The aim of a study is what theory or idea is being tested
Usually takes the form of 'the effect of x on y' where x is the independent variable and y is the dependent variable
The effect of studying on test scores
The effect of alcohol on driving precision
State a research and null hypothesis of a study (HL only)
Null hypothesis - there is no difference or change between the control and experimental conditions
Research hypothesis - there is a difference between the control and experimental conditions
Can be in the form of a directional or non-directional
Directional - predict the direction of the effect of the independent variable on the dependent variable
Studying more will increase test scores
Non-directional - only predict that there there will be an effect but not in which direction
Amount of study time will affect test scores
State the independent and dependent variable in an experiment
Independent variable - what you manipulate
Alcohol consumption
Dependent variable - what you measure
How many mistakes drunk driver makes
State operational definitions of variables
A variable is operating as a measure for some phenomenon
IQ could be a variable that is operating as a definition (and measure) of intelligence
Describe potential confounding variables
Confounding variables are any factors other than the independent variable that could have affected the dependent variable
In the drunk driving study, driving ability and experience would affect how many mistakes the driver makes
In the amount of study time test scores study, intelligence would affect test scores as well as study time
Carryover effects - in a repeated measures (within subjects) design where participants act as their own control, they may perform better or worse in the experimental condition because of practice
In the IQ test example, test scores are likely to increase from one condition to the next because they have become familiar with the test
Explain the controls needed for an experiment
Maturation - the dependent variable can be affected simply by participants aging
I'm testing how video games affect IQ in 2nd graders
They could improve because their brains are maturing and are cognitively more able to tackle IQ tests
We would need a matched pairs design where we control for existing cognitive ability then have one group play video games and the other not and compare the test scores
Contamination -
Placebo effect - the belief that something is affecting our behavior can be enough to effect the change
This is why when anti-depressants are tested there is always a placebo control group
Counterbalancing - to control for carryover effects we would have some drivers get drunk first and then drive while others would drive sober first and then drink and drive
Explain effects of participant and researcher expectations and bias
Demand characteristics - when the participant guesses what you are investigating and modifies his behavior to either help or hinder you from gaining positive evidence
If the drivers figure out I'm testing alcohol's effect on driving ability, some may help me out by trying really hard not to make mistakes while others may drive extra recklessly
Either way we only want to test for alcohol's effect so we can use a single blind method and not tell the participants what we are investigating and hope they do not figure it out
Expectancy effect - either the researcher or the participant expects a certain outcome from the experiment and unconsciously affects the results
A researcher investigating aggression in children may be more inclined to accept ambiguous acts as aggressive
Can be resolved by making it double-blind and using independent observers
Explain the use of single-, double- and triple blind techniques
Single blind - participant doesn't know which treatment they are given
Double bind - neither participant nor researcher knows which treatment participant is given
Triple blind - participant, researcher and statistician do not know which treatment the participant was given
Discuss the strengths and limitations of experimental design
Independent samples (Between Subjects)
2 separate groups of individuals
Each group is participates in a different condition (experimental or control)
Strengths
Each participant is only given one treatment so there are no carryover effects
Limitations
Cannot account for individual differences
Requires more people and therefore usually costs more
Repeated measures (Within Subjects)
1 group of individuals
All individuals participate in all conditions
Strengths
Less participants needed
Accounts for individual differences because each participant acts as their own control
Limitations
Effects of repeated testing (e.g. practice) can confound the results
Matched Pairs
A form of between subjects that attempts to control some of the individual differences by matching participants' characteristics in the experimental group to the control group
Can be age, gender, socioeconomic background, intelligence, culture, personality type
Strengths
Accounts for individual differences that repeated measures does not
No carryover effects
Limitations
There are some individual differences that are problematic to control for
Single Participant
Usually natural or quasi-experiments where the researcher cannot manipulate the independent variable usually because it would be unethical to do so
Putting a tamping iron through someone's head vs. it occurring naturally with Phineas Gage
Strengths
Allows the study of unique individuals
Can elucidate phenomena that would otherwise be unethical to intentionally cause
Limitations
Lack of control over variables for natural experiments
Can be difficult to find a control participant to compare to
Cannot extrapolate the results to the entire population because the sample size is too small
Sampling procedures
Discuss sampling techniques appropriate to quantitative research
Random
You randomly choose participants based on some algorithm like their area code
Opportunity
Most common type of sample where you choose people for your study based on their willingness to participate
Most studies use university students who either have to participate for course credit or receive some money for their cooperation
Systematic
Type of random sample but you have taken measures to make sure that every individual in your target population has an equal chance of being chosen
Stratified
When you have a sample composed of diverse individuals it is ideal to stratify the sample
Say I have a sample of high school students aged 14-18 with different ethnicities, cultural backgrounds and genders
Ideally the sample should be divided into homogeneous subcategories called stratums
One stratum would 17-year-old boys, caucasian, who grew up in an individualistic country (preferably the same country)
Another stratum could be 17-year-old boys only or caucasians only
Can reduce variability of scores within the stratum
Discuss how participants are allocated to experimental and control groups
Random Allocation
Random allocation can be generated by a computer
Using other criteria like the first 10 who arrive go in the experimental group is invalid because there may be characteristics of the participants that make them show up early for the experiment that could confound the results
Matched Pairs
Relevant characteristic is matched in the experimental group and control group
Raine et al., (1997) study used a control group matching for age, sex and where relevant schizophrenia
Explain the concept of representative sampling
The sample chosen should accurately represent the characteristics of the target population
It makes no sense to only use university students for a study as they do not accurately represent the population
Some characteristics of university students like intelligence, age and socioeconomic background could skew the results
Evaluation of research
Discuss the concepts of internal and external validity
Generally there is always a trade-off between internal and external validity
Laboratory experiments tend to have high internal validity but lack some form of external validity
Field experiments tend to have high external validity (though only for that specific setting) but lack internal validity because of the nature of the real world
You can't create a world where participants do not know that everything is under control like The Truman Show, though it would be perfect for psychological research
Internal Validity
Experiment must be valid in that it measures what it claims to measure
Experiment must be reliable in that the same measuring tool was used for all participants
External Validity
Refers to the extent that results from the experiment can be generalized to the general population, different conditions and settings
Small sample sizes threaten external validity especially if they are not representative of the population
Ecological validity refers to the problem of generalizing results from an experimental settings to real world situations
It is also argued that even if you conduct an experiment in one real-world setting does not mean that they generalize to all real-world settings
Solution is to conduct the same experiment in different real-world settings and hope for converging results
Discuss conditions that increase a study's reliability
Counterbalancing in a within subjects design to reduce carryover effects
Using matched pairs to establish controls
Replication increases validity and reliability
Cross-cultural verification to account for individualism/collectivism and power distance
Using different genders
Apply descriptive statistics to analyse data
Measures of Central Tendency
Mean - average of all the scores
used for ratio and interval data
Median - middle value of all the scores
used for ordinal data
Mode - value that appears most
used for nominal data
Measures of Dispersion
Range - maximum value minus the minimum value
Standard deviation - how much scores vary
Distinguish between levels of measurement
Nominal
Lowest level of measurement
Surveying beer preferences among college students
Data is just stated
Ordinal
Likert scale
Data can be put in order from least to greatest
People in a race are given places 1st, 2nd, 3rd
But we don't know how much faster 1st place was compared to 3rd place
1st place, 2nd place etc are ordinal data
Allows for some comparision but not by how much in meaningful terms
Rating happiness on a Likert scale
Person who rates 5 compared to 10 we cannot say that person is twice as happy, only that 10 is happier than 5
Interval/Ratio
Like ordinal data but now we have meaningful units to compare
Test scores are interval/ratio data
If you get 5/10 correct and someone else gets 7/10 correct we can say that person did 20% better than you
The difference between interval and ratio data is that ratio data has an absolute zero whereas interval data does not
Apply appropriate graphing techniques to represent data
Bar charts for comparing means
Histograms for showing frequency of nominal data
Scatter plots for correlational data (ordinal or interval/ratio)
Apply an appropriately chosen statistical test
Parametric
Tests for interval/ratio data
Comparing Means
t-test for independent samples
t-test for repeated measures
Relationship
Pearson's
Non-Parametric
Tests for ordinal data and below
Comparing Means
Mann-Whitney U - Independent measures
Wilcoxon t-test - Repeated measures
Sign test - Matched pairs
Relationship
Spearman's rho for ordinal data
if at least one of the two variables is ordinal then use Spearman's
