Exam 1 Flashcards

1
Q

what are the 7 steps of the scientific method?

A
  1. identify the problem/question/idea
  2. read existing literature
  3. test hypothesis/experiment
  4. analyze results
  5. conclusion (do results agree with the hypothesis?)
  6. publication (review and evaluated by peers and editing)
  7. replicate or revisit hypothesis
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2
Q

reliable sources

A

peer reviewed journal articles

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3
Q

grey literature

A

not subject to peer review
e.g. chapters in edited books, full length books, book chapters, dissertations, theses

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4
Q

construct/concept

A

a singular complex idea that cannot be directly observed; abstract theoretical concept (e.g. friendship, success)

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5
Q

variable

A

a concrete, observable aspect of the construct or concept that is different across people or situations (e.g. construct: friendship; variable: pos relationship quality)

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6
Q

conceptual definition

A

the fields definition of the construct in question at a theoretical level (usually found in an article from literature research)

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7
Q

measure

A

the method of gathering data about the construct of interest (e.g. PN-SMD)

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8
Q

operational definition

A

the definition of the construct as it will be measured (e.g. PN-SMD is the measure; OD: pos quality rating of a platonic relationship)

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9
Q

qualitative variables

A

descriptions of behavior and mental processes that stay in narrative written form

pros: rich and personal info
cons: time consuming, difficult to replicate, has small samples

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10
Q

quantitative variables

A

descriptions of behavior and mental processes that turn into numerical form, scores, ratings, etc.

pros: replicable, has larger samples, can analyze with stats

cons: more restricted answers

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11
Q

how do you measure quantitative data

A

NOIR
Nominal
Ordinal
Interval
Ratio

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12
Q

categorical variables

A

places an individual into one of several groups or categories (e.g. gender: female, male, other)

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13
Q

continuous variables

A

obtained by measure or counting (e.g. test score, height/weight)

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14
Q

binary

A

for categorical variables, nominal (e.g. yes/no question)

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15
Q

ordinal (NOIR)

A

for categorical variables, groups have an order of hierarchy (e.g. grade in school)

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16
Q

nominal (NOIR)

A

for categorical variables, no order to the categories (e.g. ethnicity)

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17
Q

interval (NOIR)

A

for continuous variables, NO true zero, CAN be negative (e.g. temperature in fahrenheit)

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18
Q

ratio (NOIR)

A

for continuous variables, HAS a true zero, CANNOT be negative (e.g. age)

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19
Q

cross sectional study

A

observational study that analyzes data from a population at a single point in time

pros: less time consuming, larger samples, less resource intense

cons: almost only correlations possible

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20
Q

longitudal study

A

repeated measures study, more than one piece of data for a participant (e.g. Subject A is observed at age 5, 10, and 20)

pros: time effects, measure change

cons: time consuming, resource intensive, participant drop out

21
Q

measured variable

A

observed/recorded as is (e.g. record the height of a plant as it grows)

22
Q

manipulated variable

A

something is intentionally changed (e.g. record the height of a plant as it grows while giving it fertilizer, fertilizer is the manipulated variable)

23
Q

sample

A

a group within the population

24
Q

probability sampling

A

every individual in the population has an equal chance of being selected for a study (e.g. simple random sampling: put every member’s name of the population in a pool and randomly select a number- number is associated to a person)

25
non-probability sampling
all people in the population do not have an equal chance of recruitment (introduce bias) e.g. self-selection, convenience sampling, purposive sampling
26
self-selection (non-probability sampling)
sample is made up of volunteers for the study
27
convenience sampling (non-probability sampling)
sample is made up of those that are easy to contact
28
purposive sampling (non-probability sampling)
only recruit the type of person you want to study
29
snowball sampling (non-probability sampling)
recruitment via social networks and word of mouth
30
science
empirical, based on observations
31
pseudoscience
theories, ideas, or explanations that are represented as scientific but not derived from science or the scientific method
32
what is the purpose of the abstract in a research paper?
it is a brief summary of the study
33
what is the purpose of the introduction in a research paper? what are its 3 key components?
it introduces phenomenon of interest, provides background research leading to the hypotheses key components: - literature review - identify gaps in the literature - research question
34
plagiarism and paraphrasing
all info should be paraphrased from sources with appropriate in-text citations (no quotes) paraphrasing should be your own words, relevant to the ideas that you are talking about
35
validity
how well does the data represent the way things actually are?
36
construct validity
how well a construct is operationalized- does the operational and conceptual definition match? (e.g. mental health and mental health tally)
37
measurement reliability
measure results are consistent and can be trusted
38
3 types of measurement reliability
1. test-retest reliability 2. interrater reliability 3. internal reliability
39
test-retest reliability (measurement reliability)
consistent scores every time the measure is used
40
interrater reliability (measurement reliability)
consistent scores no matter who administers the measure
41
internal reliability (measurement reliability)
researcher asks the same question multiple times in different ways- a consistent pattern of responses indicates that the participant is listening/paying attention
42
measurement validity
the measure accurately describes the variable
43
3 types of measurement validity
1. criterion validity 2. convergent validity 3. discriminant validity
44
criterion validity (measurement validity)
relevance, is the measure related to a concrete outcome that it should be related to?
45
convergent validity (measurement validity)
a measure should correlate strongly with other measures of the same variable (e.g. comparing tests of stress and anxiety)
46
discriminant validity (measurement validity)
a measure should NOT correlate strongly with measures of different variables (e.g. diagnostic measures, ADHD diagnostic test compared to autism test)
47
external validity
generalizability, how well the results of a study represent the population
48
statistical validity
the extent to which the author's interpretations of the data are reasonable and accurate
49
internal validity
the accuracy of the relationship between variables