The document discusses probability theory and sampling methods used in research. It defines probability as a measure between 0 and 1 of the likelihood of an event occurring. Probability sampling involves randomly selecting samples according to probability theory, such as simple random sampling and systematic random sampling. Non-probability sampling does not follow probability theory and includes methods like purposive and snowball sampling. The goal of sampling is to select a representative sample that has the same characteristics as the total population in order to generalize results. Probability theory helps ensure representativeness and reduces bias through random selection, where all elements have an equal chance of being selected.
1
Running head: DB FORUM 7 14.6
Discussion Board Forum 7 Question 14.6
Institution
Amanda Williams
From attendance research conducted at concerts held by the Glacier Symphony during the previous two years, you can obtain gender data on attendees for each type of music. How would you conduct a reasonably reliable nonprobability study?
Less effort and time is spent by the researcher when using nonprobability sampling. Nonprobability sampling is a technique which allows the samples to be gathered in a process. This process does not give everyone a fair chance at being selected (Non-Probability Sampling, n.d). Nonprobability sampling is known to be less superior, inferior even to probability sampling, and the significant advantage of being more cost effective (Cooper & Schindler, 2014). There can be room for bias when utilizing nonprobability sampling. The bias is known to affect the surveyor when choosing the sample pool, it is no longer randomized like it is when using probability sampling. Another is bias lies with the respondents themselves. They may choose to not complete the survey which may tamper with a true view of the results.
With 200 concerts coming up at the Glacier Symphony within the next year, a “reasonably reliable nonprobability sample” has been requested. Research from the past two years can be used for the nonprobability sampling. This research will provide the gender of each attendee at each concert and the music genre. We also know how many tickets are available for each concert.
As stated earlier, nonprobability sampling is much more cost effective than probability sampling (Cooper & Schindler, 2014). There are several types of nonprobability sampling that can be done. The most effective for Glacier Symphony would be the use of a Quota Sample. A Quota sample will allow a researcher to sample a subgroup and allows the researcher the opportunity to watch the relationships between subgroups (Non-Probability Sampling, n.d.).
Luckily, the Glacier Symphony has data from the past two years which can be used and will be very helpful. It can provide the researcher with the gender of the population at each concert and what kind of music genre was played as well. When using this data to populate a nonprobability sample, the gender of the population should be more of the constant and the variable should be the type of music. For example, if you have 70% women and 30% men, the results will be skewed. However, if you have 50% men and 50% women, the music genres will be equally represented by both men and women. Cutting out any concern of bias against gender will provide a well-rounded sample and allow for more accurate results. Feild et al reminds us that nonprobability sampling makes it impossible to determine if there are any sampling errors (2004).
This week, incorporating a bible verse has been difficult for me. While considering what to use here I thought to myself that we are all very lucky, God has no va ...
definition of survey
survey and its type
its purpose and uses.
sampling
approaches
survey methods
research designs
probability and non probability
population
cross sectional design
longitudinal design
successive independent sampling design
What is Population ?
What is Sample ?
Sampling Techniques
What is Probability sampling ?
What is Non-probability sampling ?
Advantages & Disadvantages sampling
Difference b/w Probability & Non-Probability
Characteristics of sampling
1
Running head: DB FORUM 7 14.6
Discussion Board Forum 7 Question 14.6
Institution
Amanda Williams
From attendance research conducted at concerts held by the Glacier Symphony during the previous two years, you can obtain gender data on attendees for each type of music. How would you conduct a reasonably reliable nonprobability study?
Less effort and time is spent by the researcher when using nonprobability sampling. Nonprobability sampling is a technique which allows the samples to be gathered in a process. This process does not give everyone a fair chance at being selected (Non-Probability Sampling, n.d). Nonprobability sampling is known to be less superior, inferior even to probability sampling, and the significant advantage of being more cost effective (Cooper & Schindler, 2014). There can be room for bias when utilizing nonprobability sampling. The bias is known to affect the surveyor when choosing the sample pool, it is no longer randomized like it is when using probability sampling. Another is bias lies with the respondents themselves. They may choose to not complete the survey which may tamper with a true view of the results.
With 200 concerts coming up at the Glacier Symphony within the next year, a “reasonably reliable nonprobability sample” has been requested. Research from the past two years can be used for the nonprobability sampling. This research will provide the gender of each attendee at each concert and the music genre. We also know how many tickets are available for each concert.
As stated earlier, nonprobability sampling is much more cost effective than probability sampling (Cooper & Schindler, 2014). There are several types of nonprobability sampling that can be done. The most effective for Glacier Symphony would be the use of a Quota Sample. A Quota sample will allow a researcher to sample a subgroup and allows the researcher the opportunity to watch the relationships between subgroups (Non-Probability Sampling, n.d.).
Luckily, the Glacier Symphony has data from the past two years which can be used and will be very helpful. It can provide the researcher with the gender of the population at each concert and what kind of music genre was played as well. When using this data to populate a nonprobability sample, the gender of the population should be more of the constant and the variable should be the type of music. For example, if you have 70% women and 30% men, the results will be skewed. However, if you have 50% men and 50% women, the music genres will be equally represented by both men and women. Cutting out any concern of bias against gender will provide a well-rounded sample and allow for more accurate results. Feild et al reminds us that nonprobability sampling makes it impossible to determine if there are any sampling errors (2004).
This week, incorporating a bible verse has been difficult for me. While considering what to use here I thought to myself that we are all very lucky, God has no va ...
definition of survey
survey and its type
its purpose and uses.
sampling
approaches
survey methods
research designs
probability and non probability
population
cross sectional design
longitudinal design
successive independent sampling design
What is Population ?
What is Sample ?
Sampling Techniques
What is Probability sampling ?
What is Non-probability sampling ?
Advantages & Disadvantages sampling
Difference b/w Probability & Non-Probability
Characteristics of sampling
Many decisions are based on beliefs concerning the likelihoo.docxalfredacavx97
Many decisions are based on beliefs
concerning the likelihood of uncertain
events such as the outcome of an elec-
tion, the guilt of a defendant, or the
future value of the dollar. These beliefs
are usually expressed in statements such
as "I think that . .. ," "chances are
. . .," "it is unlikely that . .. ," and
so forth. Occasionally, beliefs concern-
ing uncertain events are expressed in
numerical form as odds or subjective
probabilities. What determines such be-
liefs? How do people assess the prob-
ability of an uncertain event or the
value of an uncertain quantity? This
article shows that people rely on a
limited number of heuristic principles
which reduce the complex tasks of as-
sessing probabilities and predicting val-
ues to simpler judgmental operations.
In general, these heuristics are quite
useful, but sometimes they lead to severe
and systematic errors.
The subjective assessment of proba-
bility resembles the subjective assess-
ment of physical quantities such as
distance or size. These judgments are
all based on data of limited validity,
which are processed according to heu-
ristic rules. For example, the apparent
distance of an object is determined in
part by its clarity. The more sharply the
object is seen, the closer it appears to
be. This rule has some validity, because
in any given scene the more distant
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objects. However, the reliance on this
rule leads to systematic errors in the
estimation of distance. Specifically, dis-
tances are often overestimated when
visibility is poor because the contours
of objects are blurred. On the other
hand, distances are often underesti-
mated when visibility is good because
the objects are seen sharply. Thus, the
reliance on clarity as an indication of
distance leads to common biases. Such
biases are also found in the intuitive
judgment of probability. This article
describes three heuristics that are em-
ployed to assess probabilities and to
predict values. Biases to which these
heuristics lead are enumerated, and the
applied and theoretical implications of
these observations are discussed.
Representativeness
Many of the probabilistic questions
with which people are concerned belong
to one of the following types: What is
the probability that object A belongs to
class B? What is the probability that
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What is the probability that process B
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For an illustration of judgment b.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Many decisions are based on beliefs concerning the likelihoo.docxalfredacavx97
Many decisions are based on beliefs
concerning the likelihood of uncertain
events such as the outcome of an elec-
tion, the guilt of a defendant, or the
future value of the dollar. These beliefs
are usually expressed in statements such
as "I think that . .. ," "chances are
. . .," "it is unlikely that . .. ," and
so forth. Occasionally, beliefs concern-
ing uncertain events are expressed in
numerical form as odds or subjective
probabilities. What determines such be-
liefs? How do people assess the prob-
ability of an uncertain event or the
value of an uncertain quantity? This
article shows that people rely on a
limited number of heuristic principles
which reduce the complex tasks of as-
sessing probabilities and predicting val-
ues to simpler judgmental operations.
In general, these heuristics are quite
useful, but sometimes they lead to severe
and systematic errors.
The subjective assessment of proba-
bility resembles the subjective assess-
ment of physical quantities such as
distance or size. These judgments are
all based on data of limited validity,
which are processed according to heu-
ristic rules. For example, the apparent
distance of an object is determined in
part by its clarity. The more sharply the
object is seen, the closer it appears to
be. This rule has some validity, because
in any given scene the more distant
objects are seen less sharply than nearer
objects. However, the reliance on this
rule leads to systematic errors in the
estimation of distance. Specifically, dis-
tances are often overestimated when
visibility is poor because the contours
of objects are blurred. On the other
hand, distances are often underesti-
mated when visibility is good because
the objects are seen sharply. Thus, the
reliance on clarity as an indication of
distance leads to common biases. Such
biases are also found in the intuitive
judgment of probability. This article
describes three heuristics that are em-
ployed to assess probabilities and to
predict values. Biases to which these
heuristics lead are enumerated, and the
applied and theoretical implications of
these observations are discussed.
Representativeness
Many of the probabilistic questions
with which people are concerned belong
to one of the following types: What is
the probability that object A belongs to
class B? What is the probability that
event A originates from process B?
What is the probability that process B
will generate event A? In answering
such questions, people typically rely on
the representativeness heuristic, in
which probabilities are evaluated by the
degree to which A is representative of
B, that is, by the degree to which A
resembles B. For example, when A is
highly representative of B, the proba-
bility that A originates from B is judged
to be high. On the other hand, if A is
not similar to B, the probability that A
originates from B is judged to be low.
For an illustration of judgment b.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
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Soc 331 lecture probability theory
1. SOC 340 LECTURE: WK 2
Representativeness & Randomness*
*Source:
Creswell, J. W. (2003). Research design: Qualitative, quantitative & mixed method. Thousand Oaks, CA: Sage
Babbie, E. (2014). The basics of social research. Belmont, CA: Sage Publishing 1
2. PROBABILTY THEORY OF SAMPLING
WHAT IS PROBABILITY?
PROBABILITY IS THE MEASURE OF THE LIKELIHOOD THAT AN EVENT WILL OCCUR. PROBABILITY IS
QUANTIFIED AS A NUMBER BETWEEN 0 AND 1, WHERE, LOOSELY SPEAKING, 0 INDICATES IMPOSSIBILITY
AND 1 INDICATES CERTAINTY. THE HIGHER THE PROBABILITY OF AN EVENT, THE MORE LIKELY IT IS THAT THE
EVENT WILL OCCUR. A SIMPLE EXAMPLE IS THE TOSSING OF A FAIR (UNBIASED) COIN.
WHAT IS PROBABILITY SAMPLING?
PROBABILITY SAMPLING DESCRIBES THE PROCESS OF SELECTING SAMPLES IN ACCORDANCE WITH
PROBABILITY THEORY WHICH TYPICALLY INCLUDE A RANDOM PROCESS FOR SELECTION. TYPES OF
PROBABILITY SAMPLING THAT WE WILL BE CONCERNED WITH ARE:
SIMPLE RANDOM SAMPLING
SYSTEMATIC RANDOM SAMPLING
WHAT IS NON-PROBABILITY SAMPLING?
ANY SAMPLING SELECTION PROCESS THAT DOES NOT CONFORM TO PROBABILITY THEORY. TYPES OF
NONPROBABILITY SAMPLING THAT WE WILL BE CONCERNED WITH ARE:
PURPOSIVE SAMPLING
SNOWBALL SAMPLING
AVAILABLE RESPONDENTS SAMPLING
QUOTA SAMPLING
2
3. SAMPLING BIAS
REPRESENTATIVENESS
PURPOSE OF THE SAMPLING PROCESS:
TO PROVIDE INFORMATION ABOUT A TOTAL POPULATION BY SELECTING A SAMPLE OF THAT POPULATION THAT
CONTAINS THE SAME VARIATIONS AS THE TOTAL POPULATION. IN OTHER WORDS, THE SAMPLE MUST BE
REPRESENTATIVE OF THE POPULATION. FOR INSTANCE, IF THE TOTAL PUPULATION IS SPLIT 50/50 BETWEEN
GENDER TYPES, THE SAMPLE SHOULD ALSO BE SPLIT IN CLOSE TO THE SAME WAY, IF NOT EXACTLY.
BIAS:
RESULTS FROM INADEQUATE PREPARATION OF THE SAMPLE SELECTION, WHICH LEADES TO A LACK OF
REPRESENTATIVENESS.
REPRESENTATIVENESS:
THE QUALITY OF THE SAMPLE HAVING THE SAME DISTRIBUTION OF CHARACTERISTICS AS THE TOTAL
POPULATION. REPRESENTATIVENESS IS IMPROVED BY PROBABILITY SAMPLING & CAN BE THE BASIS FOR
GENERALIZING THE RESULTS OF THE RESEARCH STUDY.
EQUAL PROBABILITY OF SELECTION:
A METHOD THAT ENSURES EACH MEMBER OF THE TOTAL POPULATION HAS THE SAME CHANCE OF BEING
SELECTED AS A MEMBER OF THE SAMPLE GROUP
3
4. PROBABILITY THEORY
PROBABILITY THEORY:
A MATHEMATICAL PROCESS THAT ENSURES RESEARCHERS WILL SELECT SAMPLE POPULATIONS THAT
ARE REPRESENTATIVE OF THE TOTAL POPULATION FROM WHICH THE SAMPLE IS DERIVED. IT ALSO
ENABLES RESEARCHERS TO STATISTICALLY ANALYZE THE RESULTS OF THE RESEARCH STUDY & TO
APPLY THOSE RESULTS TO THE TOTAL POPULATION (I.E., GENERALIZE THE RESULTS.
RANDOM SELECTION:
IS THE PROCESS BY WHICH EACH ELEMENT (MEMBER OF THE TOTAL POPULATION) HAS AN EQUAL
CHANCE OF BEING SELECTED INTO THE SAMPLE POPULATION. FOR EXAMPLE: FLIPPING A COIN 100
TIMES. SINCE THERE ARE ONLY TWO POSSIBILITIES, HEADS OR TAILS, THERE IS AN EQUAL CHANCE
THAT ONE OR THE OTHER WILL BE THE RSULT OF A FLIP.
REASONS FOR USING RANDOM SLECTION:
1. IT REDUCES THE CHANCE OF UNCONSCIOUS BIAS
2. IT CONFORMS TO PROBABILITY THEORY & IMPROVES THE ACCURACY OF THE SAMPLE
4
5. KEY TERMS
ELEMENT:
THE UNIT OF WHICH THE POPULATION (E.G.; WILMU STUDENTS) IS COMPRISED & WHICH IS
SELECTED FOR THE SAMPLE
TOTAL POPULATION (ALSO CALLED THE STUDY POPULATION):
THE AGGREGATION OF ALL THE ELEMENTS BEING STUDIED (E.G.; WILMU STUDENTS)
SAMPLE POPULATION:
A SUB POPULATION OF THE TOTAL POPULATION RANDOMLY SELECTED THAT IS REPRESENTATIVE OF THE
TOTAL POPULATION
SAMPLING UNIT:
THE ELEMENT OF SET OF ELEMENTS SELECTED IN SOME STAGE OF SAMPLING
PARAMETER:
THE SUMMARY DESCRIPTION OF A GIVEN VARIABLE IN A POPULATION
5
