Research Methods 1: Key Words

Pilot Studies
Small scale practice investigation completed before the main research to identify potential problems.
1 of 63
Ceiling Effect
The task is too easy.
2 of 63
Floor Efect
The task is too difficult.
3 of 63
A person who is playing a role in the experiment, they are not a participant and they know exactly what is going on. They are also known as a stooge, actor or pseudo participant.
4 of 63
This is a precise statement of why a study is taking place, including what is being studied and what is trying to be achieved.
5 of 63
This states what you believe to be true. It is a precise and testable prediction of the relationship between two variables.
6 of 63
A hypothesis has to be written in a testable form and the variables have to be precisely defined and unambiguous.
7 of 63
Independent Variable (IV)
This is the variable that the researcher manipulates/changes.
8 of 63
Dependent Variable (DV)
This is the variable the researcher measures.
9 of 63
Experimental Hypothesis
This is a prediction of what the researcher thinks will happen to the DV when the IV changes. (This is operationalised).
10 of 63
Null Hypothesis
This hypothesis states that the IV will have no effect on the DV and any observed differences will be due to chance.
11 of 63
Directional Hypothesis (One Tailed)
This means that the hypothesis is making a specific prediction. It predicts that direction of the results difference between two conditions or two groups of participants.
12 of 63
Non-Directional Hypothesis (Two-Tailed)
This hypothesis is making a vague prediction. It predicts that there will be a difference between two conditions or groups of participants, without actually stating the direction.
13 of 63
Extraneous Variables (EVs)
These are variables which need to be eliminated or controlled, otherwise they may have an affect on the DV and confound the results.
14 of 63
Confounding Variables
Variables that were not controlled and have therefore affected the DV.
15 of 63
Experimenter Variables
Variables which are to do with the researcher. Including personality, age, gender, social class, ethnicity, intelligence, spoken language. Controlled by using standardised procedures, like using the same researcher for each condition.
16 of 63
Participant Variables
These variables are to do with the participants. They include, age, gender, social class, ethnicity, intelligence, personality. They can be controlled by radomly allocating participants to groups so that any differences are cancelled out.
17 of 63
Situational Variables
These variables are to do with the situation which might interfere with and affect the bahviour of the participants. These include, time, lighting, temperature, materials, instructions. Controlled with standardised procedures and instructions.
18 of 63
Demand Characteristics
Features of the experiment that allow the participant to work out the aim/hypothesis of the experiemnt. They could then change their behaviour which frustrates the aim of the research by affecting the results. Controlled by the single blind method.
19 of 63
Single Blind Method
Where the participant does not know what condition they are in but the researcher does know. This is unethical as it involves deception.
20 of 63
Investigator Effects (Investigator Bias)
These can affect the DV so are an example of extraneous variables. They are uncoscious cues from an investigator that encourage participants to behave in a particular way. They would then behave the way that is expected of them.
21 of 63
Examples of Investigator Effects
Physical characteristics of investigators (Experimenter Variables), and less obvious personal characteristics of the investigator such as accent.
22 of 63
Double Blind Method
This controlls investigator effects. It is where neither the researcher or participant know what the hypothesis is or which condition they are in. The researchers assitant conducts the investigation and collects the data.
23 of 63
Refers to consistency. If a study is repeated using the same method, design and measurements, and the same results are obtained then the results are reliable.
24 of 63
Internal Reliabilty
The extent to which something is consistent within itself. e.g. a set of scales should meaure 50 grams between 50-100 and then 50 grams between 150-200.
25 of 63
External Reliabilty
The extent to which a test measures consistently over time. e.g. If you weigh 10 stone at 10am and weigh 10 stone at 10:05am, the scales are reliable.
26 of 63
Refers to whether a test measures what it claims to be measuring.
27 of 63
Internal Validity
Whether we can say for certain that the IV caused the effect seen in the DV. This can be improved by reducing extraneous variables and by using standardised procedures.
28 of 63
Low Internal Validity
There was too little control on the extraneous variables so there could be confounding variables.
29 of 63
External Validity
The extent to which results can be generalised. to other settings (ecological validity), people (population validity), and over time (temporal validity). Improved by setting the experiments in more naturalistic settings.
30 of 63
Target Population
The group of people who the researchers want to apply their results to, e.g. murderers.
31 of 63
The small number of people from the target population who actually take part in the investigation. These results are then generalised to the target population.
32 of 63
Sampling Bias
This may occur if the sample is not representative of the rest of the population. To avoid sampling bias, the sample should be as large as possible.
33 of 63
Random Sampling
Each member of the population has an equal chance of being selected. Also known as the lottery method. e.g. picking names out of a hat.
34 of 63
Opportunity Sampling
Asking whoever happens to be around. e.g. Waiting outside Tesco and asking the people who walk past.
35 of 63
Volunteer Sampling (Self-Selected)
Where you volunteer to take part in the experiment. e.g. Seen the advert in the newspaper and choose to take part.
36 of 63
Systematic Sampling
Selected from the target population by using the nth term. e.g. Selecting every 5th person from the year 12 register.
37 of 63
Stratified Sampling
Small scale reproduction of a population where the population is dived into characteristics. e.g. Age, gender, then radomlysampled from each category.
38 of 63
Quantitative Data
This is numerical date. The data is objective meaning it is set data that can't be changed or interpreted differently. It involves measuring e.g. how much? Statistical analysis can be used and it's collected from experiments.
39 of 63
Qualitative Data
This is non-numerical data e.g. speech and text. The data is subjective which means it is open to interpretation and can be changed as it isn't fixed. It involves finding out what people think and how they feel. It is collected from case studies.
40 of 63
Primary Data
This is the original data that was collected specifically for the study and it has not previously been published. This makes it more relable and valid as it hasn't been manipulated.
41 of 63
Secondary Data
This is data that is collected for another esearch aim and it has been published. It is drawn from several sources so can give a clearer insight.
42 of 63
Meta Analysis
This is when findings from several studies are comined. Smaller studies which have the same research questions and methods are combined to create a larger study.
43 of 63
Measures of Central Tendency
Information about the typical score (averages). Summary of results in descriptive statistics.
44 of 63
Measures of Dispersion
Information about how spread out ther scores are (variabilty). Summary of results in descriptive statistics.
45 of 63
A type of descriptive statistic that shows the rate, number or amount of something within every 100. This data will be plotted onto a pie chart.
46 of 63
Correlational Data
Provides data that can be expressed as a correlation coefficient, which shows either a positive, negative or no correlation. The stronger the correlation the nearer it is to =1 or -1. This data is plotted on a scattergram.
47 of 63
Three Measures of Central Tendency
Mean, Median, Mode
48 of 63
Mean Average
The statistical average of a set of data.
49 of 63
Median Average
The middle value of a set of data.
50 of 63
Mode Average
The score that occurs the most often.
51 of 63
Standard Deviation
A measure of the variability (spread) of a set of scores from the mean.
52 of 63
Pie Chart
Used to show the frequency of categories as percentages. Each section represents the frequency of a category.
53 of 63
Bar Chart
Show data in the form of categories that are to be compared. The catergories are placed on the x-axis. Bar charts show totals, means, percentages or ratios and can show two values together.
54 of 63
Frequency Polygon
Similar to a histogram as the data is continuous. Two or more frequency distributions can be compared together on a frequency polygon.
55 of 63
Used for continuous data. The continuous score is placed on the x-axis and the frequency on the y-axis. There are no spaces in-between the bars due to the data being continuous.
56 of 63
Result tables summarise the main findings of data so are different to data tables. Data tables present raw unprocesses scores from research studies. Research tables normally present data totals and relevant measures of dispersion and central tendency
57 of 63
Normal Distribution
Most of the scores will be around the mean with decreasing amounts away from the mean. Data is usually symetrical so when plotted on a graph it forms a bell-shaped curve.
58 of 63
Skewed Distribution
This is when the distribution curve is not symetrical. Outliers can cause a skewed distribution. Positively skewed distribution will contain more low scores than high and negatively skewed distribution will contain more high scores than low.
59 of 63
Inferential Statistics
Use statiscal tests to infer differences between two or more groups/samples and test if that difference is significant or not.
60 of 63
Statistical Testing
Provides a way of determining whether the hypothesis should be accepted or rejected. In psychology it indicates the difference or relationship between variables as occurede by chance or are actually statistically significant.
61 of 63
Sign Test
A statistical test to analyse the difference in scores between related items.
62 of 63
Determining the difference or relation between variables is significant, it is usually done so at a level/probability called a significance level (5%).
63 of 63

Other cards in this set

Card 2


The task is too easy.


Ceiling Effect

Card 3


The task is too difficult.


Preview of the back of card 3

Card 4


A person who is playing a role in the experiment, they are not a participant and they know exactly what is going on. They are also known as a stooge, actor or pseudo participant.


Preview of the back of card 4

Card 5


This is a precise statement of why a study is taking place, including what is being studied and what is trying to be achieved.


Preview of the back of card 5
View more cards


No comments have yet been made

Similar Psychology resources:

See all Psychology resources »See all Research methods and techniques resources »