UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
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Research and scientific method - Research Methodology - Manu Melwin Joymanumelwin
For a clear perception of the term research, one should know the meaning of scientific method. The two terms, research and scientific method, are closely related.
Research Design: Quantitative, Qualitative and Mixed Methods DesignThiyagu K
A Research Design is simply a structural framework of various research methods as well as techniques that are utilized by a researcher. This presentation slides explain the resign design of quantitative, qualitative, and mixed-method design.
Sampling for Quantities & Qualitative Research Abeer AlNajjar.docxanhlodge
Sampling for Quantities & Qualitative Research
Abeer AlNajjar
1
Population
Target group (universe in texts)
Census (to study every member of a population)
because measuring every member of a population usually is not feasible most researchers employ a Sample
Sample ( a subgroup of the population)
2
Communication researchers are interested in a population (also called a universe when applied to texts) of communicators, all the people who posses a particular characteristic, or, in the case of those who study texts, all the messages that share a characteristic of interest.
The population of interest to researchers (often called the target group) might be members of a business, communication majors at a university, all students at a university, all people living in a city, all eligible voters in a country.
Texts ( editorials published in a specific newspaper for a week, or a large universe such as every editorial published In every newspaper in the UAE, or even larger such as all persuasive messages).
The best way to generalize to a population is to study every member of a population (Census)
If every member is studied, we know, by definition, the population’s response at the point in time the study was done
Sample
The results from the sample are then generalized back to (used to represent) the population
Representative sample ( population validity)
Its similarity to its parent population
3
The results from the sample are then generalized back to (used to represent) the population). For such generalization to be valid (demonstrate population validity), the sample must be representative of its population. That is, it must accurately approximate the population.
Types of sampling
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
Nonrandom sampling (nonprobability sampling)
Is what ever researchers do instead of using procedures that ensure that each member of a population has an equal chance of being selected
Sampling error
Is a number that express how much the characteristic of a sample probably differ from the characteristics of a population
5
There are 2 different types of sampling procedures, and differ in terms of how confident we are about the ability of the selected sample to represent the population from which it is drawn
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
By giving everyone an equal chance , random sampling eliminates the danger of researchers biasing the selection process because of their own opinions or desires. By eliminating bias, random sampling provides the best assurance that the same characteristics of the population exist in the sample, and, therefore, that the sample represents the population.
Nonrandom sampling: it sometimes is .
Survey research is based on the simple idea that if you want to find out what people think about some topic, just ask them.
A survey is a structured set of questions or statements given to a group of people to measure their attitudes, beliefs, values, or tendencies to act.
The survey is a non-experimental, descriptive research method. Surveys can be useful when a researcher wants to collect data on phenomena that cannot be directly observed.
The purpose of the survey may be to produce statistics- that is, quantitative, (numerical descriptions of some aspects of the study population) or to generate the themes- that is, qualitative.
The main way of collecting information is by asking people questions;(in the form of questionnaire or interview) their answers constitute the data to be analyzed.
Survey researcher is primarily interested in assessing the characteristics’ of the whole population. So it is ideal to study every member of the population, but it is not feasible always to study every member of the population. So the information is collected about only a fraction of the population- that is, a sample.
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
SURVEY RESEARCH- Advance Research MethodologyRehan Ehsan
This Presentation states the details of Survey Research for students to get help in advance research methodology. Rearchers may also get help from this work.
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It is a conceptual structure within which research is conducted; it constitutes the blueprint for the collection, measurement and analysis of data.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. 2
Cross-Sectional Study Design
Cross-Sectional Survey Design A cross-
sectional survey collects data to make
inferences about a population of interest
(universe) at one point in time.
Cross-sectional surveys have been described
as snapshots of the populations about which
they gather data.
Cross-sectional surveys may be repeated
periodically.
3. 3
Cross-Sectional Study Design
Cross-sectional surveys can thus be contrasted
with panel surveys, for which the individual
respondents are followed over time.
Cross-sectional surveys can be conducted using
any mode of data collection, including telephone
interviews, face-to-face interviews, mailed
questionnaires.
4. 4
Defining Characteristics
Takes place at a single point in time
Does not involve manipulating variables
Allows researchers to look at numerous things at once (age,
income, gender)
Often used to look at the prevalence of something in a given
population
5. 5
Topics of Cross-Sectional Study Design
The health needs of a Community.
The Attitudes of students towards the
facilities available in their library.
Consumer satisfaction with their products.
6. 6
Limitation Cross-Sectional Study Design
While the design sounds relatively simple, finding participants
who are very similar except in one specific variable can be
difficult.
Groups can be affected by cohort differences that arise from the
particular experiences of a unique group of people.
Individuals born in the same time period may share important
historical experiences, while people born in a specific geographic
region may share experiences limited solely to their physical
location.
7. 7
Longitudinal Study Design
Longitudinal Study Any social or developmental research
involving collection of data from the same individuals (or
groups) across time.
Observing change in these individuals gives a better basis
for causal inference than a cross-sectional study, because of
the temporal sequencing involved. In this sense the
longitudinal study is a form of ‘quasi-experimental design’.
8. 8
Longitudinal Study Design
Longitudinal studies can range from repeated measures of a
treatment group and a control group measured at two time
points in an experimental design, to a large-scale long-term
birth cohort study, involving follow-ups of the same sample
of individuals from birth through to adult life.
Longitudinal studies allow social scientists to distinguish
short from long-term phenomena, such as poverty. If the
poverty rate is 10% at a point in time, this may mean that
10% of the populations are always poor, or that the whole
population experiences poverty for 10% of the time. It is not
possible to conclude which of these possibilities is the case
using one-off cross-sectional study.
9. 9
Limitation of Longitudinal studies
longitudinal studies require enormous amounts of time and are
often quite expensive.
These studies often have only a small group of subjects, which
makes it difficult to apply the results to a larger population.
Another problem is that participants sometimes drop out of the
study, shrinking the sample size and decreasing the amount of
data collected.
10. 9
Limitation of Longitudinal studies
longitudinal studies require enormous amounts of time and are
often quite expensive.
These studies often have only a small group of subjects, which
makes it difficult to apply the results to a larger population.
Another problem is that participants sometimes drop out of the
study, shrinking the sample size and decreasing the amount of
data collected.