Scientific Processes?

Question 1

What does aim mean?

Answer 1

statement about what the study is trying to achieve

Question 2

What does experimental/alternative hypothesis mean?

Answer 2

Predicts that differences in the DV will be beyond the boundaries of chance (they will occur as a result of manipulation of the IV).

Differences beyond the boundaries of chance are significant differences and this can be incorporated into a hypothesis.

For example, ‘caffeine consumption will significantly affect reaction times’. Statistical tests are used to see if results are significant (see page 334). The term ‘experimental hypothesis’ is only used with the experimental method. Other research methods use the term ‘alternative hypothesis’, but the definition is the same.

Question 3

Null Hypothesis?

Answer 3

Is ‘the hypothesis of no differences’. It predicts that the IV will not affect the DV. Any differences in results will be due to chance factors, not the manipulation of the IV, and will therefore be not significant and this can be incorporated into a null hypothesis.

For example, ‘there will be no significant difference in reaction times as a result of caffeine consumption’.

One of the two hypotheses, null or experimental, will be supported by the findings and thus be accepted, with the other one being rejected.

Question 4

Hypothesis?

Answer 4

Testable prediction of what is expected to
happen

Question 5

Dependent Variable?

Answer 5

Variable that is manipulated by the experimenter.

In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.

Question 6

Independent Variable?

Answer 6

Variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

Question 7

What are the two types of experimental hypotheses and what do they mean?

Answer 7

1. Directional (‘one-tailed’) hypothesis - predicts the direction of the results. For example, there will be a significant reduction in the speed of reaction times as a result of caffeine consumption’. It gets its name from predicting the direction the results will go.
2. Non-directional (‘two-tailed’) - predicts that there will be a difference, but does not predict the direction of the results. For example, ‘there will be a significant difference in the speed of reaction times as a result of caffeine consumption’. Reaction times will be either quicker or slower.

Question 8

When is directional hypothesis used?

Answer 8

Directional hypotheses are used when previous research suggests that results will go in one direction, or when replicating a previous study that also used a directional hypothesis.

Question 9

What is sampling?

What should it be ideally?

What does target population mean?

Answer 9

Testing part of the population, Psychologists use different sampling techniques such as random sampling

Ideally a sample is representative (contains the same characteristics as the population from which it was taken)

The term target population is used to indicate the group of people the results are targeted at.

Question 10

What is random sampling?

What is one way it can be achieved?

What does it result in?

Answer 10

Random sampling is where each member of a population has an equal chance of being selected.

One way to achieve this is to place all names from the target population in a container and draw out the required sample number, while computer programs are also used to generate random lists.

This results in a sample selected in an unbiased fashion.

Question 11

What are 2 strength and 2 weaknesses of Random Sampling?

Answer 11

Strengths of random sampling

1. Unbiased selection - there is no bias in selection, increasing the chances of getting an unbiased and thus representative sample.
2. Generalisation - as the sample should be fairly representative, results will be generalisable to the target population.

Weaknesses of random sampling

1. Impractical -random sampling is difficult to achieve, as it is sometimes difficult to get full details of a target population and not all members may be available or wish to take part.
2. Not representative - unbiased selection does not guarantee an unbiased sample; for example all females could be randomly selected, making the sample unrepresentative and thus the results not generalisable.

Question 12

What does sampling techniques, generalisation and bias mean?

Answer 12

Sampling Techniques- Various methods of selecting samples of participants from target populations

Bias - the degree to which participants in a sample have been selected without prejudice

Generalisation - the extent to which findings generated from a sample are representative of a target population

Question 13

Opportunity Sampling?

Answer 13

Opportunity sampling involves selecting participants who are available and willing to take part, for example asking people in the street who are passing. Sears (1986) found that 75 per cent of university research studies use undergraduates as participants, simply for the sake of convenience.

Question 14

2 strengths and 2 weaknesses of Opportunity Sampling?

Answer 14

Strengths of opportunity sampling

1. Ease of formation - opportunity samples are relatively easy to create, as they use people who are readily available
2. Natural experiments - with natural experiments opportunity sampling usually has to be used, as the researcher ha no control over who is studied.

Weaknesses of opportunity sampling

1. Unrepresentative - the sample is likely to be biased by excluding certain types of participants and thus be unrepresentative, so that findings cannot be generalised to the target population. An opportunity sample collected > town during the day on a week day would not include those at work or college.
2. Self-selection - participants have the option to decline to take part and the sampling technique thus turns into a self-selected sample.
   Volunteer (self-selected) sampling

Question 15

Volunteer/ Self Selected Sampling?

Answer 15

Volunteer or self-selected sampling involves people volunteering to participate. They select themselves as participants, often by replying to adverts.

Question 16

2 Strengths and weaknesses of Volunteer/ Self Selected Sampling?

Answer 16

Strengths of self-selected sampling

1. Ease of formation - creating the sample requires little effort from the researchers (other than producing an advert) as participants volunteer themselves.
2. Less chance of ‘screw you’ phenomenon - as participants are eager to take part there will be less chance of them deliberately trying to sabotage the study.

Weaknesses of self-selected sampling

1. Unrepresentative - the sample will be biased, as volunteers tend to be a certain ‘type’ of person and thus unrepresentative, making results not generalisable to a target population.
2. Demand characteristics - volunteers are eager to please, which increases the chances of demand characteristics,

Question 17

What is systematic sampling ?

How is it calculated?

Answer 17

Systematic sampling involves taking every nth person from a list to create a sample. This involves calculating the size of the population and then assessing what size the sample needs to be to work out what the sampling interval is. For example, if a company has a workforce of 1,000 employees and a sample of 20 participants is required, then 1000 +20= 50; therefore take every fiftieth name from the list of employees to form the sample.

Question 18

2 Strengths and Weaknesses of Systematic Sampling?

Answer 18

Strengths of systematic sampling

1. Unbiased selection - there is no bias in selection, increasing the chances of getting an unbiased and thus representative sample.
2. Generalisation - the results are representative of the population unless certain characteristics of the population are repeated for every nth person, which is unlikely.

Weaknesses of systematic sampling

1. Periodic traits - the process of selection can interact with a hidden periodic trait within the population. If the sampling technique coincides with the frequency of the trait, the sampling technique is neither random, nor representative of the target population. For example, if every fifth property in a street is a flat occupied by a young person then selecting participants who live at every fifth property will not gain a representative sample.
2. Not representative - unbiased selection does not guarantee an unbiased sample; for example choosing every nth employee could result in the sample accidentally being made up of employees over 40 years old, making the sample unrepresentative of the workforce and the results not generalisable.

Question 19

Stratified Sampling?

Answer 19

Stratified sampling - sampling method where random selection of participants occurs from categories of people representing the sub-groups that comprise a target population

Question 20

2 Strengths and Weaknesses of Stratified Sampling?

Answer 20

Strengths of stratified sampling

1. Representative - as selection occurs from representative sub-groups within a population, the sample should also b fairly representative.
2. Unbiased - as random sampling is performed upon the sub-groups of a population, selection is unbiased.

Weaknesses of stratified sampling

1. Knowledge of population characteristics required – stratified samples require a detailed knowledge of the population characteristics, which may not be available.
2. Time-consuming the dividing of a population into strata and then randomly selecting from each can be time- consuming.

Question 21

What are Pilot Studies and what do they identify?

Answer 21

1. Pilot studies are small-scale practice investigations, carried out prior to research to identify potential problems with the design, method or analysis, so they can be fixed.
2. Participants may also suggest appropriate changes; for example, if participants admit that they guessed the purpose of the study and acted accordingly (demand characteristics), changes could be made to avoid this.

Pilot studies also identify whether there is a chance of significant results being found.

Question 22

What are the three main types of experimental design?

Answer 22

There are three main types of experimental design: the independent groups design, the repeated measures design and the matched pairs design.

Question 23

Independent groups design?

Answer 23

An independent groups design uses different participants in each of the experimental conditions, so that each participant only does one condition (either the experimental or control condition). Different participants are therefore being tested against each other.

Question 24

3 Strengths and 2 Weaknesses of independent groups design?

Answer 24

Strengths of the independent groups design

1. No order effects - as different participants do each condition there are no order effects whereby the order in which the conditions are done may have an effect on the outcome (see below).
2. Demand characteristics - participants do one condition each; therefore there is less chance that they can guess the purpose of the study and act accordingly.
3. Time saved - both sets of participants can be tested at the same time, saving time and effort.

Weaknesses of the independent groups design

1. More participants needed - with participants each doing only one condition, twice as many participants as needed as for a repeated measures design (RMD).
2. Group differences - differences in results between the two conditions may be due to participant variables (individual differences) rather than manipulations of the IV. For example, participants in one condition may be more intelligent than those in another condition. This is minimised by random allocation of participants to each condition.

Question 25

Random Groups Design?

Answer 25

In a repeated measures design each participant is tested in all conditions of an experiment. Participants are therefore being tested against themselves.

Question 26

2 Strengths and 3 Weaknesses of Random Groups Design?

Answer 26

Advantages of the repeated measures design

1. Group differences - as the same people are measured in all conditions, there are no participant variables (individual differences) between the conditions.
2. More data/fewer participants - as each participant produces two or more scores, more data is produced compared with an independent measures design (IMD). Therefore fewer participants are needed to get the same amount of data.

Weaknesses of the repeated measures design

1. Order effects - with an RMD, participants do all conditions and the order in which they do these conditions can affect the results. Participants may perform worse in the second condition due to fatigue or boredom (negative order effect) or perform better due to practice or learning (positive order effect). Counterbalancing can control this, where half the participants do Condition A followed by Condition B, and the other half do Condition B and then Condition A.
2. Demand characteristics – by participating in all conditions, it is more likely that participants may guess the purpose of the study and act accordingly.
3. Takes more time - a gap may be needed between conditions to counter the effects of fatigue or boredom. Each condition may also need different materials; for example, in a memory test the same list of words could not be used for both conditions.

Question 27

Repeated Measures Design?

Answer 27

In a repeated measures design each participant is tested in all conditions of an experiment. Participants are therefo being tested against themselves.

Question 28

2 Strengths and 3 Weaknesses of Repeated Measures Design?

Answer 28

Advantages of the repeated measures design

1. Group differences - as the same people are measured in all conditions, there are no participant variables (individual differences) between the conditions.
2. More data/fewer participants - as each participant produces two or more scores, more data is produced compared with an independent measures design (IMD). Therefore fewer participants are needed to get the same amount of data.

Weaknesses of the repeated measures design

1. Order effects - with an RMD, participants do all conditions and the order in which they do these conditions can affect the results. Participants may perform worse in the second condition due to fatigue or boredom (negative order effect) or perform better due to practice or learning (positive order effect). Counterbalancing can control this, where half the participants do Condition A followed by Condition B, and the other half do Condition B and then Condition A.
2. Demand characteristics – by participating in all conditions, it is more likely that participants may guess the purpose of the study and act accordingly.
3. Takes more time - a gap may be needed between conditions to counter the effects of fatigue or boredom. Each condition may also need different materials; for example, in a memory test the same list of words could not be used for both conditions.