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research and design > Question 2. > Flashcards

Question 2. Flashcards

1
Q

Define: Longitudinal

A
  1. repeated measure design: collecting data from the same individuals or groups over an extended period
  2. temporal sequence: By collecting data at multiple time points, researchers can examine how earlier conditions or experiences influence subsequent outcomes.
  3. tracking individual trajectories: insights into patterns of development, stability, or change at the individual level.
  4. Cohort comparisons:
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2
Q

Appropriate: Longitudinal

A
  1. Capturing Developmental Processes
  2. Understanding Causal Relationships
  3. Exploring Stability and Change
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3
Q

Define: Between groups

A
  1. Distinct Groups: Participants are assigned to different groups, with each group experiencing a different condition or treatment.
  2. Random Assignment: Participants are typically randomly assigned to different conditions. Helps to distribute potential confounding variables, increasing internal validity.
  3. Independent Measures: Each participant experiences only one condition or treatment, and their responses are compared across groups.
  4. Statistical Comparisons: Data collected from each group are compared statistically to determine whether there are significant differences between groups e.g. t-tests.
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4
Q

Appropriate: Between groups

A
  1. Isolation of Treatment effects: By comparing distinct groups experiencing different conditions, researchers can assess the specific impact of each treatment without interference from other factors.
  2. Avoidance of Order Effects
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5
Q

Define: Matched Pairs

A
  1. Matching criteria: Participants are matched based on characteristics that are relevant to the research question e.g. demographic variables or conditions.
  2. Random Assignment Within Matched Groups: After participants are matched, they are randomly assigned to different conditions or treatments within each matched group, to ensure that any differences are due to the manipulated variables rather than pre-existing differences between participants.
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6
Q

Appropriate: Matching Pairs

A
  1. Studying rare or small groups of people
  2. Increased internal validity and control of confounding variables.
  3. More sensitive to detecting effects, especially if the treatment effects are expected to be small or if varability is high within the population.
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7
Q

Define: Between-Participants Post-test Only

A
  1. Between-Participants Design
  2. Post-test: a treatment is implemented (or an independent variable is manipulated) and then a dependent variable is measured once after the treatment is implemented.
  3. Random Assignment: To ensure comparability between groups and minimise biases, participants are typically randomly assigned to different experimental conditions.
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8
Q

Appropriate: Between-Participants Post-test Only

A
  1. Isolation of Treatment Effects
  2. Reduction of Demand Characteristics: By administering the post-test immediately after the experimental manipulation, researchers reduce the likelihood of demand characteristics influencing participants’ responses.
  3. Avoidance of Order Effects: Unlike designs involving pre-test measurements, post-test-only designs avoid potential order effects or practice effects that could influence participants’ responses.
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9
Q

Define: Between-Participants Pretest-Posttest Control

A
  1. Between-Participants Design
  2. Pretest-Posttest: Participants are assessed on the dependent variable(s) of interest both before and after they have been exposed to the experimental manipulation. This allows researchers to assess any changes in participants’ outcomes as a result of the experimental treatment.
  3. Control Group
  4. Random Assignment
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10
Q

Appropriate: Between-Participants Pretest-Posttest Control

A
  1. Assessment of Treatment Effects Over Time
  2. Detection of Interaction Effects**: Researchers can examine whether there are interaction effects between the experimental treatment and other variables by analyzing the differences in pretest-posttest changes between the experimental and control groups.
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11
Q

Define: Solomon Four Group

A
  1. Between-Participants and Within-Participants Design: 1- pretest and experimental treatment, 2- Receives only the experimental treatment, 3- Receives only the pretest, Group 4- control group.
    impact of the pretest on the effectiveness of the experimental treatment.
  2. Groups 1 and 3 receive pretest measures to assess their baseline levels on the dependent variable(s) of interest before the experimental manipulation. This allows researchers to control for any pre-existing differences between participants and assess the impact of the experimental treatment on participants’ outcomes over time.
  3. Random Assignment
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12
Q

Appropriate: Solomon Four Group

A
  1. Assessment of Pretest Effects
  2. Detection of Interaction Effects
  3. Control for Confounding Variables
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13
Q

Define: Repeated Measures Design

A
  1. Within-Participants Design: In a Repeated Measures Design, each participant serves as their control, meaning that they are exposed to all conditions or treatments being studied.
  2. Multiple Measurements: Participants are measured on the dependent variable multiple times, typically before and after exposure to the experimental manipulation.
  3. Control for Individual Differences + Reduced error Variance
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14
Q

Appropriate: Repeated Measures Design

A
  1. Fewer participants needed
  2. Assessment of Change Over Time
  3. Control for Individual Differences
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15
Q

Define: Single N

A
  1. Individual Focus: studies the responses of individual participants to an intervention or treatment
  2. Repeated Measures: Data are typically collected on the dependent variable(s) multiple times, both before and after the implementation of the intervention or treatment.
  3. Experimental Control: Single N designs often involve the systematic manipulation of an independent variable (e.g., the implementation of an intervention) to determine its effect on the dependent variable(s).
  4. Visual Analysis: Data collected in Single N designs are often analysed visually to identify patterns or trends in the participant’s behaviour or outcomes over time
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16
Q

Appropriate: Single N

A
  1. Individualized Treatment
  2. Clinical and Practical Relevance
17
Q

Define: Factorial

A
  1. Multiple Independent Variables: researchers manipulate two or more independent variables, each with two or more levels or conditions. These independent variables are crossed, meaning that each level of one independent variable is combined with each level of the other independent variables.
  2. Factorial structure: The combination of different levels of each independent variable forms experimental conditions, known as cells, in the factorial design. The total number of cells in a factorial design is equal to the product of the number of levels of each independent variable.
  3. Main Effects and Interaction Effects: researchers examine both the main effects of each independent variable and interaction effects (the combined effects of two or more variables). Interaction effects indicate that the effects of one independent variable depend on the levels of another independent variable.
18
Q

Appropriate: Factorial Design

A
  1. Examination of Multiple Factors
  2. Assessment of Interaction Effects
  3. Generalisability and External Validity: captures the complexity of real-world phenomena. Findings from factorial designs are often more generalisable to diverse populations and settings compared to studies that manipulate only a single factor.
19
Q

Define: Correlational (observation)

A
  1. Lack of manipulation
  2. Assessment of Relationship: The primary aim of correlational research is to assess the degree and direction of the relationship between the variables.
  3. Observational Approach: Correlational research is often conducted through observation, where researchers systematically observe and record participants’ behaviour or characteristics in naturalistic settings.
20
Q

Appropriate: Correlational (observation)

A
  1. Exploration of Relationships
  2. Ecological Validity: allows researchers to gather data in real-world contexts
  3. Non-Intrusive
  4. Hypothesis Generation
21
Q

Define: Correlational (survey)

A
  1. Survey Instruments: researchers use structured questionnaires or surveys to collect data on participants’ perceptions, attitudes, behaviours, or other relevant variables
  2. Measurement: IV and DV are measured independently.
  3. Assessment of Relationship
  4. Sampling methods: involves selecting a sample of participants from a larger population using various sampling methods (e.g., random sampling, convenience sampling)
22
Q

Appropriate: Correlational (survey)

A
  1. Large-Scale Data Collection: including diverse populations.
  2. Self-report data: participants provide information about their perceptions, attitudes, behaviours, and experiences. Self-report measures are well-suited for studying subjective constructs
  3. Examination of Multiple Variables
  4. Longitudinal Studies
  5. Applied and Social Sciences Research
23
Q

Define: Archival Research

A
  1. Use of Existing Data: relies on existing data sources, such as historical documents, official records, newspapers, diaries, letters, administrative records, photographs, audio recordings, video recordings, and other artifacts
  2. Exploratory and Explanatory: Archival research can be exploratory, where researchers search for patterns, themes, or trends in the data, or explanatory, where researchers test hypotheses or answer specific research questions using existing data.
  3. Non-intrusive
  4. Historical Perspective
24
Q

Appropriate: Archival Research

A
  1. Longitudinal and Historical Analysis
  2. Ethical Considerations: Archival research may be particularly suitable in cases where direct data collection from participants is not feasible or ethical, such as when studying sensitive topics, historical events, or marginalised populations.
  3. Practical Considerations: Archival research is often more cost-effective and time-efficient than primary data collection methods, as researchers can access existing data sources without the need for participant recruitment, data collection, or data entry.
25
Q

Define: Case Studies

A
  1. Case studies involve in-depth examination and analysis of a single individual, group, event, or phenomenon within its real-life context.
  2. Holistic perspective: Researchers study the case in its entirety, considering multiple dimensions and aspects of the phenomenon under investigation.
  3. Contextual understanding: Case studies focus on understanding the context and unique characteristics of the case, rather than generalizing findings to broader populations.
26
Q

Appropriate: Case Studies

A
  1. Exploratory research: Case studies are valuable for exploring complex or novel phenomena where little existing research exists.
  2. Theory development: Case studies can contribute to theory development by providing rich, contextually grounded data that illuminate underlying processes or mechanisms.
  3. Explanatory research: Case studies can be used to investigate causal relationships or factors contributing to specific outcomes within a particular context.
27
Q

Define: Person-by-treatment

A
  1. Individual-level Data: Researchers collect data on each participant’s response to different treatments or conditions
  2. Treatment Comparisons: Researchers compare each participant’s outcomes across different treatment conditions. For example, they may compare a participant’s symptom severity before and after receiving different medications
  3. Individual Response Patterns: By analyzing data at the level of individual participants, researchers can identify individual response patterns to different treatments.
28
Q

Appropriate: Person-by-treatment

A
  1. Clinical or Personalized Medicine**: Person-by-treatment analysis is particularly relevant in clinical research and personalized medicine, where the goal is to identify the most effective treatment for each individual patient
29
Q

Define: Natural

A
  1. Naturally Occurring Events: Natural experiments arise from events or conditions that are not manipulated by researchers but occur naturally in the real world. These events can include policy changes, environmental disasters, economic shifts, or social interventions.
  2. Quasi-Experimental Design: Natural experiments often resemble quasi-experimental designs, where researchers cannot control the allocation of participants to different conditions.
  3. Observational Data: Data collection in natural experiments often relies on observational methods, such as surveys, interviews, or secondary data analysis
30
Q

Appropriate: Natural

A
  1. Ethical Considerations
  2. Unanticipated Effects: Natural experiments allow researchers to study the effects of interventions or events that cannot be replicated in controlled experimental settings
  3. Data Availability: Natural experiments rely on the availability of data from pre-existing conditions or events.
  4. External Validity + Real-world impact
31
Q

Define: Time-series

A

Sequential Data: Time series data consists of observations or measurements that are collected over successive time intervals, such as hours, days, months, or years. Each data point is associated with a specific time period, allowing researchers to study changes or fluctuations over time.
Forecasting and Prediction: involves fitting mathematical models to historical data and extrapolating these models to predict future outcomes.
Data Stationarity: Time series data should ideally be stationary, meaning that the statistical properties of the data (e.g., mean, variance, autocorrelation) remain constant over time.

32
Q

Appropriate: Time-Series

A

Used in various fields, including economics, finance, meteorology, engineering, epidemiology, and environmental science. It can be applied to analyse economic indicators, stock prices, weather patterns, sensor data, disease outbreaks, and many other types of sequential data.

33
Q

Define: Single-case

A

Repeated Measurements: This allows researchers to assess changes in behaviour or outcomes within the same individual across different phases of the study.
2. Baseline and Intervention Phases: consist of baseline phases, where the dependent variable(s) are measured before the intervention or treatment is introduced.
3. Replication**: replication of the intervention across multiple phases or conditions within the same subject. This allows researchers to assess the consistency and reliability of the intervention effects.
4.Controlled Conditions: does not involve random assignment to treatment and control groups, researchers can still control for potential confounding variables by implementing multiple baseline designs, alternating treatment designs, or withdrawal designs.

34
Q

Appropriate single case

A
  1. Individualized Interventions
  2. Clinical and Applied Settings
  3. Practical or Ethical Constraints
  4. Pilot Studies or Initial Evaluations
  5. Complex Behaviors or Conditions
35
Q

Define: Meta-Analysis

A
  1. Meta-analysis is a statistical method used to synthesize and summarize the findings from multiple independent studies on a specific research question or topic. It involves systematically collecting, evaluating, and analyzing data from individual studies to provide a quantitative summary of the overall effect size or magnitude of an intervention, treatment, or phenomenon
36
Q

Appropriate: Meta-analysis

A

Publication Bias Evaluation: Meta-analysis assesses the presence of publication bias, which occurs when studies with statistically significant or positive results are more likely to be published than studies with null or negative results
2. Meta-analysis is widely used across various disciplines, including medicine, psychology, education, social sciences, and economics, to provide evidence-based summaries of research findings, inform decision-making, and guide policy development.

research and design (7 decks)

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