0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.1261.3941.7441.0831.0541.2491.3191.7031.0461.8782.1202.0803.4560.8631.4821.9691.9852.7440.9282.3740.0000.0000.0000.0000.0001.0851.0832.1320.8771.0571.3311.4595.0480.6841.4790.0000.0000.0000.0000.0001.5971.3621.5160.7341.0203.4143.1091.7820.4981.19626Table 1 Means, Standard Deviations, and Minimum and Maximum Scores for Time AllocationMaximum10.0007.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.00010.0009.0005.00010.000ResultsBasic Trends in Time AllocationProspective Housework Childcare Paid work Learning LeisureIdeal Housework Childcare Paid work Learning LeisureSocial norms for men Housework Childcare Paid work Learning LeisureSocial norms for women Housework Childcare Paid work Learning LeisureMeanMinimum SDData AnalysisData were analyzed using the software HAD （Shimizu, 2016）. First, the average time allocated to each of the five activities （housework, childcare, paid work, learning, and leisure） was calculated, reflecting prospec-tive, ideal, and gender norms for men and women. Second, correlation coefficients were obtained to examine the association between gender and prospective and ideal time allocation. Adopting Cohen’s （1992） method, correlations of medium （r ＞ .300） and large effect sizes （r ＞ .500） were interpreted. Third, to determine gender differences in future prospective and ideal time allocations, a two-way mixed-design analysis of variance （ANOVA） was conducted on the time allocation scores for the five activities using a 2 （gender） × 2 （prospective/ideal） design. To indicate effect size, partial eta squared （ηp2） was utilized. As a supplementary analysis, this study focused on the activity areas where gender differences between ideal and prospective time allocation were clearly identified and examined the process that determines prospective time allocation.Means, SD, and minimum and maximum scores for each time allocation were calculated.
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