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.
