This presentation was intended for employees of Dubai Municipality. It is about how to use SPSS and other statistical data analysis tools like Excel and Minitab in data analysis. The course presented some statistical concepts and definitions.
univariate and bivariate analysis in spss Subodh Khanal
this slide will help to perform various tests in spss targeting univariate and bivariate analysis along with the way of entering and analyzing multiple responses.
In the presentation, hypothesis test has been explained with scrap. Tree diagram is there to understand in which situation u can apply which parametric test
Commonly Used Statistics in Survey ResearchPat Barlow
This is a version of our "commonly used statistics" presentation that has been modified to address the commonly used statistics in survey research and analysis. It is intended to give an *overview* of the various uses of these tests as they apply to survey research questions rather than the point-and-click calculations involved in running the statistics.
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Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
This presentation was intended for employees of Dubai Municipality. It is about how to use SPSS and other statistical data analysis tools like Excel and Minitab in data analysis. The course presented some statistical concepts and definitions.
univariate and bivariate analysis in spss Subodh Khanal
this slide will help to perform various tests in spss targeting univariate and bivariate analysis along with the way of entering and analyzing multiple responses.
In the presentation, hypothesis test has been explained with scrap. Tree diagram is there to understand in which situation u can apply which parametric test
Commonly Used Statistics in Survey ResearchPat Barlow
This is a version of our "commonly used statistics" presentation that has been modified to address the commonly used statistics in survey research and analysis. It is intended to give an *overview* of the various uses of these tests as they apply to survey research questions rather than the point-and-click calculations involved in running the statistics.
Bio statistics 2 /certified fixed orthodontic courses by Indian dental academy Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Parametric and non parametric test in biostatistics Mero Eye
This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test
Chapter 8
Sampling
Sampling
Sampling involves decisions about who or what will be tested, observed, or interviewed in your study (Morse, 2007)
Key questions to address:
Who should and should not be included?
How many should be included?
Probability
Probability is the likelihood that an event or a condition will occur
You can express probability in terms of the chance the event will occur or in percentages
Levels of Significance
Levels of significance are the difference that will be accepted as too large to be attributed to chance
These levels are set by the researcher at the outset of a study
Probability Samples
Probability samples are formed to ensure that each subject has an equal chance of being included so an unbiased sample can be used
Probability Samples
A sampling design explains how the subjects are chosen and should include:
Number of subjects
How they will be assessed, screened, and selected
Inclusion and exclusion criteria
Probability Samples
Random selection is accomplished by having:
Identification of all possible participants
Every potential participant is given an equal chance of being selected
Probability Samples
Variations of random sampling include:
Stratified: randomly select from each stratum
Cluster: sample groups rather than individuals
Multistage: sample from multiple sets of clusters
Nonprobability Sampling
Reasons why researchers use nonprobability samples are:
Limited resources for developing an accurate sampling frame or purchase lists of potential subjects
Information needed to identify all potential subjects is not available
Nonprobability Sampling
Reasons why researchers use nonprobability samples are:
Limited number of subjects
Subjects are difficult to find or difficult to persuade to participate in study
Subjects do not complete study
Experimental mortality
Nonprobability Sampling
Types of nonprobability samples include:
Quota sampling: select a specified number of participants from each group
Convenience sampling: enroll those who are available
Snowball network or referral sampling: begin with known individuals and ask them to refer others who meet selection criteria
Tracking and Reporting
Sample Development
In order to improve the reporting of randomized controlled trials (RCTs), the Consolidated Standards of Reporting Trials (CONSORT) were developed
A flow diagram that can be used for tracking sample development
CONSORT Flow Diagram
Source: Altman, D.G., Schulz, K.F., Moher, D., Egger, M.. Davidoff, F., Elbourne, D., Gøtzsche, P.C., & Lang, T. (2001). The revised CONSORT statement for reporting randomized trials: Explanation and elaboration. Annuals of Internal Medicine; 134(8), 663-694.
Example of Flowchart
Source: Buchbinder, R., Osborne, R.H., Ebeling, P. R., Wark, J.D., Mitchell, P.M., Wriedt, C., Graves, S.D., Staples, M.P., & Murphy, B. (2009). A randomized trial of vertebroplasty for painful osteoporotic vertebral factures. The New England Journal of Medicine, 361 ...
Need a nonplagiarised paper and a form completed by 1006015 before.docxlea6nklmattu
Need a nonplagiarised paper and a form completed by 10/06/015 before 7:00pm. I have attached the documents along the rubics that must be followed.
Coyne and Messina Articles, Part 2 Statistical Assessment
Details:
1) Write a paper of 1,000-1,250 words regarding the statistical significance of outcomes as presented in Messina's, et al. article "The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals."
2) Assess the appropriateness of the statistics used by referring to the chart presented in the Module 4 lecture and the resource "Statistical Assessment."
3) Discuss the value of statistical significance vs. pragmatic usefulness.
4) Prepare this assignment according to the APA guidelines found in the APA Style Guide located in the Student Success Center. An abstract is not required.
5) This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
·
The research question itself
·
The sample size
·
The type of data you have collected
·
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generaliza.
Statistics What you Need to KnowIntroductionOften, when peop.docxdessiechisomjj4
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
The research question itself
The sample size
The type of data you have collected
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generalizations, and possibilities regarding the relationship between the independent variable and the dependent variable to indicate how those inferences answer the research question. Researchers can make predictions and estimations about how the results will fit the overall population. Statistics can also be described in terms of the types of data they can analyze. Non-parametric statistics can be used with nominal or ordinal data, while parametric statistics can be used with interval and ratio data types.
Types of Data
There are four types of data that a researcher may collect.
Nominal Data Sets
The Nominal data set includes simple classifications of data into categories which are all of equal weight and value. Examples of categories that are equal to each other include gender (male, female), state of birth (Arizona, Wyoming, etc.), membership in a group (yes, no). Each of these categories is equivalent to the other, without value judgments.
Ordinal Data Sets
Ordinal data sets also have data classified into categories, but these categories have some form or order or ranking attached, often of some sort of value / val.
Clinical Research Statistics for Non-StatisticiansBrook White, PMP
Through real-world examples, this presentation teaches strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.
Marketing Research Project on T test and Sample Designing, Detail Analysis of all the aspect of T test and usage of all the tools for finding out the different variants.
INTRODUCTION
DEFINITION
HYPOTSIS
ANALYSIS OF QUANTITATIVE DATA
STEPS OF QUANTITATIVE DATA ANALYSIS.
STEPS OF QUANTITATIVE DATA ANALYSIS.
INTERPRETATION OF DATA
PARAMETRIC TESTS
Commonly Used Parametric Tests.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
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.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
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.
2. Sampling Theory Concepts
Population
Target Population
Accessible Population
Elements of a Population
Sampling Criteria
3. Sampling Criteria
Characteristics essential for
inclusion or exclusion of
members in the target
population
Between the Ages of 18 & 45
Ability to speak English
Dx of diabetes within last month,
or
No Hx of chronic illness
4. Sampling Theory Concepts
Sampling Plans or
Methods
Sampling Error
Random Variation
Systematic Variation
5. Sampling Error
Random Variation
The expected difference in values that
occurs when different subjects from
the same sample are examined.
Difference is random because some
values will be higher and others lower
than the average population values.
6. Sampling Error
Systematic Variation (Bias)
Consequence of selecting
subjects whose measurement
values differ in some specific
way from those of the
population.
These values do not vary
randomly around the
population mean
8. Sampling Theory Concepts
Sample Mortality
Subject Acceptance Rate:
Percentage of individuals
consenting to be subjects
Representativeness
9. Representativeness
Needs to evaluate:
setting
characteristics of the subjects:
age, gender, ethnicity, income,
education
distribution of values
measured in the study
12. Sample Size
Factors influencing sample size
Effect size
Type of study conducted
Number of variables studied
Measurement sensitivity
Data analysis techniques
13. Power Analysis
Standard Power of 0.8
Level of Significance
alpha = .05, .01, .001
Effect Size
.2 Small; .5 Medium; .8 Large
Sample Size
14. Example Sample
A convenient sample of 55 adults
scheduled for first time elective CABG
surgery without cardiac
catheterization, who had not had
other major surgery within the
previous year, and who were not
health professionals met the study
criteria and were randomly assigned
to one of two instruction conditions...
15. Example Sample
Based on a formulation of 80% power, a
medium critical effect size of 0.40 for each of
the dependent variables, and a significance
level of .05 for one-tailed t-tests means, a
sample size of 40 was deemed sufficient to
test the study hypotheses...
16. Example Sample
The study included a convenience
sample of 32 post-op Lung Cancer
patients. A power analysis was
conducted to determine size. A
minimum of 27 subjects was necessary
to achieve the statistical power of 0.8
and a medium (0.5) effect size at the
0.05 level of significance....The
subjects were 25 men and 7 women
with an age range from 18-58 years
(mean = 32.74)....
17. Critiquing the Sample
Were the sample criteria
identified?
Was the sampling method
identified?
Were the characteristics of
the sample described?
18. Critiquing the Sample
Was the sample size identified?
Was the percent of subjects
consenting to participate
indicated?
Was the sample mortality
identified?
Was the sample size adequate?
22. Levels of Measurement
Nominal
data categorized, but no order or zero (ex- gender
numbers)
Ordinal
categories with order, but intervals not necessarily
equal and no zero (ex – pain)
Interval
equal intervals, but no true zero (ex- temp scales)
Ratio
equal intervals with a true zero. These are real
numbers, for things such as weight, volume, length.
24. Likert Scale
How often do you feel in control of
your life?
(1) Never
(2) Seldom
(3) Often
(4) Almost always
25. Age
How old are you?
25-34
35-44
45-54
55 or older
26. Income
1 = under Rs-35,000/ 2 = Rs-35-50,000/ 3 = Rs-50 - 100,000/-
27. What is reliability?
Reliability - is concerned
with how consistently the
measurement technique
measures the concept of
interest.
28. Types of Reliability
Stability -- is
concerned with the
consistency of
repeated measures or
test-retest reliability
29. Types of Reliability
Equivalence -- is focused
on comparing two versions
of the same instrument
(alternate forms reliability)
or two observers (interrater
reliability) measuring the
same event.
30. Types of Reliability
Homogeneity -- addresses the
correlation of various items
within the instrument or
internal consistency;
determined by split-half
reliability or Cronbach’s alpha
coefficient.
47. Process for Quantitative Data
Analysis
• Preparation of the Data for Analysis
• Description of the Sample
• Testing the Reliability of the Instruments
for the Present Sample
• Testing Comparability of Design Groups
• Exploratory Analysis of Data
• Confirmatory Analyses Guided by
Objectives, Questions, or Hypotheses
• Post Hoc Analyses
48. Cleaning Data
Examine data
Cross-check every piece of data with the
original data
If file too large, randomly check for
accuracy
Correct all errors
Search for values outside the appropriate
range of values for that variable.
49. Missing Data
Identify all missing data points
Obtain missing data if at all possible
Determine number of subjects with data
missing on a particular variable
Make judgement - are there enough
subjects with data on the variable to
warrant using it in statistical analyses?
50. Transforming Data
Transforming skewed data so that it is linear
(required by many statistics).
Squaring each value
calculating the square root of each
value
51. Calculating Variables
Involves using values from two or
more variables in your data set to
calculate values for a new variable
to add to the data set.
Summing scale values to obtain
a total score
Calculating weight by height
values to get a value for Body
Mass Index
52. Statistical Tools
Used to allow easy calculation of statistics
Computer-based tools allow rapid analysis but
sometimes too easy
Must still know what each type of test is for and how to
use them
Don’t fall into the trap of using a test just because it is
easy to do now
Many papers appearing with questionable tests just
because a computer program allows the calculation
53. Statistics Exercises
Stat Trek
http://stattrek.com/
Tutorial for exercises
Understand rationale for the selection of each test type.
Be prepared to utilize test if asked, and know major advantages
of each main test.
Miller Text (Chapter 21, Fifth Edition, pgs 753-792)
Material very thorough.
Many little-used tests described.
Read for idea of why other tests are available
Don’t get bogged down in the details
54. Descriptive Statistics
Describes basic features of a data group.
Basis of almost all quantitative data analysis
Does not try to reach conclusions (inferences), only
describe.
Provide us with an easier way to see and quickly interpret
data
55. Descriptive Statistics
Data Types
Based on types of measurement
Measurement scales can show magnitude, intervals, zero point, and
direction
Equal intervals are necessary if one plans any statistical analysis of
data
Interval scales possess equal intervals and a magnitude
Ratio scales show equal intervals, magnitude and a zero point
Ordinal scales show only magnitude, not equal intervals or a zero
point
Nominal data in non-numeric (not orderable) whereas
ordinal data is numeric and can be ordered but not based
on continuous scale of equal intervals
56. Descriptive Statistics
Goal of use is to be able to summarize the data in a way
that is easy to understand
May be described numerically or graphically
Describe features of the distribution
Examples include distribution shape (skewed, normal
(bell-shaped), modal, etc), scale, order, location
57. Descriptive Statistics
Location Statistics
How the data “falls”
Examples would be statistics of central tendency
Mean
Median
Average of numerical data
Σx/n
Midpoint of data values
Value of data where 50% of data values is above and 50% below (if
number of data points is even, then the middle two values are averaged)
Mode
Most frequent data value
May be multi-modal if there is an identical number of max data values
58. Descriptive Statistics
Location Statistics
Data outliers may need to be accounted for and possibly
eliminated
This can be done by trimming or weighting the mean to
effectively eliminate the effect from outliers
59. Descriptive Statistics
Count Statistics
One of the simplest means of expressing an idea
Works for ordinal and nominal data
60. Descriptive Statistics
Statistics of Scale
Measures how much dispersal there is in a data set
(variability)
Example statistics include sample range, variance,
standard deviation (the square root of the variance), SEM
(SD/sq root of N)
Outliers can influence variance and standard deviation
greatly, so try to avoid their use if there are lots of outliers
that can not be weighted out
61. Descriptive Statistics
Distribution Shape Statistics
Determines how far from “normal” the distribution of data
is based on normal distribution shapes (Gaussian)
Skewness measures how “tailed” the data distribution is
(positive to right, negative to left)
Kurtosis measures whether the “tail” is heavy or light
62. Inferential Statistics
Attempts to come to conclusions about a data set that are
not exactly stated by the data (inferred)
Many tests use probability to help determine if data
points to a likely conclusion.
Often used to compare two groups of data to see if they
are ‘statistically different’
Often used to decide whether or not a conclusion one is
trying to reach from the data set is reliable (within
statistical probability)
63. Inferential Statistics
Simplest form is the comparison of average data between
two data sets to see if they are different
Students t-test is often used to compare differences
between 2 groups
Usually one control group and one experimental
Should be only one altered variable in experimental
group
64. Inferential Statistics
Most common inferential statistical tests belong to the
General Linear Model family
Data is based on an equation in which a wide variety of
research outcomes can be described
Problems with these types of analysis tools usually comes
from the wrong choice of the equation used
Errors in the wrong equation used can result in the data
conclusions being biased one way or the other, leading to
accepting or rejecting the null hypothesis wrongly
65. Inferential Statistics
Common Linear Model tests include:
Students t-test
Analysis of variance (ANOVA)
Analysis of covariance (ANCOVA)
Regression analysis
Multivariate factor analysis
66. Inferential Statistics
Type of research design used also determines the
type of testing which can be done:
Experimental analysis
Usually involves comparison of one or more groups against a
control, and thus t-test or ANOVA tests are the most commonly
used
Quasi-experimental analysis
Typically lack a control group, and thus the random analysis that is
usually used to assign individuals to groups
These types of analysis are much more complex to compensate for
the random assignments