEPI Flashcards
(109 cards)
1
Q
An art of summarizing data
A
Statistics
2
Q
Tool in decision making: Use for formulation of judgement
A
Statistics
3
Q
Uses of Biostatistics:
Data reduction ____
Tool for _____ research projects and clinical trials
Tool for _____ appraisal and evaluation of programs
Tool in ______ process and policy making
A
technique
analyzing
objective
decision-making
4
Q
Life + Science dealing w/ the collection organization, analysis, and interpretation of numerical data
A
Biostatistics
5
Q
deals w/ quantitative and qualitative aspects of vital phenomena
A
Biostatistics
6
Q
Application of statistical methods to the life sciences: biology, medicine and public health
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Biostatistics
7
Q
Application of Biostatistics:
study of distribution and determinants of health related states and events in the specified population
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Epidemiology
8
Q
study of the human population: size, structure, composition, and distribution in space
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Demography
9
Q
study the functioning of the health care system, health affecting behaviors
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Health Economics
10
Q
study of hereditary and the genes’ function
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Genetics and Genomics
11
Q
2 Branches of Biostats:
Different methods of summarizing and presenting data for easy analyzing and interpreting
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Descriptive Statistics
12
Q
2 Branches of Biostats:
-Computation of measures of central tendency and variability, location
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Descriptive Statistics
13
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2 Branches of Biostats:
-Tabulation and graphical presentation, dispersion
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Descriptive Statistics
14
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2 Branches of Biostats:
-Facilitate understanding, analysis, and interpretation of data
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Descriptive Statistics
15
Q
2 Branches of Biostats:
Ex: Constructing a statistical table to show the number of OLFU students according to the degree program.
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Descriptive Statistics
16
Q
2 Branches of Biostats:
methods of arriving at conclusions and generalizations about a target population based on info from a sample
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Inferential Statistics
17
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2 Branches of Biostats:
Estimation (point (exact value) & interval (range value)) of parameters and hypotheses testing
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Inferential Statistics
18
Q
2 Branches of Biostats:
Sample population will be tested and results will be
used for generalization of target population
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Inferential Statistics
19
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2 Branches of Biostats:
Ex: Determining if there is a difference between prevalence of smoking among students in public and private high schools based on results from a school survey
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Inferential Statistics
20
Q
all members of a specified group
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Population
21
Q
subset of population
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Sample
22
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measure of characteristic of a population
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Parameter
23
Q
cannot change, value of characteristics that remains the same
A
Constant
24
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can change; characteristics that takes on diff values, cannot be predicted w/ certainty
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Variable
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Research Process : PORRSDDW
```
Problem Identification/ Hypothesis
Objective Formulation
Review of Related Literature
Research Design
Sampling Design and Estimation
Data Collection and Processing
Data Analysis
Writing the Report Dissemination of result
```
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Types of Data According to Source:
| obtained first-hand by the investigator; he’s the one who did the survey
Primary Data
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Types of Data According to Source:
| already existing and have already been obtained, obtained by someone but not for primary purpose of their study
Secondary Data
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Types of Data:
| Categories are simply descriptions or labels to distinguish one group from another
Qualitative
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Types of Data According to Functional Relationship:
Dependent
| Independent
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Types of Variable:
| Categories can be measured and ordered according to quantity or amount and can be expressed numerically.
Quantitative
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Types of Variable:
| Can assume infinite or countable number/ other possible values
Quantitative
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Scale of Measurement of Variables:
Simply used as names or identifiers of a category
Categories are simply labels and cannot be used for meaningful rankings
Nominal (Always Qualitative)
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Scale of Measurement of Variables:
Represents an ordered series of relationships
It has inherent or implied ranking system or order
Ordinal (May be Qualitative or Quantitative )
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Scale of Measurement of Variables:
Does not have a true-zero value starting point
Categories can be measured but 0 point is arbitrary
Interval (Always Quantitative)
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Scale of Measurement of Variables:
Modified interval level w/c includes zero as a starting point
Has fixed 0 point (no value)
Ratio (Always Quantitative)
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Systematic procedure to ensure that the info/ data gathered are complete, consistent and suitable for analysis
Data Processing (Necessary step before data analysis )
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Flowchart: (Which is the correct order)
a. Data Collection → Data Processing (coding, encoding, editing) → Analysis
b. Data Processing → Data Collection (coding, encoding, editing) → Analysis
a
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Conversion of verbal/ written info into numbers w/c can be more easily encoded, counted and tabulated
Data Coding
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to permit rapid storage of data, to organize and helps avoid errors, so statistical software can perform various analysis on the data
Data Coding
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Types of Code:
Actual value or info given by the respondent, as is
Cannot assign any numerical values (1 response only)
Field Code
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Types of Code:
| Recorded as range of values rather than actual values
Bracket Code
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Types of Code:
| Codes are assigned to a list of categories of a given variable
Factual Code
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Types of Code:
| Applicable for questions w/ multiple responses
Pattern Code
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TRUE OR FALSE:
Number of code must be kept to minimum (preferably
less than 8)
TRUE
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TRUE OR FALSE:
| Codes should be exhaustive and mutually exclusive
TRUE
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Codes should be:
| Fully comprehensive ______ and do not overlap _____
exhaustive, mutually exclusive
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Document w/c contains a record of all codes assigned to the responses to all questions in the data collection forms
Coding Manual
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Minimum info that must be included in a coding manual
Variable name: must be kept as short as possible
Variable description: description of the variable in the coding
Coding instructions: actual codes to be used
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Entering the data/responses in a spreadsheet: MS Excel, MS Access, Epi Info
Data Encoding
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Inspection and correction of any errors or inconsistencies in the info collected
Data Editing
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Types of Editing:
| Done as soon as the data has been gathered while still in the field
Field Editing
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Types of Editing:
| Checking of inconsistencies and incorrect entries after receiving the questionnaire from the field
Central Editing
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TRUE OR FALSE:
| Data Editing makes corrections as early as possible
TRUE
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TRUE OR FALSE:
| Data Editing reduces non-response or incomplete answers: don’t leave it blank
TRUE
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TRUE OR FALSE:
| Data Editing eliminates inconsistencies, incorrect information
TRUE
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TRUE OR FALSE:
| Data Editing makes the entries clear, legible and comprehensive
TRUE
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TRUE OR FALSE:
| Data Editing prepares data for analysis
TRUE
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Method of summarizing and organizing and communicate info using variety of tools
Data Presentation
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Methods of Presenting Data:
| Describing data by the use of statements w/ few numbers
Narrative or Textual
| to stress or emphasize significant info
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Methods of Presenting Data:
| Convey info that has been converted into words or numbers in rows and columns
Tabular Presentation
| Less appealing than graphs
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Methods of Presenting Data:
Useful for summarizing and comparing quantitative info of different variables and info w/ different units can be presented together
Tabular Presentation
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Components of a Tabular Presentation
```
Table number
Title
Column/ box headings/ caption
Row headings/ stubs
Body of the table
Source note
Footnote
```
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TRUE OR FALSE:
| A table should be self-explanatory. All sources are specified
TRUE
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TRUE OR FALSE:
| Figures in the table should be aligned by decimal point, and consistency in decimal places
TRUE
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Types of Table:
| table listing all classes and their frequencies
Frequency Distribution
| Nominal and ordinal data, display discrete or continuous data
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Types of Table:
| Break down the range of values of the observations into a series of distinct, non-overlapping intervals.
Frequency Distribution
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Types of Table:
| Single table which allows the distribution of observations across many variables of interest in a given study
```
Master Table
(Contains all variables used in the study)
```
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Types of Table:
| Complete except for data, Doesn’t contain figures
Dummy Table/ Skeleton Table
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Types of Table:
| For proposals to show what will happen in the study
Dummy Table/ Skeleton Table
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Types of Table According to Number of Variables:
___
___
___
One-way Table: single variable
Two-way Table/ Contingency Table/ Cross Tabulation: 2 variables
Multi-way/: more than 2 variables
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% of respondents falling under the column category divided by the total of the category of the row variable
Row %
| r ÷ total (row) x 100
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% of respondents falling under the row category divided by the total category of the column variable
Column %
| c÷ total (column) x 100
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Methods of Presenting Data:
| Pictorial representations of certain quantities plotted w/ reference to a set of axes
Graphical Presentation
| Useful for summarizing, explaining, or exploring quantitative data
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TRUE OR FALSE:
| Graphical Presentation visually summarizes the variables (data set is large)
TRUE
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TRUE OR FALSE:
| Graphical Presentation emphasizes particular statement about data set
TRUE
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TRUE OR FALSE:
| Graphical Presentation enhances readability
TRUE
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TRUE OR FALSE:
| Graphical Presentation appeals the visual memory
TRUE
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Types of Graphical Presentation:
Circles subdivided into a number of slices: area of each slice represents the relative proportion data points falling into given category
Pie chart
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Types of Graphical Presentation:
| Consists of bars of the same sizes
Bar Graph aka One-Dimensional Diagram
With gap: quantitative discrete
Without gap: quantitative continuous
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Types of Bar Graph
Simple Bar Graph
| Multiple Bar Graph
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Kinds of Bar Graph
Horizontal Bar Graph: for qualitative variables
(presenting towns, proportions, rates of categories)
Vertical Bar Graph: for discrete quantitative variables
(Comparing numerical measurements)
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Types of Graphical Presentation:
| Each bar is divided into smaller rectangles representing the parts
Component Bar Graph/ Stacked-Bar Graph
| Generally used for nominal data
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Types of Graphical Presentation:
| Plot of dots joined w/ lines over some period of time in sequential series
Line Graph/ Time Series Charts
Horizontal axis: time series
Vertical axis: variable values
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Types of Graphical Presentation:
| Presentation of frequency distribution of a continuous quantitative variable
Histogram (Preferred for grouped interval data)
Horizontal axis: continuous quantitative
Vertical axis: number of relative frequencies
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Bar Graph : ___ gap ; Histogram : ___gap
| Bar Graph : ___ data ; Histogram : ___data
with ; without
| categorical ; continuous
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Types of Graphical Presentation:
| Frequencies are plotted against the corresponding midpoints of the classes
Frequency Polygon (continuous quantitative variable)
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Types of Graphical Presentation:
| Provides rank-ordered lists and its easier to restore the original value of the observation
Stem-and-leaf Plot (Primarily for small set of data)
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Types of Graphical Presentation:
| Include center, spread, shape, tail length, and outlying data points can be presented horizontal or vertical
Box Plot (Shows description of a large quantitative data)
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Types of Graphical Presentation:
| Shows the relationship between two quantitative variables (ex: weight and height)
Scatter Plot
Plotted points in line: there is linear relationship
Ascending in line: perfect + (increase left to right)
Descending in line: perfect - (decreases right to left)
Scattered points: no relationship bet x and y
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Act of studying or examining only a segment of the population to represent the whole, inferential biostatistics
Sampling
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2 Key Features of Sampling
Representative of the population
| Adequate sample size
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group where representative info is desired and w/c inferences will be made
Target Population
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population from w/c a sample will actually be taken
Sampling Population
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units w/c are chosen in selecting the sample
Sampling Unit
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where w/c a measurement/ observation is made (object or person)
Elementary Unit / Element
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collection of all the sampling/ elementary unit
Sampling Frame
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deviation from the true value
Sampling Error
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Basic Sampling Design:
Probability of each member of the population being selected as part of the sample is difficult to determine or cannot be specified.
Non-probability Sampling
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Basic Sampling Design:
| Each member of the population has a known non-zero chance of being selected as a sample
Probability Sampling
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Non-Probability Sampling Designs :
| based on expert’s subjective judgement
Judgmental/ Purposive
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Non-Probability Sampling Designs :
| those who is available, those who come at hand
Accidental / Haphazard
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Non-Probability Sampling Designs :
| samples of a fixed size
Quota
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Non-Probability Sampling Designs :
individual to be included is identified by a member who was previously included, Referral thru other samples, increases as the study progresses
Snowball
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Non-Probability Sampling Designs :
| units are easily accessible
Convenience
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Probability Sampling Designs :
In this technique elements of the sample are selected using either the lottery method or random numbers generated by a calculator, excel, EpiInfo, etc..
Simple Random Sampling: SRS
106
Probability Sampling Designs :
| Done by taking every element in the population assignment of numbers as a part of the sample.
Systematic Sampling: SYS
| -sampling interval (k=N/n)
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Probability Sampling Designs :
| The population is first divided into non-overlapping groups called: stratum
Stratified Random Sampling
| p=n/N
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Probability Sampling Designs :
| The selection of groups of study units (clusters) instead of the selection of study units individually.
Cluster Sampling (whole group is selected)
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Probability Sampling Designs :
| A procedure carried out in phases and usually involves more than one sampling method.
Multi-Stage Sampling
| Often used in community-based studies
