Research methods paper 2 (booklet 2)

word data (anything that is not numerical data

• Interviews 

• Questionnaires (open questions)

• Unstructured observation

More detailed information

• Costs more money- less research funding provided in result 

• Slower and more difficult to analyse

numerical data

Structured observations (closed questions)

• Quicker and easier to analyse 

• Cheaper

• Less detailed information

data you collect yourself from direct observation 

More reliable as you carried out the experiment yourself

• Expensive 

• Time consuming

data that has been collected from another research that you are just analysing  

• Cheaper 

• Faster to obtain

• Potential of investigator bias because it wasn't collected by you

• Less reliable

Qualitative values- usually tallied- frequencies, not able to rank 

• E.g. gender, weather, ethnicity, marital statu

scaled or ranked data (ordered). Will be subjective ratings. Often seen as a source. 

• E.g. 1-5 on a likert scale (usually a score)

ranked data with equal measurement intervals/standardised measurements and units, objective with arbitrary zero. Uses pre-existing measurement scales 

• E.g. time, temperature, bank balance

same as interval, but includes an absolute zero 

• E.g. cash, distance, weight

tells us about the most typical values in a data set

• Mean

• Mode

• Media

is the average value, only used in interval and ratio data

Advantages:

• Includes all values in data set so most representative 

Disadvantages:

• Easily affected by outliers

• Only usable on interval level data 

is the middle value, only used in ordinal data 

Advantages:

• Not affected by extreme values 

• Easy to calculate 

Disadvantages:

• Less sensitive than mean so less representative

is the most frequent value, only used in nominal data

Advantages:

• Easy to calculate 

• Can be sued on all levels of data 

• Only one that can be used on nominal data  

Disadvantages:

• If several modes, then it can’t be used 

• Does Not represent the whole data set

tell us how for scores vary and differ  between one another

• Range

• Standard deviation

the spread of data. Difference between highest and lowest value 

Advantages:

• Simple to calculate 

• Can be used on ordinal or interval level data 

Disadvantages:

• Not a clear representative of most of the score 

 average spread of values around the mean. Interval and ratio

Advantages:

• Clear representation of how far most values are from the mean 

• More precise than range 

Disadvantages:

• Affected by extreme values but this may not be revealed

• Graphs and tables

• Bar chart

• histogram

• contingency table

• scatter graph/scattergram

• Graphs need to be labelled: title, x-axis, y-axis 

• Tables have subtitles describing table conteny

• Nominal data only (=separated bars)

• Height of bars represent frequencies 

• Continuous data- ordinal, interval, ration (no gaps needed between bars)

• Shows frequency of data in successive numerical intervals 

• IV plotted along the x-axis, DV along the bottom 

• Raw scores displayed in columns and rows

• Often asks for you to draw conclusions from the data 

• Gives a good visual picture of the relationship between the two variables 

• Aids the interpretation of the correlation coefficient

The overall pattern of data seen in a graph. It is always predicted that data will follow a normal distribution

Most of the distribution is concentrated to the right (higher end) and the long tail is to the left (lower end)

Most of the distribution is concentrated to the left (lower end) and the long tail it to the right (higher end)

determining whether a study has significant findings so that you can generalise these findings to the population (outside the confines of the research settings)

how likely something is to happen 

• a number between 0 and 1

• 0 meaning impossible and 1 meaning certain

this means that the likelihood/probability of the behaviour not happening again is equal to or less than 5%- to convey significant findings

• This means that 95 people out of 100 did predict this behaviour 

• This cannot be fluke or coincidence 

• Any higher still counts as significant 

• It is even harder to achieve (e.g. p≤0.01)

when challenging well known theories, psychologists tend to adopt more stringent significance levels e.g. p≤0.02 or 0.1 (only 1 in 100 fail to do this behaviour)

• does not exist in psychological research, unless 100% accuracy is found (p=0.00 does not exist). 

• P≤0.05 shows significant support for a theory, but not proof 

• False positive 

• Belief that a significant difference or correlation is found- but this is an error one does not exist)

• Rejecting the null hypothesis (when we shouldn’t as its actually true)

• False negative 

• Belief that no significant difference or correlation has been found- but this is an error (there is a significant difference/correlation in the data in reality)

• Accepting a null hypothesis (when we shouldn’t because it's actually false)

• Making the significant levels stricter, reduces the chance of a type 1 error (e.g. p≤0.01) , but will increase the chance of making a type 2 error 

• To reduce the chance of both error types, simply increase the sample size (=increase in validity also) 

directional test

non-directional test

1. interval/ratio level data

2. Conforms to a normal distribution (not skewed)

3. Spread of data is not significantly large

• When you have nominal data (non-parametric)

• When you are checking for differences in data (not relationships)

• This test is used when repeated measures experimental design has been used. (i.e. the same participant has 2 results). This means the data is paired or related 

• It can also be used when a  matched pairs design has been used (using the pairs of participants as one person tested twice)

1. State the alternative and null hypothesis 

2. Represent each pair of data with a plus or minus. Can do this by subtracting the values in condition 1 from condition 2, if the results are negative then give a - , if the results are positive give a + sign. If any scores come out the same, give them a 0. This is used in order to work out the calculated value of S = number of times the least frequent sign occurs.

3. Look up the critical value of S in the table. To do this, need to know N (total number of scores given a sign, not including any with a 0) and whether the test hypothesis is one-tailed (directional) or two-tailed (non-directional)

4. Compare the critical value of S with your calculated value of S- following the rule stated under the table of critical values 

• If the calculated value is equal to or less than the critical value, the result is significant 

• It can therefore be concluded that (...insert hypothesis) can be accepted/rejected 

1. Abstract= key details of the report 

2. Introduction= Past research on the topic, aims and hypotheses

3. Method= What the researcher did; Design, sample, materials, procedure, ethics

4. Results= Descriptive and inferential statistics

5. Discussion= Summary of the results and what they tells us in terms of theory

6. Referencing= Lists of sources that are referred to in the article

Need to include an informed consent form, debrief form and standardised instructions

To ensure all participants have the same experience, standardised instructions are read as a script to participants. These are given in the chosen location, and there may be more than one set for different (IV) conditions of the investigation. Also, they should help eliminate confounding variables such as experiment effects. Instructions can be read to the participants or they can read the instructions themselves 

reference section (at the end of the report). This allows psychologists reading the report to find the same sources. The convection for psychological report referencing is to use Harvard style referencing 

Harvard style referencing

1. Author

2. Year

3. Title of book

4. Edition

5. Place

6. Publisher