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CPHM > 2: INTRODUCTION TO BIOSTATISTICS > Flashcards

2: INTRODUCTION TO BIOSTATISTICS Flashcards

1
Q

2 BRANCHES OF STATISTICS

A
  1. Descriptive statistics
  2. Inferential statistics
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2
Q

refers to the different methods applied in order to summarize and present data in a form that will make them easier to analyze and interpret.

A

Descriptive statistics

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3
Q

refers to methods involved in order to make generalizations and conclusions about a target population, based on results from a sample.

A

Inferential statistics

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4
Q

a phenomenon whose values or categories cannot be predicted with certainty.

A

VARIABLE

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5
Q

TYPES OF VARIABLES

A
  1. Qualitative variables
  2. Quantitative variables
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6
Q

if the values can assume only integral or whole numbers.

A

Discrete

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7
Q

if it can attain any value including fractions or decimals.

A

Continuous

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8
Q

SCALE OF MEASUREMENT:

A
  1. Nominal variables
  2. Ordinal scale
  3. Interval scale
  4. Ratio scale
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9
Q

those that can be ranked or ordered.

A

Ordinal scale

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10
Q

the exact distance between 2 categories can be determine but the zero point is arbitrary.

A

Interval scale

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11
Q

similar with interval scale but the zero point is fixed.

A

Ratio scale

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12
Q

CATEGORIES OF DATA ACCORDING TO SOURCE:

A
  1. Primary Data
  2. Secondary data
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13
Q

those that are obtained first hand by the investigator to help him answer specifically the purpose/s of his study.

A

Primary Data

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14
Q

those that are already existing and which have obtained by other people for purposes not necessarily those of the investigators.

A

Secondary data

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15
Q

SOURCES OF DATA:

A
  1. Census
  2. Registries of Vital Events
  3. Reports of Occurrence of Notifiable Diseases
  4. Health records/employment records
  5. Family records
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16
Q

METHODS OF DATA COLLECTION

A
  1. Observations
  2. Interview
  3. Use of questionnaires
17
Q

QUALITIES OF STATISTICAL DATA

A
  1. Timeliness
  2. Completeness
  3. Accuracy
  4. Precision
  5. Relevance
  6. Adequacy
18
Q

the act of examining or studying only a segment of the population to represent the whole.

A

SAMPLING-

19
Q

USES OF SAMPLING IN PUBLIC HEALTH

A
  1. Evaluating the health status of the population
  2. Investigating the factors affecting health
  3. Evaluating the effectiveness of health measures
  4. Assessing specific aspects in the administration of health services
  5. Evaluating the reliability and completeness of record systems
20
Q

in the context of sampling, it refers to entire group of individuals or items of interest in the study.

A

POPULATION

21
Q

POPULATION 2 Types:

A
  1. Target Population
  2. Sampling population
22
Q

is the group from which representative information is desired and to which inferences will be made.

A

Target Population-

23
Q

population from which a sample will be actually taken.

A

Sampling population

24
Q

an object or a person on which a measurement is actually taken or an observation is made.

A

ELEMENTARY UNIT/ELEMENT

25
Q

units which are chosen in selecting the sample and may be made up non-overlapping collection elements or elementary units.

A

SAMPLING UNIT

26
Q

listing or map which represents the collection of all sampling units

A

SAMPLING FRAME

27
Q

the difference between the value of the parameter being investigated and the estimates of the value based on different samples.

A

SAMPLING ERROR

28
Q
  • A good sampling design must follow the following criteria: should be representative of the population, sample size must be adequate, practicability and feasibility of sampling procedure, economy and efficiency of the sampling design.
A

BASIC SAMPLING DESIGNS

29
Q

BASIC SAMPLING DESIGNS 2 TYPES:

A

the probability of each member of the population to be selected in the sample is difficult to determine or cannot be specified.

30
Q

Non-probability sampling designs-

A

a. Judgment or purposive
b. Accidental/Haphazard
c. Quota sampling
d. Snowball technique

31
Q

useful in studying hidden populations

A

Snowball technique-

32
Q

Designs-where rules in selecting the sample is specified and each element has a known, non-zero chance of being included in the sample.

A

Probability Sampling Designs

33
Q

every element in population has a known equal chance of being included in the sample.

A

Simple Random Sampling

34
Q

sampling interval(k) is determined first then one number from 1 to k will be drawn at random. The element corresponding to that number and every kth number thereafter will be included in the sample.

A

Systematic sampling

35
Q

the population is first divided into non-overlapping groups called strata. A simple random sample is then selected from each stratum.

A

Stratified random sampling

36
Q

the population is first divided into CLUSTERS that serve as the sampling unit and a sample of units are selected. Every element found in ach sampling unit drawn as sample
may or may not be included in the study.

A

Cluster sampling

37
Q

SAMPLE SIZE ESTIMATION DEPENDS ON THE FOLLOWING FACTORS:

A
  1. Study design used
  2. Magnitude of the parameter being investigated
  3. Variability of the parameter being investigated
  4. Level of precision desired
  5. Data analysis plan

CPHM (12 decks)

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