Online Module Flashcards

Question

Independent + dependent variable - causal relationship

Answer 1

Independent Variable: - thought to be CAUSAL Dependent Variable: - hypothesized to be AFFECTED by the independent variable. Example: In the hypothesis "sunlight affects mood," - sunlight = independent variable - happiness = dependent variable.

Answer 2

- I have variables in a study (eg. temperature, type of environment, family size) - For easy data analysis, I want to ASSIGN NUMBERS to my variables - then i will PUT THEM ON A SCALE - the different types of scales can TELL US different things

Answer 3

1) Nominal 2) Ordinal 3) Interval 4) Ratio

Answer 4

- CATEGORIES are assigned numerical codes - SCALE ITSELF that doesn't really mean anything!! - eg. HR = 1 Marketing = 2 Finance = 3 OR Female = 1 Male = 2 Other = 3

Answer 5

WHAT DOES IT TELL US? - Numbers are put onto a scale, and this indicates ORDER - BUT the scale does not reveal the magnitude of difference between points. EG. Finishing positions in a race (1st, 2nd, 3rd) - doesn't tell you the time between the runners, BUT shows order OR Time spent in the sun: A little = 1 Average = 2 A lot = 3 - we can't quantify the difference between scale 1 + 2, BUT in order

Answer 6

WHAT DOES THE SCALE TELL US? - EQUAL units of measurement SEPARATING each score on the scale - BUT there is no real zero point (zero does not mean the complete absence of something).

Answer 7

1) scales could just be LITERAL NUMBERS - but must be EQUAL in distance + no 0 point eg. Temperature in Celsius; 10, 15,20,25 AND 0°C doesn't mean no heat.

Answer 8

WHAT DOES THE SCALE TELL US? - EQUAL units of measurement SEPARATING each score on the scale - BUT there IS a real zero point (zero does mean the complete absence of something).

Answer 9

EXAMPLE 1 - LITERAL LIST OF NUMBERS - Distance in centimeters 0cm, 5cm, 10cm, 15cm = equal intervals! - 0cm means the objects are touching - ABSENCE of distance

Answer 10

ORDINAL SCALE - Here intervals between the scales are NOT equal (we can get random age from 1 and 2, and this won't be the same for 3 and 4) - BUT order is correct

Answer 11

How we FORMAT + STRUCTURE a research project - In terms of: - What participants are asked to do - The way participants are grouped - The way data is collected - How the project progresses over time

Answer 12

1. Research question 2. Hypothesis 3. Variables used 4. Measures used 5. Plans for data analysis

Answer 13

1. Descriptive 2. Correlational 3. Non-experimental 4. Experimental 5. Quasi-experimental

Answer 14

1) OBSERVATION ONLY - Descriptive 2) CORRELATION (NOT cause and effect) - Correlational - Non-experimental 3) CAUSE AND EFFECT - Experimental - Quasi-experimental

Answer 15

- Introducing a control treatment - Only present for EXPERIMENTAL designs (cause + effect) - NOT present in both correlation designs

Answer 16

- NO experimental manipulation done

Answer 17

WHAT IS IT? - Just observations EXAMPLE? - Investigate typical height of people in class using measuring tape

Answer 18

Correlational - SIMILARITIES = Finding correlations, NOT cause + effect - DIFFERENCES = One group, 2 variables * Eg. how does exercise impact happiness? * Per person, 2 data has to be collected → exercise in a week + happiness quiz Non-experimental - SIMILARITIES = Finding correlations, NOT cause + effect - DIFFERENCES = Two groups, 1 variable * Eg. are science students smarter than art students? * For each group, 1 piece of data = IQ test

Answer 19

- Eg. How does exercise impact happiness? -> This doesn’t consider other factors like sleep, friendship, alcohol in the experiment -> Hence furthest we can go is talk about correlation

Answer 20

Experimental - SIMILARITIES = Finding cause and effect - DIFFERENCE = 2 CORE FEATURES -> 1) Manipulation of independent variable → to create a control treatment → eg. group with depression, group without -> 2 ) Controlled experimental conditions → random assignment of participants to different groups Quasi-experimental - SIMILARITIES = Finding cause and effect - DIFFERENCE = less controlled -> Same manipulation of control treatment, but no random assignment!!

Answer 21

- Most effective to see if medicine works - One group = treatment given - Second group = treatment not given - This acts as a control treatment - able to really see if the medicine works or not OR… - Want to see if depression (IV) has a direct link to happiness - One group = depression - Other group = no depression - Check happiness level using survey - Random assignment further eliminates control for BIAS / might impact results!

Answer 22

- Same as experimental - BUT participants may have been assigned to groups on the basis of a characteristic difference/shared quality - This is NOT assigning them to groups randomly - SO…may have other factors associated with independent variables NOT CONTROLLED!

Answer 23

- Degree of confidence we have in the direct relationship between the independent variable in a research study - (IE. causation and not just correlation)

Answer 24

- Through RANDOM ASSIGNMENT of participants to different groups - Ie. don’t share all of the same characteristics

Answer 25

- Are we observing, testing for correlation, or cause + effect? - If correlation = is it 1 group + 2 variables (classic correlation) OR 2 groups + 1 variable - If cause + effect = is there random assignment of groups or not?

Answer 26

- Correlation = no control treatments = YES, NATURALISTIC OBSERVATION - Causation = yes, control treatments = NO, NOT NATURALISTIC OBSERVATION → EXPERIMENTAL MANIPULATION

Answer 27

POPULATION - Everyone of relevance to a research question BUT… - usually not possible to investigate an entire population so… SAMPLE - A sample is a group of people drawn from the population to participate in a research project.

Answer 28

- If the research investigates the prevalence of anxiety in emergency workers, the population would be all emergency workers. - If the research investigates cognitive features of schizophrenia, the population would be everyone who meets the criteria for a diagnosis of schizophrenia.

Answer 29

1. PROBABILITY SAMPLING - the probability of selecting an individual CAN be determined. - Population characteristics are known 2. NON-PROBABILITY SAMPLING - the probability of selecting a given individual CANNOT be known. - Population characteristics are NOT known

Answer 30

1. List Everyone: Write all 500 names on slips of paper (or assign numbers 1–500). 2. Random Selection: Put the slips in a hat, mix, and draw 10 OR Use a computer to randomly pick 10 numbers between 1 and 500. * We know the probability of choosing everyone is EQUAL * BUT we can’t do this all the time (eg. can’t have people at a school write their names)

Answer 31

PROS - No selection bias (probability of choosing everyone in the population is EQUAL) CONS - Doesn’t guarantee the sample will be representative - Eg. all boys in sample, no girls

Answer 32

CONVENIENCE SAMPLING - The sample consists of individuals who are easy or convenient to recruit.

Answer 33

- Example: First-year research experience programs - Recruiting participants through hospitals for cancer research questions

Answer 34

PROS - Easy, efficient CONS - Very prone to SELECTION BIAS - Eg. You might accidentally recruit mostly 18-year-olds (easier to find on campus), ignoring older students.

Answer 35

USE QUOTAS - Definition: Pre-set limits for how many people you recruit from each subgroup (e.g., age, gender). - Goal: Mimic the natural distribution of groups in the population. NOTE - there are many ways to do this though, quotas are not mandatory, but recommended

Answer 36

Imagine your POPULATION of interest has this age split: - 70% under 25 - 20% aged 26–50 - 10% over 50 Quota Sampling: - In your sample you will set target numbers for each age group to match these percentages in the population! - Example: For a sample of 100 students: -> 70 participants under 25. -> 20 participants aged 26–50. -> 10 participants over 50.

Answer 37

Without Quotas: - You might accidentally recruit mostly 18-year-olds (easier to find on campus), ignoring older students. With Quotas: - SO…improves generalisability (by reflecting OG population) + inclusion of all age groups

Answer 38

- We take a sample then make INFERENCES on the whole population - SO…goal of the sample is to always REPRESENT/MIMIC the overall population dynamic!!!

Answer 39

- DO NOT just get rid of the research - Instead, just be TRANSPARENT and make this clear in your report

Answer 40

- NHMRC (national health and medical research council) - They publish recommendations in the “National statement on ethical conduct in human research”

Answer 41

1) Merit 2) Integrity 3) Justice 4) Beneficence 5) Respect

Answer 42

Research has benefit to humanity Either in 2 forms: - Providing knowledge to society - Tangible (eg. improve social welfare)

Answer 43

- Honesty - no manipulation of results / hypothesis - Ie. anything that ruins public knowledge + understanding

Answer 44

EQUITY - In terms of inclusion + exclusion of research participants - No burden placed on one participant group - Benefits of participation distributed fairly PARTICIPANT TREATMENT - Participants not exploited - Money paid not proportional to time + effort - eg. paying 10 dollars for a 10 hour study - Remuneration - eg. paying 300 dollars for a 15 minute task - pressuring participation

Answer 45

- Maximising benefits - Minimising risks and harm - INFORMED CONSENT So… - Must make clear of risks - Not ethical if information about POTENTIAL risk/harm is not mentioned!!

Answer 46

- Individual’s culture, beliefs, welfare - Respect privacy + confidentiality - BUT asking qs on private, personal sensitive info is not considered unethical! Just need informed consent

Answer 47

NO - Research proposal must be shown to an ETHICS ADVISORY COMMITTEE - They will decide if the research can proceed using the 5 ethical principles

Answer 48

- Background of project - Methodology - How potential risks will be reduced - Measures used (eg. surveys) - Explanation of participant tasks - Documentation

Answer 49

1) Plain language statement - clearly what the research is about in language w/o jargon 2) Consent form 3) Debriefing statement - after the research. Esp if there was deception involved

Answer 50

1) APPEARANCE Histograms = bars joined together/touch Bar graphs = bars separated 2) X-AXIS Histograms = continuous variable, range of numbers (eg. age, height) OR just one number (eg. score of 1,2,3,4) Bar graphs = discrete variable, categories (eg. fruit, colours) 3) Y-AXIS Frequency 4) EXAMPLE Histograms = survey total 100 people. Want to find how many people in age groups 20-30, 40-50 etc. Bar graphs = survey total 100 people. Want to find how many people like cats, dogs, birds etc.

Answer 51

- Way of organising data - Tell us the number of times (frequency) a value occurs! FORMALLY… - Groups the no. of individuals - Located at each point on the scale of measurement

Answer 52

- Impossible to make inductions from raw data - Need to organise it into frequency distribution!

Answer 53

1) Histogram 2) Frequency table 3) Box plot

Answer 54

1) NORMAL DISTRIBUTION - Looks like a bell-curve 2) POSITIVELY SKEWED - Most data values take on LOWER values - SO…tail at the end / right 3) NEGATIVELY SKEWED - Most data values take on HIGHER values - SO…tail at the front / left

Answer 55

- Includes RELATIVE FREQUENCY for each category - Can also present frequency as a percentage (shows proportion to total observations) - Includes CUMULATIVE frequency for each category (shows how observations accumulate to total of 100%)

Answer 56

1) LOWER WHISKER + LOWER PART = Bottom VALUE of 25% 2) BOX = Middle 50% 3) MIDDLE LINE = Median 4) UPPER WHISKER + UPPER PART = Top VALUE of 25% 5) OUTLIERS = represented by dots outside the whiskers. (whisker = lines w/ caps at the end) Top means top in terms of dependent variable

Answer 57

- Let’s say there are 20 students doing a test - 25% = 5 students - SO…for lower quartile, find the 5 LOWEST test scores (Eg. 15,17,20,30), then plot this onto the graph

Answer 58

- The quartile is always FIXED at 25% (eg. 5 people) - BUT the RANGE of the lower values vary - E.g. bottom 25% covers a low range of scores of 15-30, while top 25% covers large range of scores 60-100 SO…rmb box plots show SPREAD of values, not the actual frequency

Answer 59

- the hump on the graph is on the LEFT HAND SIDE of the graph - tail on the RIGHT

Answer 60

- the hump on the graph is on the RIGHT HAND SIDE - tail on the LEFT

Answer 61

- mean is greater than the median as a few extremely large values PULL the mean upwards - we say that mean gets "dragged" toward the tail at the end - the median remains closer to most values SO... MEAN > MEDIAN > MODE

Answer 62

MEAN = MEDIAN = MODE

Answer 63

- Bottom whisker shorter than the top whisker - Rmb we only know SPREAD, not FREQUENCY - Bottom whisker means lower values not as spread out - So here we look at SPREAD!

Answer 64

1) Nominal = scale no. represents discrete data (eg. 1=female, 2=male) 2) Ordinal = scale no. doesn’t show magnitude of interval - SAME AS NOMINAL, BUT shows order (eg. who came first + last in a race, 1st, 2nd, 3rd // 1=support, 2=moderate, 3=no support) 3) Interval = intervals between data is equal BUT no 0 point (eg. 10,15,20 degrees, 0 degrees doesn’t mean no temp) 4) Ratio = intervals between data is equal AND has a 0 point (eg. 0,5,10,15cm, 0 does mean absence of distance) (Nominal + ordinal tends to have discrete data REPRESENTED BY numbers) (Interval + ratio tends to be ACTUAL DATA - ie. no need to be represented)

Answer 65

What is most TYPICAL or REPRESENTIVE of data

Answer 66

Mean (ie. average) Median Mode

Answer 67

- Sum of all data points - Divide by no. of data points Eg. 1,2,3 Mean = 1+2+3 / 3

Answer 68

- Arrange data in rank order, then find the middle numnber - OR Identify the 50% percentile of data (because 50% of data is above the median, 50% is below the median)

Answer 69

When data is SKEWED or there are OUTLIERS - Eg. workers earn 20 dollars per hour, but the boss earns 80 dollars per hour - Including outliers in calculations would make the mean NO LONGER REPRESENTATIVE SO…data for the mean must be NORMALLY DISTRIBUTED!!

Answer 70

- Outliers do not have an impact. As it is based on RANK ordering - SO…suitable/can be used for skewed data w/ outliers (unlike mean)

Answer 71

1) NOMINAL = discrete data, so MODE 2) ORDINAL = discrete data but ordered, so MEDIAN 3) INTERVAL = actual data, no representation by scale, so mean 4) RATIO = actual data, no representation by scale, so mean BUT…if data is not normally distributed for the mean, MEDIAN will replace it, closer to show the representative

Answer 72

- Q1 is the 25th PERCENTILE - Eg. if data is test scores, Q1 is the score 25% of students scored less than this - If Q3, 25% of students scored greater than this AND - It is the MEDIAN of the LOWER HALF of the data

Answer 73

- It is Q2, ie it is 50th percentile - 50% of data is less than the median, 50% of data is greater than the median

Answer 74

1) Rank data points (2,7,9) 2) Median POSITION = n+1 / 2 3) So…Median position = 4/2 = 2nd position 4) So…Median = 7

Answer 75

1) Rank data points (1,2,4,7) 2) Median POSITIONS = n/2, n/2 + 1 3) So…Median position = 4/2 = 2nd position, 3rd position 4) So…Median = 2+4 / 2 = 3

Answer 76

- How scores are SPREAD throughout distribution of data - Refers to how data differ or not - Eg. Low variability = scores are very close to the mean - Eg. High variability = scores are spread out, short+ large distance to the mean // eg. variance, range, IQR

Answer 77

- Low variability = mean is a good representation - High variability = mean is a bad representation SO…central tendency alone is not enough to tell us about the data

Answer 78

1) Range 2) Interquartile range 3) Standard deviation

Answer 79

Difference of the largest and smallkest piece of data Eg. 1,2,3,4,5,6,7,8,9,10 Range= 10-1=9

Answer 80

Distance between Q1 (25th percentile) and Q3 (75th percentile)

Answer 81

- EVEN = split data into half with an imaginary line. Find the median of the lower + upper half - ODD = remove/ignore median. Find the median of the lower + upper half

Answer 82

1) Order data = 2,4,7,9,10,12 2) Split data into half using an imaginary line = 2,4,7 // 9,10,12. 3) Calculate half by n/2 Find median of both halves. This will be Q1 and Q3 4) Q1 = 4, Q3 = 10 5) IQR = 10-4 = 6

Answer 83

1) Order data = 3,5,7,9,11 2) Find the median (n+1 / 2) , and cross it out, REMOVE it! 3) Find median of both halves. This will be Q1 and Q3 4) Q1 = 3+5/2 = 4 5) Q3 = 9+11/2 = 10 6) IQR = 10-4 = 6

Answer 84

Average distance of data from the mean

Answer 85

1) Find distance of EACH data to the mean 2) Add all this up 3) Divide it by number of data points

Answer 86

1) Find distance of EACH data to the mean = called DEVIATION SCORES - Eg. if the mean is 5 and raw score is 7, DS = 2 - Eg. if the mean is 5 and raw score is 3, DS = -2 2) Negative distances is an ISSUE. When we total it up, it will cancel out. Hence we must SQUARE deviation scores - (-2)^2 = 4. 3) NOW we can add all the SQUARED deviation scores up. - The sum is called SUM OF SQUARES (SS) 4) Now divide this by number of data, BUT -1 SS/n-1 - n-1 represents degrees of freedom. - This corrects for underestimation bias - SS/n-1 is called VARIANCE 5) Standard deviation - BUT this is all squared - So… SD = root of variance

Answer 87

Measure of raw data’s distance to the mean

Answer 88

- Sum of - SQUARED deviation scores - In a distribution

Answer 89

- Sum of squares - Divided by one less than the sample size - SS/n-1 OR…squared standard deviation

Answer 90

The AVERAGE deviation score in a distribution

Answer 91

INTERQUARTILE RANGE - Because median and IQR are both not affected by outliers or unequally distributed data - Because data is ranked Ie. fit the same data conditions!!

Answer 92

STANDARD DEVIATION - Both work best when data is normally distributed + no outliers

Answer 93

An active process of scrunity

Answer 94

Basiclaly look at the entire research process circle we studied above 1) Whole research methodology 2) Deductive argumentation AND Inductive argumentation 3) Internal valdiity 4) External validity

Answer 95

Deductive argumentation to derive a hypothesis AND Inductive argumentation to go from research findings to a conclusion - forms the entire research process

Answer 96

Internal valdiity =looking for things that threaten confidence in IV-DV relationsihp

Answer 97

looking for things to see how generalisable the findings are to the real world + various populations

Answer 98

2 ASPECTS 1) Premises 2) Conclusion - We start with PREMISES (statements) - Premises are meant to support the CONCLUSION

Answer 99

1) Deductive argument 2) Inductive argument

Answer 100

1) PREMISE - Broad - Form of broad theory 2) CONCLUSION - Specific - Form of specific hypothesis

Answer 101

- This method is used to DERIVE a hypothesis (from broad theory) - It doesn’t matter if the hypothesis doesn’t hold up, because the logic may be valid, however in REAL LIFE it doesn’t prove to be true!

Answer 102

- As it forms the hypothesis - Tends to be found in the INTRODUCTION SECTION of research reports

Answer 103

VALID/INVALID - Premises must provide ABSOLUTE SUPPORT for the conclusion

Answer 104

- The premises DO NOT need to be true 1) We focus more on the STRUCTURE of the argument 2) Think - although the premises are true, is there a possibility of the conclusion being false? 3) Is there a GAP in the logic? - Ie. is something ignored or not considered?

Answer 105

DEDUCTIVE ARGUMENT - Premise = Taking broad statements - Conclsuion = forming a specific hypothesis VALID 1) Examine it in terms of STRUCTURE

Answer 106

DEDUCTIVE ARGUMENT - Premise = Taking broad statements - Conclsuion = forming a specific hypothesis INVALID - Examine it in terms of STRUCTURE - Assumes that if both groups possess the same quality, the groups are the same - This is not true - possessing the same quality foesn’t automatically mean you are the same entity

Answer 107

DEDUCTIVE ARGUMENT - Premise = Taking broad statements - Conclsuion = forming a specific hypothesis INVALID - Examine it in terms of GAPS in the logic - Doesn’t consider that things other than sunlight can lead to happiness in workers + wrong to assume night shift workers don’t sleep during the day

Answer 108

Not just a VALID deductive argument - It is called a SOUND deductive argument

Answer 109

1) PREMISE - Specific - Tends to be an OBSERVATION/interpretation after conducting research 2) CONCLUSION - Broad

Answer 110

STRONG OR WEAK - This is because data collected from research studies do not provide ABSOLUTE SUPPORT for the conclusion - All data can do is either prove the conclusion to be MORE or LESS LIKELY to be true

Answer 111

- TOTALLY different to deductive argument - Deductive we look at logical structure etc - ABSOLUTE support - Here we look at the quality of case study -> 1) Sample size -> 2) Representation / generalisbility

Answer 112

INDUCTIVE ARGUMENT - Premise = specific observation - Conclusion = broad conclusion/observation STRONG - Good sample size - Same findings in both studies - LIKELY that conclusion is the case

Answer 113

INDUCTIVE ARGUMENT - Premise = specific observation - Conclsuion = broad conclusion/observation WEAK - Bad sample size of only 2 - UNLIKELY that conclusion is the case

Answer 114

1) INDUCTIVE ARGUMENT - Premise = specific observation - Conclsuion = broad conclusion/observation 2) WEAK - Super small sample size - Premise 1 is based on correlation, not causal relationship (cause + effect) → broad GENERALISATION - UNLIKELY that conclusion is the case

Online Module Flashcards

(139 cards)