Final Deck 2 Flashcards
1
Q
Define and contrast descriptive vs. inferential vs. analytical statistics
A
- Descriptive stats summarize/organize/simplify data from a single sample or population (ex: using mean, median, mode, variance, SD)- summarize data simply
- Inferential Stats- lets us compare two or more samples to make generalizations about the populations that were pulled from (t-test, anova, correlation)-draw conclusions based on observations
- Analytical stats-goal is to gain insight into the processes that drive the observed data patterns (ex: cluster analysis, regression analysis etc.
2
Q
What is the difference between a population and sample?
A
- The population is the entire set of individuals of interest in the study (all caregivers)
- The sample is the set of people selected to represent the population (the caregivers selected to participate in the study)
3
Q
Simple random sample and it’s primary advantage
A
- Everyone in the population has an equal chance of being selected for the study
- the advantage is that this is the least biased because it is intended to look like the normal distribution
- This increases the validity (accuracy) of the study because it is representative of the population
4
Q
What is the difference between probability and non-probability samples?
A
- A probability sample is when each member of the population has a chance to be selected
- A nonprobability sample is when sampling is not completely random-easier to do, but a chance for systemic bias to creep in
5
Q
3 examples of probability sampling
A
- Simple random: everyone in the population has an equal chance of being selected
- Cluster: selecting people who tend to congregate in specific areas (ex: going to DDI for a study about Autism)
- Stratified: tailoring your sample to be proportionate to the population you are targeting (ex: if there are 2 boys to 1 girl diagnosed with ASD, then you should be copying this proportion in your sample)
- Systematic: every “nth”. ex; every 5th person that attends speech this week
- oversampling: you might oversample to look more into a specific quality
6
Q
3 types of nonproability sampling
A
- Convenience: You ask whoever happens to be around you to participate
- Purposeful: You judge someone to seem like a good source of info, then ask them (ex: parents on the PTA- you choose this bc you think they have special knowledge)
- Snowball: You send it to 3 people, ask them to send it to 3 more, etc.
- Self selection: You just put it out there and see what happens
7
Q
Why is sample size important for a study in terms of sampling error/probability?
A
- A larger sample size is important for a study because it reduces the chance of bias.
- The more people you have in your study, the more likely the sample is to look like the population.
- Also, the more people in the study, the less chance of outliers coming in to skew your data. —> this is the law of large numbers
8
Q
Rule of thumb on minimal sample size
A
- Ideally you want 300+ participants for a survey, but 100 is the minimum
10% rule of thumb-researchers should not change the sample size more than 10% than the predicted SPSS value
9
Q
How about sampling for qualitative designs?
A
- You can have a smaller sample size for a qualitative design because the study is more reliant on the researcher’s judgement than statistical analysis.
- You’re not really trying to generalize the population in this case
10
Q
What is saturation in relation to qualitative sampling?
A
- Saturation: When additional responses no longer add variability to the results → over time, they get no more new information from the data (get the same thing over and over again)
- people keep saying the same thing and there is no more variation
11
Q
List/describe 3 features specific to qualitative research designs
A
- Descriptive: Since numeric data alone are insufficient, social behaviors are described in detail and understood in the context of design and usage
- Social: Focused on social phenomena
- Systematic: Data collection is procedural and consistent
- Authentic: Data collection takes place in contextualized, naturalistic settings (i.e., not in a laboratory or controlled setting)
12
Q
Define observations (consider direct/indirect and structured/unstructured)
A
- Examination of an object, process, or other phenomenon for the purpose of collecting data about it or drawing conclusions
- is the observation direct or indirect (social media, transcript, narrative, etc.)
- Is the observation naturalistic (in the participants natural setting?) or structured (in a lab setting)
13
Q
What is a participant Observation- how is it unique?
A
- A participant observation is when the researcher is also immersed in the activity and experiencing the activities and interactions that participants do as well.
14
Q
Define interviews
A
- A series of questions designed to elicit information a researcher is interested in.
- Interviews have 3 types: structured, unstructured, and semi-structured
15
Q
Self-Disclosure in an interview
A
- Interviewers frequently engage in the process of self-disclosure, where they review their collected data in light of their own background to prevent bias.
16
Q
Define focus groups and contrast with interviews
A
- A focus group is about 6-12 people that engage in a discussion lead by a moderator.
- Focus groups highlight narratives (not behaviors).
- They use a phenomenological approach (focusing on the participant’s perception of a construct)
- interviews follow more specific questions and answers where a focus group is more of a facilitated group discussion
17
Q
What is a qualitative tradition? and vs. techniques
A
- Traditions are much broader → they are collections of ideas, theories and data collection techniques.
- A single tradition can have multiple data techniques (case study, ethnography, grounded theory approach etc → these things can use multiple techniques to collect their data)
- The technique is the specific way you collect data
18
Q
Biographical Study and one feature
A
- An in depth investigation of one individual over time.
- Characteristic: researcher asks exploratory/open ended questions that allow the participant to share their thoughts and build a narrative
19
Q
Case Study and one feature
A
- A case study focuses on a specific “object” (person, topic, location, or event). - Data is gathered from one or a small number of cases, and is usually more structured than a biographical study, with more scripted and researcher-directed questions
- note that one participant is studied at a time, but there can be multiple participants in the study.
20
Q
What is ethnography and one feature
A
- Uses interviews and observations to explore a cultural/social aspect
- it focuses on a cultural or social stuff rather than a single phenomenon
- *It does not require a formal research question or hypothesis
21
Q
Grounded Theory
A
- This design is meant to be a more systematic qualitative tradition “grounded” in data to maximize researcher’s theoretical sensitivity, meaning, their focus on data and hypotheses testing.
- Data is coded to make comparisons across sets
22
Q
3 Coding Themes of Grounded Theory
A
- Open Coding: Break all of the transcript data into themes (sometimes referred to as “codes”), typically by identifying common keywords in transcript sections
- Axial Coding: Identify relationships between themes/codes (causal, temporal, spatial), and group into Categories
- Selective/Core Coding: Use these relationships to identify a small number (usually one) overarching Core Category/Categories
23
Q
Mixed matched surveys
A
- MMR combines at least one quantitative and qualitative technique (strand) in the same study.
- For example, a MMR study might employ both qualitative interviews and quantitative surveys.
24
Q
Descriptive vs. Inferential/Analytic Research Methods
A
- Descriptive- describing variables
- Inferential/Analytic- comparing variables
25
Q
Case Report
A
- They are detailed, retrospective description of a disease or a medical condition
- Usually just a description of one person
26
Q
Case Series
A
- Multiple case reports on individuals with the same disease or condition- multiple people involved
27
Q
Describe cross sectional studies and pro/cons
A
- A descriptive “snapshot” of variables for a singular measurement in one instance in time- it measures the distribution of variables only
- pros- quick, inexpensive, avoids participant attrition/mortality
- cons- can examine prevalence only (not incidence), so you cannot establish a causal relationship, vulnerable to recall bias, misinterpretation
28
Q
Prevalence vs. Incidence
A
- prevalence- how many people have something at one point in time
- incidence- how many people are expected to have something/new cases develop
29
Q
Cohort Study
A
- Prospective study that compares variables between two groups: one group is at risk/exposed to a particular variable, the other is controlled.
- Then you see if there is any difference in development
30
Q
Case Control
A
- Retrospective study comparing variables between a condition vs. a control group → you collect 2 groups of people, one group has X condition, and group 2 does not.
- You go back into their history to see if there are any variables/red flags that might have something to do with the condition
31
Q
Relative Risk
A
Associated with a cohort study
-Relative risk is the probability event in the exposed group vs/divided by the probability of event in the unexposed group
-Advantage: you can analyze incidence, gives you more info about establishing a relationship (but not causal)
-Disadvantages: more expensive, time consuming
32
Q
Odds Ratio
A
- Associated with case control study
- It is when probability of exposure in case group divided by probability of exposure in control group
- different than with a cohort study because You are starting with who has the disorder and who doesn’t, rather than anticipating if the disorder will come up
33
Q
Randomized Control Trials (RCT)
A
- prospective studies in which participants are randomly assigned to treatment/experimental or control groups after recruitment and assessment of eligibility, but before intervention (one intervention is used)
34
Q
Crossover Designs
A
- A study that has multiple treatments applied to the same group → you see if there is an order effect.
- Ex: is the treatment better for people who get intervention A then B or is the treatment better for people who get intervention B than A?
35
Q
Pros of using an experimental design
A
- good randomization reduces population bias, easier to blind/mask participants that observational studies, results can be analyzed with well known statistics, populations of participating individuals are clearly identified
36
Q
Cons of using an experimental design
A
- $$, time consuming, volunteer biases: the population that offers to participate may not be representative of the whole population.
- Also, better at identifying cause, confounding, loss to follow-up attributed treatment
37
Q
Define a type 1 error
A
- Claiming that a result is statistically significant when it’s not
38
Q
What is p value
A
- The p value is the probability of making a type 1 error
- P must be less than .05 to be considered statistically significant
39
Q
Alpha
A
- Associated with type 1 error
the estimated probability of making a type 1 error
40
Q
What does a smaller p value mean in terms of type one error?
A
- The smaller the p value, the less chance of making a type 1 error
41
Q
What is a type 2 error?
A
- Saying that results are not statistically significant when they actually are
42
Q
Define statistical power
A
- Power is a function of beta, effect size, and sample size
- It is the probability that a study will correctly reject a null hypothesis (opposite of beta)
43
Q
Define beta
A
- Estimated probability of a type 2 error set at 0.8 or 20%
- We want this probability to be less than 20%
- We want a less than 20% chance of making a type 2 error
44
Q
What is statistical power used to estimate?
A
- The likelihood of correctly rejecting a null hypothesis
- AKA correctly identifying significant results
45
Q
What is effect size?
A
A measure of the magnitude between variables or groups used in a study
46
Q
What is the difference between a survey and a questionnaire?
A
- A questionnaire is just the list of questions
- A survey includes the process of surveying- it has methodology (sampling, distribution, analysis)
- Surveys include questionnaires
47
Q
What is pilot/field testing?
A
- Pilot testing allows you to test out your administration, directions, the way questions are worded etc.
- It is important to practice it on people before the real survey is distributed
- You can still change things in your questions at this point
48
Q
What is the difference between select response items and constructed response items?
A
- Select response items have the participant choose from previously created answer choices
- Constructed response items require the participant to create their own response
49
Q
Give 3 examples of select response items and the pro/cons
A
- Multiple choice
- true/false
- Rankings
- Likert scales/Ordinal Scales (agree-slightly agree- disagree- slightly disagree)
- Matching
- Forced choice (one of 2 choices)
- pros: easier for participants and for us to score. Time efficient. Simple individual or group administration. Greater reliability of content and format. Simple conversions to quantitative scale
- cons: provides no background info (only qualitative can give background info)
50
Q
Give 3 examples of constructed response items and the pros/cons
A
- Short or long answer write ins
- Writing samples or artifacts
- Oral discussion
- Short or long answer write in
- Writing samples/artifacts
- Oral discussion
- Performance
- pros: More creative, expansive responses available
- “Authentic” samples of behavior
- Better suited to assess writing skills, mechanical expertise, and other performance-based skills
- cons: more time consuming to grade
51
Q
What is a leading question/how does it compromise responses?
A
- A leading question is a type of question that influences a particular response through the wording of the question
- Example: do you agree that voter ID laws will prevent eligible voters from casting their vote?
- it sways people to respond in a certain way
52
Q
What is a double barreled question/how does it compromise responses?
A
- Double barreled questions using the word “AND/OR”-is very dangerous because it introduces multiple concepts
- Ex: do you like coffee and tea?
- it is a question about two things and it doesn’t create good responses
53
Q
What is acquiescence/dissent bias? Name one thing that contributes to it
A
- tendency to agree/disagree regardless of meaning → caused by a desire to please the examiner, predilection towards/against the construct, or response options all being “keyed” towards agreement/disagreement statements, they want to just finish it quickly, they don’t understand what you are asking so they just click agree for everything
54
Q
What is extreme/neutral response bias? Name one thing that contributes to this
A
- Tendency to choose extreme/moderate options, regardless of actual opinion
- likert scales tend to contribute to this because people want to answer strongly in either direction
- One thing you can do about this is add in a qualitative question-this allows the person to give their background as to why the answers are so extreme or realize they want to change their answers
55
Q
What is social desirability? What contributes to this?
A
- Tendency to respond to sensitive questions in socially desirable ways (different than wanting to please the researcher- bc this is about being socially acceptable)
Can be affected by - content (construct): some traits are more socially desirable than others
- context: Knowing the respondent’s identity or consequences of results → if their identity is anonymous, they are more likely to answer honestly)
- *caused by poorly worded questions, not being anonymous and leads to an overestimate of socially desired results
56
Q
What are 3 ways to control response bias?
A
- Manage content format (limit the number of items, use good wording, consider some qualitative items, use a combination of positive and negative keyed items)
- Manage context AKA testing situation (ensure anonymity, promote honesty, minimize distractions)
3.
57
Q
What is skewness/what does it look like on a graph?
A
- Skewness is a lack of symmetry on one side of a graph
- caused by outliers (dragging the graph in either direction)
- It looks like the curve is shifted far to the left or right of a graph
58
Q
What is kurtosis/what does it look like on a graph?
A
- Kurtosis is how spread out the scores are
- Looks like how wide the bell curve is
