Leadership Trait Analysis (LTA)

All traits at once

get_lta() computes all eight LTA traits in one call and returns a one-row tibble per speech. Set bootstrap = TRUE to obtain bootstrap means and delta-method variances for the trait proportions.

res_lta <- data.table::rbindlist(
  lapply(c(jfk1, jfk2, jfk3), function(x)
    get_lta(own_entity = own_ent, text = x, bootstrap = TRUE, B = B))
)

print(res_lta)

The output includes:

• Bootstrap means and variances for raw counts (e.g., meanP, varP, meanOP, varOP)
• Trait proportions and their delta-method variances (e.g., Pp, varPp, D, varD, C, varC, Ta, varTa, Ss, varSs, Na, varNa, B, varB)

The seven trait proportions are:

Individual traits

You can also compute traits one at a time:

res_nat     <- get_nat(own_entity = own_ent, text = jfk1, bootstrap = TRUE, B = B)
res_ctrl    <- get_ctrl(own_entity = own_ent, text = jfk1, bootstrap = TRUE, B = B)
res_power   <- get_power(own_entity = own_ent, text = jfk1, bootstrap = TRUE, B = B)
res_aff     <- get_aff(own_entity = own_ent, text = jfk1, bootstrap = TRUE, B = B)
res_dist    <- get_dist(own_entity = own_ent, text = jfk1, bootstrap = TRUE, B = B)
res_complex <- get_complex(text = jfk1, bootstrap = TRUE, B = B)
res_conf    <- get_conf(text = jfk1, bootstrap = TRUE, B = B)
res_task    <- get_task(text = jfk1, bootstrap = TRUE, B = B)

Aggregation methods

The way speech-level traits are aggregated matters for classification. type_lta() supports two approaches:

Simple mean (precision_weighted = FALSE, the default): Takes the arithmetic mean of each trait across speeches. All speeches contribute equally regardless of how precisely each trait was estimated.

Precision-weighted mean (precision_weighted = TRUE): Uses inverse-variance weighting via random-effects meta-analysis (metafor::rma() with REML). Speeches with lower bootstrap variance (i.e., more precisely estimated traits) receive higher weight. This requires that get_lta() was run with bootstrap = TRUE so that the variance columns (varPp, varB, etc.) are available.

# Simple mean
type_lta(res_lta)

# Precision-weighted
type_lta(res_lta, precision_weighted = TRUE)

Classification dimensions

type_lta() classifies the leader along three dimensions:

Constraint (Respect vs. Challenge): Based on need for power (Pp) and belief in control (B). A leader who scores below both thresholds respects constraints; otherwise, they challenge constraints.

Openness (Open vs. Closed): Based on conceptual complexity (C) and self-confidence (Ss). A leader is open to information if complexity exceeds self-confidence, or if both exceed their respective high thresholds.

Motivation toward world (four categories): Based on distrust (D) and nationalism (Na):

Leadership styles

Constraint, openness, and task orientation (Ta) map to one of eight leadership styles:

out_lta <- type_lta(res_lta, precision_weighted = TRUE)
out_lta[, c("constraint", "openness", "motivation_toward_world", "typology")]

Custom thresholds

All classification thresholds are configurable. The defaults correspond to the norming sample:

type_lta(
  res_lta,
  precision_weighted = TRUE,
  need_for_power  = 0.50,
  control         = 0.44,
  complex_high    = 0.56,
  confidence_high = 0.81,
  task            = 0.59,
  distrust        = 0.41,
  ingroup         = 0.42
)