Sample Tables

These sample tables illustrate how to set up tables in APA Style. When possible, use a canonical, or standard, format for a table rather than inventing your own format. The use of standard formats helps readers know where to look for information.

There many ways to make a table, and the samples shown on this page represent only some of the possibilities. The samples show the following options:

• The sample factor analysis table shows how to include a copyright attribution in a table note when you have reprinted or adapted a copyrighted table from a scholarly work such as a journal article (the format of the copyright attribution will vary depending on the source of the table).
• The sample regression table shows how to include confidence intervals in separate columns; it is also possible to place confidence intervals in square brackets in a single column (an example of this is provided in the Publication Manual).
• The sample qualitative table and the sample mixed methods table demonstrate how to use left alignment within the table body to improve readability when the table contains lots of text.

Use these links to go directly to the sample tables:

• Sample demographic characteristics table
• Sample results of several t tests table
• Sample correlation table
• Sample analysis of variance (ANOVA) table
• Sample factor analysis table
• Sample regression table
• Sample qualitative table with variable descriptions
• Sample mixed methods table

These sample tables are also available as a downloadable Word file (DOCX, 37KB). For more sample tables, see the Publication Manual (7th ed.) as well as published articles in your field.

Table 1

Sociodemographic Characteristics of Participants at Baseline

Baseline characteristic	Guided self-help		Unguided self-help		Wait-list control		Full sample
	n	%	n	%	n	%	n	%
Gender
Female	25	50	20	40	23	46	68	45.3
Male	25	50	30	60	27	54	82	54.7
Marital status
Single	13	26	11	22	17	34	41	27.3
Married/partnered	35	70	38	76	28	56	101	67.3
Divorced/widowed	1	2	1	2	4	8	6	4.0
Other	1	1	0	0	1	2	2	1.3
Children a	26	52	26	52	22	44	74	49.3
Cohabitating	37	74	36	72	26	52	99	66.0
Highest educational     level
Middle school	0	0	1	2	1	2	2	1.3
High school/some      college	22	44	17	34	13	26	52	34.7
University or      postgraduate degree	27	54	30	60	32	64	89	59.3
Employment
Unemployed	3	6	5	10	2	4	10	6.7
Student	8	16	7	14	3	6	18	12.0
Employed	30	60	29	58	40	80	99	66.0
Self-employed	9	18	7	14	5	10	21	14.0
Retired	0	0	2	4	0	0	2	1.3
Previous psychological    treatment a	17	34	18	36	24	48	59	39.3
Previous psychotropic    medication a	6	12	13	26	11	22	30	20.0

Note. N = 150 (n = 50 for each condition). Participants were on average 39.5 years old (SD = 10.1), and participant age did not differ by condition.

^a Reflects the number and percentage of participants answering “yes” to this question.

Table 2

Results of Curve-Fitting Analysis Examining the Time Course of Fixations to the Target

Logistic parameter	9-year-olds		16-year-olds		t(40)	p	Cohen's d
	M	SD	M	SD
Maximum asymptote, proportion	.843	.135	.877	.082	0.951	.347	0.302
Crossover, in ms	759	87	694	42	2.877	.006	0.840
Slope, as change in proportion per ms	.001	.0002	.002	.0002	2.635	.012	2.078

Note. For each subject, the logistic function was fit to target fixations separately. The maximum asymptote is the asymptotic degree of looking at the end of the time course of fixations. The crossover point is the point in time the function crosses the midway point between peak and baseline. The slope represents the rate of change in the function measured at the crossover. Mean parameter values for each of the analyses are shown for the 9-year-olds (n = 24) and 16-year-olds (n = 18), as well as the results of t tests (assuming unequal variance) comparing the parameter estimates between the two ages.

Table 1

Descriptive Statistics and Correlations for Study Variables

Variable	n	M	SD	1	2	3	4	5	6	7
1. Internal–      external status a	3,697	0.43	0.49	—
2. Manager job      performance	2,134	3.14	0.62	−.08**	—
3. Starting salary b	3,697	1.01	0.27	.45**	−.01	—
4. Subsequent promotion	3,697	0.33	0.47	.08**	.07**	.04*	—
5. Organizational tenure	3,697	6.45	6.62	−.29**	.09**	.01	.09**	—
6. Unit service      performance c	3,505	85.00	6.98	−.25**	−.39**	.24**	.08**	.01	—
7. Unit financial      performance c	694	42.61	5.86	.00	−.03	.12*	−.07	−.02	.16**	—

^a 0 = internal hires and 1 = external hires.

^b A linear transformation was performed on the starting salary values to maintain pay practice confidentiality. The standard deviation (0.27) can be interpreted as 27% of the average starting salary for all managers. Thus, ±1 SD includes a range of starting salaries from 73% (i.e., 1.00 – 0.27) to 127% (i.e., 1.00 + 0.27) of the average starting salaries for all managers.

^c Values reflect the average across 3 years of data.

^*p < .05. ^**p < .01.

Table 1

Means, Standard Deviations, and One-Way Analyses of Variance in Psychological and Social Resources and Cognitive Appraisals

Measure	Urban		Rural		F(1, 294)	η2
	M	SD	M	SD
Self-esteem	2.91	0.49	3.35	0.35	68.87***	.19
Social support	4.22	1.50	5.56	1.20	62.60***	.17
Cognitive appraisals
Threat	2.78	0.87	1.99	0.88	56.35***	.20
Challenge	2.48	0.88	2.83	1.20	7.87***	.03
Self-efficacy	2.65	0.79	3.53	0.92	56.35***	.16

^***p < .001.

Table 1

Results From a Factor Analysis of the Parental Care and Tenderness (PCAT) Questionnaire

PCAT item	Factor loading
	1	2	3
Factor 1: Tenderness—Positive
20. You make a baby laugh over and over again by making silly faces.	.86	.04	.01
22. A child blows you kisses to say goodbye.	.85	−.02	−.01
16. A newborn baby curls its hand around your finger.	.84	−.06	.00
19. You watch as a toddler takes their first step and tumbles gently back         down.	.77	.05	−.07
25. You see a father tossing his giggling baby up into the air as a game.	.70	.10	−.03
Factor 2: Liking
5. I think that kids are annoying (R)	−.01	.95	.06
8. I can’t stand how children whine all the time (R)	−.12	.83	−.03
2. When I hear a child crying, my first thought is “shut up!” (R)	.04	.72	.01
11. I don’t like to be around babies. (R)	.11	.70	−.01
14. If I could, I would hire a nanny to take care of my children. (R)	.08	.58	−.02
Factor 3: Protection
7. I would hurt anyone who was a threat to a child.	−.13	−.02	.95
12. I would show no mercy to someone who was a danger to a child.	.00	−.05	.74
15. I would use any means necessary to protect a child, even if I had to         hurt others.	.06	.08	.72
4. I would feel compelled to punish anyone who tried to harm a child.	.07	.03	.68
9. I would sooner go to bed hungry than let a child go without food.	.46	−.03	.36

Note. N = 307. The extraction method was principal axis factoring with an oblique (Promax with Kaiser Normalization) rotation. Factor loadings above .30 are in bold. Reverse-scored items are denoted with an (R). Adapted from “Individual Differences in Activation of the Parental Care Motivational System: Assessment, Prediction, and Implications,” by E. E. Buckels, A. T. Beall, M. K. Hofer, E. Y. Lin, Z. Zhou, and M. Schaller, 2015, Journal of Personality and Social Psychology, 108(3), p. 501 (https://doi.org/10.1037/pspp0000023). Copyright 2015 by the American Psychological Association.

Table 3

Moderator Analysis: Types of Measurement and Study Year

Effect	Estimate	SE	95% CI		p
			LL	UL
Fixed effects
Intercept	.119	.040	.041	.198	.003
Creativity measurement a	.097	.028	.042	.153	.001
Academic achievement measurement b	−.039	.018	−.074	−.004	.03
Study year c	.0002	.001	−.001	.002	.76
Goal d	−.003	.029	−.060	.054	.91
Published e	.054	.030	−.005	.114	.07
Random effects
Within-study variance	.009	.001	.008	.011	<.001
Between-study variance	.018	.003	.012	.023	<.001

Note. Number of studies = 120, number of effects = 782, total N = 52,578. CI = confidence interval; LL = lower limit; UL = upper limit.

^a 0 = self-report, 1 = test. ^b 0 = test, 1 = grade point average. ^c Study year was grand centered. ^d 0 = other, 1 = yes. ^e 0 = no, 1 = yes.

Table 2

Master Narrative Voices: Struggle and Success and Emancipation

Discourse and dimension	Example quote
Struggle and success a
Self-actualization as member of a larger gay community is the end goal of healthy sexual identity development, or “coming out”	“My path of gayness ... going from denial to saying, well this is it, and then the process of coming out, and the process of just sort of, looking around and seeing, well where do I stand in the world, and sort of having, uh, political feelings.” (Carl, age 50)
Maintaining healthy sexual identity entails vigilance against internalization of societal discrimination	“When I'm like thinking of criticisms of more mainstream gay culture, I try to ... make sure it's coming from an appropriate place and not like a place of self-loathing.” (Patrick, age 20)
Emancipation b
Open exploration of an individually fluid sexual self is the goal of healthy sexual identity development	“[For heterosexuals] the man penetrates the female, whereas with gay people, I feel like there is this potential for really playing around with that model a lot, you know, and just experimenting and exploring.” (Orion, age 31)
Questioning discrete, monolithic categories of sexual identity	“LGBTQI, you know, and added on so many letters. Um, and it does start to raise the question about what the terms mean and whether ... any term can adequately be descriptive.” (Bill, age 50)

^a The struggle and success master narrative states that same-sex desire/behavior is a natural if relatively uncommon developmental variant distinguishable from heterosexuality. Healthy sexual development entails “coming out” as well as joining a larger gay community in a shared struggle to overcome societal discrimination and be socially recognized as normal.

^b The emancipation master narrative states that discrete, monolithic, and mutually exclusive categories of homosexuality and heterosexuality are social constructions, conceptually suspect in their ability to fully capture the idiosyncrasies of sexual subjectivities, desires, and behaviors. This circumscription of sexual self within culturally contingent and hegemonic sexual identity categories must be resisted.

Table 3

Integrated Results Matrix for the Effect of Topic Familiarity on Reliance on Author Expertise

Quantitative results	Qualitative results	Example quote
When the topic was more familiar (climate change) and cards were more relevant, participants placed less value on author expertise.	When an assertion was considered to be more familiar and considered to be general knowledge, participants perceived less need to rely on author expertise.	Participant 144: “I feel that I know more about climate and there are several things on the climate cards that are obvious, and that if I sort of know it already, then the source is not so critical ... whereas with nuclear energy, I don't know so much so then I'm maybe more interested in who says what.”
When the topic was less familiar (nuclear power) and cards were more relevant, participants placed more value on authors with higher expertise.	When an assertion was considered to be less familiar and not general knowledge, participants perceived more need to rely on author expertise.	Participant 3: “[Nuclear power], which I know much, much less about, I would back up my arguments more with what I trust from the professors.”

Note. We integrated quantitative data (whether students selected a card about nuclear power or about climate change) and qualitative data (interviews with students) to provide a more comprehensive description of students’ card selections between the two topics.