Module 1 Flashcards
1
Q
What four methods does Neuman (2011) outline that people use to aquire knowledge?
A
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Personal Experience and common sense: can be distorted through perception, illusion and judgement
- Common sources of bias: overgeneralisation, premature closure, the halo effect, false consensus
- From Experts and Authorities: Easily accept information from authority figures as true
- Peers and Media Messages: Distorted by level of education of source of information, bias in reporting and cultural myth
- Ideological Beliefs and Values: views shaped by beliefs are often presented as factual knowledge
2
Q
How does Neuman define the scientific method?
A
- Science is both a system and the knowledge derived from that system.
- Theory = coherent system of ideas used to condense knowledge
- Data = form of empirical knowledge collected via science
- Empirical = evidence/observations grounded in sensory data
- Scientific Literacy = capacity to understand and use scientific method
- Innumeracy = lack of quantitative literacy
- A scientific approach is characterised by:
- Clearly defined constructs
- Theory driven research Qs
- Falsifiable hypotheses
- Valid meausurement of constructs
- Reproducible design, methods and results
- Peer review and consensus
3
Q
What are the five basic norms of the scientific community according to Neuman?
A
- Norms are informal rules/principles/values that govern the way scientists conduct research
- Universalism: all research should be judged on merit regardless of researcher or institution
- Organised Skepticism: challenge and question all evidence
- Disinterestedness: neutrality, impartialiality, openness and receptiveness to unexpected/new ideas
- Communalism: Knowledge belongs to all, it should be shared and critiqued by all and peer reviewed
- Honesty: no dishonesty or cheating in research
4
Q
What biases and non-scientific sources of information should be avoided according to Neuman?
A
- Overgeneralisation: Statement that goes far beyond what can be justified based on the data
- Selective Observation: Examination that reinforces preexisting thinking
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Premature Closure: Making a judgment or ending an investigation before
gathering sufficient evidence - Halo Effect: When prior experience colours one’s evaluations rather than evaluating all in a neutral, equal manner
- False Consensus: assuming that everyone else thinks like he or she does
- Pseudoscience: Information with outward appearance of science that was not created with systematic rigor
- Junk Science: A public relations term used to criticize scientific research that produces findings that an advocacy group opposes.
5
Q
What are the differences between quantitative and qualitative research?
A
6
Q
What are some common research designs?
A
- Non-experimental: Does not involve manipulation of an independant or dependant variable
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Correlational: Examining variables through interpretation of their association (no causality).
- Examine Direction, Strength and Statistical Significance (all interrelated)
- Quasi-experimental: Use pre-existing or other non-randomly assigned groups or interventions. ie a correlational study with manipulation
- Experimental: allow inference of causal relationships between variables. The exprimenter actively manipulates the independent variable(s).
7
Q
What is the difference between exploratory, descriptive and explanatory research?
A
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Exploratory: Primary purpose is to examine a little understood area and develop preliminary ideas.
- Addresses “what” questions
- Formulate and focus questions for future research
- Most uses qualitative data (surveys, interviews)
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Descriptive: Presents a picture of the specific details of a situation, social setting, or relationship.
- Focus on “how” and “who” questions (ie how often, describe patterns)
- Use most data-gathering techniques: surveys, field research, content analysis, and historical-comparative research
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Explanatory/Experimental: To explain why events occur and to build,
elaborate, extend, or test theory.- Testing a (usually causal) hypothesis
8
Q
What are five potential errors in causal explanation?
A
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Tautological errors: the causal factor and the result are actually the same or restatements of one another
- eg Sally is conservative because she believes in less regulation
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Teleological errors: the causal relationship is empirically untestable because
the causal factor occurs after the result or cannot be empirically measured- Crime occurs because it is just human nature.
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Ecological Fallacies: When data at a higher or an aggregated unit of analysis isd used to explain something about a smaller or disaggregated unit
- Country A has more motorbikes than B; therefore Person from A does.
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Reductionistic errors: when a person observes a lower or disaggregated
unit of analysis but makes statements about higher units- eg Civil Rights Movement due to Martin Luther King Jnr alone
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Spuriousness: occurs when two variables are associated but are not causally related because an unseen third factor is the real cause
- eg IQ causes TV vs Reading and Level of Knowledge
9
Q
What is a hypothesis? What are the different types of hypothesis?
A
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A hypothesis is an empirically testable statement predicting the relationship between 2 or more variables
- Research determines support never proof. Research is a dynamic process of generation, investigation and elimination of hypotheses.
- 5 factors of a Causal Hypothesis: 2+ variables, causal relationship, predictive, linked to theory and falsifiable.
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Types of Hypotheses
- Null v Alternative: Ho predicts no relationship
- Directional V Non Directional: based on previous research either direction or not (increased X –> decreased Y)
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Double Barrel Hypothesis: Poorly designed. Two IVs mean 3 predictions.
- Use an Interaction hypothesis instead
10
Q
What are the different types of missing data?
A
- Missing Completely At Random (MCAR): no pattern in the missing data
- Missing at Random: a pattern to missingness not associated with the DV
- Missing Not at Random: a pattern associated with the DV
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How to find out which in SPSS:
- Analyse -> Missing Value Analysis -> Add experimental group to categorical -> Estimation EM (chi-square)
- Look for the MCAR test under EM Means -> if p-value is significant it is MCAR
11
Q
How can you deal with Missing Data?
A
- List-Wise Deletion: deleting the case (not good option, deletes all other data)
- Mean: Replace value with the mean of the item
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Common-Point: Replace value with the midpoint of the scale
- Transform -> Recode into Same Variable -> Old and New Variables -> Select System or User Missing and add the midpoint value
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Multiple Imputation (best option): Using regression to predict the response based on their other data and the group
- Analyse -> Multiple Imputation -> Impute Missing Data Values
- Options: Add all IVs and DVs into model, 1 imputation, New Data Set
- Constraints: min and max values of scale, use as predictor and impute and use as predictor
12
Q
What are the four possible reasons for outliers?
A
- Data Entry Errors (always screen for data entry errors before anything else)
- Not Specifying missing-value codes in SPSS (always screen missing values first)
- The outlier is not a member of the population you intended to sample
- The outlier is from the intended population but the distribution for the variable is more extreme than a normal distribution
13
Q
What are the different types of outliers and how can you deal with them?
A
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Univariate Outlier: A response on DV > 3.29 SDs from the mean of the group
- Split cases: data -> select cases -> if experimental group = 0
- Analyse -> descriptive stats -> DVs -> save standardised
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Multivariate outlier: responses across DVs indicate inconsistent responding
- Analyse -> Regression Linear -> Reverse IV’s and DVs -> Save MAHs distance
- MAH -> Transform -> Compute Var -> prob = 1- CDF.CHISQ(MAH_1, #predictors)
- Look for probability <0.001
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Dealing with Outliers
- Check for errors in data entry
- Fix univariate outliers; Winsorizing (replace with normal score)
- Remove Multivariate outliers altogether.
14
Q
What is the assumption of Normality?
A
- Normality assumes that the random error (residuals) are normally distributed throughout the data set (not the data itself)
- Especially important when n<200 (before Central limit theorem sets in)
- Affects Type 1 Error (false positive)
- Distributional information
- Skewness: symmetry of distribution (positive skew = clustered at low end)
- Kurtosis: peakedness of the distribution (leptokurtic = tall,slender) (platykurtic = flat)
15
Q
How can you test for Normality?
A
- It is reccomended to use multiple methods of normality
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Shapiro Wilk test: Ho is a normal distribution. If the result is significant, the error distribution is not normal
- Split the groups: Data -> Select case -> if condition satisfied
- Analyse -> Explore -> Group filter -> plots -> normality plots
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Multivariate normality: no direct test in SPSS
- If each DV is normal via Shapiro Wilk, generally overall is normal
- Can run a multivariate normality test via Syntax if needed
