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75 Cards in this Set
- Front
- Back
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Karl Landsteiner |
- Discovered ABO blood type in 1901 - Him and Alexander Wiener discovered RH factor (positive and negative blood types) |
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Why is change good? |
It is important to know that we don’t always know the truth, but we can gather more of an understanding as we continue to learn new information |
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Nonscientific ways of knowing |
Intuition, superstition, Authority, and rational-inductive argument |
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Scientific ways of knowing |
Scientific method |
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Superstition |
- A belief that violates the known laws of nature; false association of causation (A causes B) - EX: 13 is an unlucky number - EX: knocking of wood - EX: black cats are unlucky |
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Where do superstitions come from? |
Subjective feelings, Cultural/learned, and personal experiences |
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Intuition |
- Something that arises without conscious reasoning, it may be difficult to state why you feel the way you do - EX: Fast decision - EX: Picking from a group of good choices |
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Conformation Bias |
Remembrance of times your intuition was right while forgetting of minimizing times it was wrong |
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Authority |
- Information is derived from sources one deems to be trustworthy and credible - EX: Friends, relatives, specialists (mechanics, doctors, etc.), teachers, newscasters EX: I know there are 50 states in the US because my teacher told me |
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Rational-inductive argument |
- Use of previous knowledge and experience, logic, and reasoning - a source of knowledge by reasoning and proofs - EX: We expect all swans to be white |
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Scientific method |
- used in sciences to acquire knowledge - information is collected in an objective and systematic way |
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Hypothesis |
a testable explanation for how or why a phenomenon occurs |
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Can we prove a hypothesis? |
- No we cannot, but we can find or fail to support our hypothesis by doing an experiment and testing it |
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Objectivity |
Makes the data collection and testing of the hypothesis objective |
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Confounds |
- Flaws in the design of a research experiment that introduce alternative explanations for the obtained results - cancel out all confounds in an experiment to get the best results |
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Vehicle |
Everything that your drug group gets, minus the drugs. This is given to the individuals in the control group |
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Independent variable |
A condition in the experiment that the researcher is going to manipulate/change in order to see what it does/doesn’t do to the DV |
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Levels/conditions of the IV |
The different ways the IV is altered |
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Experimental group |
The group of participants that is given a treatment of interest |
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Control group |
Participants in a study that do not receive the treatment of interest |
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Dependent variable |
An observable, measurable outcome interest in an experiment. The measure is believed to be dependent on the conditions of the IV |
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Subject variables |
- A measurable characteristics of the participant that CANNOT be manipulated by the researcher - EX: substance use history - EX: medical conditions - EX: age, weight, height, gender identity |
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Casual |
Manipulate the IV, ability to say X caused Y |
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Literature search |
Process which allows researchers to understand what is known and unknown about the topic they’re interested in |
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Boolean search terms |
AND, OR, NOT |
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Review papers |
- no new original studies - cover the scope of existing literature - good source for reference material - CHECK PRIMARY SOURCES |
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Meta-analysis |
- Systematic synthetization of existing studies - run statistical analyses using the results from all included studies - determine the overall effect - CHECK PRIMARY SOURCES |
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What to avoid in writing |
Redundancy, ambiguity, long and complex sentences |
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What you should do in writing |
Clear, concise, specific, simple |
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Title |
Descriptive, concise, relevant |
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Abstract |
- provides short summary of the paper - read to make sure the paper is relevant |
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Introduction |
- Established why the topic is important - outlines previous literature - ends with the authors hypotheses |
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Methods |
Make sure to tell EVERYTHING in detail. |
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Correlational |
No manipulation of the IV, cannot say X caused Y |
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Results |
- Output from statistical tests - Graphs/figures/tables |
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What do Graphs/figures/tables need to have? |
Titles, axes labels, legend, Y and X axis, figure legend, time |
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Discussion |
- interpretations - connection to previous literature - limitations - future directions |
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References |
ONLY contains works cited within the text |
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APA citation authors (1-multiple) |
One: (White, 2023) Two: (White & Brodbeck, 2023) Three or more: (White et al., 2023) |
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Plagiarism |
Presenting someone else’s ideas as your own |
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When to cite |
- summarizing/rephrasing of another persons work - direct quotation - Do NOT cite if its common knowledge or cant be challenged |
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Scientific Journals |
- can be published monthly, bimonthly, or weekly - Quality of journal can be assessed by its impact factor - Check how many citations they get Excellent is greater or equal to 10 Good is 3-10 Acceptable is 1-2 |
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Blind review |
Reviews don’t know who wrote the manuscript and the authors don’t know who reviewed the manuscript |
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Peer reviewers |
Critique manuscript |
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Description |
Carefully recording of true observation of something |
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Business model of scientific journals |
- relies on subscriptions - no advertisements - reviewers are rarely paid |
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Population |
- situation specific - EX: age, occupation, substance use, etc. |
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Representative samples (Selection bias) |
Convenience, under-coverage, non-response, self-selection, random assignment |
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Random selction |
Randomly selected them from the larger population |
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Random assignment |
Randomly assigning them to the control group or treatment group |
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Representative sample EX |
Cooking soup on a stove, you do not take a spoon and take soup off the top, you stir it around and test it stirred |
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Probabilty |
The chance a given event will occur EX: flipping a coin |
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Measurements |
Systematically assigning number to objects, event or characteristics according to a set of rules EX: points per game, When playing basketball, we assign a number to the shots being taken and count them if they make the shot |
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Reliability |
Outcomes are consistent when repeatedly measured (think consistency) |
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Validity |
The intrude time use is measuring what you claim to be measuring (think accurate or correct) |
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Explanation |
Once the phenomena is observed and described, we can try to explain why it is happening |
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Nominal scale |
- Categorical (they have group names) - Order does not matter Labels: Eye color, education level, age group (senior, toddler, babies, etc.), sports jerseys |
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Ordinal scale |
- order is important - distance between categories may not be equal - EX: Grades: 50.83% and 59.89% - EX: rank order: attractiveness or preference - EX: Horse racing (the first place horse can finish way faster than the seconds place horse, but it doesn’t matter) |
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Interval scale |
- equal units of measurement across the scale - order and relative quantity of characteristic being measured - no true zero value and negative numbers are possible - EX: Temp (C and F always NOT K) - there is not true 0 degrees because in another form of temperature it could be 30 |
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Ratio scale |
- order matters - equal distance between units - there IS a true 0 - EX: length, weight, time (we have 0 seconds but not -15), temp (K always goes under ratio) |
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Describing the data |
- descriptive statistics - data averages - spread of data |
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Mode |
The score the occurs most frequently |
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Median |
- Middle point of the ordered scores - data must be in order before determining the middle point |
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Mean |
Mathematic average of the scores |
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Outliers |
- values that are inordinately small or large are given as much weight in mean determination as every other score - EX: test scores: 86, 91, 97, 88, 94, 95, 100, 27 Mean with outlier = 84.75 Mean without outlier = 93 |
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Standard deviation |
- measure of data dispersion - Relative to the mean - how far is the score from the mean - variance = how much spread in data |
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Hypothesis |
A scientific testable explanation for what causes the phenomena |
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Variance |
- Effect: variance between groups - Error variance: variance within groups |
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Range |
- The number of possible values for scores in a data set - Does NOT indicate how data is distributed |
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Correlations |
- degree and direction of relationship between variables - positive, negative - R value: -1.00 to +1.00 - farther from 0 |
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Theories vs laws (theories) |
A set of related statements that can explain and predict phenomena |
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Theories vs laws (laws) |
Specific scientific statements within scientific theories, generally explained in mathematical terms, that have an over abundance of data to support them, their accuracy is beyond reasonable doubt |
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Simplicity |
EX: i stick my key into the lock but it will not turn The reason it wont turn is because i have the wrong key |
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Support |
EX: i stick my key into the lock and it will not turn The lock will not turn because i have the wrong office |
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Assumptions |
EX: i stick my key into the lock and it will not turn The lock will not turn because the department moved my office and all of my belongings without telling me |
