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Flashcards in Research Methods Deck (56):
1

Scientific Inquiry

is a process of developing an explanation of a question in the natural world by testing, investigating and collecting data that will either support or reject your idea.

2

Non Scientific Inquiry

Is one that does not use a systematic collection of evidence or one that tries to find answers to questions about things other than the external world

3

7 Steps of Psychological Research

Identify the Research Problem
Hypothesis
Method:
- select partipants
- how many participants
- which data collection method
Collect Data
Analyse Data
Interpret Data
Report Findings

4

Sample

A sample is the group of people who take part in the investigation

5

Population

The target population is the total group of individuals from which the sample might be drawn.

6

Ethics

Confidentiality, Withdrawal Rights, Voluntary Participation, Debriefing, Deception, Informed Consent.

7

Experimental Hypothesis

A general prediction about the direction of interactions between the IV, the DV and the population.

8

Operational Hypothesis

A testable prediction that explains exactly how the variables will be measured/manipulated + the population from which the sample has been drawn (workable/testable/repeatable)

9

4 Elements of a Operational Hypothesis

- testable prediction
- population
- operationalised IV
- operationalised DV

10

Extraneous Variables + Types

Any variable other than the IV that causes change in results and an unwanted effect on experiment.
1. Partipant
2. Experimenter
3. Situational
4. Confounding
5. Artificiality

11

Confounding Variable and Effect

is an uncontrolled variable that has affect on DV.
If EV is not controlled then EV not IV had effects on results leading to wrong assumptions on what has effected the DV

12

Experimental vs Control Groups

An experimental group is the group in an experiment that receives the variable being tested (IV). The control group does not.

13

Demand Characteristics

Participants know they are being studied and so they change.

14

Longitudinal Design

Studies same group of people at different points in time.
Limitations:
- expensive/time consuming
- loose participants (is sample still representative)
- practice effects can distort findings
- cross generalisation problems ie children in differing cohorts may experience different at each point in life span than other children in earlier/later cohorts.
Positives:
- no cohort effect

15

Cross Sectional Design

Studies cohorts who differ in age at the same time. Takes into account developmental differences amongst age and can compare children's abilities of different ages.
Limitations:
- Cohort effect: cannot be sure if its not due to developmental or participant differences ie cultural/environmental
- cannot determine whether children in youngest group will reason like older children that age
Positives:
- easy, cheap

16

Longitudinal Sequential Design

Groups of participants are studied over time, and at each measurement a new group is added that was the same age as the first group when beginning the study.
Lets us look for changes in individuals across time (as a longitudinal) and age differences (as in cross sectional).
Limitations:
- complex and expensive
Positives:
- no cohort effect

17

Cohort

group of people at same age who have experienced same cultural conditions and environment

18

Independent Measures Design

Randomly sampled to control or experimental groups, with equal chance of being allocated to each.
Limitations:
- doesn't minimise potential differences in characteristics
Strengths:
- popular and easy to administer

19

Matched Participant Design

Involves pairing each participant based on a characteristic they share. Randomly allocated to control and experimental group.
Limitations:
- pretesting required
Strengths:
- seeks to eradicate participant differences by separating into 2 groups

20

Repeated Measures

Same participants exposed to control and experimental groups.
Limitations:
- creates order effects; occur when their is change in results due to the sequence in which 2 tasks are completed
Strengths:
- eliminates partipant differences

21

Counterbalanced

Involves dividing the groups of participants in half and arranging them in order of the conditions so that each condition occurs equally as often in each position e.g half exposed to control then experimental condition and the other half experimental then controlled

22

Empirical Data

Is the information or observable facts that a psychologist systematically collects in a experiment, studies and investigations.

23

Subjective Data

Collected through observations of behaviour or information based on participants self reports. Can be biased and difficult to statistically analyse.

24

Objective Data

Collected under controlled conditions, are easily measured and compared with other data. Often numerical and can be statistically analysed.

25

Qualitative Data

Describes changes in the quality of behaviour and often expressed in words. Difficult to statistically analyse as responses take a wide variety of forms.

26

Quantitative Data

Describes changes in numerical or categorical form. Can be statistically analysed,easily measured and compared with other data.

27

Non Experimental Designs Examples

Case studies, Correlational, Interviews, Scales, Observational, Self Reports, Brain Imaging, Archival Research

28

Case studies

Provide detailed knowledge about single case or small number of related cases by studying an individual or small group. Uses a variety of data collection processes.
Limitations
- not generalisable/expensive
Positives:
- detailed/specific data
- can offer insight into cases otherwise unethical

29

Interviews

Has open ended questions and focussed on particular area of interest
Limitations:
- interviewer bias
Strengths:
- comparable data

30

Scales

provide scale on which individuals standing on an issue can be measured. Quantitative subjective data can make inferences/stats.
Limitations:
- limited scales
Strengths:
- easy to understand

31

Observational

Involves individual observing another individual or a group of people in natural environment (no interference or observation) and recording behaviours.
Limitations:
- Observer bias/ Experimenter Effect
- Expensive, Time Consuming
- No scientific control of extraneous variables
- Hawthorne Effect
Strengths:
- forms a hypothesis that guides future studies
- qualitative subjective data

32

Hawthorne effect

Aware of observer and so acts different (side effect of artificiality).

33

Experimenter effect + strategy to fix

Experimenter treats one individual different to others (less interaction thus reduced). Controlled by standardised procedure/single and double blind side.

34

Placebo Effect

A phenomenon that occurs when a person believes he or she is receiving real treatment and reports an improvement in his or her condition.

35

Single Blind Procedure

Experimenters are aware of which subjects are receiving the treatment or independent variable, but the participants of the study are not - a technique for eliminating subjective bias

36

Double Blind Procedure

Both the experimenters and participants are unaware of who is receiving the independent variable

37

Self Reports

Individuals asked to comment on own thoughts/emotions etc by answering series of questions on a particular topic
Limitations:
- unrepresentitive
- partipant variables
- subjective data that cannot be overtly seen
- difficult to compare
Strengths:
- easy/cheap
- lots of data and flexible

38

Correlational Studies Definition and 3 types

Looks at relationships between different variables and are usually used in cross sectional studies.
- naturalistic observation
- survey methods
- archival research

39

Limitations and Strengths of Correlational Studies

Strengths:
- allows for researchers to determine strength/direction of relationship so later studies can narrow down findings
Limitations:
- only uncover relationships not provide a conclusive reason for relationships

40

Reliability and Types (Internal/External)

the extent to which an assessment tool measures what it is supposed to measure consistently each time it is used

Internal:
- Internal Consistency
- Test- Retest Reliability
- Parallel forms of reliability
External:
- Inter-rater reliability

41

Validity and Types (Internal/External)

The extent to which an assessment tool actually measures what it is designed to measure

Internal:
- Content Validity
- Construct Validity
External:
- External Validity
- Criterion-related Validity

42

Archival Research

is research involving primary sources held in an archives ie reports/documents etc
Limitations:
- dates may be missing
- unreliable research
- cannot change anything
Strengths:
- analyses studies
- less expensive

43

Inferential Statistics

Allows us to make inferences, form conclusions, generalise findings and determine validity. Can determine whether differences in results due to manipulation of IV or to chance (P Values, Probability, Correlations)

44

Internal Consistency

All items contribute equally to what is being measured

45

Test Re-Test Reliability

Same results in same condition by same person at a different time

46

Inter Rater Reliability

Same results when assessment tools used by different administrators

47

Parallel Forms Reliability

Do two tests developed from the same content produce the same results

48

Content Validity

Are research tools assessing what they are meant to

49

Construct Validity

Are research tools assessing the content/theories they are meant to

50

External Validity

Can finding be generalised to wider population

51

Criteria Related Validity

Consistent with other research

52

Descriptive Statistics

used to summarise, organise an describe data obtained. Allows data to be easily interpreted (percentages, graphs, measures of central tendency)

53

Measures of Central Tendency

Mean, Mode, Median, Range, Variance, SD)

54

Statistical Significance

- Refers to the significance of the difference between two scores
- whether we can attribute the results to the IV or to chance alone
- Demonstrates the cause and effect relationship

55

P Value

is the level of probability that the results (differences) are due to chance alone, and determines the statistical significance = 0.05%,

56

Correlations

- seek to establish whether two variables are related
- correlation does not cause causation, only association
- e.g scatterplot and correlation coefficient (close to 1 is strong).