dat.lehmann2018.RdResults from studies in which participants rated the attractiveness of photos that featured red or a control color. See OSF project at https://osf.io/xy47p/.
dat.lehmann2018The data frame contains the following columns:
| Short_Title | character | Shortened citation formatted Author name(s), year of publication - Experiment number. All cells in the column are unique for use as labels in the meta-analysis. |
| Full_Citation | character | Full citation in APA format. |
| Short_Citation | character | Shortened citation of different format, exactly as it would appear in an in-text citation. |
| Year | numeric | Year study published (whether in journal or published online). |
| Study | character | Experiment number. If only one experiment presented in a paper, then ‘Exp 1’, otherwise numbered according to numbering within paper. |
| Peer_Reviewed | character | Whether the experiment was published in a peer-reviewed journal or not. ‘Yes’ = peer-reviewed journal, ‘No’ can mean in press, online publication, or other. Column for moderator analysis. |
| Source_Type | character | Location where experiment is available, including journal articles, conference proceedings, online-only, and other options. More specific than whether peer-reviewed or not. |
| Preregistered | character | Whether experiment was pre-registered or not. |
| Moderator_Group | character | In some studies, a moderator was intentionally investigated that was meant to reduce the red-romance effect. Data for studies where the red-romance effect is expected to be moderated are marked ‘Yes’ in this column. All others are blank. |
| Gender | character | Gender of rater (male or female). In all cases, gender of stimuli will be opposite. |
| Color_Contrast | character | The color used as the contrast against red. In some cases, not every contrast color was listed. We chose to examine only contrasts that were present in the original studies, when possible. This column contains only the contrasts we examined in this meta-analysis. |
| Color_Form | character | Location of color in photo. Background = background or border color manipulated; Face = facial redness manipulated; Shirt, Dress, Item = color of specified object manipulated; Dot = a dot of color on shirt manipulated. |
| Photo_Type | character | Amount of body visible in photo. Head Shot = head only; Bust = head, shoulders, sometimes torso; Full Body = entire body visible. |
| DV_Type | character | Scale used for DV. ‘Perceived attractiveness’ = the perceived attractiveness scale used in the original studies; alternate scales are differentiated. |
| DV_Items | numeric | Number of items in DV scale. |
| DV_Scale | character | Full length of DV scale, if clear. |
| DV_ScaleBottom | numeric | Lower anchor of DV scale. |
| DV_ScaleTop | numeric | Upper anchor of DV scale. |
| Location | character | Country where study took place, if clear. ‘Worldwide’ in some cases of online participation without IP filtering of participants. |
| Continent | character | Continent where study took place, for the sake of creating larger categories for analysis. |
| Participants | character | Basic notes about participants. Students = high school, undergraduate, or graduate students; online = participants were gathered online; adult = no other common identifying factor given. Put into fewer categories for ease of analysis. |
| Participant_Notes | character | A finer grained description of participant characteristics. |
| Design | character | Whether study was a between- or within-subjects design. |
| Eth_Majority | character | Basic notes about participant ethnicity for ease of analysis. This represents the ethnic majority within the sample. |
| Eth_Majority_Detail | character | A finer grained description of participant characteristics, including in some cases participant counts when the ethnic majority was close to another category. |
| Eth_Stim | character | Ethnicity of the people pictured in the stimulus materials. |
| Eth_Match | character | Whether the ethnic majority of the participant pool matched the ethnicity of stimulus photos. |
| Red_Age | numeric | Mean age of participants in red group. If not given for specific group, then mean age overall. |
| Control_Age | numeric | Mean age of participants in control group. If not given for specific group, then mean age overall. |
| Color_Red | character | Specific values of red color, if given. ‘No data’ if not given or unclear. |
| Color_Control | character | Specific values of control color, if given. ‘No data’ if not given or unclear. |
| Red_Original | character | Whether the red color used in the study is within 5 units of the LCh values for red used in the original study. |
| Color_Match | character | Whether the control color used in the study is within 5 units of the red color on the L and C parameters. In cases where the control color used was white, it was not possible for the L and C parameters to match. |
| Presentation_Control | character | Whether the color of the stimulus viewed by each participant was consistent, as in participants viewing everything on paper or the same computer, versus uncontrolled presentation of the stimulus, as in viewing stimulus on different computers. |
| Stimuli_Presentation | character | Method for presenting stimuli. ‘Paper’ = stimuli printed on paper, shown in-person; ‘Screen’ = stimuli shown on-screen, not carefully controlled; ‘Screen Control’ = stimuli shown on-screen, but screen carefully color-matched. |
| Red_N | numeric | Number of participants in red group. |
| Red_M | numeric | Mean rating of DV in red group. |
| Red_SD | numeric | Standard deviation of DV in red group. |
| Control_N | numeric | Number of participants in control group. |
| Control_M | numeric | Mean rating of DV in control group. |
| Control_SD | numeric | Standard deviation of DV in control group. |
| SD_diff | numeric | Calculated for within-subjects studies, standard deviation of difference scores. |
| RM_r | numeric | Calculated for within-subjects studies, correlation between participant ratings of red and control attractiveness. |
| Control_Attractiveness | numeric | Attractiveness of stimuli in control condition, calculated as (Control_M - DV_ScaleBottom) / DV_ScaleTop, in order to compare attractiveness ratings across different scales. |
| Notes | character | Any additional notes on the study. |
| Total.SampleSize | numeric | Total unique participants in the study. |
| pooled | numeric | Pooled standard deviation for within-subjects studies. |
| yi | numeric | Standardized mean difference. |
| vi | numeric | Corresponding sampling variance. |
This is data from a meta-analysis of studies that test the red-romance hypothesis, which is that the color red enhances heterosexual attraction in romantic contexts. Analyzing male participants only, the meta-analysis should show a small, statistically significant effect (d = 0.26 [0.12, 0.40], p = .0004, N = 2,961). Analyzing female participants only should show a very small effect (d = 0.13 [0.01, 0.25], p = .03, N = 2,739). The analyses in the published meta-analysis found clear evidence of upward bias in the estimate for female participants and equivocal evidence for male participants. Moderator analyses suggest effect sizes may have declined over time (both genders), may be largest when an original shade of red is used (men only), and may be smaller in pre-registered studies (women only).
Lehmann, G. K., Elliot, A. J., & Calin-Jageman, R. J. (2018). Meta-analysis of the effect of red on perceived attractiveness. Evolutionary Psychology, 16(4). https://doi.org/10.1177/1474704918802412 https://osf.io/xy47p/
psychology, attraction, standardized mean differences
### copy data into 'dat' and examine data
dat <- dat.lehmann2018
head(dat)
#> Short_Title
#> 1 Roberts et al., 2010 - Exp 2
#> 2 Roberts et al., 2010 - Exp 2
#> 3 Wartenberg et al., 2011 - Exp 1 - In Group
#> 4 Gilston & Privitera, 2016 - Exp 1 - Healthy
#> 5 Roberts et al., 2010 - Exp 1
#> 6 Roberts et al., 2010 - Exp 1
#> Full_Citation
#> 1 Roberts, S. C., Owen, R. C., & Havlicek, J. (2010). Distinguishing between Perceiver and Wearer Effects in Clothing Color-Associated Attributions. Evolutionary Psychology, 8(3), 350-364. http://doi.org/10.1177/147470491000800304
#> 2 Roberts, S. C., Owen, R. C., & Havlicek, J. (2010). Distinguishing between Perceiver and Wearer Effects in Clothing Color-Associated Attributions. Evolutionary Psychology, 8(3), 350-364. http://doi.org/10.1177/147470491000800304
#> 3 Wartenberg, W., Hoepfner, T., Potthast, P., & Mirau, A. (2011). If you wear red on a date, you will please your mate. Proceedings of Empiriepraktikumskongress, 6th, Aug. 7, pp 26-27. University of Jena, Germany.
#> 4 Gilston, A., & Privitera, G. J. (2015). A 'Healthy' Color: Information About Healthy Eating Attenuates the 'Red Effect.' Global Journal of Health Science, 8(1), 56-61. https://doi.org/10.5539/gjhs.v8n1p56
#> 5 Roberts, S. C., Owen, R. C., & Havlicek, J. (2010). Distinguishing between Perceiver and Wearer Effects in Clothing Color-Associated Attributions. Evolutionary Psychology, 8(3), 350-364. http://doi.org/10.1177/147470491000800304
#> 6 Roberts, S. C., Owen, R. C., & Havlicek, J. (2010). Distinguishing between Perceiver and Wearer Effects in Clothing Color-Associated Attributions. Evolutionary Psychology, 8(3), 350-364. http://doi.org/10.1177/147470491000800304
#> Year PRPublication Source_Type Preregistered Moderator_Group Context Gender
#> 1 2010.1 Yes Journal Not Pre-Registered No Romantic Females
#> 2 2010.1 Yes Journal Not Pre-Registered No Romantic Males
#> 3 2011.0 No Conference Proceedings Not Pre-Registered No Romantic Females
#> 4 2016.0 Yes Journal Not Pre-Registered No Romantic Males
#> 5 2010.1 Yes Journal Not Pre-Registered No Romantic Females
#> 6 2010.1 Yes Journal Not Pre-Registered No Romantic Males
#> Color_Contrast Color_Form Photo_Type Photo_Similarity
#> 1 Blue/Green/White Clothing Bust Different between conditions
#> 2 Blue/Green/White Clothing Bust Different between conditions
#> 3 Blue Background Bust Same between conditions
#> 4 White Clothing Head Shot Same between conditions
#> 5 Blue/Green/White Clothing Bust Different between conditions
#> 6 Blue/Green/White Clothing Bust Different between conditions
#> DV_Type DV_Items DV_Scale DV_ScaleBottom DV_ScaleTop Location
#> 1 Single item rating of attractiveness 1 1-10 1 10 England
#> 2 Single item rating of attractiveness 1 1-10 1 10 England
#> 3 Perceived attractiveness 4 1-7 1 7 Germany
#> 4 Perceived attractiveness 1 1-7 1 7 USA
#> 5 Single item rating of attractiveness 1 1-10 1 10 England
#> 6 Single item rating of attractiveness 1 1-10 1 10 England
#> Continent Participants Participant_Notes Design Eth_Majority
#> 1 Europe Students Undergrads Within Subjects White
#> 2 Europe Students Undergrads Within Subjects White
#> 3 Europe Students Undergrads Within Subjects White
#> 4 North America Students Undergrad Within Subjects <NA>
#> 5 Europe Students Undergrads Within Subjects White
#> 6 Europe Students Undergrads Within Subjects White
#> Eth_Majority_Detail Eth_Stim Eth_Match Red_Age Control_Age Color_Red Color_Control
#> 1 Noted in manuscript all caucasian White Matched NA NA No Data No Data
#> 2 Noted in manuscript all caucasian White Matched NA NA No Data No Data
#> 3 White White Matched 21.47 21.47 No Data No Data
#> 4 <NA> White <NA> 19.85 19.85 No Data No Data
#> 5 Noted in manuscript all caucasian White Matched NA NA No Data No Data
#> 6 Noted in manuscript all caucasian White Matched NA NA No Data No Data
#> Red_Original Color_Match Presentation_Control Stimuli_Presentation Red_N Red_M Red_SD
#> 1 No No Yes Screen 15 3.660000 0.599760
#> 2 No No Yes Screen 15 3.653300 0.448600
#> 3 No No Yes Screen 39 3.000000 1.180000
#> 4 No No Yes Screen 54 5.650000 1.510000
#> 5 No No Yes Screen 30 4.496552 1.227654
#> 6 No No Yes Screen 30 4.596552 1.095445
#> Control_N Control_M Control_SD SD_diff RM_r Control_Attractiveness
#> 1 15 3.446667 0.6085667 NA 0.660 0.2718519
#> 2 15 3.455567 0.4820896 NA 0.920 0.2728407
#> 3 39 2.760000 1.1300000 0.63 0.856 0.2933333
#> 4 54 3.090000 1.0900000 NA 0.893 0.3483333
#> 5 30 4.263218 1.1838188 NA 0.660 0.3625798
#> 6 30 4.404598 0.9151259 NA 0.920 0.3782886
#> Notes Total.SampleSize
#> 1 Age range includes both male and female participants. 15
#> 2 Age range includes both male and female participants. 15
#> 3 Translation of summary provided by Elliot; within subjects info still needed 39
#> 4 <NA> 54
#> 5 Age range includes both male and female participants. 30
#> 6 Age range includes both male and female participants. 30
#> pooled yi vi
#> 1 0.6041794 0.3337773 0.049046910
#> 2 0.4656460 0.4014099 0.016037663
#> 3 1.1552705 0.2036116 0.007916124
#> 4 1.3168523 1.9163675 0.037967265
#> 5 1.2059356 0.1884325 0.023258447
#> 6 1.0093204 0.1852130 0.005905064
### load metafor package
library(metafor)
### meta-analyses for male and female participants
red_romance_malep <- dat[dat$Gender == "Males", ]
red_romance_femalep <- dat[dat$Gender == "Females", ]
res_malep <- rma(yi, vi, data=red_romance_malep, test="knha")
res_malep
#>
#> Random-Effects Model (k = 45; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of total heterogeneity): 0.1383 (SE = 0.0422)
#> tau (square root of estimated tau^2 value): 0.3718
#> I^2 (total heterogeneity / total variability): 88.94%
#> H^2 (total variability / sampling variability): 9.04
#>
#> Test for Heterogeneity:
#> Q(df = 44) = 172.4651, p-val < .0001
#>
#> Model Results:
#>
#> estimate se tval df pval ci.lb ci.ub
#> 0.2610 0.0680 3.8382 44 0.0004 0.1240 0.3980 ***
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
res_femalep <- rma(yi, vi, data=red_romance_femalep, test="knha")
res_femalep
#>
#> Random-Effects Model (k = 36; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of total heterogeneity): 0.0483 (SE = 0.0235)
#> tau (square root of estimated tau^2 value): 0.2197
#> I^2 (total heterogeneity / total variability): 52.92%
#> H^2 (total variability / sampling variability): 2.12
#>
#> Test for Heterogeneity:
#> Q(df = 35) = 73.0481, p-val = 0.0002
#>
#> Model Results:
#>
#> estimate se tval df pval ci.lb ci.ub
#> 0.1295 0.0580 2.2319 35 0.0321 0.0117 0.2473 *
#>
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>