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Research design > Exam 1 > Flashcards

Exam 1 Flashcards

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1
Q

Research question

A

Research has selected a topic and formulated a research question.
Very specific and clear to what extent it will be studied.

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2
Q

What are Variables?

A

Variable is a measurable property that differs among entities or across time.
Variables need to be specific.

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3
Q

Levels of variable

A

Conceptual and Operational (measurement)

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4
Q

Conceptual Definition

A

Describe the theoretical meaning of a variable

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5
Q

Operational Definition

A

Provides a tool for quantification and measurement of a variable

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6
Q

Identifying variables

A

Identify the variables of interest. Informed by the research question and guides hypothesis. Need clearly defined IV and DVs

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7
Q

Independent variable

A

What you are manipulating. Independent of other variables

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8
Q

Dependent variables

A

What you are measuring (outcome) Affected by changes in an IV

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9
Q

Forming the Hypothesis

A

The hypothesis is the expected results of the study. It is based on theory and/or previous research. Hypothesis must be testable. Two types

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10
Q

Null hypothesis

A

The prediction that there are no differences among treatments or no relationship among variables. Denoted by H0

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11
Q

Alternative Hypothesis

A

Prediction that there are differences among treatments or there is a relationship among variables. Denoted by H1. Directional or Non-Directional

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12
Q

Directional hypothesis

A

Predicts specific relationship/outcome and the direction. Example: the people that come to class will do better on the final exam

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13
Q

Non-directional hypothesis

A

There will be a difference but not sure where. Example: there will be a difference in the Mid-Term from people who come to class compared to those who don’t

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14
Q

Understanding the Null and Alternative Hypothesis

A

In scientific research, we always either: Reject the null hypothesis or fail to reject the null hypothesis or accept the alternative. We do not ‘accept’ the null hypothesis and we do not ‘prove’ or ‘disprove’ a hypothesis

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15
Q

data collection

A

need to decide on the specific procedures to gather the data to test hypotheses. consider: design, validity, reliability, sampling techniques

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16
Q

study designs

A

experimental, quasi-experiment, qualitative

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17
Q

Study validity

A

internal and external

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18
Q

internal validity

A

extent to which the results of a study can be attributed to the treatments used in the study

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19
Q

external validity

A

the generalizability of the results of a study. we want to infer our sample findings to the population

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20
Q

test validity

A

degree to which a test/instrument measures what it is supposed to measure

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21
Q

reliability

A

refers to consistency/repeatability of a measure. a test cannot be valid if it is not reliable. however, a test can be reliable but not valid

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22
Q

population

A

a large group of people from which a sample is taken. estimate population characteristics from a sample. larger samples more representative or generalizable. sample type, too specific: lose generality

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23
Q

probability sampling

A

every person has an equal probability of being selected. includes: random selection, systematic sampling, and stratified sampling

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24
Q

non-probability sampling

A

no assurance is given that each item has a chance of being selected. includes: convenience sampling and purposive sampling

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25
Q

random sampling

A

each member of the population has an equal chance of being selected

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26
Q

random sampling steps

A

assign a number to each member of the population. use a random number table or software to select numbers

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27
Q

random sampling benefits

A

every case in the populate has an equal chance of selection

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28
Q

systematic sampling steps

A

assign a number to each member of the population. choose a random starting point. from that point, choose every Kth person.

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29
Q

systematic sampling benefits

A

faster than random sampling

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30
Q

systematic sampling drawbacks

A

possible systematic error

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31
Q

stratified sampling

A

the population or sampling frame is divided and grouped on a characteristic before random selection takes place

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32
Q

Stratified sampling steps

A

the population is divided on some characteristics. sample is then randomly selected proportionally from the different strata. this approach can be particularly important if there is a certain characteristic that needs to be represented in the sample

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33
Q

convenience sampling

A

a process of drawing a sample from groups of people that are familiar or convenient. clinicians might ask patients to participate in their studies. kin profs might ask students, coaches, teams

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34
Q

convenience sampling benefits

A

quick and easy

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35
Q

convenience sampling disadvantages

A

not random, not always representative

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36
Q

purposive sampling

A

involves identifying units that represent a characteristic of interest. sample is identified with that purpose in mind. serval types of purposive sampling; snowball sampling, quota sampling- if you have to split in groups 50/50

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37
Q

replication

A

study should be replicable. results should be able to be reproduced

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38
Q

analyzing and interpreting results

A

data analysis phase, interpretation phase, and communication phase

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39
Q

data analysis phase

A

analyze the data using appropriate statistical techniques

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40
Q

interpretation phases

A

compare results with the hypotheses on the basis of your theory. do your results support the hypotheses, theory/ previous research?

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41
Q

communication phase

A

prepare written and/ or oral report for publication/ presentation

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42
Q

experimental research

A

intervention/ treatment introduced. attempts to provides explanations. allows causal inferences

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43
Q

non-experimental research

A

no intervention/ treatment introduced. often trying to describe. hearing their story. not manipulating the variable

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44
Q

qualitative research

A

based on the generation and interpretation of non-numerical data. three main sources of qualitative data: open-minded interviews, direct observation, Witten documents

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45
Q

defining features of qualitative research

A

well-suited for understanding peoples meaning of experience. data collected in participants natural setting. the researcher is an integral part of the research process. researchers play an integral role in generating data. use of the term data generation rather than data collection. emphasizes the manner in which researchers and participants work together to generate data

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46
Q

qualitative research types

A

five common types of qualitative research.

1.Narrative 2. Ethnography 3.Phenomenology 4. Case study 5. Grounded theory

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47
Q

narrative

A

stories are used to bring understanding or meaning to the lived experience of individuals. various specific forms of narrative inquiry; life history, oral history. stories typically generated via in-depth and unstructured interviews

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48
Q

ethnography

A

seek to understand cultures or a cultural group. specifically, the behaviours, values, and beliefs. data generation primarily through participant observation. interviews and documents may also be used

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49
Q

Phenomenology

A

purpose is describe a lived experience of a phenomenon from participants perspectives. use multiple methods to collect data. uses bracketing. a method used in qualitative research to mitigate the potentially deleterious effects of preconceptions that may taint the research process. research goes into the field with no preconceived attitudes, beliefs or opinions themselves.

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50
Q

case study

A

study of the complexity and distinctiveness of a case with important circumstance. cases of interest: people, team, event, organization, or community. data generation through interviews, observation, visual methods

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51
Q

grounded theory

A

purpose in theory development. relies on constant comparative method to develop theory. simultaneously collect and analyze data. examine the data against each other in an effort to identify similarities and differences. continue this until saturation is met, until no new themes are emerging. data generated via interviews, in dept interviews

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52
Q

sampling

A

purposeful sampling. researchers may choose to identify a specific form of purposeful sampling. extreme case sampling. maximum variation sampling. snowball sampling

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53
Q

data generation

A

interviews are the most common method for generating data in qualitative research. qualitative studies often use more than one method of data generation

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54
Q

interviews

A

one on one. groups interviews need group rules. important to blood and maintain rapport, making sure they feel comfortable. typically comprised of three main phases- introduction, questioning and closing. interviews are often recorded and then you transcribe them

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55
Q

structured interviews

A

same questions, same order, same wording

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56
Q

semi-structured interviews

A

list of questions but flexibility to ask additional questions

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57
Q

unstructured interviews

A

concepts and ideas you want to touch upon buts its more like a conversation

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58
Q

observation

A

going into the natural setting to try to better understand the topic of the study. spending a prolong amount of time in a setting. field notes are taken throughout the observation and are focused on what is seen

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59
Q

written documents

A

various types of written documents can be used to generate data in qualitative research . public documents, written documents from participants, medical records, memos

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60
Q

trustworthiness, four aspects

A

one method to evaluate qualitative research. four aspects of trustworthiness; truth value, applicability, consistency, neutrality

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61
Q

truth value

A

credibility of the study, confidence in the “truth” of the study findings for participants

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62
Q

applicability

A

transferability of study, forming understanding that may be relevant to other contexts or participants

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63
Q

neutrality

A

findings are based on participants meaning and experience, findings are not a mere function of researchers’ biases, interests and perspectives

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64
Q

consistency

A

dependability of a study, seek to understand variability of study findings, understand unique experiences

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65
Q

evidence of trustworthiness

A

audit trail, member checking, peer debrief, present negative or discrepant information, prolonged engagement, purposeful sampling, research flexility, rich thick descriptions, triangulation

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66
Q

audit trail

A

researchers maintain detailed description of entire research process. someone external to study examines various components of study

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67
Q

member checking

A

study participants review data or study interpretations. opportunity to add, alter, delete

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68
Q

peer debrief

A

researchers pushed by professional “peer” to critically reflect on study

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69
Q

present negative or discrepant information

A

presenting information that counters main study findings. highlights opposing views and unique experiences

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70
Q

prolonged engagement

A

sustained time spent with participants “in the field”

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71
Q

purposeful sampling

A

recruiting information-rich participants who can best inform research question

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72
Q

researcher flexility

A

researchers position themselves. reflect on biases, experiences, and background to consider how these shape research

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73
Q

rich, thick descriptions

A

collecting through descriptive data. presenting findings in a rich manner, quotes

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74
Q

triangulation

A

crosscheck study findings and interpretations. use variety of data sources, perspectives and methods

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75
Q

usefulness of qualitative research

A

understanding the individual experience. understanding the subjective experience. problem: generalizability

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76
Q

what are statistics?

A

a set of mathematical processes that deal with collecting, organizing and interpreting quantitive data. descriptive techs, correlation techs, differences among groups

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77
Q

inferential statistics

A

generalization of results to some larger population

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78
Q

types of variables

A

continuous, and discrete (categorical)

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79
Q

continuous data

A

attributes of characteristics that can theoretically have infinitely fine gradations. can be expressed as fractions

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80
Q

discrete data

A

variables in which there are no intermediate values possible. cannot be expressed as fractions

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81
Q

levels of measurement

A

nominal, ordinal, interval, ratio

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82
Q

nominal variables

A

classify object or events into categories. assuming numbers to classify characteristics into categories. the assigned values are simple labels. when there are only two levels of nominal variable this is referred to as dichotomous or binary variable and the number associated does not matter

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83
Q

ordinal variable

A

variable that as categories that are ordered. differences between categories is meaningless.

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84
Q

interval variables

A

equal intervals on the scale. distance between any two points are equal. cants say one is twice the other. no absolute 0, the zero point is arbitrary

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85
Q

ratio variable

A

equal intervals on the scale. distance between any two points are equal. ratios. absolute zero point

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86
Q

central tendency

A

values that describe the middle characteristics of a set of data. mean, median and mode

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87
Q

mean

A

the arithmetic average of a variable in a group or sample. mean= sum of all sources/ # of sources

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88
Q

median

A

middle value in a set on ordered numbers

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89
Q

mode

A

most frequently occurring number

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90
Q

variability

A

an index of how the score vary or disperse. measures of variation; range, variance, standard deviation

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91
Q

standard deviation

A

spread of score above and below the mean. the larger the SD, the more variability you have in you sample. ever time you report a mean you MUST report a SD

92
Q

normal distribution

A

in normally distributed data, the standard deviation is approximately 3SD above and below the mean

93
Q

kurtosis

A

leptokurtic curves and platykurtic curves

94
Q

leptokurtic curves

A

limited to variability, positive kurtosis, more peak

95
Q

platykurtic curves

A

large variable, negative kurtosis, more spread

96
Q

skewness

A

the direction of the hump of the curve and the nature of the tails of the curve. positive or negative

97
Q

positive skewness

A

the hump of the curve is shifted to the left with the long tail to the right

98
Q

negative skewness

A

the hump of the curve is shifted too the right with the long tail to the left

99
Q

categories of statistical tests

A

parametric or nonparametric

100
Q

parametric

A

normal distribution of population, equal variances, independent observations, large sample size, DV has to be measured on at least interval or ratio scale

101
Q

nonparametric

A

distribution is not normal, DV cane categorized or ordinal, above assumptions of parametric stats are not met, sample size requirements are less stringent than the parametric tests

102
Q

Alpha level

A

level of significance, probability value (P value), power. alpha is the predetermined crucial value for significance. usually 0.05. if the study was done 100 times, the decision to reject the null hypotheses would be wrong 5% of the time

103
Q

p-value

A

the actual critical value obtained from data analysis. it indicates the probability that the result could be a function of chance. compared to your predetermined aphelia value. p-value is considered significant if it is less than or equal to alpha. the p-value for a statistical test should be reported as the exact number

104
Q

power

A

the probability of rejecting the null hypothesis when the null hypothesis is false.

105
Q

type 1 error

A

every time something is compared we increase our chances of making a Type 1 error

106
Q

type II error

A

accepting the null hypothesis when the null hypothesis is false. false negative FN or beta error. it is the result of. a lack of power. caused from under sampling, exaggerating the estimate of an effect size which leads to under sampling

107
Q

significance vs. effect sizes

A

statistical significance just tells us if the differences are unlikely to have occurred by chance. the ES refers to the practice significance or how meaningful results are. the magnitude/ change that happens to your DV that an be attributed to you intervention/ treatment. an ES of 0 is no difference

108
Q

Relationship between power, ES, alpha, and sample size

A

sample size is influenced by alpha level, power, and the ES. if power and the ES stays the same but a more stringent alpha is used a greater number of particles are required to detect a significant difference

109
Q

Planning research

A

alpha level is predetermined. effect size: should be based on prior research. power generally sat at a value of 0.8. sample size: you want a balance between statistical and practical significance

110
Q

experimental research

A

A treatment is intentionally introduced to see the effect on the dependent variable

111
Q

true experimental design

A

most powerful means of generating new knowledge. the only quantitative design used to identify a cause and effect relationship. typically conducted in a lab within a controlled environment. this level of control helps to ensure the study’s internal validity. external validity is not as easy to claim

112
Q

true experimental design includes

A

an experimental group, a control group

113
Q

group membership

A

using a method of random assignment

114
Q

Randomized control trail (RCT)

A

is a type of true experimental research design. pre and post test design. post test only design

115
Q

criteria to establish cause and effect

A
  1. the cause must precede the effect. 2. the cause and effect must be correlated 3. the correlation between cause and effect cannot be explained by another variable
116
Q

derivates of true experimental designs

A

between subjects designs and within subjects designs

117
Q

between subjects designs

A

separate groups of participants, each being tested by a different treatment/conditions. each group is given a separate treatment

118
Q

within subjects designs

A

all participants are exposed every treatment to every treatment or condition. multiple testing sessions.

119
Q

common characteristics of an experiment

A

random selection from the population of interest. random assignment into groups. blinding. treatment-control comparisons. pre-test assessment (sometimes). post-test assessment

120
Q

random sampling

A

any member of the population of interest has an equal chance of being selected

121
Q

random assignment

A

every participant must have an equal probability of being assigned to either the control or experimental group. helps to ensure groups do not differ at the beginning

122
Q

blinding

A

open label. single blind. double blind. triple blind

123
Q

open label

A

no blind, everyone is aware of treatment allocation

124
Q

single blind

A

either participants or tester unaware of the treatment allocation

125
Q

double blind

A

neither participants nor tester knows which treatment the participants are receiving

126
Q

triple blind

A

participants, experimenters and investigators are all unaware, reduces placebo effect

127
Q

treatment-control comparisons

A

comparing treatment to control provides information about how treatment improves over time. make sure the treatment is better than no treatment

128
Q

baseline evaluations

A

pre-test to establish baseline. can then compare baseline to post-test. complications: practice effects

129
Q

internal validity

A

extent to which the results of a study can be attributed to the treatment used in the study. ability to say that any difference in the DV is a result of the IV. true experiment designs strives for high internal validity

130
Q

external validity

A

the generalizability of the results of a study. external validity is difficult to claim in true experimental designs. the real world does not allow control.

131
Q

validity tradeoff

A

researchers often attempt to maximize internal and external validity but… nearly impossible to have both. impossible to have both high internal and external validity. impossible to have both high internal and external validity

132
Q

quasi- experimental designs

A

studies that are “sort of” experimental in design. no randomization to groups. low on internal validity but higher in external validity. basic formula for a quasi- experiment study; people are studied in real-world settings, an independent variable in introduced or manipulated, there is a dependant variable (the effect) that is measured

133
Q

treats to internal validity

A

testing, instrument accuracy, experiment drop-out/ mortality, selection bias, placebo effects, statistical regression,

134
Q

treats: testing

A

the effects of one test on subsequent administration of the same test. pre-testing can provide a practice effect

135
Q

instrument accuracy

A

changes in calibration, inappropriate use of entrustment(s), different techniques used between first and follow-up measure, between researcher differences

136
Q

experimental drop-out/mortality

A

participants dropping out or leaving the study

137
Q

selection bias

A

forming groups without random assignment. may cause groups to be biased on some or many characteristics. groups could be different due to treatment

138
Q

placebo effect

A

participants reacting in a way they expect they would react

139
Q

statistical regression

A

groups selected on the basis of extreme score are not as extreme on subsequent testing. scores change in the direction towards the mean

140
Q

controlling treats to internal validity

A

randomization, blind setups, calibration of instruments

141
Q

treats to external validity

A

setting and treatment interaction, selection and treatment interaction

142
Q

setting and treatment interaction

A

studies conducted in highly controlled environments. difficult to know if the same outcome would be found in real world settings

143
Q

selection and treatment interactional

A

unique characteristics of participants makes the treatment only effective for them. can’t generalize results to people who do not have the same characteristics

144
Q

controlling treats to external validity

A

conduct experiments on groups that have different characteristics. conduct experiments in new settings. select participants that represent a larger population

145
Q

Epidemiology Definition

A

is the study of the frequency, distribution and determinants of health nd disease in populations, and the application of this study to control health problems

146
Q

what do epidemiologists do?

A

search for the cause of disease. identify people who are at risk. determine how to control or stop the spread. identify new diseases that have never been seen before and causes of them. examine how and where disease outbreaks start. make recommendations to control spread or prevent future occurrences

147
Q

John Snow

A

1813-1858
father of modern epidemiology. 1854 cholera epidemic in London England. miasma theory, “cholera caused by bad air”. skeptical of this theory so began researching, collecting data, and speaking with local residents

148
Q

Snow’s work

A

discovered nearly all deaths occurred around broad street. used dot map to illustrate the cluster of cholera around broad street pump. advised officials to close pump that supplied water to the neighbourhood. once he pump was shut down cases of cholera diminished. later discovered that this public well had been dug 3 feet from an old cesspit that was leaking fecal bacteria

149
Q

measurements

A

proportions and rates

150
Q

proportions definition

A

number of health events divided by total populations. in percentages

151
Q

rates

A

a measure of the frequency with which an event occurs in a defined population in a defined time. in rates

152
Q

prevalence

A

the proportional of population that are affected by a disease at specific time. total number of cases (old and new) of the disease in a given population/ total number of people in that population. is a proportion, usually a percentage

153
Q

Incidence

A

the number of new cases of a disease that occur during a specific period of time in a population at risk for developing the disease. number of new cases of a disease in a population during a specific period of time/ total number of propel at risk of developing the disease in that population during the same period of time. is a rate. expressed as n case per N population

154
Q

three points apply when calculating incidence

A
  1. pre-existing case of the disease cannot be included in the numerator 2. people who already have the disease or who are incapable of having the disease are excluded from the denominator 3. the calculation is based on the population that all at-risk individuals are observed for an period of time
155
Q

increase in prevalence

A

due to:
increased incidence, decreased mortality, increased duration of disease (chronic disease), better or increased screening of disease

156
Q

incidence vs. prevalence

A

harder to measure new cases (incidence) than total cases (prevalence). Canada has a good estimate of cancer incidences because of cancer registries. poor estimates of diabetes incidence because new cases are not often reported to central depository

157
Q

observation study

A

gather dat by observing events

158
Q

experimental study

A

researcher imposes an intervention

159
Q

why observational studies?

A

expose is too dangerous (unethical), exposure is too expensive, experimental would take to long to get result

160
Q

cross sectional studies

A

data collected from a sample at a specific point in time. disease and exposure determined simultaneously. participants are then classified based in their exposure and disease status at that point in time

161
Q

cohort studies

A

a cohort is assembled with no of these individuals having the outcome/disease. once in the study, people in the cohort are classified according to their exposure. all members are then observed over time to see which of them develop the outcome/disease

162
Q

cohort definition

A

a group of people who have something in common when they are first assembled and then who are observed for a period of time

163
Q

famous cohort study

A

framingham heart study

164
Q

framingham heart study

A

well-recognized influence of hypertension and hypercholesterolemia on the development of coronary heart disease is confirmed. a “risk score” = using standardized variables to predict. 10 year cardiovascular disease risk. a gold standard in medicine for assessing need for treatment

165
Q

smoking causing lung cancer

A

follow forward 187,766 men. link proven “beyond a reasonable doubt”

166
Q

case control studies

A

participants are grouped on disease status: case= someone affected by the disease/ outcome. control= someone not affected by the disease/ outcome. retrospectively investigate whether participants were exposed to the factor of interest. case and control participants are then classified as either exposed or not exposed

167
Q

famous case control

A

disease carcinoma of the lung. 709 lung carcinoma patients. 709 non cancer control patients. patients interviews about smoking. 688 lung carcinoma patients were smokers

168
Q

cross selectional advantages

A

can target high risk populations, relativity fast and inexpensive

169
Q

cross sectional disadvantages

A

temporal issues cannot determine whether exposure preceded disease

170
Q

cohort advantages

A

know temporal relationship, good for rare exposures

171
Q

cohort disadvantages

A

not useful for rare outcome, often ling follow-up times, expensive

172
Q

case-control advantages

A

good for rare diseases, relatively inexpensive and fast

173
Q

case control disadvantages

A

not good for rare exposures, most susceptible to bias

174
Q

Independent sample

A

samples chosen are independent, no relationship between those chosen. Subjects chosen in one sample have no bearing on who is chosen in the other sample. Example; right handed people vs. left handed people

175
Q

Dependent sample

A

the subjects chosen in one sample depend directly on who was chosen in the other sample. Examples: test-retest experiments, brothers and sisters, mothers and daughters

176
Q

Choose which test: suppose we are interested in determining whether men watch more TV than women in one week.

A

independent samples T-test

177
Q

Independent samples T-test

A

compares the means between two independent groups on the same continuous dependent variable.

178
Q

which test answers the question: is there a significant difference between the two sample means?

A

Independent samples T-Test

179
Q

independent samples t-test assumptions

A
  1. DV is continuous (interval or ratio scale). 2. IV is categorical consisting of two independent groups (dichotomous). 3. independent of observations (participants can only be in one group) 4. no signifiant outliers. 5. homogeneity of Variance (variances pf two populations are equal
180
Q

Homogeneity of Variance

A

Assumes the samples were drawn from two populations that have approximately equal variance. tested is SPSS via levees test of homogeneity of variance.

181
Q

when do you not record an ES?

A

if your result is not significant

182
Q

Mann Whitney U statistic

A

nonparametric equivalent to an independent t-test. this test is used to: 1. compare two groups on a DV that is ordinal. 2. Compare two groups on a DV that is continuous but does not meet the assumption of normality

183
Q

which test:Suppose 10 dancers are given a jump-and-reach test. Then they take part in 10 weeks of dance activity that involves leaps and jumps 3 days/week. The dancers are then given the jump-and-reach test after the 10 weeks.

A

dependent samples t-test

184
Q

Dependent samples t-test

A

compares the means of two related groups to determine whether there is a statistically significant difference between these means. often the same participants are tested more than once. Therefore, the same participants are in both groups.

185
Q

Possible relationships

A

one group of participants is test twice on the same variable. Ex; comparing baseline and post-test score of a single group after undergoing an intervention. A single group compared on two continuous outcomes. ex. comparing whether students miss more class because of oversleeping or illness.

186
Q

Dependent samples t-test assumptions

A
  1. DV is continuous (measured on a interval or ratio scale). 2. IV is categorical consisting of two ‘related groups’, 3. No significant outliers in the difference between the two related groups. 4. The distribution of the differences in DV between the two related groups should be normally distributed.
187
Q

Wilcoxon Signed Rank Test

A

Non-parametric test equivalent to dependent samples t-test. this test is used when the two groups of scores are related and: 1. the DV is measured on an ordinal scale. 2. the DV is measured on an continuous scale but does not meet the assumption of normality

188
Q

which test for this scenario: Suppose we are interested in knowing if Canada, France, or Australia won more gold medals in the 2016 Rio Olympics.

A

Analysis of Variance (ANOVA)

189
Q

ANOVA is used…

A

to determine whether there are any statistically significant differences between the means of two or more independent groups. extension of an independent samples t-test.

190
Q

Why not use several independent samples t-test?

A

increase the risk of Type 1 error, too time consuming

191
Q

ANOVA is…

A

an omnibus test statistic. tells you whether groups means are different but not which pairs of means are different

192
Q

post-hoc test (ANOVA)

A

post-hoc tests are used to determine which pairs of groups means are significantly different. many types of post-hoc tests to control for type 1 error. ex. bonferonni, tukey

193
Q

ANOVA assumptions

A
  1. DV is continuous (interval or ratio) and is normally distributed. 2. IV is categorical consisting of two or more independent groups. 3. independence of observations. 4. no significant outliers. 5. homogeneity of variance, tested using Levene’s test in SPSS
194
Q

Reporting ANOVA results

A

include statement about Levens test and P-value. P-value. F-value. The degrees of freedom between groups. The degrees of freedom with groups. and effect size when ANOVA is significant

195
Q

Kruskal-Wallis test

A

non-parametric equivalent to an ANOVA. this test is used to: 1. compare 3 or more independent groups on a DV that is ordinal. 2. Compare 3 or more independent groups on a DV that is continuous but does not meet the assumption of normality.

196
Q

Which test: Suppose we are interested in knowing whether the distance that children throw a ball using an overhand pattern increases over time. We decided to measure the children’s distance thrown every 4 months during a year. Thus, each child’s throwing distance was assessed 3 times.

A

repeated measures analysis of variance (RMANOVA)

197
Q

RMANOVA used..

A

to determine whether there are any statistically significant differences between the means of two or more related groups. extension of dependent t-test. RMANOVA is an omnibus test statistic.

198
Q

omnibus

A

shows if there is a difference but you have to look at the table to see where the difference is

199
Q

post-hoc test (RMANOVA)

A

RMAVOA only tells you whether there is a difference, but not specifically what pairs of means scores are significantly different. post-hoc tests are used to determine which pairs of mean score are significantly different. many types of post-hoc tests to control for Type 1 error

200
Q

RMANOVA assumptions

A
  1. DV is continuous (interval or ratio). 2. IV is categorical consiting of two or more related groups. 3. no significant outliers in the related groups. 4. the distribution of the DV in the two or more related groups should be normally distributed. 5. Sphericity, variances of the differences between all combinations of related groups must be equal. Measured using Mauchly’s test is SPSS. Mauchly’s test should be insignificant to meet the assumption of sphericity
201
Q

what test do we look at mauchly’s test for sphericity was significant

A

you would then look at and report the Greenhouse Geisser row

202
Q

Friedman

A

non-parametric test equivalent to RMANOVA. This test is used when the three (or more) groups of scores are related (dependent) and: 1. the DV is ordinal. 2. the DV is continuous but does not meet the assumption of normality

203
Q

correlation

A

a statistical test to determine the relationship (association) between two or more variables. no cause and effect assumed. one variable is not a direct cause of another

204
Q

Correlation coefficient (r)

A

quantitative value of the relationship between two or more variable

205
Q

Correlation coefficient ranges

A

between -1 and 1. -1 indicates a perfect negative relationship (inverse relationship) +1 indicates a perfect positive relationship. 0 indicates no relationship

206
Q

correlation coefficient tells us…

A

direction of the relationship (positive, negative, no relationship). strength of relationship between two variables, it does not indicate the cause of that relationship

207
Q

person moment correlation

A

used to determine correlations between two continuous variables. Has one outcome (DV) and one predictor (IV). every participant has two scores

208
Q

assumptions of Pearson moment correlation

A
  1. two variables are continuous. 2. correlated variables must have normal distribution. 3. no significant outliers. 4. linear relationship between X and Y. 5. Homoscedasticity, correlated variables must have equal variability
209
Q

Coefficient of Determination: Effect size

A

(r2), the squared correlation coefficient. represents proportion of shared variance.

210
Q

Using correlation for prediction

A

one purpose of correlation is prediction, the stronger the relationship between two variables, the more accurately you can predict one from the other. we do not encounter perfect relationships in the real world, there is always some error

211
Q

simple linear prediction (regression)

A

statistical method used to predict a dependent (or outcome) variable (Y), from one independent (or predictor) variable, (X). if two variables have a relationship between them, we can use that information to make a prediction.

212
Q

fitting a regression line

A

to be precise, you would need to calculate the slope and y-intercept and calculate Y-predict using the equation Y-predict = a+bx

213
Q

Spearman Rank-order correlation

A

non-parametric test. this test is used to examine a relationship:

214
Q

issues with research

A

publication bias, replication bias, fraus and misconduct, pseudoscience, media influence

215
Q

Publication bias

A

tendency for statistically significant findings to be published over nonsignificant findings

216
Q

publication bias happened due to

A

researchers not submitting their studies for publication, particularly if they have null results. journals being more likely to publish significant results than studies without statistical significance

217
Q

problems causes by publication bias

A

can lead to the overestimation of how effective an intervention is. leads to skewed perception of a field of research. limits the replicability assumption of science. due to pressures to publish, this can led researchers to p-hack

218
Q

replicability crisis

A

concerned with the large number of scientific studies that cannot be reproduced

219
Q

replication

A

using the same methodology as a prior study but using different participants in order to confirm their results

220
Q

why can’t studies be reproduced

A

inappropriate or incorrect SS analysis. insufficient sample size. publication bias

221
Q

fraud and misconduct

A

violations of the standard codes of scholars conduct and ethical behaviour research. fraud is intentional and misconduct in not

222
Q

Dr Wakefield

A

dumbfuck with the autism study

223
Q

Brain Wansink

A

professor at Cornell, many problems with data reporting, committed p-hacking. he refused to let failure be an option. 15 studies retracted

224
Q

science

A

is the discovery of knowledge

225
Q

pseudoscience

A

the pursuit of knowledge or collection of related beliefs about the world mistakenly regarded as being based on scientific methods

226
Q

dangers of pseudoscience

A

opportunity cost, blocks scientific thinking, direct harm

Research design (1 decks)

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