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Yr 3, Sem 1 research skills > class test 2 (week 5-8) > Flashcards

class test 2 (week 5-8) Flashcards

1
Q

what are the uses for data in the SPSS?

A

> management
> analysis
> storage

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2
Q

what does data management in the SPSS refer to?

A
  • defining variables
  • coding values
  • entering and editing data
  • creating new variables
  • recording variables
  • selecting cases
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3
Q

what does data analysis statistics refer to in the SPSS?

A
  • univariate stats
  • bivariate stats
  • multivariate stats
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4
Q

what are the two screens on the data editor on the SPSS?

A

> data view: previous screen

> variable view: used to define variables

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5
Q

what are the 10 characteristics used to define a variable in variable view?

A 
Name
Type 
Width
Decimals
Labels
Values
Missing
Columns
Align
Measure
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6
Q

what occurs in the data entry process?

A
  • define your variables in variable view

- enter data, the values of the variables, in data view

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7
Q

what is name as a characteristic to define a variable in variable view?

A

-each variable must have a unique name of not more than 8 characters and starting with a latter

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8
Q

what are some things to remember when using names as a characteristic in variable view?

A

try to give meaningful names

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9
Q

what are the two formats for type as a characteristic to define a variable in variable view?

A

internal and output formats

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10
Q

what do internal formats as a type of variable characteristic refer to?

A

> numeric
> string (alphanumeric)
> date

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11
Q

what do output formats as a type of variable characteristic refer to?

A 
> comma
      > dot 
      > scientific notation
      > dollar
      > custom currency
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12
Q

what are numeric variables?

A

numeric measurements and codes

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13
Q

what do string (alphanumeric) variable contain?

A

words or characters; strings can include numbers but, taken here as characters, mathematical operations cannot be applied to them

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14
Q

what is the max size of a string variable?

A

255 characters

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15
Q

what are labels for variables?

A

-descriptors for the variables

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16
Q

what is the maximum amount of characters for labels of variables?

A

-maximum 255 characters

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17
Q

when are labels used for variables?

A

output

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18
Q

what are values of variables?

A
  • value labels are descriptors of the categories of a variable
  • coding
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19
Q

what are the options for missing values and variables?

A

> up to 3 discrete missing values

> a range of missing values plus one discrete missing value

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20
Q

what are columns of variables in data view?

A

-columns sets the amount of space reserved to display the contents of the variable

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21
Q

what does align set in data view of variables?

A

whether the contents of the variable appear on the left, centre or right of the cell in data view

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22
Q

are numeric variables right or left hand justified by default in data view?

A

right-hand

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23
Q

are string variables right or left hand justified by default in data view?

A

left-hand

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24
Q

what are the levels of measurement?

A

> nominal
> ordinal
> interval
> ratio

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25
Q

in SPSS where are interval and ratio levels of measurement designated?

A

designated together at scale

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26
Q

what is the default measurement level fro string variables?

A

nominal

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27
Q

what is the numeric measurement level fro string variables?

A

scale

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28
Q

wat is an interview transcript?

A

written record of an interview that has been transcribed from the verbal conversation

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29
Q

what are the benefits of transcribing?

A

> aids data familiarisation
> opportunity to reflect on interviewing style
> record emotional expression noted during the interview

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30
Q

what does confidentiality focus on dealing with?

A

participant identity

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31
Q

how can researchers practice confidentiality?

A

-interview transcripts use a pseudonym, change names of organisation, places and other people mentioned

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32
Q

what is re-identifiable data?

A

identifying info is removed from data and replaced with code so they can be re-identified

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33
Q

what is the iterative process in qual data analysis?

A

moving between data collection and analysis

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34
Q

what is inductive process in qual data analysis?

A

looking for patterns or themes in data rather than imposing ideas onto the data

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35
Q

what are some important parts of qual data analysis?

A
  • make constant comparisons within and across interviews
  • move from raw data to codes, subcategories, categories and finally themes
  • draw well support conclusions based on data collected to answer research question
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36
Q

what are some types of methods of qual data analysis?

A

> content
> thematic
> discourse

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37
Q

what are some examples of approaches to data analysis for qual research designs?

A

> narrative analysis
> grounded theory methodology
> interpretive phenomenological analysis

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38
Q

what is thematic analysis?

A

-method for identifying, analysing and reporting patterns/themes within data

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39
Q

what is involved in the process of thematic analysis?

A

> familiarising oneself with data
> breaking the data into codes
> grouping similar codes together to form categories with associated subcategories
> define categories and associated subcategories
> group categories together form themes

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40
Q

what are the 4 steps of thematic analysis?

A
  • data familarisation
  • generating initial codes
  • developing categories
  • generating themes
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41
Q

what is involved in the data familiarisation step of thematic analysis?

A

undertake an initial quick read of each interview transcript and record initial response to data

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42
Q

what is involved in the generating initial codes step of thematic analysis?

A

code each interview transcript by writing a summary word or phrase that captures key idea foe each line or sentence within transcript

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43
Q

what is involved in the generating initial codes step of thematic analysis?

A

code each interview transcript by writing a summary word or phrase that captures key idea for each line or sentence within transcript

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44
Q

what is involved in the developing categories step of thematic analysis?

A
  • re-read transcripts with initial codes looking for similarities/differences within single interview and across all interviews
  • begin by grouping initial codes
  • develop hierarchy system of categories and subcategories
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45
Q

what is involved in the developing themes step of thematic analysis?

A

focuses on examining relationships between each of the categories developed and how they might be grouped too fit under overall theme

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46
Q

what are some tips for initial coding?

A
  • keep codes close to participant words/or use their words (in-vivo codes)
  • use active language
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47
Q

what are some tips for coding sub-categories and categories?

A
  • subcategories must relate to category under which they fall
  • not all participants will have info that fits into all categories/subcategories
  • participants might have differing views on same topic this can provide Moore complete understanding of sub-categories
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48
Q

what are some trustworthiness strategies?

A
  • field notes
  • audit trail
  • triangulation
  • member checking
  • peer review
  • prolonged engagement
  • reflexivity
  • thick description
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49
Q

what is the idea of probability?

A

everyone in pop will have the opportunity to have data collected

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50
Q

what are the three categories of non-respondents?

A
  1. data collection procedures do not reach, not giving them chance to answer questions
  2. asked to provide data who refuse to do so
  3. unable to perform task or provide info needed
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51
Q

how is the response rate calculated?

A

number of people responding divided by number of people sampled

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52
Q

what is the response rate usually reported as?

A

% of a selected sample where data was collected or completion rat

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53
Q

will completion rates be lower or higher than response rates?

A

higher

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54
Q

how is non-response to surveys based?

A

non-respondents are systemically different from the whole pop

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55
Q

what is the response rates of mailed surveys?

A

5% to 20%

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56
Q

are response rates higher or lower in rural compared to city?

A

higher

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57
Q

what do acceptable response rates range from?

A

15-75%

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58
Q

what are some drawbacks of mailed surveys in regard to response rates?

A
  • people more interested in topic likely to return= bias

- better educated people often send back more quickly

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59
Q

what time of the day will telephone survey information be more distinctive

A

f data is collected between 9am and 5 pm on Sunday > Thursday

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60
Q

what are groups the tend to be under-represented in surveys?

A 
> unemployed 
     > single 
     > recent migrant 
     > inner city areas
     > low income 
     > low education 
     > don’t speak English
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61
Q

what are two issues that must be addressed to achieve high rates of response?

A

Gaining access to the selected individuals

Enlisting their cooperation

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62
Q

how do you approach increasing response rate due to lack of availability for telephone surveys?

A
  • make numerous calls during evening and weekend
  • 6 to 10 calls per household often needed
  • have interviewers with flexible schedules to make it convenient to respondents
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63
Q

how can you increase response rate by enlisting cooperation?

A
  • send info letter in advance
  • effectively present purpose
  • ensure respondents are not threatened
  • effective interviewers
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64
Q

what amount of people who initially refuse to survey involvement later agree?

A

approx .25 to .33

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65
Q

what can help increase response rate of mailed surveys?

A
  • look more professional or personalised
  • clear layout
  • questions should be attractively spaced, easy to read and uncluttered
  • tasks should be easy to do
  • responses should be a check box, circle a number or other simple task
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66
Q

what is the process of sending out a mailed survey to increase response rate?

A
  • about 10 days later mail reminder
  • about 10 days after reminder send out reminder letter to emphasise importance
  • last step if to contact respondents by telephone or email
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67
Q

what is a way to track who has or hasn’t responded to mailed survey?

A

an identification number can be written on questionnaire or return envelope

-good practice to tell them what number is for

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68
Q

how can we enhance response rate of internet surveys?

A

make task easy, repeating contacts, using more than one mode to contact respondents and offering alternative modes of responding for this who don’t initially respond appear to maximise rates

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69
Q

what are three approaches to minimise resulting error when correcting nonresponse?

A
  1. Using proxy respondents
  2. Doing stat adjustments
  3. Re-surveying sample of non-respondents
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70
Q

how are proxy respondents utilised to enhance response rate?

A
  • many surveys collect data from one household respondent about other members
  • if respondent is unable/unwilling to be interviewed asking another household member to report is an option
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71
Q

what type of information gathering is proxy respondents appropriate for?

A

factual but no subjective info

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72
Q

what are some things to take into account when planning your analysis?

A

budget, personnel, timeline, topic, target audience

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73
Q

what is the formatting technique for quant data?

A

> must quantify data

> convert (data reduce) from collection format into numeric database

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74
Q

what is the formatting technique for qual data?

A

> must process data (type/enter-describe)

> convert from audio/video to text

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75
Q

what is the formatting process of a quant and qual combination technique?

A

> process each element as appropriate

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76
Q

what are types of quant data analysis?

A

> counts, frequencies, tallies

> stat analyses ( as appropriate)

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77
Q

what are types of qual data analysis?

A

> coding, categories

> patterns, themes, theory building

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78
Q

what are types of qual and quant combination data analysis?

A

> process each element as appropriate

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79
Q

what are types of qual and quant combination data analysis?

A

> process each element as appropriate

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80
Q

what must occur before analysis?

A

must quantify data

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81
Q

what is quantification?

A

the process of converting data to numeric format

82
Q

what are some simple transformations when quantifying?

A

> assign numeric rep to nominal or ordinal variables:
> turning male into 1 and female into 2

 > assign numeric values to continuous variables:
   > turning born in 1973 to 35
   > number of children to 02
83
Q

what is the goal of coding quant data?

A

reduce a wide variety of info to a more limited set of variable attributes

84
Q

what is the purpose of codebook construction?

A

-guide data set construction, locating variables, and interpreting codes in data file during analysis

85
Q

what does optimal scan sheets do when entering data?

A

limits possible responses

86
Q

when is data entered in a CATI system/online

A

while collected

87
Q

what is data entered directly onto in CATI system/online?

A

SPSS data matrix, excel, or ASCII file

88
Q

what do CATI system/on-line typically work off?

A

coded questionnaire

89
Q

what are the measures of central tendency?

A

mean, median, mode

90
Q

what are the goals of presenting univariate data?

A

> provide reader with the fullest degree of detail regarding data
> present data in manageable form
> simple and straightforward

91
Q

what is the point of subgroup comparisons?

A

describe subsets of cases, subjects or respondents

92
Q

what is the bivariate analysis?

A

describe a case in terms of two variables simultaneously

93
Q

how can you construct bivariate table?

A
  • divide cases into groups according to the attributes of the independent variable
  • describe each subgroup in terms of attributes if the dependent variable
  • read table by comparing the IV subgroups in terms of a give attribute of the DV
94
Q

does the dependent variable in a bivariate table go in the row or column?

A

row

95
Q

does the independent variable in a bivariate table go in the row or column?

A

column

96
Q

what type of variables are typically made into categorical variables in bivariate tables?

A

continuous

97
Q

what are levels of measurement for continuous variables?

A

interval and ratio

98
Q

what are four types of quant research methods?

A
  • descriptive
  • correlational
  • cause-comparative
  • experimental
99
Q

what is involved in descriptive quant research method?

A

collecting data for hypothesis testing

100
Q

what is involved in correlational quant research method?

A

determining whether and to what degree a relationship exists

101
Q

what is involved in cause-comparative quant research method?

A

establishing the cause-effect relationship

102
Q

what is involved in experimental quant research method?

A

establishes the cause-effect relationship, but manipulates the cause

103
Q

wha are common methods of data collection in quant research?

A

> surveys/questionnaires
> structured interviews
> observation or interaction analysis
> secondary data or content analysis
> experiments

104
Q

what is the purpose of descriptive stats?

A

to describe sample at hand

105
Q

what do inferential stat procedures let us do?

A

generalise our findings beyond particular sample to larger pop

106
Q

what are the three levels of descriptive stats?

A

> central tendency measures
> variability measures
> frequency and percentages

107
Q

what are the four levels of measurement?

A
  • nominal
  • ordinal
  • interval
  • ratio (scale)
108
Q

what is the nominal level of measurement?

A

basic clarification data;

> don’t have meaningful numbers attached, but are broader categories

109
Q

what is the ordinal level of measurement?

A

have numbers attached in certain order, not equal intervals between numbers

110
Q

what is the interval level of measurement?

A

have equal intervals between numbers; distance between attributes have meaning

111
Q

what is the ratio level of measurement?

A

have equal intervals between numbers absolute zero is meaningful

112
Q

what are types of descriptive stats?

A 
> number 
     > frequency count 
     > percentage 
     > deciles and quartiles
     > measures of central tendency (mean, midpoint, mode)
     > variability 
     > variance and standard deviation 
     > graphs 
     > normal curve
113
Q

what is mode?

A

most frequently occurring value in a distribution (any scale, most unstable)

114
Q

what level of data is mode best used for?

A

nominal

115
Q

what is median?

A

midpoint in the distribution below which half of the cases reside

116
Q

what level of measurement is median used for?

A

ordinal or above

117
Q

is median sensitive to extremes?

A

insensitive

118
Q

what is mean?

A

arithmetic average- the sum of all values in a distribution divided by the number of cases

119
Q

what levels of data measurement is mean used for?

A

interval or ratio

120
Q

is mean influenced by extremes?

A

yes

121
Q

in a symmetrical distribution what falls at the same point?

A

median, mode, and mean

122
Q

if a distribution is skewed to the right is it positive or negative?

A

positive

123
Q

if a distribution is skewed to the left is it positive or negative?

A

negative

124
Q

how are mode, median and mean related in a positively skewed distribution?

A

the mode is smaller than the median, which is smaller than the mean.

The median is the point x-axis that cuts the distribution in half, such that 50% of the area falls on each side

125
Q

Does a hard test skew distribution to right or left?

A

left

126
Q

Does a easy test skew distribution to right or left?

A

left

127
Q

how is mean, mode, median related in a negatively skewed distribution?

A

mean being smaller than the median, which is smaller than the mode

128
Q

what are some factors varying of results?

A
  • dispersion
  • range
  • variance/standard deviation
129
Q

what is dispersion?

A

extent of scatter around the average

130
Q

what is range?

A

highest and lowest scores in a distribution

131
Q

what is variance/standard deviation?

A

spread of scores, the greater the scatter the larger the variance

132
Q

is standard deviation sensitive to extremes?

A

yes

133
Q

what does standard deviation reflect?

A

how much subjects differ from the mean of the group

134
Q

what does inferential stats allow for?

A

comparisons across variables

-hypothesis testing

135
Q

what is the level of significance?

A

is predetermined level at which null hypothesis is not supported. The most common level is p < .05

136
Q

what is an error type I?

A

reject the null hypothesis when it is really true

137
Q

what is an error type II?

A

-fail to reject the null hypothesis when it is really false

138
Q

what type of stats used to make decisions and report probability when we have made a type I error?

A

inferential stats

139
Q

what does reporting the p value to readers alert?

A

the odds that we were incorrect when we decided to reject the null hypothesis

140
Q

what levels of measurement are used in a chi-square test of independence?

A

two variables (nominal and nominal, nominal and ordinal, or ordinal and ordinal)

141
Q

what are chi-square tests of independence affected by?

A

number of cells, number of cases

142
Q

what type of hypothesis does a 2-tailed distribution chi-square test of independence result in?

A

null

143
Q

what type of hypothesis does a 1-tailed distribution chi-square test of independence result in?

A

directional

144
Q

what is correlation in inferential stats?

A

the extent to which two variables are related across a group of subjects

145
Q

what is the range of a Pearson r relating to correlation in inferential stats?

A

-1.00 to 1.00

146
Q

what does a 0.00 score indicate in a Pearson r relating to correlation in inferential stats?

A

absence of relationship

147
Q

what does a t-test test for?

A

difference between two sample means for significance

148
Q

what does analysis of variance (ANOVA) test for?

A

difference/s among two or more means

149
Q

what type of analysis is used for ANVOA tests?

A

regression analysis (including step-wise regression)

150
Q

what are the two types of stat presentations of data?

A

graphical and numerical

151
Q

what is the graphical presentation of data?

A

ook for overall pattern and for striking deviations from that pattern. Over all pattern usually described by shape, centre, and spread of the data. An individual value that falls outside the overall pattern is an outlier

152
Q

what are two types of data presentations used for categorical variables?

A

bar diagram and one charts

153
Q

what are data presentation methods used for numerical data?

A

histogram, stem, leaf and box-plot

154
Q

what is a bar graph?

A

lists the categories and presents the % or count of individuals who fall in each category

155
Q

what is a pie graph?

A

lists the categories and presents the % or count of individuals who fall into each category

156
Q

what is a histogram?

A

overall pattern can be described by its share, centre, and spread. The following age distribution is right skewed, the centre lies between 80 to 100, not outliers

157
Q

what is a box plot?

A

describes the five number summary

158
Q

what are the 5 approaches to instrument selection?

A 
Standardised tests/performance measures 
Contextualised/enviro assessment 
Focus groups 
Biometric measures 
Document/record reviews
159
Q

what do semantic differential scales ask?

A

respondents to rate given concept on a series of bipolar adjectives that are used to characterise one’s feelings, attitudes or reactions

eg. Good vs bad, dull vs exciting

160
Q

what is the goal of retesting?

A

goal of protesting is to improve research instrument rather than to provide descriptions of pop

161
Q

what can pretesting the sample design indicate?

A

whether design is feasible, provide an assessment of its difficulty and give a rough estimate of time and costs

162
Q

what is sampling?

A

process where subgroup of participants is selected for study from larger group

163
Q

what is random bias?

A

> happens by chance

164
Q

what are some causes of systemic bias?

A

> volunteers
> non-respondents
> people that share common characteristics
> people with vested interest

165
Q

what are some issues that can affect sampling?

A
  • study questions/hypotheses
  • research approach> quant, qual, single-case design

-practical considerations

> availability, time, space, budget, etc.

-other issues:

> gender, age, location, SES etc.
166
Q

what are some sampling techniques?

A
  • volunteers/convenience sampling
  • targeted recruitment
  • inducement/compensation
  • randomised: simple/stratified
  • purposive
  • snowball/networking
167
Q

what are the sample requirements?

A

> representative
> specify inclusion and exclusion criteria
> establish min number of participants required

168
Q

what occurs with probability sampling?

A

> each person can have equal chance of being selected
> pop known
> reduces sampling bias/increase study rigour

169
Q

is the population known in probability sampling?

A

yes

170
Q

when is non-probability sampling used?

A

> pop is unknown

> not feasible to use probability sampling

171
Q

what occurs in simple random sampling?

A
  • sampling without replacement
  • manual methods: draw out of hat
  • random tables or computer generated lists
  • use computer randomisation assignment programs
172
Q

how is sample selected in stratified random sampling?

A

selection from identified subgroups

173
Q

how is sample selected in systemic random sampling?

A

selection from every nth person on a list

174
Q

how is sample determined in cluster sampling?

A

select groups or programs

175
Q

what is purposive sampling?

A

-deliberate selection and recruitment

176
Q

when is quota sampling used?

A

-when different proportions of participants are needed based on specific criteria

177
Q

when is non-probability sampling used?

A
  • clearly define process of sampling
  • acknowledge limitation of sampling procedure used
  • justify if the sampling limitations do harm the research question being answered
178
Q

what are four important factors in sampling in quant research?

A

Sample size
Effective size
Level of sig.
Power

179
Q

what is the p-value standard level used in most studies for the level of significance?

A

0.05

180
Q

what is power calculation used for in sampling?

A

determines minimum sample size required

181
Q

what is usually the power calculation used in OT studies

A

0.8

182
Q

what are two principles of sampling in qual research?

A

-appropriateness and adequacy

183
Q

what is appropriateness as a principle in qual sampling?

A

identification of participants who will best inform research about phenomena under inquiry

184
Q

what is adequacy as a principle in qual sampling?

A
  • enough data will be available to provide rich description of phenomena of interest
  • continue to collect data until data saturation is reached or data source is exhausted (diminishing returns)
185
Q

what are some strategies for sampling in qual research?

A
  • purposeful selection
  • max variation
  • homogenous
  • theory-based selection
  • convenience selection
  • snowball or network selection
186
Q

what are common types of sampling in qual research?

A
  • purposeful
  • convenience
  • theoretical
  • max variation
  • homogenous
  • snowball
187
Q

what is purposeful sampling in qual research?

A

-info rich participants

188
Q

what is theoretical sampling in qual research?

A
  • sampling used in grounded theory

- seeks people, events, or info to refine theoretical categories emerging from data

189
Q

what is max variation sampling in qual research?

A
  • heterogenous (diverse) sample based on range of characteristics
  • focus on exploring diversity and capturing breadth
190
Q

what is homogenous sampling in qual research?

A

-selecting participants who are as similar as possible

191
Q

what is snowball sampling in qual research?

A

-existing participants suggest new participants who meet criteria

192
Q

when is homogenous sampling useful?

A

when resources are limited

193
Q

what is generalisability?

A

To be able to accurately draw conclusions about the population by studying a subgroup or
sample of that population

194
Q

what is effect size?

A

is the effect of difference between 2 means or the degree of correlation
between 2 variables in the results of a study

195
Q

what is effect size?

A

is the effect of difference between 2 means or the degree of correlation
between 2 variables in the results of a study

196
Q

what is referred to as the p value?

A

Probability statistic

197
Q

what is power?

A

is the calculation completed to determine the minimum sample size required to
complete a study

198
Q

what does reliability relate to?

A

how consistently items of a questionnaire measure what they claim
to measure

199
Q

What type of questions is it when asked to self-reflect on experience, opinions, thoughts,
ideas, attitudes, or needs and then select the best option from a finite number of response
categories?

A

closed

200
Q

what does a pilot study allow?

A

the examination of the procedures used when completing a survey;
it basically is a dry run of doing a larger survey, but instead mimicking it on a smaller scale.

Yr 3, Sem 1 research skills (2 decks)

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