Positive and Negative Affect Schedule:
Scoring and Interpretation

The PANAS captures two broad dimensions of emotional experience. Positive Affect (PA) reflects enthusiasm, activity and alert engagement, whereas Negative Affect (NA) reflects subjective distress and aversive mood. Watson, Clark and Tellegen (1988) built a 10-item scale for each dimension, so the instrument describes affect and does not stand in for a depression or anxiety diagnosis.

It is used in personality, experimental, health-psychology and intervention research. The instructions can ask about feelings at the present moment or over a longer period, such as the past week or in general, and that choice moves the variable toward a state or a trait reading. The time frame is therefore essential metadata in the dataset and in the publication (Watson et al., 1988).

• Positive Affect (PA): items 1, 3, 5, 9, 10, 12, 14, 16, 17 and 19.
• Negative Affect (NA): items 2, 4, 6, 7, 8, 11, 13, 15, 18 and 20.
• Response scale: five levels coded 1 to 5, from very slightly or not at all to extremely.
• Reverse-scored items: none in the standard form.
• Scores: two subscale sums, each from 10 to 50, with no standard combined total.
• Recall period: chosen at administration (the moment, today, the past few days, the past week, the past few weeks, the past year or in general) and not changed afterwards.
• Other forms: the PANAS-X, the PANAS-C and the I-PANAS-SF are distinct instruments with their own scoring rules.

1. Confirm that panas1 to panas20 follow the original item order, hold integers from 1 to 5 and refer to a single, documented time frame.
2. Sum the ten PA items to obtain panas_pa and the ten NA items to obtain panas_na, each ranging from 10 to 50. No reversal is applied and no omnibus PANAS score exists.
3. Set a missing-item rule. The original article does not set a universal prorating rule, so the code below requires all ten items within each subscale. Document and justify any alternative policy.

* Illustrative syntax: invalid values must already be recoded to missing (valid range 1 to 5).
COMPUTE panas_pa = SUM.10(panas1, panas3, panas5, panas9, panas10, panas12, panas14, panas16, panas17, panas19).
COMPUTE panas_na = SUM.10(panas2, panas4, panas6, panas7, panas8, panas11, panas13, panas15, panas18, panas20).
EXECUTE.

# data: one row per respondent; items panas1 to panas20 coded 1 to 5.
pa_items <- paste0("panas", c(1, 3, 5, 9, 10, 12, 14, 16, 17, 19))
na_items <- paste0("panas", c(2, 4, 6, 7, 8, 11, 13, 15, 18, 20))
data$panas_pa <- rowSums(data[pa_items], na.rm = FALSE)
data$panas_na <- rowSums(data[na_items], na.rm = FALSE)

For PANAS interpretation, a high PA score signals greater positive emotional activation, and a high NA score signals more negative emotional experience over the stated period. Neither direction establishes psychiatric illness on its own. The developers did not specify standardized diagnostic thresholds or population-independent severity bands, which is why each subscale is read as a continuous variable against your own sample.

Prespecify PA and NA as two separate outcomes or predictors, especially in emotion-induction and behavioral-intervention designs with repeated assessments. When the same people rate their affect several times, a Linear Mixed Models (Multilevel Modeling) analysis accounts for the clustering of observations within persons; cross-sectional questions can use correlation or regression instead. Write the recall period and the measurement schedule into the analysis plan.

Estimate alpha or omega separately for PA and NA from your own data, using Reliability Analysis (Cronbach’s Alpha) for the first. If your study tests whether two affect dimensions hold, Exploratory Factor Analysis (EFA) can examine the item structure, and translated or clinical samples call for Measurement Invariance testing before means are compared. The PANAS has no universal minimally clinically important difference, so avoid quoting one without population-specific validation.