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Academic Skills and Data Description Semester 2 > introduction > Flashcards

introduction Flashcards

1
Q

2 types of statistics

A

descriptive
inferential

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2
Q

descriptive stats

A

describe study of population

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3
Q

inferential stats

A

using what we know from data we’ve collected to make inferences about what we don’t

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4
Q

cycle of experimental research design

A

current state of knowledge
construct hypothesis to test
design experiment
execute experiment
carryout stats analysis
interpret and report

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5
Q

what’s a research design

A

framework / blueprint for conducting research project. details procedures to obtain correct info needed to solve research problem

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6
Q

importance of research design

A

provides smooth operation
makes research efficient
blue print for advanced planning
precautions to reduce errors
reliability depends on design

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7
Q

what makes a good research design

A

specifies sources and info needed
strategic roadmap for collecting and analysing data
defines timelines and cost
include statement of problem, procedures and techniques, range of processes and analysis

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8
Q

criteria of research design

A

reliability
replication
validity

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9
Q

types of validity

A

measurement
internal
external
ecological

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10
Q

what are variables

A

main focus of research

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11
Q

characteristics of variables

A

continuous (measure in a specific way and even intervals)
discrete (specific items)
categorical (eg mode of transport)
dichotomising continuous and discrete (convert with to categorical)

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12
Q

4 levels of measurement

A

interval scale
ratio scale
nominal scale
ordinal scale

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13
Q

interval scale

A

scores in order of magnitude with equal intervals

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14
Q

ratio scale

A

same as interval but has absolute zero point

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15
Q

nominal scale

A

attributes are only named

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16
Q

ordinal scale

A

attributes are only ordered

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17
Q

extraneous variables

A

might impact on variables we are interested in but failed to take into account

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18
Q

confounding variables

A

types of extraneous variable related to both main variable and one we are interested in

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19
Q

research designs - experimental

A

correlational
experimental
quasi

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20
Q

research designs - cross sectional

A

longitudinal
case study

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21
Q

correlational design

A

relationship / association between variables

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22
Q

stats techniques used for correlational design

A

Pearson product of moment correlation
spearman’s rho
simple linear regression
multiple linear regression
chi squared analysis

23
Q

issues with correlational design

A

not causation (only observing and recording changes)

24
Q

experimental research design

A

experimenter manipulates the IV and see an effect on the DV (differences between conditions)

25
features of experimental research
has a hypothesis random allocation
26
quasi research design
seeing if there are differences on the DV between conditions of the IV (pre existing difference) no random allocation
27
quasi stats techniques
t test mann whitney wilcoxon analysis of variance
28
structure of experimental design
has a time line: obs = observation in relation to DV (pre and post test) exp = experimental treatment no exp = control t = timing of obs
29
within participant design
repeated measure / related same ppt in every condition
30
between participant design
independent / unrelated different groups of ppts
31
+ve of within participant design
control individual confounding variables fewer ppts = low cost
32
-ve of within participant design
order effects - practice, boredom, fatigue counterbalancing demand characteristics
33
+ve of between participant design
less likely to get bored or tired = less order effects and demand characteristics
34
-ve pf between participant design
more ppts lose some control over confounding variables individual differences
35
cross sectional design
collection of data on more than one case at a single point in time with 2 or more variables interested in variation and relationship between variables
36
structure of cross sectional design
collection of data on a series of variables at a single point in time creates a rectangle of data
37
features of a questionnaire
reduced bias (presented to everyone the same way) open and closed questions descriptive research large samples self administered or by researcher less intrusive postal versions easy to do and analyse
38
pre coding questionnaire
set format closed responses can be scanned into SPSS data sheet
39
designing a self completion questionnaire
easy to follow don't cramp identify response sets in a likert scale clear instructions how to respond keep questions and answers together well worded questions easily understood happy to share info start and end with 'friendly' questions avoid 2 in 1 use simple language avoid leading don't presuppose ensure produce variablilty test in person with trial vary format to reduce boredom include response id on every page
40
+ve of self completion questionnaire
cheaper quicker no interviewer effects no interviewer variability convenience for respondents
41
-ve of self completion
cannot prompt cant ask too many difficult to ask other kinds of questions can be read as a whole cant collect additional data can report desirable answers not real may not take seriously not appropriate for all risk of missing data low response rate
42
how to improve response rate
good covering letter return S.A.E with clear instructions follow up for no responses questionnaire correct length capture interest limit open ended questions provide incentive eg voucher
43
what does a cover letter include
friendly short describe what's being done tailor to audience incentives s.a.e use deadline describe confidentiality name and phone number
44
features of follow ups
include 2nd copy of questionnaire cant guarantee anonymity only confidentially sounds surprised at non response
45
how can bias occur with random allocation
due to missing data
46
not randomly missing data
fails to reply to question because they don't want to answer
47
pilot study
tests measures too are going to use on a very small sample and helps identify the feasibility
48
guidelines for creating data for analysis
decide variable and document them design data set with one subject per line variable with proper designated name descriptive labels for each variable select type for each variable additional tips for categorical variables define missing values codes consider need for grouping variables
49
variable with proper designated name
begin with letter short no blanks or special characters unique and no duplicates case doesn't matter
50
descriptive labels for each variable
eg Age: Age on 1st Jan 2022 Gender: male or female
51
select type for each variable
numeric (quantitative) character (categorical)
52
additional tips for categorial variables
case consistent avoid long data codes consider binary coding
53
what not to do with missing data
don't fill with 0 eg use '''
54
data screening
check errors (scores out of range) find error in data file correcting the error in data file

Academic Skills and Data Description Semester 2 (9 decks)

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