This document discusses correlational research methods. It describes correlational research as non-experimental, backward-looking, and dynamic. There are three types of correlational research: positive correlation, where increases in one variable correspond to increases in another; negative correlation, where increases in one variable correspond to decreases in another; and zero correlation, where changes in one variable do not correspond to changes in the other. Data can be collected through naturalistic observation, archival data, or surveys. Understanding correlation coefficients and multivariate analyses is also important for correlational research.
After the formulation of research questions and sample selection, the next step in research chain is developing data collection instruments or research instruments.
They are measurement tools (i.e., tests, questionnaires or interviews)
They can be designed by the researcher or can be previously-developed by other researchers.
Concept cannot be measured until its Converted in to variables.
Variables: It is a Property that takes on different values. Variables are classified in terms of their relationship with one another
Topics:
Quantitative research
Characteristics of Quantitative Research
Strengths of Quantitative Research
Weaknesses of Quantitative Research
Importance of Quantitative Research Across Fields
TYPES OF QUANTITATIVE RESEARCH DESIGN
This is the basic explanation on what are ANCOVA and MANCOVA in research study in which provides the definitions and the illustration on how can these both be use in SPSS tool analysis. If you's like to get practice file, do not hesitate to contact me.
After the formulation of research questions and sample selection, the next step in research chain is developing data collection instruments or research instruments.
They are measurement tools (i.e., tests, questionnaires or interviews)
They can be designed by the researcher or can be previously-developed by other researchers.
Concept cannot be measured until its Converted in to variables.
Variables: It is a Property that takes on different values. Variables are classified in terms of their relationship with one another
Topics:
Quantitative research
Characteristics of Quantitative Research
Strengths of Quantitative Research
Weaknesses of Quantitative Research
Importance of Quantitative Research Across Fields
TYPES OF QUANTITATIVE RESEARCH DESIGN
This is the basic explanation on what are ANCOVA and MANCOVA in research study in which provides the definitions and the illustration on how can these both be use in SPSS tool analysis. If you's like to get practice file, do not hesitate to contact me.
Discuss the strengths and weaknesses of correlational and regression.pdfaksharatelicom
Discuss the strengths and weaknesses of correlational and regression studies; discuss concepts
such as positive and negative correlations, correlation coefficients, confounding, and causality.
Solution
M. Ed (1 st ) SESSION: 2011-2012 DEPARTMENT OF EDUCATIONAL TRAINING Nature
of Correlational Studies Types of Correlations Positive and negative Correlation: A positive
correlation means high scores on the one variablehave a propensity to be associated with high
scores on other variable. A negative correlationmeans high score on the one variable is related
with low scores on the other variable and lowscores on the one are associated with high scores
on the either.Linear and Non Linear Correlation: The correlation between two variables is
believed to belinear if be consistent to a unit change in one variable, there is constant change in
other variableover entire array of values. The relationship between two variables is thought to be
non-linear if corresponding to a unit change in first variable, but the other variable does not
change at aconstant rate, but at varying rate. Types of Correlational Designs There are many
methods for correlational studies with their own strengths and weaknesses. Onetrendy method is
called naturalistic observation, which necessitates a researcher to observe andrecord the natural
environment without interference. One benefit of naturalistic observation isthat the researcher is
observing variables in a natural state. Some limitations are that it can bedifficult to control the
variables or to avoid outside influences from faking the results.Another type of correlational
research is called the survey method. Surveys are easy on the pocket and swift, and can be used
to gather information from very large sample size. However, poorly written survey questions can
distort results. Another hitch is that survey results are alsodependent on survey respondents, who
are not always trustworthy.A third method for correlational design is archival research, which
analyzes historical records.An improvement in this method is that it’s a workable way to analyze
large amounts of datawithout expending a lot of money. A lapse of this particular research
method is that theresearcher has no way of knowing if the original data collection methods were
reliable.Correlational studies are a helpful tool for conducting research. However, it must be kept
in mindthat no study method is perfect. Researchers must take into consideration the limitations
of boththeir preferred research method and correlational studies as a general rule.Correlational
designs may be cross-sectional, in which all observations are made at the same point with
context to time, or they may be longitudinal, in which calculations are made at two or more
different time points.
1.We don’t necessarily know ‘what works’ – “confident predictions about policy made by experts often turn out to be incorrect. RCTs have demonstrated that interventions which were designed to be effective were in fact not”
2. RCTs don’t have to cost a lot of money – “The costs of an RCT depend on how it is designed: with planning, they can be cheaper than other forms of evaluation.”
3. There are ethical advantages to using RCTs – “Sometimes people object to RCTs in public policy on the grounds that it is unethical to withhold a new intervention from people who could benefit from it.” “If anything, a phased introduction in the context of an RCT is more ethical, because it generates new high quality information that may help to demonstrate that an intervention is cost effective.”
4. RCTs do not have to be complicated or difficult to run – “It is much more efficient to put a smaller amount of effort [than a post-intervention impact evaluation] into the design of an RCT before a policy is implemented.”
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
19. THE STRATEGY OF CORRELATIONAL
RESEARCH: GENERAL CHARACTERISTICS
TACTICS: COLLECTING DATA
TACTICS: READING ABOUT AND
UNDERSTANDING MULTIVARIATE ANALYSES
STRATEGY:
THREE TYPES OF CORRELATIONAL RESEARCH
***CONDUCTING CORRELATIONAL RESEARCH MAGNITUDE,
SCATTERPLOTS, AND TYPES OF RELATIONSHIPS
MISINTERPRETING CORRELATIONS
1
2
3
4
CORRELATIONAL
RESEARCH
METHODS OF
***PREDICTION AND CORRELATION STATISTICAL ANALYSIS:
CORRELATION COEFFICIENTS
ADVANCED CORRELATIONAL TECHNIQUES: REGRESSION ANALYSIS
22. GENERAL
CHARACTERISTICS
THE STRATEGY OF
CORRELATIONAL RESEARCH:
CORRELATIONAL RESEARCH IS
NON-EXPERIMENTAL
Correlational research is non-experimental as it does not involve
manipulating variables using a scientific methodology in order to
agree or disagree with a hypothesis. In correlational research,
the researcher simply observes and measures the natural
relationship between 2 variables; without subjecting either of the
variables to external conditioning.
25. GENERAL
CHARACTERISTICS
THE STRATEGY OF
CORRELATIONAL RESEARCH: CORRELATIONAL RESEARCH IS
BACKWARD-LOOKING
Correlational research doesn't take the future into consideration as it only
observes and measures the recent historical relationship that exists between 2
variables. In this sense, the statistical pattern resulting from correlational
research is backward-looking and can cease to exist at any point, going
forward.
Correlational research observes and measures historical patterns between 2
variables such as the relationship between high-income earners and tax
payment. Correlational research may reveal a positive relationship between the
aforementioned variables but this may change at any point in the future.
28. GENERAL
CHARACTERISTICS
THE STRATEGY OF
CORRELATIONAL RESEARCH:
CORRELATIONAL RESEARCH IS DYNAMIC
Statistical patterns between 2 variables that result from correlational
research are ever-changing. The correlation between 2 variables changes
on a daily basis and as such, it cannot be used as fixed data for further
research.
For example, the 2 variables can have a negative correlational relationship
for a period of time, maybe 5 years. After this time, the correlational
relationship between them can become positive; as observed in the
relationship between bonds and stocks.
31. GENERAL
CHARACTERISTICS
THE STRATEGY OF
CORRELATIONAL RESEARCH:
PURPOSE
USED TO TEST
THE STRENGTH
OF ASSOCIATION
BETWEEN VARIABLES
VARIABLES
VARIABLES ARE ONLY
OBSERVED WITH NO
MANIPULATION OR
INTERVENTION BY
RESEARCHERS
CONTROL
LIMITED CONTROL IS
USED, SO OTHER
VARIABLES MAY PLAY
A ROLE IN THE
RELATIONSHIP
34. three TYPES OF
CORRELATIONAL
RESEARCH
STRATEGY:
POSITIVE CORRELATIONAL RESEARCH
Positive correlational research is a research method involving
2 variables that are statistically corresponding where an increase or
decrease in 1 variable creates a like change in the other.
An example is when an increase in workers' remuneration results in an
increase in the prices of goods and services and vice versa.
35. three TYPES OF
CORRELATIONAL
RESEARCH
STRATEGY:
POSITIVE CORRELATIONAL RESEARCH
The more hours you spend in direct sunlight, the more severe your sunburn.
As the temperature goes up, ice cream sales also go up.
When an employee works more hours his paycheck increases proportionately.
The more gasoline you put in your car, the farther it can go.
High school students who had high grades also had high scores on the SATs.
As attendance at school drops, so does achievement.
When enrollment at college decreases, the number of teachers decreases.
Common Examples of Positive Correlations
36. three TYPES OF
CORRELATIONAL
RESEARCH
STRATEGY:
NEGATIVE CORRELATIONAL RESEARCH
Negative correlational research is a research method involving
2 variables that are statistically opposite where an increase
in one of the variables creates an alternate effect or decrease
in the other variable. An example of a negative correlation
is if the rise in goods and services causes a decrease
in demand and vice versa.
37. three TYPES OF
CORRELATIONAL
RESEARCH
STRATEGY:
NEGATIVE CORRELATIONAL RESEARCH
A student who has many absences has a decrease in grades.
As the weather gets colder, air conditioning costs decrease.
If a train increases speed, the length of time to get to the final point
decreases.
If a chicken increases in age, the number of eggs it produces
decreases.
As the temperature decreases, more heaters are purchased.
As a biker's speed increases, his time to get to the finish line
decreases.
Common Examples of Negative Correlation
38. three TYPES OF
CORRELATIONAL
RESEARCH
STRATEGY:
ZERO CORRELATIONAL RESEARCH
Zero correlational research is a type of correlational research that involves 2
variables that are not necessarily statistically connected. In this case, a change
in one of the variables may not trigger a corresponding or alternate change in
the other variable.
Zero correlational research caters for variables with vague statistical
relationships. For example, wealth and patience can be variables under zero
correlational research because they are statistically independent.
Sporadic change patterns that occur in variables with zero correlational are
usually by chance and not as a result of corresponding or alternate mutual
inclusiveness.
39. three TYPES OF
CORRELATIONAL
RESEARCH
STRATEGY:
ZERO CORRELATIONAL RESEARCH
The amount of coffee that individuals consume and their IQ level have a
correlation of zero.
The height of students and their average exam scores have a correlation of
zero. In other words, knowing the height of an individual doesn’t give us an
idea of what their average exam score might be.
The shoe size of individuals and the number of movies they watch per year
have a correlation of zero. In other words, knowing the shoe size of an
individual doesn’t give us an idea of how many movies they watch per
year.
The weight of individuals and their annual income has a correlation of zero.
In other words, knowing the weight of a person doesn’t give us an idea of
what their annual income might be.
Common Examples of Zero Correlation
40. three TYPES OF
CORRELATIONAL
RESEARCH
STRATEGY:
POSITIVE CORRELATIONAL RESEARCH
NEGATIVE CORRELATIONAL RESEARCH
ZERO CORRELATIONAL RESEARCH
Correlational research can also be classified based on data collection
methods.
Based on these, there are 3 types of correlational research: Naturalistic
observation research, survey research, and archival research.
43. COLLECTING DATA
TACTICS:
NATURALISTIC OBSERVATION
Naturalistic observation is a correlational research methodology that involves observing
people's behaviors as shown in the natural environment where they exist, over a period of
time. It is a type of research-field method that involves the researcher paying closing
attention to natural behavior patterns of the subjects under consideration.
This method is extremely demanding as the researcher must take extra care to ensure that
the subjects do not suspect that they are being observed else they deviate from their
natural behavior patterns. It is best for all subjects under observation to remain anonymous
in order to avoid a breach of privacy.
The major advantages of the naturalistic observation method are that it allows the
researcher to fully observe the subjects (variables) in their natural state. However, it is a
very expensive and time-consuming process plus the subjects can become aware of this
act at any time and may act contrary.
45. COLLECTING DATA
TACTICS:
ARCHIVAL DATA
Archival data is a type of correlational research method that involves making use of already
gathered information about the variables in correlational research. Since this method
involves using data that is already gathered and analyzed, it is usually straight to the point.
For this method of correlational research, the research makes use of earlier studies
conducted by other researchers or the historical records of the variables being analyzed.
This method helps a researcher to track already determined statistical patterns of the
variables or subjects.
This method is less expensive, saves time and provides the researcher with more
disposable data to work with. However, it has the problem of data accuracy as important
information may be missing from previous research since the researcher has no control
over the data collection process.
47. COLLECTING DATA
TACTICS:
SURVEY METHOD
The survey method is the most common method of correlational research; especially in
fields like psychology. It involves random sampling of the variables or the subjects in the
research in which the participants fill a questionnaire centered on the subjects of interest.
This method is very flexible as researchers can gather large amounts of data in very little
time. However, it is subject to survey response bias and can also be affected by biased
survey questions or under-representation of survey respondents or participants.
These would be properly explained under data collection methods in correlational research.
50. READING ABOUT AND
UNDERSTANDING MULTIVARIATE
ANALYSES
TACTICS:
A correlation coefficient is an important value in correlational research that indicates
whether the inter-relationship between 2 variables is positive, negative or non-existent. It is
usually represented with the sign [r] and is part of a range of possible correlation
coefficients from -1.0 to +1.0.
The strength of a correlation between quantitative variables is typically measured using a
statistic called Pearson’s Correlation Coefficient (or Pearson’s r). A positive correlation is
indicated by a value of 1.0, a perfect negative correlation is indicated by a value of -1.0
while zero correlation is indicated by a value of 0.0.
It is important to note that a correlation coefficient only reflects the linear relationship
between 2 variables; it does not capture non-linear relationships and cannot separate
dependent and independent variables. The correlation coefficient helps you to determine
the degree of statistical relationship that exists between variables.
58. Conclusion
Correlational research enables researchers to establish the statistical pattern
between 2 seemingly interconnected variables; as such, it is the starting point
of any type of research. It allows you to link 2 variables by observing their
behaviors in the most natural state.
Unlike experimental research, correlational research does not emphasize the
causative factor affecting 2 variables and this makes the data that results from
correlational research subject to constant change. However, it is quicker,
easier, less expensive and more convenient than experimental research.