Psychology research methods aim to test hypotheses scientifically. Researchers must account for biases that skew logic, such as hindsight bias, overconfidence, and the Barnum effect. There are two main research methods: experimental and correlational. Experimental research manipulates an independent variable to determine its causal effect on a dependent variable, while controlling for confounding variables through random assignment and double-blind procedures. Correlational research observes relationships between variables without manipulating them. Proper research requires following ethical guidelines to protect human and animal subjects.
Babitha's Note on Research Problem & ObjectivesBabitha Devu
A research problem statement is an enigmatic stage for an emerging scholar. This presentation will help to brush up your skills when you state a good research question.
Babitha's Note on Research Problem & ObjectivesBabitha Devu
A research problem statement is an enigmatic stage for an emerging scholar. This presentation will help to brush up your skills when you state a good research question.
An overview of, and introduction to, survey-based research in the social sciences.
http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Survey_research
A Research problem is a problem that a researcher wants to solve moreover, it is an issues or a concern that an investigator / researcher presents and justifies in a research study.
It describes the types of research, differences between quantitative and qualitative research and gives an introduction to Participatory Rural Appraisal tools
How Teaching UX is One Giant Participatory Design ExperimentTricia Okin
Talk given at the UX Antwerp Meet Up - 05-26-2015
Teaching UX (and anything in general) is a large exercise in understanding how others learn and building empathy towards them. You’re teaching people from 24 years old a few years out of university all the way up to 50-something years old and whom are lawyers. You have to be able to make people comfortable enough to acknowledge what they don’t know and not be ashamed of learning. We’ll go through the fun part of teaching and the dark side while translating teaching methods into ways to help your clients understand your design process.
Experiment Design - strategy and markets determine what you should testFirmhouse
Slides from our talk at Leanconf 2016 in Manchester. Get in touch if you have any questions regarding our talk or this topic. The Experiment Design guide can be found here: http://bit.ly/experiment-design
An overview of, and introduction to, survey-based research in the social sciences.
http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Survey_research
A Research problem is a problem that a researcher wants to solve moreover, it is an issues or a concern that an investigator / researcher presents and justifies in a research study.
It describes the types of research, differences between quantitative and qualitative research and gives an introduction to Participatory Rural Appraisal tools
How Teaching UX is One Giant Participatory Design ExperimentTricia Okin
Talk given at the UX Antwerp Meet Up - 05-26-2015
Teaching UX (and anything in general) is a large exercise in understanding how others learn and building empathy towards them. You’re teaching people from 24 years old a few years out of university all the way up to 50-something years old and whom are lawyers. You have to be able to make people comfortable enough to acknowledge what they don’t know and not be ashamed of learning. We’ll go through the fun part of teaching and the dark side while translating teaching methods into ways to help your clients understand your design process.
Experiment Design - strategy and markets determine what you should testFirmhouse
Slides from our talk at Leanconf 2016 in Manchester. Get in touch if you have any questions regarding our talk or this topic. The Experiment Design guide can be found here: http://bit.ly/experiment-design
This presentation is about a lecture I gave within the "Green Lab" course of the Computer Science master program, of the Vrije Universiteit Amsterdam.
http://www.ivanomalavolta.com
FDA’s emphasis on quality by design began with the recognition that increased testing does not improve product quality (this has long been recognized in other industries).In order for quality to increase, it must be built into the product. To do this requires understanding how formulation and manufacturing process variables influence product quality.Quality by Design (QbD) is a systematic approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.
This presentation - Part VI in the series- deals with the concepts of Design of Experiments. This presentation was compiled from material freely available from FDA , ICH , EMEA and other free resources on the world wide web.
Introductory Psychology: Research DesignBrian Piper
lecture 3 from a college level introduction to psychology course taught Fall 2011 by Brian J. Piper, Ph.D. (psy391@gmail.com) at Willamette University, includes correlation and experiments
Clinical trials are about comparability not generalisability V2.pptxStephenSenn3
It is a fundamental but common mistake to regard clinical trials as being a form of representative inference. The key issue is comparability. Experiments do not involve typical material. In clinical trials; it is concurrent control that is key and randomisation is a device for calculating standard errors appropriately that should reflect the design.
Generalisation beyond the clinical trial always involves theory.
Clinical trials are about comparability not generalisability V2.pptxStephenSenn2
Lecture delivered at the September 2022 EFSPI meeting in Basle in which I argued that the patients in a clinical trial should not be viewed as being a representative sample of some target population.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
2. Why do we have to learn this
stuff?
Psychology is first and foremost a science.
Thus it is based in research.
Before we delve into how to do research, you should be
aware of three hurdles that tend to skew our logic.
3. Hindsight Bias
• The tendency to
believe, after
learning the
outcome, that you
knew it all along.
Monday Morning
Quarterbacking!!!
After the Chris Brown/Rihanna
incident….my friend said she
knew Chris Brown was a violent
kid!!! Did she really?
4. Overconfidence
• We tend to think we
know more than we do.
• 82% of U.S. drivers consider
themselves to be in the top 30% of
their group in terms of safety.
• 81% of new business owners felt
they had an excellent chance of
their businesses succeeding. When
asked about the success of their
peers, the answer was only 39%.
(Now that's overconfidence!!!)
5. The Barnum Effect
• It is the tendency for
people to accept very
general or vague
characterizations of
themselves and take
them to be accurate.
6. Applied V. Basic Research
• Applied Research
has clear, practical
applications.
• YOU CAN USE IT!!!
• Basic Research
explores questions
that you may be
curious about, but
not intended to be
immediately used.
Studying how
kissing changes
when you get
older is
interesting…but
that’s about it.
Research on therapies for drug addicts has
a clear purpose.
8. Hypothesis
• Expresses a
relationship between
two variables.
• A variable is anything
that can vary among
participants in a study.
• Participating in class
leads to better grades
than not participating.
9. Independent Variable
• Whatever is being
manipulated in the
experiment.
• Hopefully the
independent variable
brings about change.
If there is a drug in an
experiment, the
drug is almost always
the independent
variable.
10. Dependent Variable
The dependent variable
would be the effect
of the drug.
• Whatever is being
measured in the
experiment.
• It is dependent on the
independent variable.
11. Operational Definitions
• Explain what you mean
in your hypothesis.
• How will the variables
be measured in “real
life” terms.
• How you
operationalize the
variables will tell us if
the study is valid and
reliable.
Let’s say your hypothesis
is that chocolate causes
violent behavior.
• What do you mean by
chocolate?
• What do you mean by
violent behavior?
12. Sampling
• Identify the
population you want
to study.
• The sample must be
representative of
the population you
want to study.
• GET A RANDOM
SAMPLE.
• Stratified Sampling
13. Experimental Method
• Looking to prove
causal relationships.
• Cause = Effect
• Laboratory v. Field
Experiments
Smoking causes health issues.
14. Beware of
Confounding Variables
If I wanted to prove that
smoking causes heart
issues, what are some
confounding variables?
• The object of an
experiment is to prove
that A causes B.
• A confounding variable
is anything that could
cause change in B, that
is not A.
Lifestyle and family
history may also
effect the heart.
15. Random Assignment
• Once you have a
random sample,
randomly assigning
them into two groups
helps control for
confounding variables.
• Experimental Group v.
Control Group.
• Group Matching
16. Hawthorne Effect
• But even the control
group may
experience changes.
• Just the fact that
you know you are in
an experiment can
cause change.
Whether the lights were brighter or
dimmer, production went up in the
Hawthorne electric plant.
17. Experimenter Bias
• Another confounding
variable.
• Not a conscious act.
• Double-Blind
Procedure.
19. Correlational Method
• Correlation
expresses a
relationship between
two variable.
• Does not show
causation.
As more ice cream is eaten,
more people are murdered.
Does ice cream cause murder, or murder cause people to eat ice cream?
20. Types of Correlation
Positive Correlation
• The variables go in
the SAME direction.
Negative Correlation
• The variables go in
opposite directions.
Studying and
grades hopefully
has a positive
correlation.
Heroin use and
grades probably has
a negative
correlation.
21. Survey Method
•Most common type of
study in psychology
•Measures correlation
•Cheap and fast
•Need a good random
sample
•Low-response rate
22. Naturalistic Observation
• Watch subjects in their
natural environment.
• Do not manipulate the
environment.
• The good is that there is
Hawthorne effect.
• The bad is that we can
never really show cause
and effect.
23. Correlation Coefficient
• A number that
measures the
strength of a
relationship.
• Range is from -1 to +1
• The relationship gets
weaker the closer you
get to zero.
Which is a stronger
correlation?
• -.13 or +.38
• -.72 or +.59
• -.91 or +.04
24. Case Studies
• A detailed picture of
one or a few
subjects.
• Tells us a great
story…but is just
descriptive
research.
• Does not even give
us correlation data.
The ideal case study is John and
Kate. Really interesting, but what
does it tell us about families in
general?
25. Statistics
• Recording the
results from our
studies.
• Must use a common
language so we all
know what we are
talking about.
26. Descriptive Statistics
• Just describes sets
of data.
• You might create a
frequency distribution.
• Frequency polygons or
histograms.
27. Central Tendency
• Mean, Median and Mode.
• Watch out for extreme scores or outliers.
Let’s look at the salaries of the
employees at Dunder Mifflen Paper
in Scranton:
$25,000-Pam
$25,000- Kevin
$25,000- Angela
$100,000- Andy
$100,000- Dwight
$200,000- Jim
$300,000- Michael
The median salary looks good at
$100,000.
The mean salary also looks good at
about $110,000.
But the mode salary is only $25,000.
Maybe not the best place to work.
Then again living in Scranton is kind
of cheap.
28. Normal Distribution
• In a normal
distribution, the
mean, median and
mode are all the
same.
29. Distributions
• Outliers skew
distributions.
• If group has one high
score, the curve has a
positive skew
(contains more low
scores)
• If a group has a low
outlier, the curve has
a negative skew
(contains more high
scores)
30. Other measures of variability
• Range: distance from
highest to lowest
scores.
• Standard Deviation:
the variance of scores
around the mean.
• The higher the
variance or SD, the
more spread out the
distribution is.
• Do scientists want a
big or small SD?
Shaq and Kobe may both
score 30 ppg (same mean).
But their SDs are very
different.
31. Scores
• A unit that measures
the distance of one
score from the
mean.
• A positive z score
means a number
above the mean.
• A negative z score
means a number
below the mean.
33. Inferential Statistics
• The purpose is to
discover whether the
finding can be applied to
the larger population
from which the sample
was collected.
• T-tests, ANOVA or
MANOVA
• P-value= .05 for
statistical significance.
• 5% likely the results are
due to chance.
34. APA Ethical Guidelines for
Research
• IRB- Internal Review
Board
• Both for humans and
animals.
35. Animal Research
• Clear purpose
• Treated in a humane
way
• Acquire animals
legally
• Least amount of
suffering possible.
36. Human Research
• No Coercion- must
be voluntary
• Informed consent
• Anonymity
• No significant risk
• Must debrief