This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
Validity:
Validity refers to how well a test measures what it is purported to measure.
Types of Validity:
1. Logic valididty:
Validity which is in the form of theory, statements. It has 2 types.
I. Face Validity:
It is the extent to which the measurement method appears “on its face” to measure the construct of interest.
• Example:
• suppose you were taking an instrument reportedly measuring your attractiveness, but the questions were asking you to identify the correctly spelled word in each list
II. Content Validity:
Measuring all the aspects contributing to the variable of the interest.
Example:
For physical fitness temperature, height and stamina are supposed to be assess then a test of fitness must include content about temperatures, height and stamina.
2. Criterion
It is the extent to which people’s scores are correlated with other variables or criteria that reflect the same construct
Example:
An IQ test should correlate positively with school performance.
An occupational aptitude test should correlate positively with work performance.
Types of Criterion Validity
Concurrent validity:
• When the criterion is something that is happening or being assessed at the same time as the construct of interest, it is called concurrent validity.
• Example:
Beef test.
Predictive validity:
• A new measure of self-esteem should correlate positively with an old established measure. When the criterion is something that will happen or be assessed in the future, this is called predictive validity.
• Example:
GAT, SAT
Other types of validity
Internal Validity:
It is basically the extent to which a study is free from flaws and that any differences in a measurement are due to an independent variable and nothing else
External Validity
• It is the extent to which the results of a research study can be generalized to different situations, different groups of people, different settings, different conditions, etc.
Step by step introduction to scientific methods for juniorsdakter Cmc
A step by step introduction to scientific methods starting from Observation to communicating the result. Followed by an appropriate example for the target group.
Quantitative Methods of Research-Intro to research
Once a researcher has written the research question, the next step is to determine the appropriate research methodology necessary to study the question. The three main types of research design methods are qualitative, quantitative and mixed methods.
Quantitative research involves the systematic collection and analysis of data.
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
Validity:
Validity refers to how well a test measures what it is purported to measure.
Types of Validity:
1. Logic valididty:
Validity which is in the form of theory, statements. It has 2 types.
I. Face Validity:
It is the extent to which the measurement method appears “on its face” to measure the construct of interest.
• Example:
• suppose you were taking an instrument reportedly measuring your attractiveness, but the questions were asking you to identify the correctly spelled word in each list
II. Content Validity:
Measuring all the aspects contributing to the variable of the interest.
Example:
For physical fitness temperature, height and stamina are supposed to be assess then a test of fitness must include content about temperatures, height and stamina.
2. Criterion
It is the extent to which people’s scores are correlated with other variables or criteria that reflect the same construct
Example:
An IQ test should correlate positively with school performance.
An occupational aptitude test should correlate positively with work performance.
Types of Criterion Validity
Concurrent validity:
• When the criterion is something that is happening or being assessed at the same time as the construct of interest, it is called concurrent validity.
• Example:
Beef test.
Predictive validity:
• A new measure of self-esteem should correlate positively with an old established measure. When the criterion is something that will happen or be assessed in the future, this is called predictive validity.
• Example:
GAT, SAT
Other types of validity
Internal Validity:
It is basically the extent to which a study is free from flaws and that any differences in a measurement are due to an independent variable and nothing else
External Validity
• It is the extent to which the results of a research study can be generalized to different situations, different groups of people, different settings, different conditions, etc.
Step by step introduction to scientific methods for juniorsdakter Cmc
A step by step introduction to scientific methods starting from Observation to communicating the result. Followed by an appropriate example for the target group.
Quantitative Methods of Research-Intro to research
Once a researcher has written the research question, the next step is to determine the appropriate research methodology necessary to study the question. The three main types of research design methods are qualitative, quantitative and mixed methods.
Quantitative research involves the systematic collection and analysis of data.
Experimental Research Design - Meaning, Characteristics and ClassificationSundar B N
This ppt contains Experimental Research Design Which covers Meaning, Characteristics and Classification of Experimental Research Design.
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Methodology and EthicsSocial Psychology An Empirical .docxARIV4
Methodology and Ethics
Social Psychology:
An Empirical Science
2
Social Psychology:
An Empirical Science
Results of some experiments may seem obvious
Why?
3
Scientific Method
H ypothesize
O perationalize
M easure
E valuate
R evise/Replicate
4
How are Hypotheses Formulated?
Previous theories and research
Personal observation
HYPOTHESIS: an explicit, testable prediction about the conditions under which an event will occur.
5
Operationalize
Conceptual variable: The general abstract definition of a variable. (the dictionary definition)
Operational definition: The specific procedures for manipulating or measuring a conceptual variable. (concrete application)
6
“Birds of a feather flock together.”
Hypothesis (conceptual)
similar people will be more attracted to each other
Hypothesis (operational)
personality test choose partners
height, age attraction questionnaire
Construct Validity: How well measures in a study reflect the variables they are intended to measure and manipulations in a study reflect the variables they are intended to manipulate.
7
1.Response to a mirror
2. Questionnaire
1. Questionnaire
2. non-verbal behavior
1. time spend staring
2. questionnaire
3. pupil dilation
1. speed running away
2. questionnaire
3. facial expression
Evaluation of the self
Negative feeling based on group membership
Desire between two people
Feeling scared
Self-esteem
Prejudice
Attraction
Fear
Operational
(concrete)
Conceptual (dictionary)
Variables
Click to edit Master text styles
Second level
Third level
Fourth level
Fifth level
8
Scientific Method
H ypothesize
O perationalize
M easure
E valuate
R evise/Replicate
Social psychologists use the same methods as other scientists.
Theories and hypotheses can change dramatically
Researchers often find that collected data indicate findings that are quite disparate from the projected findings
9
Three Measurement Methods
Observational
Goal: Description
Correlational
Goal: Prediction
Experimental
Goal: Answer causal questions
Observational Method
Researcher observes people and systematically records measurements of impressions of their behavior.
11
Observational Method
Ethnography
12
Observational Method
Archival Analysis (Historical Records)
13
Observational Method
Example
Research Question
14
Observational Method
Example
Method
Behaviors are concretely defined before the observation begins
Observer systematically:
Accuracy of observer is assessed
Interjudge reliability
15
Interjudge Reliability
Interjudge Reliability
The level of agreement between two or more people who independently observe and code a set of data.
16
Limits of Observational Method
Certain behaviors difficult to observe
Archival analysis
Does not allow prediction and explanation
17
Advantage:
Disadvantage:
18
Correlational Method
Two or more variables are systematically mea ...
This tutorial corresponds with Module A Lesson 2 and should be completed by students enrolled in Professor Hokerson's Psychology 300 online class at American River College.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Research Methods in Psychology Sampling and Experimental Design
1. Research Methods
in Psychology
Sampling and Experimental
Designs
Friday, 27 January 2012
2. Lesson 12: Research Methods Variables and
Hypothesising
Exam Question:
Read the following research question and respond to
the following
Does drinking alcohol effect reaction time?
a) What is the dependent variable? (1 mark)
b) What is the independent variable? (1 mark)
c) What is a possible extraneous variable? (1 mark)
d) Write an operational hypothesis for this research
question. (3 marks)
Friday, 27 January 2012
3. Model response:
a) DV: Reaction time
b) IV: Alcohol Consumption
c) Many possible responses e.g. age, gender, sleep deprivation,
strength of eye sight, natural skill at task
d) It was hypothesised that Victorian adults aged 20-30 who
drink 3, 375ml bottles of beer, 20 minutes prior to taking the
“Reaction Speed Simulator” test will produce a lower
reaction time (lower percentage score) than those who did
not consume alcohol.
Friday, 27 January 2012
4. Lesson 13: Research Methods: Sampling, Participant Selection and Experimental Designs
OUTCOMES:
Define population
Define sample
Describe the process of sampling procedures including
random, stratified and random stratified
Describe the process of participate allocation to groups
(experimental and control) including random allocation
Discuss the advantages and disadvantages of different
experimental designs including repeated measures, matched
participants and independent groups
Describe the placebo effect and ways of managing its
occurrence
Describe the experimenter effect and ways of managing its
occurrence
Friday, 27 January 2012
5. Sampling
Sampling is the selection of participants for a
research.
Population refers to the group which the research
wishes to draw conclusions from.
The term sample refers to the members of the
population that have been chosen to take part in the
research.
Sampling procedures must ensure that the sample is
representative of the population.
Friday, 27 January 2012
6. Representative Samples
Two techniques are used to
ensure a representative sample:
1)Random Sampling
2)Stratified Sampling and
Stratified Random Sampling.
Friday, 27 January 2012
7. Random Sampling
A sampling procedure in which every member
of the population has an equal chance of
being selected
Examples include:
1) Picking a name out of
a hat
2) Tattslotto
3) Closing my eyes and
selecting a number to
match that number with
student id numbers.
Friday, 27 January 2012
8. Stratified Sampling and
Stratified Random Sampling
Is used when you wish to eliminate the effects of
confounding variables.
The effects of a certain variable can be eliminated as a
possible confounding variable in an experiment.
The variable could be any personal attribute, such as
age, years of education, ethnicity, gender, IQ etc.
Involves six procedures:
Friday, 27 January 2012
9. 1) Identifying a property that we believe may
interfere with the effects of the IV on the value of
the DV.
2) Measuring that property for each member of the
population.
3) Dividing the population into particular strata (groups)
based on the value of that variable.
4) Deciding on the number of participants required for
the experiment.
5) Selecting participants in the same proportions as
exist in the population to make up the sample
(stratified sample).
6) Selecting a random sample from each stratum, in
the same proportions as exist in the population
(stratified random sample).
Friday, 27 January 2012
11. Which to use?
Sophisticated, advanced Psychological
research studies use Stratified Sampling,
however it is very time consuming and
expensive, therefore majority of research
uses random sampling.
More so common, as the name suggests,
Psychological research uses a sample of
convenience, which although is biased is
quick, easy and cheap!
Friday, 27 January 2012
13. Participant Allocation:
Experimental & Control Groups
The experimental method uses two different groups
called the experimental and control groups.
The experimental group are exposed to the IV, known
as the ‘treatment’.
The control group do not receive the treatment (IV).
The purpose of the experimental group is to show the
effects of the IV on the value of the DV.
The purpose of the control group is to form a basis for
comparison with the experimental group.
Friday, 27 January 2012
14. Experimental and Control Group
Allocation
It is super important that all participants have an
equal chance of being in the experimental or control
group. That is Random Allocation.
When there is a large enough sample, both the
experimental and control groups will be equivalent on
all participant characteristics therefore the presence
or absence of the IV is the only difference between
them.
E.G. If we had all males in the experimental group and
all females in the control group, then an obvious
extraneous variable will be gender.
Friday, 27 January 2012
15. Experimental Designs
There are three popular experimental
designs
Repeated Measures Design
Matched Participants Design
Independent Groups Design
Friday, 27 January 2012
16. Repeated Measures Design
(within participants design)
In a repeated measures design participants experience
both the experimental and control groups.
This is possible by conducting the experiment on two
occasions and then comparing the two results.
Friday, 27 January 2012
17. ADVANTAGES:
1)Using the same participants means that confounding
variables that are participant depend are eliminated.
2)Allows for fewer participants to be used than with
other designs.
DISADVANTAGES:
1) Time consuming - drop outs
2) Confounding variables such as Order Effects:
a) Participants may perform better on the task when
doing for a second time (practise effect).
b) Participants may do worse the second time because
of fatigue or boredom.
Friday, 27 January 2012
18. Counterbalancing
Used to overcome order effect.
In counterbalancing, half the participants
will first perform the task with the IV
present (experimental condition) and then
perform the task with the IV absent (control
condition). The other half of the participants
will experience the conditions in the reverse
order.
Friday, 27 January 2012
19. Matched Participants Design
Enables the researcher to identify a variable that is likely
confound and to eliminate the effects of this variable from the
experiment.
Participants can be ranked in accordance with their scores on this
variable and then allocated to the respective groups.
Friday, 27 January 2012
20. E.G. A sports coach developed a new game plan that
would help the team reach the playoffs. He decided to
test this by giving the experimental group the
instruction but not the control group. Because
individual skills would be a confounding variable, he
decided to ‘match’ the groups. The two highest skilled
players will be randomly allocated to either the
experimental or control group, the third and fourth
most skilled will then be randomly allocated to either
and so on and so forth until all players were allocated
to a group resulting in the same mean skill percentage
in both groups.
Friday, 27 January 2012
21. Advantages: The variable on which the
participants are ‘matched’ will not influence
the results because its effects will be the
same in the experimental and control groups.
Disadvantages: It is very time consuming
(and therefore expensive) to find out the
value of this variable for every participant.
Also, if one of the pair drops out, the scores
for the other must also be eliminated.
Friday, 27 January 2012
22. Independent Groups Design
(between participants design)
Allocates participants to the
experimental or control group at random
Friday, 27 January 2012
23. Advantages: The independent groups design
can be done at once and drop-outs are
unlikely.
Disadvantages: The procedure needs a large
number of participants to ensure that the
spread of participant variables in the sample
will match the spread in the population.
Friday, 27 January 2012
24. This may effect the DV
Participants
resulting in invalid
expectations
results
PLACEBO EFFECT
Can be eliminated by using single blind procedure,
that is participants are unaware of which group
they are in.
Friday, 27 January 2012
25. For example: Experimenter
Experimenter treats participants differently
influences depending on the group they are
in which in turn influences the
experiment
behaviour of the participant and
effects the results
EXPERIMENTER EFFECT
Can be eliminated by using a double blind procedure, that is,
neither the experimenter or the participants are aware of
whether they are in the experimental or control group
Friday, 27 January 2012