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The sum of category percentages up to a specified position in an ordering. It has a substantive interpretation for an ordinal categorical variable; the cumulative percentage of an arbitrarily ordered nominal categorical variable changes when the labels are reordered.

For a two-way contingency table whose row and column variables are ordinal categorical variables, assign row scores $u_i$ and column scores $v_j$. The linear-by-linear association statistic measures the weighted covarianceAfter normalization, its square is asymptotically ${\chi }_1^2$ under independence. A signed, one-sided version tests a specified direction of ordinal association and can have greater statistical power than an omnibus independence test against a monotone alternative.

A categorical variable whose labels have no intrinsic ordering. Relabelling its categories does not change the underlying classification. An ordinal categorical variable additionally records an order; neither type automatically supplies equal numerical distances between categories.

All three frequency tables describe 2466 valid observations with no reported missing values. The sample is overwhelmingly White, Medium is the most common efficacy category, and disagreement with the technology-failure statement is more common than agreement. These statements describe the sample rather than establish population composition or causal relationships.

In a frequency table, a count gives the number of observations in a category, and a percentage divides that count by the full sample total. A valid percentage instead uses the total with an observed value of that variable. Here the two denominators coincide because every table reports zero missing values. A cumulative percentage adds categories in the displayed order; its substantive meaning depends on whether that order is meaningful.

The ethnicity counts are 77 Asian, 57 Black and 2332 White respondents, or 3.12%, 2.31% and 94.57%. Ethnicity is a nominal categorical variable: the displayed order is not a low-to-high ethnic scale. Its cumulative percentages are bookkeeping for that listing rather than an interpretable ranking. The two minority groups are small relative to the White group, which will matter for the precision and expected cell counts of the stratified comparisons.

For the three-category efficacy measure, 908 observations are High, 972 Medium and 586 Low, giving 36.82%, 39.42% and 23.76%. Medium is the mode, and 76.24% fall in the combined High-or-Medium categories. This is an ordinal categorical variable, but the table does not imply equal numerical distances between categories. Its order is descending efficacy, High then Medium then Low. Therefore the cumulative 76.24% refers to that upper portion of the scale, not to the proportion at or below Medium under an ascending convention. The cutpoints used to form these categories are not supplied and need not be invented to interpret their frequencies.

For techfail_chr, 336 strongly disagree, 922 disagree, 490 are neutral and 718 agree: 13.63%, 37.39%, 19.87% and 29.12%. The wording concerns not trusting technology because it fails when needed. Thus moving toward Agree means more distrust, not more trust. Combining the two disagreement categories gives $1258/2466\approx 51.01\%$, versus 29.12% agreement and 19.87% neutrality. Adding the separately rounded displayed percentages gives 51.02%; the difference is ordinary rounding error. There is no displayed strongly-agree category, so its frequency cannot be inferred or added to the agreement count.

The technology percentages add to 100.01%, including the printed valid-total figure. The integer counts still sum exactly to 2466; rounded percentages need not sum exactly to 100%. Finally, three separate univariate frequency tables say nothing by themselves about which efficacy categories the trusting or distrusting respondents occupy. That requires the joint information in the later contingency tables.

A frequency table describes category counts and their relative frequencies. The full-sample percentage uses every listed case as denominator; a valid percentage excludes missing data for that variable. A cumulative percentage adds the valid percentages up to an ordered category. The denominator matters as much as the number: a statement about respondents with recorded use need not describe every respondent.

Employment is a binary categorical variable: 1216 cases are employed and 837 are not, or about $59.23\%$ and $40.77\%$. Neither employment category is missing in this output. Employment labels have no intrinsic numerical spacing; codes zero and one identify the two categories under a chosen ordering convention. Cumulative percentages for a nominal categorical variable depend on the arbitrary display order and add little to this two-category description.

Age groups are an ordinal categorical variable because their intervals have a natural ordering, although the grouped representation loses exact ages. Among the five visible age rows, ages 25–44 form the largest group, with $562/1730\approx 32.49\%$, and ages 14–24 the smallest, with $182/1730\approx 10.52\%$. The other visible groups account for $20.98\%$, $16.42\%$ and $19.60\%$ on that denominator. About $43.01\%$ of the visible five-row count is below 45. These percentages should not be silently applied to the stated total $2053$: for example, the same $562$ cases are only $27.37\%$ of $2053$.

The age variable key advertises six categories, whereas only five appear. Their counts sum to $1730$, leaving a difference of $323$ against the printed total; the table nevertheless prints zero missing values. This is an internal source discrepancy, not permission to label those $323$ cases as a particular unseen age group or as recorded missing data. The oldest row is labelled 65+ here but 65–74 in Figure 2. Even the age percentage total has a small display discrepancy: the five rounded percentages sum to $100.01\%$, while the printed valid total and last cumulative percentage say $99.99\%$. The much larger count inconsistency remains after allowing ordinary rounding.

Social media use is also an ordinal categorical variable. Its six ordered categories express increasing frequency, not equally spaced measurements: averaging category labels or claiming a one-unit change has a constant time meaning would require an additional scoring convention. Among the $1424$ observed responses, the mode is never, at $459/1424\approx 32.23\%$, followed by daily, at $28.37\%$. Weekly use accounts for $18.19\%$, use more than once a day for $11.38\%$, less-than-monthly use for $6.39\%$, and monthly use for $3.44\%$. The first three categories together comprise about $42.06\%$ of valid responses. Consequently the middle ordered response lies in the weekly category: cumulative frequency first passes one half there.

Combining the two daily categories gives $566/1424\approx 39.75\%$ of valid responses using social media at least daily. The same recorded responses are $566/2053\approx 27.57\%$ of all listed cases. The second percentage is the fraction recorded as daily users, not an estimate that classifies all missing responses as nonusers. Missing social media responses make up $629/2053\approx 30.64\%$. The directly calculated valid fraction is $69.36\%$; summing the individually rounded full-sample percentages produces the printed $69.37\%$, a minor rounding difference. With no account of the missing-data mechanism, the observed valid distribution should not automatically be generalized to those with missing use. Employment being fully observed does not make social media missingness ignorable. The substantive distributional summary must retain both the valid-response denominator and the missing-data qualification.

Each frequency table describes a marginal distribution, rather than a relationship among variables. The recorded two-category gender variable has 1,042 male and 1,093 female responses: 48.81% and 51.19%. It is a nominal categorical variable. Its ordering in the table supplies no quantitative ranking; a cumulative percentage over those arbitrarily ordered labels has no invariant substantive meaning.

Household income is an ordinal categorical variable with six bands. Their shares, from the lowest band upward, are $5.48\%$, $9.93\%$, $11.48\%$, $13.02\%$, $8.34\%$ and $51.76\%$. The highest band alone contains 1,105 respondents, slightly more than half the sample. The bands are not a numerical income measurement with equal steps: the top band is open-ended, so an exact sample mean income cannot be recovered from these category counts alone.

The agreement item is also an ordinal categorical variable. Combining the first two categories givesNeutral responses account for $447/2135\approx 20.94\%$; the two disagreement categories together account for $570/2135\approx 26.70\%$. These percentages describe the measured vaccination intention item, rather than actual vaccination status.

All three tables have 2,135 valid observations and no missing values for these variables, so their percentages and valid percentages coincide. This is different from the seven missing group-indicator values in question 1. The income and agreement columns sum to $100.01\%$ because the displayed percentages were rounded separately. That rounding is compatible with counts summing exactly to 2,135.

The tables alone cannot show whether the gender composition differs by income, or whether vaccination intention differs between groups. Those are questions about a joint contingency table and its conditional probabilities, not about separate marginal distributions.

These are row percentages, so the comparisons estimate the conditional probability of recorded vaccination within each NHS trust category. The percentages increase across the ordered trust categories asThe extreme-category difference is approximately 49.31 percentage points, rather than a 49.31% relative increase. The ordinal categorical variable records an order; the categories do not establish equal numerical trust increments.

Under the null hypothesis of independence, the expected count in a cell is its row total times its column total divided by 748. The Pearson chi-squared statistic compares observed and expected counts throughThere are $(5-1)(2-1)=4$ statistical degrees of freedom, giving $p\approx 6.49\times 10^{-21}$. The minimum expected count is approximately 5.015, so the usual expected-count concern is much less acute here than in the income strata. The table gives strong evidence of a statistical association. The likelihood-ratio test of independence in a contingency table agrees: its statistic is approximately 81.361 on four statistical degrees of freedom, with a very small p-value. The displayed 0.000 is rounding, not a zero probability.

For magnitude, Cramér's V isIt describes the size of the departure from independence without using the category order or giving a direction. Calling it “moderate” is only a rough contextual description; the actual percentage differences are easier to interpret.

The ordered coefficients describe a positive direction. Goodman and Kruskal's gamma uses only concordant pairs and discordant pairs:It excludes tied pairs, which are frequent when the response has only two categories. It is therefore not a claim that 54.7% of respondents were vaccinated because of their trust. The directional Somers' D for vaccination as response includes pairs tied on that response in its denominator:The smaller value reflects the retained ties; it does not contradict the positive statistical association.

The small column-dependent Goodman and Kruskal's lambda has a useful prediction interpretation. Without trust information, always predicting the more frequent vaccinated category gives 121 mistakes. Predicting the majority category separately in each trust row gives $13+15+26+34+28=116$ mistakes. HenceTrust reduces this particular modal-prediction error by only about 4.13%, despite the strong evidence against independence. Most rows retain the same majority category. Evidence of association, its ordinal direction and improvement in a chosen prediction rule are different questions. None establishes that changing trust would change an individual's vaccination status.

Both variables are ordinal categorical variables, and the proposed alternative is directional: higher scores should tend to occur at higher prices. Assign increasing numerical scores to low, medium, high price and to poor, good, excellent quality, and use a one-sided linear-by-linear association test. Its statistic is based on the sample covariance, or equivalently the correlation, of those scores under the fixed margins.

This test uses one degree of freedom and directs its rejection region toward positive association. The four-degree-of-freedom likelihood-ratio test of independence in a contingency table spends power on every possible departure from independence, including unordered and negative patterns, so the ordered one-sided test is more powerful when the stated alternative is credible.

Kilometres is an ordinal categorical variable: its five levels have a meaningful order. Treating it as an ordinary factor would use four indicator variables and hence four unrelated regression coefficients. Converting the levels to numerical scores instead imposes a single linear trend across successive mileage bands. This is more parsimonious and gives greater precision when that trend is a reasonable approximation, although it also assumes that adjacent levels are equally spaced in their effect.

Reverse the order of exactly one of two ordinal categorical variables. Every concordant pair becomes a discordant pair and conversely, while pairs tied on either variable remain tied. Therefore the numerator $C-D$ changes sign and the denominator $C+D$ does not: Goodman and Kruskal's gamma changes sign whenever defined. The same argument applies to Somers' D, Kendall's tau-b and Stuart's tau-c, whose denominators depend on tie counts or table dimensions rather than the orientation. Reversing both orders preserves the sign. This explains why an association's numerical sign must be interpreted using the actual coding directions; it does not change the substantive paired observations. Measures of nominal association such as Cramér's V are invariant under either reversal.

For an $r\times c$ contingency table of two ordinal categorical variables, write $m=\min (r,c)>1$, $n$ for the sample size and $C,D$ for concordant pairs and discordant pairs. The coefficient isIt provides an ordinal association measure whose normalization accounts for the table dimensions. It generally differs from Kendall's tau-b and Goodman and Kruskal's gamma on a table with tied pairs. An association sign presupposes the chosen category ordering and is not a causal direction. The eponym is associated with Alan Stuart.^[ref]

Two observations tied on a variable have equal recorded values of that variable. For observations of two ordinal categorical variables, distinguish pairs tied only on the first, only on the second, or on both. Goodman and Kruskal's gamma excludes all such pairs, whereas Somers' D retains ties on the response when measuring directional predictive association. Consequently these coefficients can differ markedly for the same contingency table.