Writing in Psychology- Test 2

Major Sections of an APA style research paper

Title pages, Abstract (normally), Introduction, Method, Results, Discussion, References, Appendices.

Title Page, References, Appendices

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Abstract

the general overview of the paper

Introduction

General Topic, Prior Research,New information, Hypothesis

Method

participants, materials, how study was conducted

Results & Discussion

Results: The statistics, numbers, tells what was found

Discussion: Breaks down the numbers, tells what was found, limitations, future research

Notecard Outline

Heading on the Topic-Title

Letter on Top- Indicates Source

Concise notes, remember to indicate citations, and questions

Direct Quotes(carefully written) & Numbers for specific quotes

Organizing Introduction- Funnel Approach

Introduce topic, Justify Topic, Introduce Variables, Justify Variables, Thesis

Organizing Body Paragraphs

Each Section must forecast thesis’

ELEMENTS OF STYLE

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Scientific Literacy

The knowledge and understanding of scientific concepts and processes required for personal decision making, participation in civic and cultural affairs and economic productivity

scientific v. popular knowledge

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science v. pseudoscience

science- is data drive, objective(non-biased), verifiable, and public, looks at all data even if it disagrees with your hypothesis

pseudoscience- accepts anecdotes, coincidence, isolated, non-generalizable, believes that the unexplained is proof, uses confirmation bias unverifiable claims from the past as proof, claims of origination in exotic places

antidotes- quick fix

1.        Refuse to fall for the everybody knows syndrome

·      What if it’s wrong

·      What if the answer is based on wrong evidence

·      Look at the research for ourselves

Identify the source

·      Where did the information come from

·      Who, what, when, where, and why

·      Check the credibility of the journal

Look Carefully at the numbers

·      Think statistics

·      Correlation (to high could cause problems-close to +1)

Learn to Identify the sources that you can trust

time, trail, and error

Look for Bias

Look for ways that the author could steer you in one direction, before you trust

Pratical v. Statistical Significance

Practical- does the difference make a meaningful impact on people’s lives(this has more to do with effect size)

Statistical-

·< .05(know the meaning)

·      P. Alpha- p<.01

·      .10(special cases, realized)

Even if you agree with the findings still think about it critical thinking

·      What if we are actually finding the wrong thing

·      Take your time and allow yourself to be critical thinking

What should we be wary of when reading research studies

tenacity, personal experience, authority, anecdotes

tenacity

holding on to any idea despite what the data says

-              Must be objective

Not to close to home, just in case it goes against their hypothesis

personal experience

can bias the hypothesis and data collection

-              Can be an isolated event

-              Results must be generalizable

-              Remember science is objective

authority

when a person in authority tells you something

-              Could be personal or famous

-              Always look data yourself, don’t take someone else’s word for it

-              Should have the respect to bow out, if you don’t know the field/ can’t offer good advice

scientific approach includes

evaluating sources, respectfully disagreeing, critical evaluation, breakthroughs

evaluating sources

look at measurements, do they report reliability and validity, look at the questionaries. Remember sometimes items are written to be biased.

Disagreeing doesn’t mean you are being disagreeable.

-              Figure out why you are disagreeing

-              Disagreeing is great for scientific research

-              No ad hominem- don’t attack the person

Be wary of breakthroughs

watch and wait to see what happens in the future because it  could be a fluke or one time deal

what constitutes a good statistic?

data based, well defined, accurate, sound methodology, appropriate samples

Well defined

something that seems clear, but in reality can’t easily be defined (most of the time it isn’t). We must have clear definitions

appropriate for the argument

sometimes you may have to change something for your argument to fit your data. Or change how you are looking at the numbers

Accurate as possible but not precise to where it won’t make sense

False sense of precisions- Don’t force participants to make something up. Ask questions they can easily answer. Data should not be based on precision(ex: how many)

sound methodology

the more analysis you conduct, the more likely you are to find something significant.

P value- sometimes you have to make it stricter(p <.01)

what constitutes a good samples

· reasonably large and representative of the population you wish to describe

• First figure out who you are trying to describe

• A reasonable number based on that population(10%)

• Avoid to large

some ways statistics can deceive

1. Law of Small Numbers- saying what is true for a small sample is true for the larger population

2. Correlation v. Causation- can’t assume this relationship(three variable problem)

Regression to the mean

normal people or situations that show extreme scores at one point will more likely revert to the mean the next time. People vary from day to day and everyone is different

-              Outliers

-              If we measure only one time, we need to be vary careful. The data could end up wrong

Confusing figures and gee whiz graphs

people use graphs to shift your point of view by changing the data on the graphs.

·      Chose numbers for clarity, not to make your point

·      Is data distracting? These can mislead

·      Have they exaggerated the scale or differences

selective time frames

using time frame to make your point

·      Why would you pick some time frames over others

·      Always look at the larger time frames. Long term

numbers

1.        Means- take out outliers. Senstive

2.        Median- can’t use for calculations. Not actual math  

3.        Standard Deviation- the variance, how much variation does the group have. Are groups comparable?  

antidotes

1.        Are the measures valid in the first place? Are we missing important concepts? Do the numbers make sense?

2.        Are the measurements stated in units that could be understood in everyday life?

3.        Are the averages unreasonable- considered sloppy research (important to catch). Do you have contact with every number/ each data point

4.        Is the variability too large?- How can we make an statement about group without ignoring the data. Don’t over simplify where you aren’t accurately depicting the data

 

probability

in real life we can’t make guarantees based of research because research is based of probability 

Plagiarism

When people take credit for thoughts, words, images, musical passages, or ideas that were originally created by someone else

Intentional Plagiarism

not fooling yourself, you know you aren’t doing their own work

examples include: Copy and paste Getting help from a tutor,Self-plagiarism, If its word for word, the same structure, and not in quotations is plagiarism

Unintentional Plagiarism

not as clear, vaguely remember

-              Always find a source, even if you know a fact

-              Always use quotations, page number, paragraph number

-              Still counts

examples: Lab Partner homework, to much help from others, the writer may not remember having read the material somewhere beforehand, or may not be knowledgeable about a particular citation style

How to avoid Plagiarism

1. On your own-Give yourself time, really read and digest information, take notes in own words.

2. From others- Don’t share passwords, don’t share work, communicate with faculty members throughout the semester about what you are working on

Fabrication

Inserting false information in your data, reference list, in-text citations, purposely misinterpreting/misquoting a source

self plagiarism

Using previous work for a different class without approval (it is up to professor discretion, but it is generally not allowed

What can occur during plagiarism(Harvard)

1. Reflects poorly on your character.

2. Damages your education and the learning process.

3. Cheating in any form can have serious consequences, at any point in life.

Plagiarism can be looked at differently- students and teachers

·      Really know what each article is saying from the perspective of the research hypothesis

·      Don’t share work- ever

·      Save work- different names

descriptive statistics

describing

inferential statistics

using the numbers to go beyond data and hypothesis

generalizability

can you apply the results outside your study

·      .05- 5/100 saying there is a difference when there isn’t one (taking a chance). Small margin  

·      Rather not give people the wrong data and inflict harm

operational definition

how someone measures and defines their variable

·      Ex: if the variable is academic success, how do they define academic success.

·      How do know what the right answer is, there is not a right answer. So we have to draw that conclusion and research

understanding uncertainty and probability

·      Probability (p value)

·      Results always vary

law of inevitability

even though any single outcome might be unlikely, something will inevitability happen

example: A medicine could help 95% of people, are you the five percent that will not get help

 Probability and statistics look at the whole, but a single event could surprise you

more about measure

accuracy/ validity, precision, reliability(consitency), type 1 and 2 error

accuracy and validity

are you measuring what you say are measuring

precision and reliability

· to have reliability you need precise measurement

· Reliability is consistency

type 1 and 2 errors

·      Type 1 error- addressed by P value, says there is a difference, but there isn’t one(alpha)  

·      Type 2 error is there is no difference but there is one(beta- less problematic)

illusions

sensory, sensor, and other

interpretation of sensory input

having a desire and interpret situations differently

inattention blindness

always be sure to repeat to lesson errors

inability to mulitask

we aren’t as good at dividing attention that we think we are, if its important focus on one thing at a time

patternicity

 human beings naturally find patterns, we can make unclear things “clear.” Or finding a meaningful pattern if there isn’t one

baseline measure

make sure to find what exists before researching

false consensus effect

  occurs when we have an exaggerated view of the extent to which others share our opinions and behaviors.

-              Politics

-              Not everyone agrees with you

-              Most likely happens when we fail from a task OR when we engage in an undesirable behavior

-              Over generalize

representative

how representative is your sample of the population

·      Be aware of SLOP- self-selected listener poll(happens when people select themselves in the study) can make your study biased

·      External validity

cognitive shortcuts

availbity heuristics, framing effect, dyrationalia

availability heuristics

taking the information most available to us and think that the answer

·      Don’t apply the representativeness of a sample, results don’t apply to everybody

Framing effect

the way we present or frame the context of the consequence of a decision or problem may effect answers(be careful how we word). The research can be faulty

dyrationalia

we are reason with limited resources, lead to stupid decisions

·      Hindsight bias- we avoid by being honest

fundamental attribution error

when something bad happens to me external reasons are at fault. When something bad happens to someone else, its internal. BUT we something good happens, internal. When something bad happens to someone else its external

Natural human tendency- to justify ourselves

natural human tendency

·      Dispositional- internal

·      Situational- external

·      Self serving bias- justifiable

·      Self effacement- humble

cognitive dissonance

when actions and thoughts don’t match (behavior rules)

belief perseverance

having a belief that you stick by no matter what