Ocram

SERRANO, JOHN MARCO M.
BSCE – 2B
May 5, 2023
ASSIGNMENT NO. 1
1. Difference between Probability and Non-Probability Sampling.
When choosing a sample from a population, probability sampling ensures that each
person has an equal and known chance of being picked. This shows that there is a
chance that each component of the population will make up the sample. Simple
random sampling, stratified random sampling, and cluster sampling are some of the
frequently applied probability sampling techniques.
Non-probability sampling, in other words, is a strategy for choosing a sample in

which it is unknown what percentage of the population will be chosen. This suggests
that the sample is chosen using methods other than random selection. Convenience
sampling, quota sampling, purposive sampling, and snowball sampling are a few non-
probability sampling techniques.
To sum up, probability sampling is a sampling method where each member of the
population has a known and equal chance of being chosen. Non-probability sampling,
on the other hand, is a sampling strategy that does not guarantee that each member
of the population will have the same chance of being chosen.
2. Identify and explain the types of probability sampling.
a. Simple Random Sampling
A sampling approach called simple random sampling includes choosing

a sample from a population in a way that ensures that each person has an
equal chance of being chosen. A random number generator or a database of
random numbers can be used to carry out the selection procedure. When there
is a huge population and no identifiable subgroups, simple random sampling is
usually used.
b. Stratified Random Sampling
With stratified random sampling, a population is divided into subgroups

or strata according to characteristics like age, gender, or income. Then, a
random sample is chosen from each subgroup, making sure that each subgroup
is represented in the sample in proportion to the size of its membership in the
population. When there are significant subgroups within a population that must
be well represented in the sample, stratified random sampling is useful.
c. Cluster Sampling
Choosing a sample from a population that is physically or geographically

distributed is referred to as cluster sampling. Instead of picking people at
random, a researcher selects groups of people based on certain geographic or
spatial parameters. For example, a researcher may choose a sample of homes
in a certain neighborhood by randomly choosing streets, then choosing homes
on those streets. When the population is dispersed across a large region,
cluster sampling may be more economical than other sample techniques.
3. Identify and explain the types of non-probability sampling.
a. Convenience Sampling
A sampling method called convenience sampling includes choosing

participants who are easily reachable or readily available for the study.
Convenience sampling may not fully reflect the total population, while being
quick, simple, and affordable.
b. Quota Sampling
With this approach, a sample that accurately represents the

population's proportions of various subgroups is chosen. The researcher
chooses people to fill the quotas that are specified for each grouping. Although
quota sampling has the potential to be more effective than other sampling
techniques, it may not accurately reflect the population.
c. Purposive Sampling
Using this technique, participants are chosen based on traits or

characteristics the researcher feels are crucial to the study. When researching
a particular group or issue, purposeful sampling is helpful, but it runs the risk
of introducing bias into the sample.
d. Snowball Sampling
With this approach, people who have knowledge of the target

population are chosen, and they are then asked to recommend other people
who also fit the inclusion criteria. When researching difficult-to-reach or hidden
populations, snowball sampling is helpful, but it could skew the results.
4. Difference between Qualitative and Quantitative variables.
Quantitative variables are numerical and indicate quantities or amounts, such

as height, weight, age, income, or the number of siblings, whereas qualitative variables
are non-numeric and define qualities or traits of a population, such as gender,
ethnicity, religion, or marital status.
Quantitative variables may be measured or tallied and, in contrast to qualitative

variables, are represented by numerical values. Typically, they depict quantities or
sums of a population, such as a person's height, a product's weight, their age, their
household's income, or the number of siblings they have. Quantitative variables may
be divided into two categories: continuous variables, like height, which can take on
any value within a range, and discrete variables, like the number of children in a family,
which can only take on integer values.
In conclusion, quantitative variables reflect numbers or amounts, and

qualitative variables define features or attributes.
5. What are the Scales of Measurement?
a. Nominal
The most fundamental type of measuring scale, known as the nominal

scale, divides data into distinct, non-overlapping categories or groups. Nominal
data can be described using either verbal or numerical descriptors and are
often qualitative in nature.
b. Ordinal
An ordinal scale is a measuring scale that divides data into distinct

groups or categories that have a particular ranking or order. Although the order
of the categories is known, it is impossible to measure the precise distances
between them. Ordinal data can be expressed in words or numbers and are
frequently qualitative in character.
c. Interval
A measuring technique called interval scale uses evenly spaced

numerical values to quantify data. There is no exact zero point, but it is known
how much each category differs from the others. This implies that a value of
zero on the scale does not represent a characteristic's total absence.
Temperature measurements in Celsius or Fahrenheit, where each degree is
equal in distance but 0 does not signify a lack of temperature, are an example
of interval scale data.
d. Ratio
Numerical values that are evenly spaced or standardized are used to

measure data using the ratio scale. The ratio scale, which features a genuine
zero point as opposed to the interval scale, enables the computation of ratios
between numbers. Height or weight data are examples of ratio scale data,
where 0 denotes the lack of the property being measured.

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SERRANO, JOHN MARCO M.

1. Difference between Probability and Non-Probability Sampling.

Non-probability sampling, in other words, is a strategy for choosing a sample in

2. Identify and explain the types of probability sampling.

a. Simple Random Sampling

A sampling approach called simple random sampling includes choosing

b. Stratified Random Sampling

With stratified random sampling, a population is divided into subgroups

Choosing a sample from a population that is physically or geographically

3. Identify and explain the types of non-probability sampling.

A sampling method called convenience sampling includes choosing

With this approach, a sample that accurately represents the

Using this technique, participants are chosen based on traits or

With this approach, people who have knowledge of the target

Quantitative variables are numerical and indicate quantities or amounts, such

Quantitative variables may be measured or tallied and, in contrast to qualitative

In conclusion, quantitative variables reflect numbers or amounts, and

5. What are the Scales of Measurement?

The most fundamental type of measuring scale, known as the nominal

An ordinal scale is a measuring scale that divides data into distinct

A measuring technique called interval scale uses evenly spaced

Numerical values that are evenly spaced or standardized are used to

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