PCA Biplot

A 2D visual map showing how elements and constructs relate to each other using Principal Component Analysis (PCA).

• Elements are plotted as points - elements close together were rated similarly across constructs.
• Constructs are shown as arrows (vectors) from the origin - arrows pointing in similar directions measure similar things.
• PC1 and PC2 are the two main dimensions that explain the most variance in your ratings.

How to interpret

• Elements near each other = similar rating patterns
• Constructs pointing same direction = correlated (measure similar things)
• Constructs pointing opposite directions = negatively correlated
• Elements in the direction of a construct arrow = rated high on that construct

Understanding construct arrow labels

Crossplot Analysis

A scatter plot showing where each element falls on two constructs of your choice.

• X-axis = ratings on the first construct (1 = left pole, 7 = right pole)
• Y-axis = ratings on the second construct
• Each point = one element from your grid

How to interpret

• Elements in the same quadrant share similar ratings on both constructs
• The midpoint (4) is marked with dashed lines - this divides the plot into four quadrants
• Use this to explore relationships between specific construct pairs
• Try different construct combinations to find meaningful patterns

Example use

If your constructs are 'friendly-unfriendly' (X) and 'competent-incompetent' (Y), elements in the top-right are seen as both unfriendly AND incompetent.

Synopsis Analysis

Summarises your rating patterns through histograms and variance analysis.

Display options

• Overall Distribution - Histogram of ALL ratings in your grid. Shows if you tend to use certain parts of the scale more than others. Red line = mean, blue line = median.
• Element Distributions - Separate histogram for each element. Shows how each element was rated across all constructs.
• Construct Distributions - Separate histogram for each construct. Shows how ratings vary across elements for each construct.
• Scree Plot - Shows how much variance each principal component explains. Helps determine how many dimensions are meaningful in your data.

How to interpret

• Skewed distributions may indicate response bias or genuine patterns
• Flat distributions suggest differentiated ratings
• In the scree plot, look for an 'elbow' where variance drops off - components before the elbow are most meaningful

Heatmap

A color-coded grid showing all your ratings at a glance.

• Rows = Elements
• Columns = Constructs
• Colors = Rating values (darker = higher ratings by default, or use color toggle for blue-white-red)

How to interpret

• Look for patterns - rows or columns with similar shading
• Dark/red regions indicate high ratings (toward right pole)
• Light/blue regions indicate low ratings (toward left pole)
• Uniform rows = element rated similarly across all constructs
• Uniform columns = construct doesn't differentiate between elements

Element Dendrogram

A tree diagram showing which elements are most similar to each other based on their rating patterns.

How to read it

• Elements that join early (close to the left) are very similar - they were rated similarly across most constructs
• Elements that join late (further right) are more different from each other
• Branch length indicates degree of difference

Example interpretation

If elements A and B join together before connecting to C, this means A and B have more similar rating profiles than either has with C.

Construct Dendrogram

A tree diagram showing which constructs are most similar based on how elements were rated on them.

How to read it

• Constructs that join early (close to the left) essentially measure the same thing - elements received similar ratings on both
• Constructs that join late (further right) measure different dimensions
• Very similar constructs may be redundant - consider if you need both

Example interpretation

If 'friendly-unfriendly' and 'warm-cold' join early, you may be using these constructs interchangeably. They represent the same underlying dimension in your thinking.

Focus Cluster Analysis

Focus automatically sorts your grid to reveal patterns. Similar elements appear together, and similar constructs appear together.

The display shows 4 parts

• Top dendrogram - shows how constructs (columns) cluster together
• Left dendrogram - shows how elements (rows) cluster together
• Center grid - your ratings, reordered so similar items are adjacent
• Match statistics - similarity percentages for elements and constructs

Reading the dendrograms

• Short connections = very similar items
• Long connections = less similar items
• Items that join low on the tree are more similar than those joining higher up

Parameters

• Minkowski Power - 1.0 (city block, default) treats all differences equally; 2.0 (Euclidean) emphasizes larger differences
• Match Cutoff - only shows matches above this similarity threshold

Common uses

• Finding element groups that cluster together
• Identifying redundant constructs (matches > 90%)
• Discovering main conceptual dimensions

Descriptive Statistics

Summary statistics for your grid data, showing patterns in how elements and constructs were rated.

Element Statistics

• Mean - average rating for this element across all constructs. High means = element rated toward right poles; low means = toward left poles.
• SD (Standard Deviation) - how much ratings varied. Low SD = element rated consistently; high SD = element rated very differently on different constructs.

Construct Statistics

• Mean - average rating on this construct across all elements. Near 4 = construct differentiates well; extreme values may indicate bias.
• SD - how much this construct differentiates between elements. Low SD = construct doesn't distinguish elements well; high SD = good differentiation.

What to look for

• Constructs with very low SD may not be useful - they rate all elements the same
• Elements with extreme means may be outliers worth examining
• Compare means to identify patterns in how you perceive different elements