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BIOEPI > INTRO TO BIOEPI > Flashcards

INTRO TO BIOEPI Flashcards

1
Q

An art of summarizing data

A

Statistics

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2
Q

Tool in decision making: Use for formulation of judgement

A

Statistics

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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

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4
Q

Life + Science dealing w/ the collection organization, analysis, and interpretation of numerical data

A

Biostatistics

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5
Q

deals w/ quantitative and qualitative aspects of vital phenomena

A

Biostatistics

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6
Q

Application of statistical methods to the life sciences: biology, medicine and public health

A

Biostatistics

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7
Q

Application of Biostatistics:

study of distribution and determinants of health related states and events in the specified population

A

Epidemiology

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8
Q

study of the human population: size, structure, composition, and distribution in space

A

Demography

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9
Q

study the functioning of the health care system, health affecting behaviors

A

Health Economics

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10
Q

study of hereditary and the genes’ function

A

Genetics and Genomics

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11
Q

2 Branches of Biostats:

Different methods of summarizing and presenting data for easy analyzing and interpreting

A

Descriptive Statistics

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12
Q

2 Branches of Biostats:

-Computation of measures of central tendency and variability, location

A

Descriptive Statistics

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13
Q

2 Branches of Biostats:

-Tabulation and graphical presentation, dispersion

A

Descriptive Statistics

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14
Q

2 Branches of Biostats:

-Facilitate understanding, analysis, and interpretation of data

A

Descriptive Statistics

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15
Q

2 Branches of Biostats:

Ex: Constructing a statistical table to show the number of OLFU students according to the degree program.

A

Descriptive Statistics

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16
Q

2 Branches of Biostats:

methods of arriving at conclusions and generalizations about a target population based on info from a sample

A

Inferential Statistics

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17
Q

2 Branches of Biostats:

Estimation (point (exact value) & interval (range value)) of parameters and hypotheses testing

A

Inferential Statistics

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18
Q

2 Branches of Biostats:
Sample population will be tested and results will be
used for generalization of target population

A

Inferential Statistics

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19
Q

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

A

Inferential Statistics

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20
Q

all members of a specified group

A

Population

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21
Q

subset of population

A

Sample

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22
Q

measure of characteristic of a population

A

Parameter

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23
Q

cannot change, value of characteristics that remains the same

A

Constant

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24
Q

can change; characteristics that takes on diff values, cannot be predicted w/ certainty

A

Variable

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25
Q

Research Process : PORRSDDW

A 
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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26
Q

Types of Data According to Source:

obtained first-hand by the investigator; he’s the one who did the survey

A

Primary Data

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27
Q

Types of Data According to Source:

already existing and have already been obtained, obtained by someone but not for primary purpose of their study

A

Secondary Data

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28
Q

Types of Data:

Categories are simply descriptions or labels to distinguish one group from another

A

Qualitative

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29
Q

Types of Data According to Functional Relationship:

A

Dependent

Independent

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30
Q

Types of Variable:

Categories can be measured and ordered according to quantity or amount and can be expressed numerically.

A

Quantitative

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31
Q

Types of Variable:

Can assume infinite or countable number/ other possible values

A

Quantitative

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32
Q

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

A

Nominal (Always Qualitative)

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33
Q

Scale of Measurement of Variables:
Represents an ordered series of relationships
It has inherent or implied ranking system or order

A

Ordinal (May be Qualitative or Quantitative )

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34
Q

Scale of Measurement of Variables:
Does not have a true-zero value starting point
Categories can be measured but 0 point is arbitrary

A

Interval (Always Quantitative)

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35
Q

Scale of Measurement of Variables:
Modified interval level w/c includes zero as a starting point
Has fixed 0 point (no value)

A

Ratio (Always Quantitative)

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36
Q

Systematic procedure to ensure that the info/ data gathered are complete, consistent and suitable for analysis

A

Data Processing (Necessary step before data analysis )

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37
Q

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

a

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38
Q

Conversion of verbal/ written info into numbers w/c can be more easily encoded, counted and tabulated

A

Data Coding

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39
Q

to permit rapid storage of data, to organize and helps avoid errors, so statistical software can perform various analysis on the data

A

Data Coding

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40
Q

Types of Code:
Actual value or info given by the respondent, as is
Cannot assign any numerical values (1 response only)

A

Field Code

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41
Q

Types of Code:

Recorded as range of values rather than actual values

A

Bracket Code

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42
Q

Types of Code:

Codes are assigned to a list of categories of a given variable

A

Factual Code

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43
Q

Types of Code:

Applicable for questions w/ multiple responses

A

Pattern Code

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44
Q

TRUE OR FALSE:
Number of code must be kept to minimum (preferably
less than 8)

45
Q

TRUE OR FALSE:

Codes should be exhaustive and mutually exclusive

46
Q

Codes should be:

Fully comprehensive ______ and do not overlap _____

A

exhaustive, mutually exclusive

47
Q

Document w/c contains a record of all codes assigned to the responses to all questions in the data collection forms

A

Coding Manual

48
Q

Minimum info that must be included in a coding manual

A

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

49
Q

Entering the data/responses in a spreadsheet: MS Excel, MS Access, Epi Info

A

Data Encoding

50
Q

Inspection and correction of any errors or inconsistencies in the info collected

A

Data Editing

51
Q

Types of Editing:

Done as soon as the data has been gathered while still in the field

A

Field Editing

52
Q

Types of Editing:

Checking of inconsistencies and incorrect entries after receiving the questionnaire from the field

A

Central Editing

53
Q

TRUE OR FALSE:

Data Editing makes corrections as early as possible

54
Q

TRUE OR FALSE:

Data Editing reduces non-response or incomplete answers: don’t leave it blank

55
Q

TRUE OR FALSE:

Data Editing eliminates inconsistencies, incorrect information

56
Q

TRUE OR FALSE:

Data Editing makes the entries clear, legible and comprehensive

57
Q

TRUE OR FALSE:

Data Editing prepares data for analysis

58
Q

Method of summarizing and organizing and communicate info using variety of tools

A

Data Presentation

59
Q

Methods of Presenting Data:

Describing data by the use of statements w/ few numbers

A

Narrative or Textual

to stress or emphasize significant info

60
Q

Methods of Presenting Data:

Convey info that has been converted into words or numbers in rows and columns

A

Tabular Presentation

Less appealing than graphs

61
Q

Methods of Presenting Data:
Useful for summarizing and comparing quantitative info of different variables and info w/ different units can be presented together

A

Tabular Presentation

62
Q

Components of a Tabular Presentation

A 
Table number
Title
Column/ box headings/ caption
Row headings/ stubs
Body of the table
Source note
Footnote
63
Q

TRUE OR FALSE:

A table should be self-explanatory. All sources are specified

64
Q

TRUE OR FALSE:

Figures in the table should be aligned by decimal point, and consistency in decimal places

65
Q

Types of Table:

table listing all classes and their frequencies

A

Frequency Distribution

Nominal and ordinal data, display discrete or continuous data

66
Q

Types of Table:

Break down the range of values of the observations into a series of distinct, non-overlapping intervals.

A

Frequency Distribution

67
Q

Types of Table:

Single table which allows the distribution of observations across many variables of interest in a given study

A 
Master Table
(Contains all variables used in the study)
68
Q

Types of Table:

Complete except for data, Doesn’t contain figures

A

Dummy Table/ Skeleton Table

69
Q

Types of Table:

For proposals to show what will happen in the study

A

Dummy Table/ Skeleton Table

70
Q

Types of Table According to Number of Variables:
___
___
___

A

One-way Table: single variable
Two-way Table/ Contingency Table/ Cross Tabulation: 2 variables
Multi-way/: more than 2 variables

71
Q

% of respondents falling under the column category divided by the total of the category of the row variable

A

Row %

r ÷ total (row) x 100

72
Q

% of respondents falling under the row category divided by the total category of the column variable

A

Column %

c÷ total (column) x 100

73
Q

Methods of Presenting Data:

Pictorial representations of certain quantities plotted w/ reference to a set of axes

A

Graphical Presentation

Useful for summarizing, explaining, or exploring quantitative data

74
Q

TRUE OR FALSE:

Graphical Presentation visually summarizes the variables (data set is large)

75
Q

TRUE OR FALSE:

Graphical Presentation emphasizes particular statement about data set

76
Q

TRUE OR FALSE:

Graphical Presentation enhances readability

77
Q

TRUE OR FALSE:

Graphical Presentation appeals the visual memory

78
Q

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

79
Q

Types of Graphical Presentation:

Consists of bars of the same sizes

A

Bar Graph aka One-Dimensional Diagram
With gap: quantitative discrete
Without gap: quantitative continuous

80
Q

Types of Bar Graph

A

Simple Bar Graph

Multiple Bar Graph

81
Q

Kinds of Bar Graph

A

Horizontal Bar Graph: for qualitative variables
(presenting towns, proportions, rates of categories)
Vertical Bar Graph: for discrete quantitative variables
(Comparing numerical measurements)

82
Q

Types of Graphical Presentation:

Each bar is divided into smaller rectangles representing the parts

A

Component Bar Graph/ Stacked-Bar Graph

Generally used for nominal data

83
Q

Types of Graphical Presentation:

Plot of dots joined w/ lines over some period of time in sequential series

A

Line Graph/ Time Series Charts
Horizontal axis: time series
Vertical axis: variable values

84
Q

Types of Graphical Presentation:

Presentation of frequency distribution of a continuous quantitative variable

A

Histogram (Preferred for grouped interval data)
Horizontal axis: continuous quantitative
Vertical axis: number of relative frequencies

85
Q

Bar Graph : ___ gap ; Histogram : ___gap

Bar Graph : ___ data ; Histogram : ___data

A

with ; without

categorical ; continuous

86
Q

Types of Graphical Presentation:

Frequencies are plotted against the corresponding midpoints of the classes

A

Frequency Polygon (continuous quantitative variable)

87
Q

Types of Graphical Presentation:

Provides rank-ordered lists and its easier to restore the original value of the observation

A

Stem-and-leaf Plot (Primarily for small set of data)

88
Q

Types of Graphical Presentation:

Include center, spread, shape, tail length, and outlying data points can be presented horizontal or vertical

A

Box Plot (Shows description of a large quantitative data)

89
Q

Types of Graphical Presentation:

Shows the relationship between two quantitative variables (ex: weight and height)

A

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

90
Q

Act of studying or examining only a segment of the population to represent the whole, inferential biostatistics

91
Q

2 Key Features of Sampling

A

Representative of the population

Adequate sample size

92
Q

group where representative info is desired and w/c inferences will be made

A

Target Population

93
Q

population from w/c a sample will actually be taken

A

Sampling Population

94
Q

units w/c are chosen in selecting the sample

A

Sampling Unit

95
Q

where w/c a measurement/ observation is made (object or person)

A

Elementary Unit / Element

96
Q

collection of all the sampling/ elementary unit

A

Sampling Frame

97
Q

deviation from the true value

A

Sampling Error

98
Q

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.

A

Non-probability Sampling

99
Q

Basic Sampling Design:

Each member of the population has a known non-zero chance of being selected as a sample

A

Probability Sampling

100
Q

Non-Probability Sampling Designs :

based on expert’s subjective judgement

A

Judgmental/ Purposive

101
Q

Non-Probability Sampling Designs :

those who is available, those who come at hand

A

Accidental / Haphazard

102
Q

Non-Probability Sampling Designs :

samples of a fixed size

103
Q

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

104
Q

Non-Probability Sampling Designs :

units are easily accessible

A

Convenience

105
Q

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..

A

Simple Random Sampling: SRS

106
Q

Probability Sampling Designs :

Done by taking every element in the population assignment of numbers as a part of the sample.

A

Systematic Sampling: SYS

-sampling interval (k=N/n)

107
Q

Probability Sampling Designs :

The population is first divided into non-overlapping groups called: stratum

A

Stratified Random Sampling

p=n/N

108
Q

Probability Sampling Designs :

The selection of groups of study units (clusters) instead of the selection of study units individually.

A

Cluster Sampling (whole group is selected)

109
Q

Probability Sampling Designs :

A procedure carried out in phases and usually involves more than one sampling method.

A

Multi-Stage Sampling

Often used in community-based studies

BIOEPI (13 decks)

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