Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

BIOEPI > INTRO TO BIOEPI > Flashcards

INTRO TO BIOEPI Flashcards

1
Q

An art of summarizing data

A

Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Tool in decision making: Use for formulation of judgement

A

Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

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

A

Biostatistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

deals w/ quantitative and qualitative aspects of vital phenomena

A

Biostatistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

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

A

Biostatistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Application of Biostatistics:

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

A

Epidemiology

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

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

A

Demography

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

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

A

Health Economics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

study of hereditary and the genes’ function

A

Genetics and Genomics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

2 Branches of Biostats:

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

A

Descriptive Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

2 Branches of Biostats:

-Computation of measures of central tendency and variability, location

A

Descriptive Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

2 Branches of Biostats:

-Tabulation and graphical presentation, dispersion

A

Descriptive Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

2 Branches of Biostats:

-Facilitate understanding, analysis, and interpretation of data

A

Descriptive Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

2 Branches of Biostats:

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

A

Inferential Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

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

A

Inferential Statistics

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

all members of a specified group

A

Population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

subset of population

A

Sample

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

measure of characteristic of a population

A

Parameter

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

cannot change, value of characteristics that remains the same

A

Constant

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

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

A

Variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

Types of Data:

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

A

Qualitative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

Types of Data According to Functional Relationship:

A

Dependent

Independent

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

Types of Variable:

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

A

Quantitative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

Types of Variable:

Can assume infinite or countable number/ other possible values

A

Quantitative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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 )

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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 )

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q

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

A

Data Coding

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
41
Q

Types of Code:

Recorded as range of values rather than actual values

A

Bracket Code

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
42
Q

Types of Code:

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

A

Factual Code

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
43
Q

Types of Code:

Applicable for questions w/ multiple responses

A

Pattern Code

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
44
Q

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

A

TRUE

45
Q

TRUE OR FALSE:

Codes should be exhaustive and mutually exclusive

A

TRUE

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

A

TRUE

54
Q

TRUE OR FALSE:

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

A

TRUE

55
Q

TRUE OR FALSE:

Data Editing eliminates inconsistencies, incorrect information

A

TRUE

56
Q

TRUE OR FALSE:

Data Editing makes the entries clear, legible and comprehensive

A

TRUE

57
Q

TRUE OR FALSE:

Data Editing prepares data for analysis

A

TRUE

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

A

TRUE

64
Q

TRUE OR FALSE:

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

A

TRUE

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)

A

TRUE

75
Q

TRUE OR FALSE:

Graphical Presentation emphasizes particular statement about data set

A

TRUE

76
Q

TRUE OR FALSE:

Graphical Presentation enhances readability

A

TRUE

77
Q

TRUE OR FALSE:

Graphical Presentation appeals the visual memory

A

TRUE

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

A

Pie chart

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

A

Sampling

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

A

Quota

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

A

Snowball

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)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions