Module 1

Question 1

What four methods does Neuman (2011) outline that people use to acquire knowledge?

Answer 1

1) Personal experience and common sense
- seeing is believing
- biases: overgeneralisation, halo effect, false consensus, premature closure, selective obsercation

Common sources of bias: -

• overgeneralisation
• premature closure
• the halo effect,
• false consensus

2) Authorities and experts
3) Media and peers
4) Ideological beliefs and values

Question 2

What is premature closure?

knowledge through personal experience and common sense

Answer 2

premature closure is when we think we have the answer so we stop seeking information asking questions and feel we have no need to listen.

Question 3

What is the halo effect?

knowledge through personal experience and common sense

Answer 3

we give a halo or a positive reputation things and people we respect. e.g. picking up a report from Harvard university not an unknown university and expecting its amazing. or
when we make an attempt to judge someone’s entire personality based off one trait

Question 4

what is overgeneralisation?

knowledge through personal experience and common sense

Answer 4

when we have some believable evidence but then think it applies to every situation

Question 5

what is selective observation?

knowledge through personal experience and common sense

Answer 5

kind of like confirmation bias. selective observation reinforces preexisting ways of thinking. we focus on particular cases so that they fit preconcieved ideas we have

Question 6

What are the five basic norms of the scientific community according to Neuman? (DOUCH)

Answer 6

Universalism
Organised Skepticism
Disinterestedness
Communalism
Honesty

Question 7

what is false consensus?

knowledge through personal experience and common sense

Answer 7

a tendency to project ones way of thinking on to other peoples. we assume that others think like we do. we overestimate how much of our views match other peoples

Question 8

what is pseudoscience?

Answer 8

a body of ideas or information that seem like science but aren’t created with systematic rigor or standards of science

Question 9

what is junk science?

Answer 9

junk science is used to discredit scientific research even if conducted properly.. it is usually used when an advocacy group opposes a scientific research paper

Question 10

what is universalism?

norms of the scientific community

Answer 10

all research should be judged equally on its merit regardless who did it

Question 11

what is organised scepticism?

norms of the scientific community

Answer 11

process of scrunity of studies and their methods and approaches but not of the person (which would be a kind of anti-universalism)

Question 12

what is disinterestedness?

norms of the scientific community

Answer 12

the idea that scientists remain free from bias, are impartial and neutral and open to new ideas

Question 13

what is communalism?

norms of the scientific community

Answer 13

the idea that scientific knowledge and the outcomes of studies are available to the public and shared without censorship

Question 14

what is honesty?

norms of the scientific community

Answer 14

honesty is the most important. all research and reporting must be done honestly and without cheating

Question 15

most quantitative data techniques are…

Answer 15

data condensers - when data is condensed you see the big picture

Question 16

most qualitative data techniques are…

Answer 16

data enhancers - when data are enhanced you can see the aspects and nuances

Question 17

How might the scientific community be described as concentric circles?

Answer 17

There are few researchers in the middle of the concentric circles who do the groundbreaking work.
Practitioners, clinicians and technicians are more numbered and lie in the outer circles, moving back and forth between the centre and outer edges gathering new information to use in practice

Question 18

What is the key factor of quantitative research? What is the key factor of qualitative research?

Answer 18

The key factor of quantitative research is reliability, while the key factor of qualitative research is authenticity.

Question 19

What is a non-experimental design

Answer 19

Non-experimental: Does not involve manipulation of an independant or dependant variable

Question 20

what is a correlational design?

Answer 20

Correlational: Examining variables through interpretation of their association (no causality).
Examine Direction, Strength and Statistical Significance (all interrelated)

Question 21

what is a quasi-experimental design?

Answer 21

Quasi-experimental: Use pre-existing or other non-randomly assigned groups or interventions. ie a correlational study with manipulation

Question 22

what is an experimental design?

Answer 22

Experimental: allow inference of causal relationships between variables. The exprimenter actively manipulates the independent variable(s).

Question 23

What is the difference between exploratory, descriptive and explanatory research?

Answer 23

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)

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

Explanatory/Experimental: To explain why events occur and to build,
elaborate, extend, or test theory.
Testing a (usually causal) hypothesis

Question 24

Neuman (2011) reported 7 steps in the quantitative approach to research. What are they?

Answer 24

1. select a topic
2. focus the question
3. design the study
4. collect data
5. analyse the data
6. interpret the data
7. inform others

Question 25

When should parametric statistics be used? When should non-parametric stats be used?

Answer 25

Parametric tests are used only where a normal distribution is assumed. The most widely used tests are the t-test (paired or unpaired), ANOVA (one-way non-repeated, repeated; two-way, three-way), linear regression and Pearson rank correlation.

Non-parametric tests are used when continuous data are not normally distributed or when dealing with discrete variables. Most widely used are chi-squared, Fisher’s exact tests, Wilcoxon’s matched pairs, Mann–Whitney U-tests, Kruskal–Wallis tests and Spearman rank correlation.

Question 26

What does Katrina Simpson video suggest considering after you’ve developed a research question?

Answer 26

x

Question 27

What does a null hypothesis predict? What does an alternative hypothesis predict? Which one do we test?

Answer 27

Null predicts no relationship so we test the alternative

Question 28

What is a double-barrelled hypothesis? What are some of the problems it can create? How can it be made better?

Answer 28

A confusing and poorly designed hypothesis with two IVs so its unclear whether one or the other variable or both in combo produce an effect

Use an Interaction hypothesis instead

Question 29

What are crucial experiments?

Answer 29

An experiment held to decide with certainty between two rival hypotheses about some matter.

Question 30

How can you deal with Missing Data?

Answer 30

List-Wise Deletion: deleting the case (not good option, deletes all other data)

Mean: Replace value with the mean of the item

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

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

Question 31

What are the four possible reasons for outliers?

Answer 31

1. Data Entry Errors (always screen for data entry errors before anything else)
2. Not Specifying missing-value codes in SPSS (always screen missing values first)
3. The outlier is not a member of the population you intended to sample
4. The outlier is from the intended population but the distribution for the variable is more extreme than a normal distribution

Question 32

When do we reject the null hypothesis?

Answer 32

If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. When this happens, the result is said to be statistically significant .

If the p-value is less than 0.05, we reject the null hypothesis that there’s no difference between the means and conclude that a significant difference does exist.

Question 33

What is the assumption of Normality?

what is skewness?

what is kurtosis?

Answer 33

Normality assumes that the random error (residuals) are normally distributed throughout the data set

• very important when n<200 before the central limit theorem sets in
• it impacts type 1 error (false positive)

skewness: symmetry of distribution
- positive skew = clustered at low end

kurtosis: peakedness of distribution
- platykurtic = flat
- leptokurtic = tall and slender

Question 34

how can you test for normality?

Answer 34

It is reccomended to use multiple methods of normality
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
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

Question 35

What is Homoscedasticity/homogeneity of variance?

Answer 35

homogeneity of variance is the equality of variances and is a common assumption amongst statistical analysis

Running a test without checking for equal variances can have a significant impact on your results and may even invalidate them completely. How much your results are affected depends on which test you use and how sensitive that test is to unequal variances. For example, while a fixed-factor ANOVA test with equal sample sizes is only affected a tiny amount, an ANOVA with unequal sample sizes might give you completely invalid results.

homoscedasticity means “having the same scatter.

The opposite is heteroscedasticity (“different scatter”), where points are at widely varying distances from the regression line.

Question 36

What are the assumptions of linear regression

Answer 36

Linear relationship
Multivariate normality
No or little multicollinearity
No auto-correlation
Homoscedasticity

Question 37

What are the 5 characteristics of casual hypotheses? (VCPLF - very cool people like food)

Answer 37

1. they have at least 2 variables
2. they express a causal or cause-effect relationship between the variables
3. they can be expressed as a prediction or an expected future outcome
4. they are logically linked to a research question and theory
5. they are falsifiable; they are capable of being tested against empirical evidence

Question 38

what is tautology

Answer 38

an error in explanation in which the IV (Causal factor) and the DV (result) are the same or restatements of one another, making an apparent causal relationship true by definition

Question 39

what is teology?

Answer 39

an error in explanation in which the causal relationship is empircally untestable because the causal factor does not come earlier in time than the result or because the causal factor is vague, a general force that cannot be empircally measured

Question 40

what is ecological falacy?

Answer 40

an error in explanation in which empirical data about associations found amongst largesale units of analysis are greatly overgeneralised and treated as evidence for statements about relationships about smaller units

Question 41

what is reductionism?

Answer 41

an error ine xplanation in which empirical data about associations found among small scale units of analysis are overgeneralised and treated as evidence for statements about relationships for larger units

Question 42

what is spuriousness?

Answer 42

an illusiary causal relaitonship due to the effect of an unseen or hidden causal factor; the unseen factor has a causal impact on both an independent and dependent variable and produces tehf alse impression that a relationship between them ecists

Question 43

Why do we never use the word “proof” in science? What are some better terms to use when explaining the meaning of empirical findings?

Answer 43

proof doesn’t exist, all scientific knowledge is tentative and provisional and there’s no such thing as the final answer

experimental evidence

Question 44

What are crucial experiments?

Answer 44

an experiment capable of determining whether or not a particular hypothesis or theory is superior to all other hypothesis or theories whos acceptance is widespread

Question 45

when do we reject the null hypothesis?

Answer 45

If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. When this happens, the result is said to be statistically significant.