Paper 3 PREP

preparation for Paper 3 asessments

1a) True Experiment

an IV is manipulated and a DV is measured, allowing for causality to be determined. Participants are randomly allocated to conditions. Attempt to control for extraneous variables.

1a) Focus group

makes use of a purposive sample (8-12 participants). Researcher acts as facilitator of group, keeping the discussion focused. Data is qualitative, and must be interpreted through content analysis.

1a) Case Study

Longitudinal, based on method triangulation to increase credibility; data may be qualitative and quantitative; study of individual/group/organization based on trait or behavior.

1a) Naturalistic observation

Researcher collects data in the participants natural environment without manipulating any variables; observations carried out overtly (participants are aware they are being observed) or covertly. Researchers may use an observation checklist (quantitative) or may take field notes (qualitative). 

1a) Semi-structured Interview

Pre-determined set of questions, potential follow-ups. May include both open and closed questions. Face-to-face which may lead to interview effects and social desirability bias. 

1a) Natural Experiment

An IV is naturally occuring, therefor not manipulated, and a DV is measured. Causality cannot be determined. Cannot control for extraneous variables. Participants are not randomly allocated to conditions. 

1a) Correlational Study

No manipulation of an IV; instead sets of data are compared to determine a level of correlation. Often pre-existing data sets are used. The data is quantitative and may be statistically analysed for significance. 

1a) Questionnaire/Survey

Often set as written interviews. Allow participants to be anonymous, coded in order to avoid participants being identified. Questions may be misinterpreted by the participants; however, researchers maybe available to answer questions about individual questions. 

1a) Quasi-experiment

IV may be manipulated and a DV is measured, but causality cannot be determined. Attempt to control extraneous variables. Participants are not randomly allocated to condition. They are assigned based on a participant variable.

1b) Purposive Sampling

Researchers select participants who fit specific characteristics relevant to the study. Ensures participants have rich, relevant knowledge of the topic.

1b) Random Sampling

Every member of the target population has an equal chance of selection. Reduces sampling bias but may include individuals with limited insight.

1b) Volunteer/Self-Selected Sampling

Participants volunteer after seeing an advertisement or invitation. Increases motivation but can lead to volunteer bias.

1b) Opportunity/Convenience Sampling

Participants selected based on availability and willingness. Quick, easy, but may not be representative.

1b) Snowball Sampling

Existing participants recruit future participants. Useful for accessing hard-to-reach or hidden populations (topics about: abuse, trauma, etc).

1b) Stratified Sampling

Population divided into subgroups and participants selected in proportion. Improves representativeness across key demographics. 

2) C-Consent

Participants must give informed consent and understand the nature of the study.

2) A-Anonymity

Identities must be protected; no identifying information should be revealed.

2) R-Right to Withdraw

Participants may leave the study or withdraw their data at any time.

2) D-Deception

Should be avoided unless justified; any deception must be minimal and followed by debriefing.

2) U-Undue stress or harm

The researcher must ensure participants are not exposed to psychological or physical harm.

2) D-Debriefing

Participants must be informed of the study’s purpose and given the opportunity to ask questions after participation.

3) Credibility - Data Triangulation

Using different data sources within the same method. Example: using two different populations, interviewing people at different points in time (private vs public), or comparing people with different perspectives.

3) Credibility - Method Triangulation

Using different data collection methods with the same sample in order to check the consistency of the findings. Goal to check that the research method alone did not lead to results.

3) Credibility - Researcher Triangulation

Using other researchers to help carry out the research and review the data. Makes sure that results are not simply due to one researcher’s interpretations.

3) Credibility - Member Checking

Data, interpretations, and conclusions are shared with participants to ensure that participants can clarify intentions and correct potential errors.

3) Credibility - Researcher credibility

The researcher’s personal and professional experience with regard to the topic; the training that the researcher underwent; actions taken by the researcher to prep for the specific study

3) Bias - Randomization

Participants are randomly assigned to experimental conditions. This helps ensure that any individual differences are spread evenly across groups. When selecting participants from a larger population, random sampling helps reduce the chance of bias in the sample.

3) Bias - Personal Reflexivity 

Researcher reflects on how their beliefs, expectations, or background might influence interpretation. Helps prevent imposing their own worldview onto participants’ experiences.

3) Bias - Epistemological Reflexivity

Researcher evaluates how their methodological decisions affect the data. Reduces bias created by the design or theoretical assumptions.

3) Bias - Method Triangulation

Using multiple methods (e.g. interview + observation + document analysis) If different methods produce similar findings, the influence of researcher bias is minimised.

3) Bias - Researcher Triangulation

More than one researcher collects or analyzes the data. Reduces individual bias because interpretations are compared and checked.

3) Bias - Member Checking

Researches share transcripts, interpretations, or themes with participants. Participants confirm accuracy, reducing misinterpretation or researcher bias.

3) Bias - Peer Review

A non-involved researcher critiques the analysis and coding. Making the researcher’s assumptions more transparent and corrects biased conclusions.

3) Bias - Selection Bias

Selection bias occurs when the sample is not representative of the target population because of the way participants were selected. This means certain types of people are more likely or less likely to be included, which affects the credibility and transferability of the findings.

3) Bias - Standardized Directions

Ensuring all participants recieve the same instructions in the same way reduces variability in how the study is administed.