SAS 10 Flashcards

1
Q

two types of data

A

primary data
secondary daya

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2
Q

are data collected directly by the researcher himself. These are first-hand original sources. data collection can be more effective and informative if given the necessary preparation and planning.

A

primary data

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3
Q

are information taken from published or unpublished materials previously gathered by other researchers or agencies such as books, newspapers, magazine, journals, published and unpublished thesis and dissertations.

A

secondary data

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4
Q

one of the most important parts of the research work that needs preparation and planning is choosing the right and appropriate sampling method

A

sampling techniques

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5
Q

two types of sampling technique

A

probability sampling
non-probability sampling

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6
Q

every unit has a chance of being selected and that chance can be quantified.

A

probability sampling

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7
Q

involves the selection of a sample from a population based on the principle of randomization or chance It is more complex, more time consuming and usually more costly than non- probability sampling.

A

probability sampling

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8
Q

every item in the population does not have an equal chances of being selected and the results are often biased.

A

non-probability sampling

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9
Q

types of probability sampling

A

simple random sampling
systematic random sampling
stratified random sampling
cluster random sampling

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10
Q

types of non-probability sampling

A

convenience sampling
snowball sampling/ respondent-driven sampling
voluntary sampling

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11
Q

is commonly recommended to prevent the possibility of bias or erroneous inference

A

simple random sampling

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12
Q

each member of the population has an equal chance to be included in the sample gathered

A

simple random sampling

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13
Q

2 ways can be done through simple random sampling

A

table of random numbers
lottery or fishbowl technique

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14
Q

write the name of each subject on small slips of paper and deposit them in a box. After they have been thoroughly mixed, the first pick is made followed by other until the chosen size is chosen.

A

lottery/fishbowl technique

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15
Q

2 ways using lottery fishbowl technique

A

sampling without placement
sampling with placement

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16
Q

in which the drawn papers are no longer returned in the container

A

sampling without placement

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17
Q

involves returning to the container every piece of paper drawn

A

sampling with placement

18
Q

every nth individual or item in the population is selected, starting with a randomly chosen starting point.

A

systematic random sampling

19
Q

This method is useful when the population is ordered, such as in a list or a production line.

A

systematic random sampling

20
Q

Select sample at regular intervals based on sampling fraction
• The items or individuals are arranged in some way- perhaps alphabetically or other sort

A

systematic random sampling

21
Q

the population is divided into non-overlapping strata or groups based on specific characteristics (e.g., age, gender, income level).

A

stratified random sampling

22
Q

A random sample is then drawn from each stratum, ensuring that each group is proportionally represented in the sample.

A

stratified random sampling

23
Q

Select random samples from within homogenous subgroups (strata)
• The strata are homogenous as possible and at the same time each stratum is different from one another as much as possible

A

stratified random sampling

24
Q

the population is divided into naturally occurring groups or clusters.

A

cluster random sampling

25
Q

A random sample of clusters is then selected, and all individuals within the chosen clusters are included in the sample.

A

cluster random sampling

26
Q

This method is useful when the population is spread out over a large geographic area.

A

cluster random sampling

27
Q

select all units within randomly selected geographic clusters

A

cluster random sampling

28
Q

• This can be done by subdividing the population into smaller units and then selecting only at random some primary units where the study would be concentrated

A

cluster random sampling

29
Q

• Cluster sampling is sometimes referred to as an “_________” because it is frequently applied on a geographical basis

A

area sampling

30
Q

is the process of organizing, summarizing, and displaying data in a dear, visually avealing, and meaningful way that facilitates Understanding, interpretation, and communication of this ressarch findings or insights.

A

data presentation

31
Q

are collected in an investigation and they are not organized systematically.

A

raw data

32
Q

Raw data are presented in the form of frequency distribution are called

A

grouped data

33
Q

2 methods of organizing the raw data

A

setting up an array
stem-and-leaf- diagram

34
Q

An ordering of the observations from the smallest to the largest or vice versa is an

A

array

35
Q

statistical tool used to organize and summarize data by showing the frequencies (number on tause is a sis or relative frequencies (proportions or percentages) of aditerive values or categories in a dataset.

A

frequency distribution table

36
Q

A tabular arrangement or data by classes together with the corresponding class trequencies called

A

frequency distribution or frequency table

37
Q

ways of constructing a frequency distribution table

A

first method
second method

38
Q

Choosing a convenient class size- to facilitate the construction of classes use multiple numbers for the class size that is easy to work such as 5, 10,20, or 50..

A

first method

39
Q

may be used for more convenient class size. This method gives a more appropriate and direct way of computing the class size.

A

second method: the Sturge’s formula

40
Q

a point that represents the halfway point between two successive classes is called

A

true limits or class boundary

41
Q

is the midpoint of a class interval

A

class mark