Psych Paper 3

Independent Variable (IV)

The variable manipulated or controlled by the researcher; placed on the x-axis.

Dependent Variable (DV)

The variable measured as an outcome of the IV; placed on the y-axis.

Line Graph Purpose

Shows changes or trends over time or across conditions.

Line Graph: Trend Direction

Whether the line increases, decreases, or stays stable.

Line Graph: Rate of Change

Steep lines indicate rapid change; gradual slopes show slower change.

Line Graph: Peaks and Troughs

Highest and lowest points on the graph.

Line Graph: Intersection Points

Where lines cross, showing comparison between groups.

Line Graph Example

Used to show how cognitive development changes with age.

Bar Chart Purpose

Compares average values across different groups or categories.

Bar Chart: Height Differences

Shows which group scored highest or lowest.

Bar Chart: Patterns Across Groups

Identifies similarities and differences between groups.

Bar Chart: Variability

Differences in bar heights showing variability across categories.

Bar Chart Example

Shows how memory scores differ across age groups.

Histogram Purpose

Displays frequency distribution of continuous data.

Scatter Plot Purpose

Shows the relationship or correlation between two variables.

Scatter Plot: Positive Correlation

Dots trend upward (as one variable increases, so does the other).

Scatter Plot: Negative Correlation

Dots trend downward (as one increases, the other decreases).

Scatter Plot: No Correlation

Dots appear random with no clear pattern.

Scatter Plot: Strength of Correlation

Tight clusters = strong correlation; wide spread = weak.

Scatter Plot: Outliers

Points far from the trend line.

Scatter Plot Example

Relationship between study time and test performance.

Box Plot Purpose

Shows distribution, central tendency, and spread of data.

Box Plot: Median

The middle line inside the box.

Box Plot: Interquartile Range (IQR)

Width of the box, showing middle 50% of data.

Box Plot: Range

Length of whiskers showing minimum to maximum values.

Box Plot: Outliers

Individual points beyond whiskers.

Box Plot Example

Effect of teaching methods on test scores.

Pie Chart Purpose

Displays proportions or percentages of a whole.

Pie Chart: Slice Sizes

Indicates which category is largest or smallest.

Pie Chart Example

Proportion of participants using different coping strategies.

Area Graph Purpose

Shows cumulative change over time or stacked category changes.

Area Graph: Segment Trends

How each category increases or decreases over time.

Area Graph: Total Change

Combined area showing overall change.

Area Graph Example

Used to show music sales by format over time.

Line Graph Typical Use

Longitudinal studies showing development or changes over time.

Bar Chart Typical Use

Experiments comparing group performance.

Histogram Typical Use

Descriptive studies showing data distribution (e.g., IQ scores).

Scatter Plot Typical Use

Correlational studies.

Box Plot Typical Use

Studies comparing score distributions across groups.

Pie Chart Typical Use

Survey data showing percentages.

Area Graph Typical Use

Time-series data showing cumulative effects.

Graph Reading: Scale

Understand numerical ranges and intervals.

Graph Reading: Trends

Identify increases, decreases, stability.

Graph Reading: Comparisons

How groups or time points differ.

Graph Reading: Anomalies

Look for outliers or unusual data points.

P3 Description: Precise Language

Use terms like increases, decreases, stabilizes, peaks, troughs.

P3 Description: Use Data Points

Reference specific values when describing patterns.

P3 Description: Avoid Vague Statements

Be explicit about what the graph shows.

Positive Correlation

Points rise from left to right (as x increases, y increases, vice versa

Negative Correlation

Points fall from left to right (as x decreases, y decreases)

No correlation

Regression line

helps us to see correlations more clearly (strong, dots cluster closely around line, moderate, dots are more scattered but a trend is still visible, weak, the dots are widely scattered and no trend is visible)

Correlation coefficient

used to determine the strengths of correlation, (1.0 is perfect correlation, -1.0 is perfect negative correlation) and closer to 1 means stronger correlation, closer to -1 is weaker correlation

Quantitative data

Numeric measurable data that can be statistically analyzed

4 Levels of quantitative data

Nominal, Ordinal, interval, Ratio

Nominal data

Data based on categories

Ordinal data

Ranked data (strongly agree to strongly disagree)

Interval data

Differences between data, but no true 0 point (the temperature in fahrenheit)

Ratio data

Has potential score of 0 and you can carry out basic arithmetic on the data (the words remembered or reaction time in seconds)

Qualitative data

Non-numeric descriptive data

Self-report data

where pps tell researchers their experiences in an interview or survey

Behavioral data

obtained when a psychologist observes behavior (eg: reaction time)

Physiological data

biological measures (heart rate)

Archival Data

pre-existing records (medical files)

Anecdotal Data

Someone’s personal experiences or informal observations, not systematically collected, and can be biased

Empirical data

qualitative or quantitative data that is gathered through systematic observations or experimentations that can be independently verified and statistically analyzed

Experimental data

obtained from experiments and allows us to determine causality

Correlation data

quantitative but did not manipulate an IV under controlled conditions with pps randomly allocated

Line Graph Limitations

misleading trends if the axis scaling is improper, difficulty in accurately representing a wide range of data

Bar Chart Limitations

Axis if not scaled correctly can be misleading, can hide variability within a dataset

Histogram limitations

difficult to compare multiple datasets, does not show exact data points only ranges

Scatter Plot limitations

unable to show more than 2 variables at a time, overplotting can occur with a large dataset

Box plot limitations

oversimplifies the data by showing only 5 summary stats (minimum, first quartile, median, IQR, and maximum)

Pie chart limitations

difficulty in accurately comparing slices, especially when they are similar in size

Area graph limitations

difficulty in accurately reading exact values especially for overlapped or stacked data.