Single-N Design and Qualitative Research Methods

Single-N Design

Uses only 1 participant or a small number of participants. Useful in clinical settings or when studying unique conditions.

Historical and Current Uses of Single-N

Historically used in behavior analysis and therapy; still used to study individual behavior in detail.

Baseline Phase (A)

Initial phase with no treatment; establishes a stable behavior pattern to compare against.

Treatment Phase (B)

Phase in which an intervention or treatment is introduced.

Need for Stable Baseline

A stable baseline is essential to rule out random error, maturation, or testing effects.

ABA Reversal Design

Involves a baseline (A), treatment (B), and return to baseline (A). Shows cause if behavior returns to original pattern.

ABAB Design

Reintroduces treatment to increase internal validity and is more ethical by not ending on a no-treatment phase.

Multiple Baseline Design

Used when reversal is unethical or impossible; treatment is introduced at different times.

Multiple Baseline Across Participants

Same treatment applied to different people at different times.

Multiple Baseline Across Behaviors

Same treatment applied to different behaviors in the same individual.

Multiple Baseline Across Situations

Treatment introduced in different settings at different times.

Internal Validity in Single-N

Strengthened by replication and reversal designs.

External Validity in Single-N

Generalizability is limited; best when studying fundamental processes.

Quasi-Experimental Design

Lacks random assignment; cannot ensure strict experimental control.

Traditional Quasi-Experimental Variables

Groups based on participant characteristics like gender or age.

One-Group Posttest-Only Design

Treatment followed by measurement; no control or baseline.

One-Group Pretest-Posttest Design

Measurement before and after treatment; no control group.

Threats to Internal Validity

Includes history, maturation, testing, instrumentation, regression to mean, and attrition.

Understanding Validity Threats

Some threats cause real DV changes (maturation), others cause bogus changes (instrument decay).

Minimizing Validity Threats

Understand and control threats where possible.

Developmental Designs

Explore age-related changes; age is a quasi-variable.

Cross-Sectional Design

Different age groups measured at one time.

Longitudinal Design

Same participants measured over time.

Cohort Effects

Differences due to participants being born in different times.

Sequential Design

Combines cross-sectional and longitudinal; less time-consuming but affected by both attrition and cohort.

Construct Validity

Refers to how well a test or tool measures the concept it is intended to measure.

Internal Validity

The degree to which a study allows us to determine that a change in the independent variable caused a change in the dependent variable.

External Validity

The extent to which the results of a study can be generalized to other situations, people, or time periods.

Conclusion Validity

Also known as statistical conclusion validity; concerns whether the conclusion drawn from statistical analysis is reliable.

Descriptive Statistics

Summarize and describe the main features of a dataset. Examples include mean, median, mode, and standard deviation.

Inferential Statistics

Use data from a sample to make inferences about a population.

Nominal Scale

Categories with no numerical or ordered value (e.g., gender, color).

Ordinal Scale

Categories that have a ranked order, but the intervals between ranks are not equal (e.g., race placements).

Interval Scale

Equal intervals between values, but no true zero (e.g., temperature in Celsius).

Ratio Scale

Has equal intervals and a true zero point (e.g., height, weight).

Four Goals of Behavioral Science

1) Description, 2) Prediction/Correlation, 3) Causation, 4) Explanation.

Frequency Distribution

Counts how often each score occurs in a dataset.

Relative Frequency Distribution

Used when sample sizes differ; expresses frequency as a proportion or percentage.

Pie Chart

Visual representation of data where each slice represents a proportion.

Bar Graph

Used for comparing different groups; categories on x-axis, values on y-axis.

Frequency Polygon

Line graph showing frequency of scores; helps identify shape of distribution.

Histogram

Bar graph used for continuous data; bars touch.

Central Tendency

Describes where scores cluster. Includes mean, median, and mode.

Mode

Most frequently occurring score.

Median

Middle score in a sorted dataset.

Mean

Arithmetic average of scores.

Variability

Describes the spread of scores in a dataset.

Range

Difference between highest and lowest score.

Variance

Average of squared deviations from the mean.

Standard Deviation

Square root of the variance; shows average distance from the mean.

Correlation Coefficient (r)

Quantifies the strength and direction of a linear relationship between two variables.

Scatterplot

Graph that depicts relationship between two variables.

Positive Correlation

As one variable increases, the other also increases.

Negative Correlation

As one variable increases, the other decreases.

Strength of Correlation

Measured from -1 to +1. Values closer to |1| are stronger.

Perfect Correlation

r = 1.0 or -1.0; indicates a perfect linear relationship.

No Correlation

r = 0; indicates no relationship.

Shape/Form of Relationship

Can be linear, curvilinear, S-shaped, or J-shaped.

Pearson's r

Used when variables are interval or ratio, linear, and normally distributed.

Spearman's r

Used when at least one variable is ordinal or distribution is non-normal.

Regression Analysis

Statistical technique for predicting value of one variable based on another.

Linear Regression Equation

Ŷ = a + bX. Ŷ = predicted score, a = intercept, b = slope.

Multiple Regression

Ŷ = a + b1X1 + b2X2; uses more than one predictor.

Mediator Variable

Explains the process by which one variable affects another.

Moderator Variable

Changes the strength or direction of a relationship between variables.

Qualitative Research Definition

A method of inquiry that focuses on understanding meaning, experience, and perspective from the participant's point of view.

Focus of Qualitative Research

People's words, stories, and actions. Emphasizes depth, context, and meaning over numerical data.

When to Use Qualitative Methods

When exploring new or complex phenomena, understanding lived experiences, understanding motivations behind behavior, or when little is known.

Exploratory and Descriptive

Qualitative research is primarily aimed at describing how and when phenomena occur.

Participant-Focused

Prioritizes the perspectives and meanings of participants rather than researcher assumptions.

Natural Setting

Research takes place in the real world, not in a lab.

Researcher as Instrument

The researcher directly collects and interprets the data.

Multiple Sources of Data

Includes interviews, observations, documents, audiovisual materials; looks for patterns across data.

Use of Reasoning

Moves from inductive (specific to general) to deductive (general to specific) logic.

Emergent Design

Study design is flexible and can evolve during the research process.

Reflexivity

The process of reflecting on how personal biases and experiences influence the research.

Complex Account

Goal is to produce a rich, multi-layered understanding of human behavior.

Qualitative Data Collection Methods

Observations, interviews, documents, audiovisual materials, and digital content.

Narrative Research Purpose

To understand and interpret people's life stories or personal experiences.

Narrative Key Features

Focuses on how people make sense of their lives through storytelling.

Narrative Data Collection

In-depth interviews and personal documents.

Narrative Example

Studying recovery from trauma through life stories.

Phenomenological Research Purpose

Explores and describes how people experience a phenomenon.

Phenomenological Key Features

Uses bracketing to limit researcher bias and focus on participants' perspectives.

Phenomenological Data Collection

Semi-structured interviews and first-person accounts.

Phenomenological Example

Exploring what it feels like to live with anxiety or chronic illness.

Grounded Theory Purpose

Generates or builds theory based on patterns in the data.

Grounded Theory Key Features

Involves coding and comparing data; theory emerges from data.

Grounded Theory Data Collection

Interviews, observations, documents.

Grounded Theory Example

Studying the process people go through when seeking therapy.

Ethnographic Research Purpose

Studies cultures, social groups, and practices in natural settings.

Ethnographic Key Features

Long-term immersion; focuses on shared meanings and rituals.

Ethnographic Data Collection

Participant observation, interviews, analysis of cultural artifacts.

Ethnographic Example

Studying social dynamics among adolescent peer groups.

Case Study Purpose

Provides an in-depth understanding of a single case or small number of cases.

Case Study Key Features

Focus on intensive, holistic description of specific individuals or programs.

Case Study Data Collection

Interviews, observations, documents—whatever helps create a detailed picture.

Case Study Example

Analyzing a person with a rare condition or a treatment program.

Descriptive Methods

Involves coding data and identifying patterns or themes with minimal interpretation.

Thematic Analysis

Also known as theme analysis; organizes raw data into recurring themes.