Estimating LGBTQ+ attitudes in the US and the UK

2025-09-15

2026-12-31

i) The average differences-in-means between control and treatment groups from all lists for all key sensitive statements (using the list experiment data).

ii) The answers to the direct sensitive questions (using the survey data).

iii) The differences between i) and ii).

iv) The differences in i), ii), and iii) across all key statements.

v) The differences in i), ii), and iii) over time, when possible.

i) The average differences-in-means between control and treatment groups from all lists for all key sensitive statements (using the list experiment data): this is the outcome variable that comes from our double list experiment technique. As explained in more detailed in the experimental design section of our pre-analysis plan registration, for a given sensitive statement, half of the subjects will see “List A” and “List B + Key Sensitive Item” and the remaining half will see “List A + Key Sensitive Item” and “List B”. We will take the difference in means based on the answers provided to “List A” and “List A + Key Sensitive Item”, next take the difference in means based on the answers provided to “List B” and “List B + Key Sensitive Item”, and then we will calculate the average of these two differences in means. This gives us the estimated share of the population with the key sensitive attribute.

ii) The answers to the direct sensitive questions (using the survey data): these are baseline estimates of the share of the population with the key sensitive attributes (without accounting for social desirability bias).

iii) The differences between i) and ii): this is the estimated size of the social desirability bias.

iv) The differences in i), ii), and iii) across all key statements: these are the differences in in attitudes using list experiment, direct survey, the size of the social desirability bias across all key statements. Key statements are indicated below.

v) The differences in i), ii), and iii) over time, when possible: these are the differences in in attitudes on transgender managers and employment discrimination using list experiment, direct survey, the size of the social desirability bias over time in comparison with Aksoy et al. (2025) (AEARCTR-0008820: https://doi.org/10.1257/rct.8820).

i) Population beliefs. This is the guessed percent of people in the general [US/UK] population who would agree to each key statement used in the list experiments
ii) Additional survey questions to understand views on gender affirming care
iii) Additional survey questions measuring attitudes and perceptions on LGBTQ+ topics and demographics
iv) Comparisons between UK and US data

We use a double list experiment, a variant of the original list experiment pioneered by Miller (1984). In a regular list experiment, participants are either assigned to a control or a treatment group. In the control group, participants are given a list with a set of statements and asked to indicate how many of those statements are true for them. In the treatment group, participants are given the same list with the same set of statements plus one additional sensitive statement (e.g., a statement about attitudes towards LGBTQ+ individuals or policies). The difference in means between the treatment and control group is the estimated share of the population with the key sensitive attribute.

In our case, we exploit the double list experiment technique to increase power, following Glynn (2013). For each sensitive statement, we will have two lists (i.e., Lists 1A and 1B), designed to be positively correlated. Some participants will be randomized into List 1A (control) and List 1B + Key Sensitive Statement (treatment). Other participants will be randomized into List 1A + Key Sensitive Statement (treatment) and List 1B (control). Some participants will get List 1A first, others will get List 1B first (order will be randomized). The differences-in means between control and treatment groups from both lists will be averaged and that will be the share of the population with that key sensitive attribute.

Not available

First, since each participant will see only one statement from Key Items 1-3 in List Experiment 1, we will randomize participants into one of the possible twelve combinations.

Then, since each participant will see only one statement from Key Items 4-6 in List Experiment 2, we will again randomize participants into one of the possible twelve combinations.

List Experiment 2 will always be shown after List Experiment 1.

Individual level randomization

No

We plan to obtain a representative sample in Prolific using sex, age, and political affiliation as quota selection criteria.

Following the initial pre-registration of our study, we collected pilot data from both the US and the UK. We collected pilot data on 750 participants in the US and 450 participants in the UK. We will include/exclude the pilot datasets from the analysis as a robustness check. Planned number of observations for the main studies: In the US: 900 participants will take the survey without the list experiment. 5850 participants will take the full survey including the list experiment and the direct questions. In the UK: 600 participants will take the survey without the list experiment. 3500 participants will take the full survey including the list experiment and the direct questions. The sample sizes are determined based on the max UK/US representative sample sizes (Factors: sex, age, and political affiliation) as well as budgetary constraints. Conditional on Prolific's sample size constraints and our funding availability, we may need to adjust the sample sizes accordingly.

For Lists A, we will have 50% of the participants in the treatment group and 50% of the participants in the control group. For Lists B, we will again have 50% of the participants in the treatment and control groups each. However, since we are using a double list experiment technique (which incorporates a within-subject component), participants who are in List A (List B) treatment group will naturally be in List B (List A) control group and vice versa.