Research methods - Design - Flashcards in University Psychology

What makes a good research design?

- rooted with in literature
- It will add something new to the literature
-It will be achievable
- It will be something you understand

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what to avoid when making a research design?

- Avoid projects which only replicate well-established effects - give a
- different take
- Are unethical
- Are too ambitions

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Relational Design

Is there a relationship between height and age in children
- Example of correlation

Does age predict height in children?
- Example of regression

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Experimental Design

Has two types
- Between subjects/groups
- Within participant designs

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Between subjects / group

Each participant only experiences
one condition

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Within Participants design

- Each participant experiences all conditions
- Issus with order effects but easily controlled by counterbalancing

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Variables

Independent variable is the cause while
Depdendent variable is the effect

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Independent variables

Can be changed by researcher but not anything else in the experiment
eg: study experiment looking at the effects of studying on test scores, studying would the be independent variable

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Dependent Variables

Dependent on the independent variable

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Nominal Data

Nominal - variables are used to name or label a series of values
- Not numerical related
- Categories / categorical
- Mutually exclusive
- Descriptive - they don’t have a quantitative or numeric value
Remember - nominal = name

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Ordinal Data

- Not numerically related
- Ranked or placed in order
- Differences between scores don’t represent real differences
- There are discrete
Ordinal = order

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Interval Data

give us order of values + ability to quantify the difference between each one
- Numerically related
- Order
- Differences between individual scores are equal
- Don’t have a true zero can be negative
- They can be discrete (whole numbers) or contin

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Ratio Data

give us the ultimate order, interval values, plus the ability to calculate ratios since a true zero can be defined
- Numerically related
- Order
- Equal difference between values
- Absolute zero, scores cannot be negative
They can be discrete or co

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Cross sectional research analysis

- Takes place at a single point in time

- All test measures or variables are administered to participants on one occasion

- This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a per

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Longitudinal research

- Takes place over a period of time
- Data is first collected at the beginning of the study and may be then gathered repeatedly throughout the length of the study
- Some studies may occur over a short period of time such as a few days while other coul

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Correlational

- A measurement of the relationship between two variables
- - A positive correlation is direct relationship where as the amount of one variable increases as the amount of a second variable also increases

- A negative correlation would be the amount of

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Participants

Who will participants be?
• Special populations
• Characteristics
Appropriate control participants

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How many participants will you need?

• Depends on the expected variability in the data
• Number of variables
eg. Questionary studies - high variability - many variables - larger samples
- Do I have access to enough participants to complete the study in a reasonable time

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Feasibility

- A study that is detailed analysis that considers all of the critical aspects of a proposed project in order to determine the likelihood of it succeeding

- A feasibility study asks whether something can be done, should we proceed with it, and if so, ho

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Confounding Variables

A confounding variable is. Third variable that influences both the IV and DV. Failing to account for confounding variables can cause you to wrongly estimate the relationship between your IV and DV

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Confounding variable carried on

A variable must meet two conditions to be a confounder:

It must be correlated with the independent variable. This may be a causal relationship, but it does not have to be.
It must be causally related to the dependent variable

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confounding variable example

Higher ice cream consumption is associated with a higher probability of sunburn. Does that mean ice cream consumption causes sunburn?

Here, the confounding variable is temperature: hot temperatures cause people to both eat more ice cream and spend more t

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Counterbalancing

Counterbalancing is a technique used to deal with order effects when using a repeated measures design. With counterbalancing, the participant sample is divided in half, with one half completing the two conditions in one order and the other half completing

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Benefit of counterbalancing

Counterbalancing ensures that all possible orderings of conditions occur equally often

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Validity

how well the results among the study participants represent true findings among similar individuals outside the study

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Reliability

Whether what you are testing is reliable, will you get the same result if you do it again?

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Instructions

- Are you recruiting the right people
- Is the nature of the experiment clear
- How will you verify that they are right for the experiment
Is there any test or question to rind the right participant

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Instructions carried on

- Are they doing the study well and paying attention properly
- Are they clear instructions
- Do you have rests or breaks
- Are they any health and safety considerations
Welfare issues

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Instruction final part

- How will you ensure compliance or at least detect non compliance?

- Think about instructions
- Are they clearly written
- Are they verbal is there a script or a protocol for these
- Example stimuli / questions
- Practise trials or practise se

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Data Quality

Data screening, will you be able to tell when someone is not behaving properly or remove their data?

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Data quality carried on

Vary the required response
Questionareies - have normal or reverse coded questions
Experiments - make sure the correct answer is not always the same
questions such as please select strongly agree - that will detect automatic responding
Experimenting -

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Ethics

You need to detail

Rational
Hypothesis
Research design
Analysis
Type of participants
Number of participants
Data screening
Health and safety and welfare etc

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Report central tendency

A measure of central tendency is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution

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central tendency carried on

There are three main measures of central tendency
- The mode
- The median
- And the mean

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mode

The most common occurring value in a distribution

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mode cons

some distributions the mode may not reflect the centre of the distribution very well.
- Also possible for there to be more than one mode for the same data. (bi-modal or multimodal) - the presence of one mode can limit the ability of the mode in describi

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mode pros

- It can be found for both numerical and categorical data

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median

Middle value in distribution when values are arranged in ascending or descending order

The median divides the distribution in half - in a distribution with an odd number, the median is the middle number

If the distribution is an even number, the media

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Median pros

The median is less affected by outliers and skewed data than the mean, and is usually the preferred measure of central tendency when the distribution is not symmetrical

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Median Cons

The median cannot be identified for categorical nominal data, as it cannot be logically ordered.

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mean

The mean is the sum of the value of each observation in a dataset divided by the number of observation, also known as arithmetic average
Looking at the retirement age distribution again:
4, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60

The mean is calculated b

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mean pros

The mean can be used for both continuous and discrete numeric data.

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mean cons

The mean cannot be calculated for categorical data, as the values cannot be summed.

As the mean includes every value in the distribution the mean is influenced by outliers and skewed distributions.

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mean carried on

The population mean is indicated by the Greek symbol µ (pronounced ‘mu’). When the mean is calculated on a distribution from a sample it is indicated by the symbol x̅ (pronounced X-bar).

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measure of dispersion

In statistics, the measures of dispersion help to interpret the variability of data i.e. to know how much homogenous or heterogeneous the data is. In simple terms, it shows how squeezed or scattered the variable is

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Types of dispersion

- Absolute measure of dispersion
- Relative measure dispersion

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The absolute measure of dispersion

Contains same unit as the original data set. This method expresses the variations in terms of the average deviation of observations like standard or means deviations. It includes range, standard deviation, quartile deviation etc

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Range

It is simply the difference between the maximum value and the minimum value given in a data set. Example: 1, 3,5, 6, 7 => Range = 7 -1= 6

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variance

Deduct the mean from each data in the set, square each of them and add each square and finally divide them by the total no of values in the data set to get the variance. Variance (σ2) = ∑(X−μ)2/N

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Standard Deviation

The square root of the variance is known as the standard deviation i.e. S.D. = √σ.

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Quartiles and Quartile Deviation:

The quartiles are values that divide a list of numbers into quarters. The quartile deviation is half of the distance between the third and the first quartile.

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Mean and Mean Deviation:

The average of numbers is known as the mean and the arithmetic mean of the absolute deviations of the observations from a measure of central tendency is known as the mean deviation (also called mean absolute deviation).

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A relative measure of dispersion

The relative measures of dispersion are used to compare the distribution of two or more data sets. This measure compares values without units. Common relative dispersion methods include:

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Common relative dispersion methods include

1. Co-efficient of Range
2. Co-efficient of Variation
3. Co-efficient of Standard Deviation
4. Co-efficient of Quartile Deviation
5. Co-efficient of Mean Deviation

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signal vs noise

Signal - systematic or between condition variance

Noise - unsystematic or within condition variance

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Potential confounds in experiments

- Order effects
- Tiredness
- Time of the day
- Habituation
- Training effects
- Group differences (other than the hing you are trying to test)
- Match participants
- Include baseline measures
- Measure relative performance
- Control group

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- Different participants

○ Independent samples
○ Between subjects

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- Same participants

○ Paired samples
○ Within subjects

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Multiple groups of participants but each do several conditions

○ Mixed design
Split plot

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factors vs levels

Factors are distinct independent variables

○ Each variable can take multiple values
- Levels
○ Levels are the values that you plan to test

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Blocked Conditions

- Participant undertakes a batch or block or trials belonging to the same condition followed by a batch of another condition etc

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blocked conditions advantages

- Reduced uncertainity
- Increases habitutation
- Good for splitting conditions across sessions but be careful of time of the day effects
- Good for splitting conditions across sessions but be careful of time of the day effects

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Interleaved conditions

Participants see all conditions each session in either a counterbalanced or randomised order

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Interleaved conditions advantages

- Reduces habitutation
- Increases uncertainty
It can be split into multiple sessions

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Counterbalancing between participants

Each participant undertakes conditions in a different order all orders presented but not all participants see all orders

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Counterbalancing within participants

- In some experiments - especially those involving imaging and EEG the presentation order for individual trials/stimuli matters
- Brains reaction to one stimulus may affect its response to a subsequent stimulus
- Counterbalance the order of stimuli with

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randomization

Participants to 'treatments" if you have two or more treatments or manipulations to be applied to different groups drawn from the same population randomise the allocation of people to treatment groups

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Get Revising

Research methods - Design

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