Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Glob Health Action . 2026 Apr 13;19(1):2656563. doi: 10.1080/16549716.2026.2656563 Search in PMC Search in PubMed View in NLM Catalog Add to search Understanding men’s attitudes toward the justification of wife-beating in Tanzania: insights from the 2022 Demographic and Health Survey Elihuruma Eliufoo Stephano Elihuruma Eliufoo Stephano a Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma, Tanzania b Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing Find articles by Elihuruma Eliufoo Stephano a, b, ✉ , Tegemea Patrick Mwalingo Tegemea Patrick Mwalingo a Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma, Tanzania Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing Find articles by Tegemea Patrick Mwalingo a , Azan Abubakar Nyundo Azan Abubakar Nyundo c Department of Psychiatry and Mental Health, School of Medicine and Dentistry, The University of Dodoma, Dodoma, Tanzania Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing Find articles by Azan Abubakar Nyundo c , Mtoro J Mtoro Mtoro J Mtoro d TILAM International, Dar es Salaam, Tanzania Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing Find articles by Mtoro J Mtoro d Author information Article notes Copyright and License information a Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, Dodoma, Tanzania b Clinical Nursing Teaching and Research Section, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China c Department of Psychiatry and Mental Health, School of Medicine and Dentistry, The University of Dodoma, Dodoma, Tanzania d TILAM International, Dar es Salaam, Tanzania ✉ CONTACT Elihuruma Eliufoo Stephano [email protected] Department of Clinical Nursing, School of Nursing and Public Health, University of Dodoma, P.O Box 395, Benjamin Mkapa Road, SONPH Admistrative Building, Iyumbu, Dodoma 41218, Tanzania Roles Elihuruma Eliufoo Stephano : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing Tegemea Patrick Mwalingo : Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing Azan Abubakar Nyundo : Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing Mtoro J Mtoro : Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing Received 2025 Oct 16; Accepted 2026 Apr 2; Collection date 2026. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. PMC Copyright notice PMCID: PMC13072675 PMID: 41968939 ABSTRACT Background Male justification of wife-beating refers to a man’s belief that it is acceptable or legitimate to physically discipline or assault his wife under certain circumstances. Research has predominantly focused on women, leaving a critical gap in understanding men’s attitudes and beliefs surrounding violence against women. Objective This study aims to address this gap by examining the factors associated with male attitudes toward the justification of wife-beating in Tanzania. Methods This study employed an analytical cross-sectional design using secondary data from the 2022 Tanzania Demographic and Health Survey, which included 5,763 ever-married men. A modified Poisson regression model identified factors, and results were presented as weighted prevalence ratios (PR) with 95% confidence intervals (CI). Results The overall prevalence of male justification of wife-beating was 30.4% (95% CI: 28.1–32.9). Younger men, aged 15–24 years (Adjusted Prevalence Ratio [APR] = 1.62, 95% CI: 1.27–2.07), those aged 25–34 years (APR = 1.16, 95% CI: 1.01–1.34), men who consume alcohol (APR = 1.34, 95% CI: 1.17–1.53), those who were cohabiting (APR = 1.31, 95% CI: 1.10–1.55) and in men residing in the western, northern, central, southern, and lake zones of mainland Tanzania exhibited significantly higher prevalence of wife-beating justification. Conversely, men with secondary/higher education (APR = 0.73, 95% CI: 0.57–0.94) and in the middle wealth quantile (APR = 0.81, 95% CI: 0.66–0.99) were less likely to justify wife-beating. Conclusion This study reveals a notable prevalence of male justification of wife-beating in Tanzania, with significant associations identified between key demographic and behavioral factors. These findings underscore the need for multi-pronged strategies that engage men, address socio-cultural norms, and harmful attitudes. KEYWORDS: Attitude, wife-beating, justification, men, Tanzania Paper context Main findings: The study identified a significant prevalence of male justification of intimate partner violence in Tanzania, with younger age, alcohol consumption, cohabitation, and residence in specific geographical zones being associated with higher justification, while higher education and middle wealth were associated with lower justification. Added knowledge: This research contributes by specifically addressing the under-researched area of men’s attitudes toward intimate partner violence justification within a Tanzanian context, utilizing recent national data to identify key demographic and behavioral factors influencing these views. Global health impact for policy and action: The findings provide actionable insights for designing targeted interventions and policies that engage men and boys to challenge harmful gender norms and reduce intimate partner violence justification, contributing to global efforts to prevent gender-based violence. Background Violence against women is a global crisis affecting a significant portion of the female population, whereby many victims experience this abuse from their intimate partners [ 1 ]. These violences represent a severe global public health and human rights concern [ 2 ], with approximately 27% of women worldwide experiencing physical and/or sexual violence from their partners during their lifetime [ 3 ]. The situation is particularly alarming in sub-Saharan Africa (SSA), where prevalence ranges from 36% to 71% in many countries, among the highest globally [ 3–5 ]. These elevated rates often correlate with patriarchal social structures, gender inequality, and cultural norms that normalize violence as a form of control within intimate relationships [ 5 ]. Within this context, understanding male justifications for wife-beating is critical, as these beliefs serve as cognitive mechanisms that enable and maintain abusive behaviors across diverse settings [ 6 ]. Several common risk factors contribute to male justifications for wife-beating, including male interpersonal aggression, substance abuse, childhood exposure to violence, and gendered attitudes toward aggression [ 7 ]. Additional factors that increase the likelihood of violent behavior include socioeconomic pressures, cultural conditioning, being in teen violent relationships, and access to firearms [ 8 ]. Globally, women are more likely to be victims, and common justifications for wife-beating from males include self-defense, being provoked, and anger issues. Most male perpetrators narrate wife-beating as an unintentional and unplanned action [ 9 ]. In Tanzania, male justification of wife-beating remains pervasive, with previous studies reporting that approximately 12%–61% of ever-married women have experienced physical or sexual violence from their partners [ 10 , 11 ]. This high prevalence interacts with traditional gender norms, socioeconomic factors, and limited support systems for survivors [ 3 ]. Studies have identified common male justification of wife-beating among Tanzanian, including perceived female transgressions of traditional gender roles (neglecting children, arguing with husband), suspected infidelity, financial disputes, women’s refusal of sexual advances, and challenging male authority in household decision-making [ 10 , 12 , 13 ]. These justifications often reflect deeply embedded cultural beliefs about male dominance and female subordination that transcend educational and socioeconomic boundaries [ 13 ]. Tanzania’s legal framework has evolved to address violence through several key measures, including the Law of Marriage Act, the Sexual Offences Special Provisions Act, and the Anti-Trafficking in Persons Act [ 14 ]. Additionally, Tanzania ratified the Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) and developed a National Plan of Action for the Prevention and Eradication of Violence Against Women and Children [ 15 ]. However, implementation remains inconsistent, with significant gaps between legal protection and practical enforcement, particularly in rural areas where traditional authorities often supersede formal legal systems [ 16 ]. This dissonance between legal frameworks and lived experiences creates an environment where male justification of wife-beating persists despite policy reforms. Despite these legal provisions, previous research in Tanzania using the Tanzania Demographic and Health Survey (TDHS) data reveals a concerning pattern in which a substantial proportion of Tanzanian men continues to justify wife-beating under certain circumstances [ 16 , 17 ]. This acceptance exists alongside high actual wife-beating prevalence and creates a cycle where violence is simultaneously perpetrated, experienced, and normalized. These studies further indicate significant regional variations in attitudes toward wife-beating, with higher acceptance estimates in rural areas, among less educated populations, and in specific regions such as the Lake Zone and the Western Zone [ 16–18 ]. These variations suggest that wife-beating justification is influenced by complex intersections of geographic, cultural, and socioeconomic factors that require further understanding, underscoring the need for this secondary analysis using the TDHS. A comprehensive study examining male justification of wife-beating in Tanzania is urgently needed to address knowledge gaps in existing research. While previous studies have documented violence prevalence and general attitudes, fewer have specifically explored the justifications used by men to rationalize violence [ 3 , 10 , 11 , 16–19 ]. In Tanzania, there is a scarcity of studies assessing male justification for wife-beating, particularly using nationally representative data. The TDHS offers a unique opportunity to examine these justifications across different demographic segments, enabling the development of interventions that address the root beliefs sustaining violent behaviors. Understanding male perspectives is essential because men are primarily the perpetrators [ 9 ], and sustainable violence reduction requires engaging them as agents of change. Such research would inform more effective prevention strategies that target specific reported justifications, helping Tanzania progress toward its commitments to gender equality and violence reduction. Therefore, this study aims to assess male justification of wife-beating by using a secondary analysis of the 2022 TDHS. Methods Study design and setting This study employed an analytical cross-sectional design using secondary data from the 2022 TDHS. The data used in this study were the 2022 TDHS data. Details on DHS have been described elsewhere [ 20 ]. The TDHS was conducted in the United Republic of Tanzania from 24 February to 21 July 2022. Tanzania is a country in East Africa with approximately 62 million people, according to the 2022 census, of whom half are men [ 21 ]. Participant and sampling The detailed TDHS methodology has been explained elsewhere [ 20 ]. In summary, the 2022 TDHS uses a stratified two-stage sampling strategy. The first stage involved the selection of clusters consisting of census enumeration areas (EAs) delineated from the 2022 national population census. First, 629 EAs (211 urban and 418 rural) were selected with probability proportional to size (PPS) within each stratum. During the household listing phase, field staff mapped these clusters and recorded all residential addresses to create a secondary sampling frame. From this, 26 households were systematically chosen per cluster. The final sample targeted men aged 15–49, resulting in a total of 5,763 participants. In DHS methodology, participants aged 15–17 years are considered part of the reproductive-age population and are routinely included in modules assessing attitudes, reproductive health, and related outcomes. For this analysis, we analyzed data from 3,246 weighted male participants, aged ≥ 15 years, after excluding never-married men for accurate wife-beating attitude measurement. Variable measurements Dependent variable The outcome variable for this study was male attitude toward wife-beating. This variable was derived from responses to five questions within the 2022 TDHS, each assessing whether men believed specific scenarios justified wife-beating. A binary variable was created, where ‘justification’ was defined as a ‘yes’ response to at least one of the following scenarios in Table 1 . This composite variable reflects the justification of wife-beating against women under specific circumstances, as measured by the TDHS. Table 1. Description of male justification toward wife-beating. Question Scenarios Beating justified if; Wife goes out without telling husband Wife neglects children Wife argues with husband Wife refuses to have sex with husband Wife burns food Open in a new tab Independent variables Independent variables were selected based on the available data and literature [ 3 , 10 , 11 , 16–19 ]; age in years (15–24, 25–34, or 35–49), education level (no formal education, primary education, or secondary/higher), marital status (married, cohabiting, or separated/divorced), literacy (literate; men were considered literate if they could read aloud all or part of a sentence shown to them and illiterate if otherwise) [ 22 ]. Working status (working or not working), own a mobile phone (yes or no), internet use (ever use or never use), media exposure (yes as listening to the radio, reading the newspaper, or watching Television less than once a week or at least once a week or no if otherwise), number of living children (none, 1–3, or ≥4), alcohol consumption (yes or no), household members (≤5 or ≥6), spouse current pregnant (yes or no), wealth index (poor, middle, or rich), sex of household head (male or female), residence (urban or rural), and geographical zones (western, northern, central, southern, lake, eastern, and Zanzibar). Data management and analysis To address the complex survey design of the 2022 TDHS, sample weights, primary sampling units (clusters), and strata were used to adjust for clustering and potential sampling biases. Data cleaning, coding, and analysis were performed using Stata 18.5 (STATA Corp., College Station, TX) with the ‘svy’ command used in all statistical analyses. Descriptive statistics, including means, standard deviations (SD), medians (interquartile ranges), and frequencies with proportions, were used to summarize the continuous and categorical variables. The prevalence of men’s justification of wife-beating was calculated as the proportion of men reporting justification across at least one domain. Pearson’s chi-square tests were employed to compare wife-beating justification across sociodemographic characteristics. We used a Modified Poisson regression model with a robust variance estimator as the odds ratio estimated from logistic regression might overestimate the association for non-rare outcomes (>10%). Univariate analysis was performed by fitting each explanatory variable against the response variable to provide a crude prevalence ratio (CPR). Backward selection at a p -value of <0.1 was used to select variables for inclusion in the multivariable analysis. Before the multivariable regression model, we assess multicollinearity using the Variance Inflation Factor (VIF), resulting mean VIF of 4.10, suggesting no significant multicollinearity. Finally, a Modified Poisson regression model with robust variance estimator was fitted, adjusting for potential confounders including respondent age, to estimate the adjusted prevalence ratio (APR) with corresponding 95% Confidence intervals (CI). We used the Hosmer–Lemeshow test to assess the goodness-of-fit of our model [ 23 ]. Statistical significance was set at p < 0.05. Ethics and consent The study is based on the publicly available 2022 TDHS datasets, which are accessible online and have been de-identified. The initial survey was approved by the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. We obtained permission to use the DHS data from MEASURE Tanzania. The initial DHS was carried out in accordance with the principles of the Declaration of Helsinki. All methods were conducted following the relevant guidelines and regulations. The consent for publication is not applicable. Results Sociodemographic characteristics of study participants A total of 3,246 men with a median age of 35 years (interquartile range = 29–42), with more than half (52.7%) aged 35–49 years. More than half (63.4%) had attained primary education, and 85.9% were literate. The majority (95.2%) were working, and 90.1% had media exposure. Regarding socioeconomic status, 36.0% of the participants were from poor households, while 43.7% were from rich households. Over half (61.0%) had ≤5 household members and 54.6% had ≤3 living children. One in five (20.6%) was alcohol consumers. More than half (69.0%) were from rural settings, and 30.0% were from the lake zone of mainland Tanzania ( Table 2 ). Table 2. Sociodemographic and behavioral characteristics of men in Tanzania ( N = 3,246). Characteristics Frequency Percentage Age group (years) 15–24 268 8.3 25–34 1,266 39.0 35–49 1,712 52.7 Median (IQR) 35 (29–42) Education Level No formal education 390 12.0 Primary 2,059 63.4 Secondary/Higher 798 24.6 Marital Status Married 2621 80.8 Cohabiting 316 9.7 Separated/Divorced 309 9.5 Literacy Illiterate 459 14.1 Literate 2,787 85.9 Working Status Not working 155 4.8 Working 3,091 95.2 Media Exposure No 322 9.9 Yes 2,924 90.1 Number of living Children None 215 6.6 1–3 1,771 54.6 ≥4 1,260 38.8 Mean(±SD) 3.4 (2.6) Alcohol Consumption No 2,253 69.4 Yes 993 30.6 Household members ≤5 1,980 61.0 ≥6 1,266 39.0 Spouse’s Currently Pregnant ( n = 2,936) No 2,548 86.8 Yes 388 13.2 Wealth Index Poor 1,168 36.0 Middle 660 20.3 Rich 1,417 43.7 Sex of Household Head Male 2,946 90.8 Female 300 9.2 Own a mobile phone No 402 12.4 Yes 2,844 87.6 Internet use Never use 2,359 72.7 Ever use 887 27.3 Residence Urban 1,007 31.0 Rural 2,239 69.0 Geographical Zones Western 273 8.4 Northern 348 10.7 Central 299 9.2 Southern 758 23.4 Lake 964 29.7 Eastern 508 15.7 Zanzibar 95 2.9 Open in a new tab Prevalence of men’s attitude toward justification of wife-beating The overall prevalence of men’s attitudes justifying wife-beating was 30.4%. Across specific scenarios, 21.34% of the men agreed that a wife deserves to be beaten if she neglects the children, whereas 3.78% justified wife-beating if she burns the food ( Figure 1 ). Figure 1. Open in a new tab Percentage distribution of men’s attitudes toward justification of wife-beating in Tanzania. Bivariate analysis of factors associated with men’s justification of wife-beating in Tanzania Table 3 presents the bivariate analysis of the justification of wife-beating among the respondents’ characteristics. Respondent age, marital status, education, working status, alcohol consumption, sex of household head, mobile phone ownership, internet use, household wealth index, residence, and geographical zones were significantly associated with the likelihood of justification of wife-beating ( p < 0.05). Contrarily, literacy, media exposure, number of living children, household members, and spouse’s pregnant status were not significantly associated with justification of wife-beating. Table 3. The distribution of sociodemographic characteristics by men’s justification of intimate partner’s violence in Tanzania ( N = 3246). Characteristics Justified wife-beating, n (%) χ 2 (df) p -value No Yes Age group (years) 28.1 (3) 0.001* 15–24 150 (56.1) 118 (43.9) 25–34 873 (68.9) 393 (31.1) 35–49 1,235 (72.2) 477 (27.8) Education Level 23.4 (2) <0.001* No formal education 250 (64.1) 140 (35.9) Primary 1401 (68.1) 658 (31.9) Secondary/Higher 607 (76.1) 191 (23.9) Marital Status 16.2 (2) 0.004* Married 1862 (71.0) 759 (29.0) Cohabiting 191 (60.4) 125 (39.6) Separated/Divorced 205 (66.5) 104 (33.5) Literacy 4.7 (1) 0.070 Illiterate 299 (65.2) 160 (34.8) Literate 1959 (70.3) 828 (29.7) Working Status 5.7 (1) 0.039* Not working 121 (78.3) 34 (21.7) Working 2137 (69.1) 954 (30.9) Media Exposure 2.0 (1) 0.256 No 213 (66.1) 109 (33.9) Yes 2045 (70.0) 879 (30.0) Number of living Children 4.3 (2) 0.225 None 137 (63.5) 78 (36.5) 1–3 1232 (70.0) 539 (30.0) ≥4 890 (70.6) 370 (29.4) Alcohol Consumption 32.5 (1) <0.001* No 1637 (72.7) 616 (27.3) Yes 621 (62.6) 372 (37.4) Household members 0.2 (1) 0.732 ≤5 1372 (69.3) 608 (30.7) ≥6 887 (70.0) 379 (30.0) Spouse’s Currently Pregnant No 1783 (70.0) 765 (30.0) 0.1 (1) 0.847 Yes 269 (69.3) 119 (30.7) Wealth Index 30.2 (2) 0.001* Poor 743 (63.6) 425 (36.4) Middle 485 (73.5) 175 (26.5) Rich 1030 (72.7) 387 (27.3) Sex of Household Head 10.9 (1) 0.015* Male 2075 (70.4) 871 (29.6) Female 183 (61.1) 117 (39.9) Own a mobile phone 13.4 (1) 0.003* No 248 (61.6) 154 (38.4) Yes 2011 (70.7) 833 (29.3) Internet Use 24.9 (1) 0.001* Never use 1482 (67.1) 777 (32.9) Ever use 676 (76.2) 211 (23.8) Residence 26.2 (1) 0.002* Urban 763 (75.6) 244 (24.2) Rural 1495 (66.8) 744 (33.2) Geographical Zones 94.8 (6) <0.001* Western 185 (67.8) 88 (32.2) Northern 263 (75.7) 85 (24.3) Central 199 (64.5) 100 (83.5) Southern 527 (69.5) 231 (30.5) Lake 584 (60.6) 380 (39.4) Eastern 415 (81.7) 93 (18.3) Zanzibar 84 (88.7) 11 (11.3) Open in a new tab χ 2 ; Chi-square value, df; degree of freedom, * p < 0.05. Factors associated with men’s justification of wife-beating Table 4 presents the adjusted prevalence ratios (APR) from the generalized Poisson regression analysis, revealing significant associations between several factors and the male justification of wife-beating. Compared to men aged ≥35 years, the prevalence of wife-beating justification was 60% higher among younger men aged 15–24 years (APR = 1.60, 95% CI: 1.31–1.96) and 16% higher among men aged 25–34 years (APR = 1.16, 95% CI: 1.01–1.34). Conversely, men with secondary or higher education were 27% less likely to justify wife-beating than those with no formal education (APR = 0.73, 95% CI: 0.57–0.94). Alcohol consumption was associated with a 33% increase in the prevalence of wife-beating justification (APR = 1.33, 95% CI: 1.16–1.52). Notably, men residing in western (APR = 2.25, 95% CI: 1.53–3.32), northern (APR = 1.77, 95% CI: 1.19–2.64), central (APR = 2.39, 95% CI: 1.63–3.51), southern (APR = 2.07, 95% CI: 1.46–2.91), and lake zones of mainland Tanzania (APR = 2.67, 95% CI: 1.89–3.80) were more likely to justify wife-beating compared to men in Zanzibar ( Table 4 ). Table 4. Generalized Poisson regression analysis for factors associated with justification of wife-beating among men in Tanzania ( N = 3,246). Characteristics Unadjusted p -value Adjusted p -value PR (95%CI) PR (95%CI) Age group (years) 15–24 1.57 (1.22–2.01) <0.001* 1.60 (1.31–1.96) <0.001* 25–34 1.12 (0.97–1.29) 0.134 1.16 (1.01–1.34) 0.042* 35–49 Ref Ref Education Level No formal education Ref Ref Primary 0.89 (0.74–1.06) 0.198 0.90 (0.75–1.08) 0.278 Secondary/Higher 0.67 (0.53–0.84) 0.001* 0.73 (0.57–0.94) 0.013* Marital Status Married Ref Ref Cohabiting 1.36 (1.15–1.62) <0.001* 1.29 (1.09–1.53) 0.002* Separated/Divorced 1.16 (0.93–1.43) 0.183 1.17 (0.95–1.45) 0.150 Working Status Not working Ref Ref Working 1.42 (0.99–2.03) 0.056 1.31 (0.91–1.87) 0.141 Alcohol Consumption No Ref Ref Yes 1.37 (1.20–1.57) <0.001* 1.33 (1.16–1.52) <0.001* Spouse’s Currently Pregnant - No Ref Yes 1.02 (0.83–1.26) 0.842 Wealth Index Poor 1.33 (1.14–1.55) <0.001* 0.99 (0.83–1.99) 0.991 Middle 0.97 (0.79–1.17) 0.747 0.81 (0.66–0.99) 0.040* Rich Ref Ref Sex of Household Head Male Ref Ref Female 1.31 (1.08–1.60) <0.001* 1.16 (0.95–1.41) 0.140 Residence Urban Ref Ref Rural 1.37 (1.15–1.63) <0.001* 1.17 (0.95–1.43) 0.134 Geographical Zones Western 2.84 (1.95–4.13) <0.001* 2.25 (1.53–3.32) <0.001* Northern 2.14 (1.44–3.18) <0.001* 1.77 (1.19–2.64) 0.005* Central 2.95 (2.04–4.27) <0.001* 2.39 (1.63–3.51) <0.001* Southern 2.69 (1.93–3.74) <0.001* 2.07 (1.46–2.91) <0.001* Lake 3.47 (2.48–4.85) <0.001* 2.67 (1.89–3.80) <0.001* Eastern 1.61 (1.07–2.42) 0.002* 1.47 (0.97–2.21) 0.067 Zanzibar Ref Ref Open in a new tab PR; Prevalence ratio, CI: Confidence Intervals, Ref; Reference category, * p < 0.05. Discussion This study examined the factors associated with male justification of wife-beating in Tanzania using data from the 2022 TDHS. The findings reveal that nearly one-third of Tanzanian men justify wife-beating under certain circumstances, highlighting a persistent societal acceptance of gender-based violence. This prevalence aligns with prior studies, where patriarchal norms and traditional gender roles normalize violence against women [ 24–26 ]. The Tanzanian wife-beating prevalence findings become particularly noteworthy when considering that women themselves often justify such violence in similar cultural contexts. For instance, in Nigeria, 62.4% of the women justified wife-beating in 2003, declining to 37.1% in 2013 [ 27 ]. This parallel suggests that wife-beating justification is not merely a male-perpetrated issue but a deeply internalized social norm affecting all genders. The Tanzanian figures likely reflect similar underlying patriarchal structures where violence becomes normalized through the generational transmission of gender norms [ 28 ]. The phenomenon of women justifying against themselves is observed across SSA [ 29 ] and underscores how profoundly patriarchal norms become embedded in collective consciousness. This context is important for interpreting why 30.4% of the Tanzanian men still justify wife-beating. When societies socialize both men and women to view gender-based violence as acceptable, challenging these attitudes requires transforming entire belief systems rather than simply targeting male perpetrators. The study identified significant associations between wife-beating justification and sociodemographic factors, including age, education, marital status, alcohol consumption, and geographical zone of residence. Younger men (15–24 years) were significantly more likely to justify wife-beating compared to older men (≥35 years). The results align with research indicating that younger men may exhibit higher wife-beating justification due to limited exposure to gender-equitable norms and higher impulsivity [ 30 , 31 ]. Interventions targeting youth through education and awareness campaigns could help shift these attitudes. Men with secondary or higher education were less likely to justify wife-beating than those without formal education. This finding is consistent with global evidence that education fosters gender-equitable attitudes and reduces acceptance of violence [ 32 , 33 ]. Additionally, alcohol consumption was strongly associated with wife-beating justification, corroborating previous studies that link alcohol use to increased aggression [ 34 ]. Alcohol may impair judgment and reinforce traditional masculine norms that condone dominance over women [ 35 ]. Public health interventions targeting alcohol abuse could contribute to reducing the acceptance of wife-beating. Men in mainland Tanzania (Western, Northern, Central, Southern, and Lake zones) were more likely to justify wife-beating than those in Zanzibar. Regional disparities may stem from differences in cultural norms, enforcement of gender policies, or exposure to awareness programs [ 36 ]. Zanzibar’s lower prevalence of men’s justification of wife-beating could reflect stronger community-based interventions or distinct sociocultural dynamics. Future research should explore these regional differences, including involving a sufficient sample size to inform targeted interventions. These results collectively demonstrate that wife-beating justification in Tanzania is not an isolated phenomenon but rather a manifestation of systemic gender inequalities that require multifaceted, context-specific solutions. Strength and limitations The use of the 2022 TDHS provides a large, nationally representative dataset. This enhances the generalizability of the findings to the Tanzanian male population. Additionally, the findings might have direct implications for the development of evidence-based violence prevention programs and policies in Tanzania. The study addresses a critical gap in the literature by focusing on men’s justification of wife-beating, rather than solely focusing on women’s experiences as victims. This provides valuable insights into the perpetuation of wife-beating in Tanzania. The study utilizes a well-established and rigorous methodology, including a two-stage, stratified sampling design and Modified Poisson regression analysis with a robust variance estimator. This strengthens the validity and reliability of the findings. While providing valuable insights, this study’s findings should be interpreted cautiously due to its limitations. The cross-sectional design prevents causal inference, and reliance on self-reported data may introduce recall and social desirability biases. The use of the word ‘wife-beating’ during assessment could bring limited information, especially when the perpetrator is also doing the same violence to a girlfriend or any cohabiting female. Implications for practice and policy recommendations This study’s findings suggest immediate and targeted interventions to disrupt the alarming prevalence of male justification of wife-beating in Tanzania. To effectively challenge harmful norms, we recommend developing and implementing evidence-based wife-beating prevention programs specifically for high-risk groups, including younger men, alcohol consumers, and cohabiting men, focusing on fostering respectful relationships and challenging harmful gender norms. Geographically targeted interventions should be prioritized in the Western, Northern, Central, Southern, and Lake Zones, addressing region-specific drivers of wife-beating justification. Integrating comprehensive gender equality and wife-beating prevention education into secondary and higher education curricula would be crucial for shaping positive attitudes from an early age. Strategic, multi-platform public awareness campaigns leveraging mass media could be essential to dismantling harmful gender norms and promoting respectful relationships. Legal frameworks must be reinforced to protect women from wife-beating, ensuring accountability for the perpetrators and promoting gender equality. In-depth qualitative studies should be conducted to understand the contextual drivers of wife-beating justification, and the effectiveness of existing prevention programs should be rigorously evaluated to inform evidence-based strategies. Conclusion This study provides critical insights into the prevalence and determinants of male justification of wife-beating in Tanzania, revealing that a significant 30.4% of the men endorse beliefs that condone violence against women under specific circumstances. Notably, younger age, alcohol consumption, cohabitation, and residence in specific geographic zones were significantly associated with higher rates of wife-beating justification, while higher education demonstrated a protective effect. These findings underscore the urgent need for targeted interventions to address the harmful attitudes and beliefs that perpetuate wife-beating within the Tanzanian context. The identification of high-risk groups and geographic disparities highlights the importance of tailored prevention programs that challenge harmful gender norms and promote respectful relationships. Furthermore, the protective effect of higher education emphasizes the potential of educational interventions to foster positive attitudes and behaviors. Ultimately, this study calls for a multi-faceted approach, encompassing educational initiatives engaging young men, community engagement, and strengthened legal frameworks, to effectively combat wife-beating and promote gender equality in Tanzania. Supplementary Material STROBE checklist Male Justification of IPV.docx ZGHA_A_2656563_SM1705.docx (35.3KB, docx) Acknowledgments We thank the DHS program for making the data available for this study. Responsible editor Jennifer Stewart Williams Funding Statement The author(s) reported there is no funding associated with the work featured in this article. Data availability statement The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com . Disclosure statement No potential conflict of interest was reported by the author(s). Ethics and consent Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm . As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures were adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X . Supplementary material Supplemental data for this article can be accessed online at https://doi.org/10.1080/16549716.2026.2656563 References [1]. Schafer M, Lachman J, Zinser P, et al. Using a digital parenting intervention to prevent intimate partner violence and promote gender equitable behaviors in South Africa and Jamaica: a qualitative study exploring partnered parents’ experiences of ParentText. Violence Against Women [Internet]. 2025;32:1150–10. doi: 10.1177/10778012251329363 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [2]. Eckhardt CI, Parrott DJ, Sprunger JG.. Mechanisms of alcohol-facilitated intimate partner violence. Violence Against Women [Internet]. 2015;21:939–957. doi: 10.1177/1077801215589376 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [3]. Aloyce D, Stöckl H, Malibwa D, et al. Men’s reflections on romantic jealousy and intimate partner violence in Mwanza, Tanzania. Violence Against Women [Internet]. 2023;29:1299–1318. doi: 10.1177/10778012221108421 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [4]. Devries KM, Mak JYT, García-Moreno C, Petzold M, Child JC, Falder G, et al. Global health. The global prevalence of intimate partner violence against women. Science. 2013;340:1527–1528. doi: 10.1126/science.1240937 [ DOI ] [ PubMed ] [ Google Scholar ] [5]. Mossie TB, Mekonnen Fenta H, Tadesse M, et al. Mapping the disparities in intimate partner violence prevalence and determinants across sub-Saharan Africa. Front Public Health [Internet]. 2023;11:11. doi: 10.3389/fpubh.2023.1188718 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [6]. Sehlikoğlu Ş, Nalbant A, Sehlikoğlu K, et al. A retrospective descriptive study of male perpetrators of intimate partner violence referred by judicial authorities: an example from Turkey. Arch Womens Ment Health [Internet]. 2025;28:129–138. doi: 10.1007/s00737-024-01495-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [7]. Clare CA, Velasquez G, Mujica Martorell GM, et al. Risk factors for male perpetration of intimate partner violence: a review. Aggression Violent Behav [Internet]. 2021;56:101532. doi: 10.1016/j.avb.2020.101532 [ DOI ] [ Google Scholar ] [8]. Jennings WG, Okeem C, Piquero AR, et al. Dating and intimate partner violence among young persons ages 15–30: evidence from a systematic review. Aggression Violent Behav [Internet]. 2017;33:107–125. doi: 10.1016/j.avb.2017.01.007 [ DOI ] [ Google Scholar ] [9]. Cunha O, Pereira B, Cruz AR, et al. Intimate partner violence: perceptions and attributions of male perpetrators. J Forensic Phychol Res Pract [Internet]. 2024;24:338–358. doi: 10.1080/24732850.2022.2133663 [ DOI ] [ Google Scholar ] [10]. Christopher E, Drame ND, Leyna GH, et al. Disclosure of intimate partner violence by men and women in Dar es Salaam, Tanzania. Front Public Health [Internet]. 2022;10. doi: 10.3389/fpubh.2022.928469 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [11]. Kapiga S, Harvey S, Muhammad AK, et al. Prevalence of intimate partner violence and abuse and associated factors among women enrolled into a cluster randomised trial in northwestern Tanzania. BMC Public Health. 2017;17:190. doi: 10.1186/s12889-017-4119-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [12]. Messersmith LJ, Halim N, Steven Mzilangwe E, et al. Childhood trauma, gender inequitable attitudes, alcohol use and multiple sexual partners: correlates of intimate partner violence in northern Tanzania. J Interpers Violence. 2021;36:820–842. doi: 10.1177/0886260517731313 [ DOI ] [ PubMed ] [ Google Scholar ] [13]. Tsawe M, Mhele K. Determinants of wife-beating justification amongst men in Southern African countries: evidence from demographic and health surveys. Afr J Reprod Health/La Revue Afr de la Santé Reprod [Internet]. 2022;26:85–93. Available from: https://www.jstor.org/stable/27231780 [ DOI ] [ PubMed ] [ Google Scholar ] [14]. SVRI . Strategic plan 2025–2030 [Internet]. SVRI. 2023. [cited 2025 Apr 16]. Available from: https://www.svri.org/strategic-plan-2025-2030/ [15]. Matinda MZ. Implementation of the convention on elimination of all forms of discrimination against women (CEDAW): the Tanzania experience. Willamette J Int Law Dispute Resolut [Internet]. 2019;26:99–137. Available from: https://www.jstor.org/stable/26915365 [ Google Scholar ] [16]. Kinyondo A, Ntegwa MJ, Miho A. Determinants of intimate partner violence in Tanzania: evidence from the national demographic and health survey. Afr J Econ Rev. 2021;9:200–222. [ Google Scholar ] [17]. Kihiyo P, Msoka E. Exploring key socio-cultural norms that influence the prevalence of intimate partners’ violence in Tanzania. J Afr Interdiscip Stud. 2024;8:332. [ Google Scholar ] [18]. Kilgallen JA, Schaffnit SB, Kumogola Y, et al. Positive correlation between women’s status and intimate partner violence suggests violence backlash in Mwanza, Tanzania. J Interpers Violence. 2022;37:NP20331–60. doi: 10.1177/08862605211050095 [ DOI ] [ PubMed ] [ Google Scholar ] [19]. Abramsky T, Lees S, Stöckl H, et al. Women’s income and risk of intimate partner violence: secondary findings from the MAISHA cluster randomised trial in north-western Tanzania. BMC Public Health [Internet]. 2019;19:1108. doi: 10.1186/s12889-019-7454-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [20]. Ministry of Health (MoH) [Tanzania Mainland], Ministry of Health (MoH) [Zanzibar], National Bureau of Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF . Tanzania demographic and health survey and malaria indicator survey 2022 key indicators report. Dodoma, Rockville: MoH, NBS, OCGS, and ICF; 2023. [ Google Scholar ] [21]. The United Republic of Tanzania (URT), Ministry of Finance and Planning, Tanzania, National Bureau of Statistics and President’s Office - Finance and Planning, Office of the, Chief Government Statistician, Zanzibar, Chief Government Statistician, Zanzibar . The 2022 population and housing census: administrative units population distribution report; Tanzania Zanzibar, [Internet]. The United Republic of Tanzania (URT); 2022. Available from: https://sensa.nbs.go.tz/publication/volume1c.pdf [ Google Scholar ] [22]. Diallo MA, Mbaye N, Aidara I. Effect of women’s literacy on maternal and child health: evidence from Demographic Health Survey data in Senegal. Int J Health Plann Manage. 2023;38:773–789. doi: 10.1002/hpm.3624 [ DOI ] [ PubMed ] [ Google Scholar ] [23]. A generalized Hosmer–Lemeshow goodness-of-fit test for a family of generalized linear models | TEST | Springer Nature link [Internet]. [cited 2026 Feb 15]. Available from: https://link.springer.com/article/10.1007/s11749-023-00912-8 [ DOI ] [ PMC free article ] [ PubMed ] [24]. Darteh EKM, Dickson KS, Rominski SD, et al. Justification of physical intimate partner violence among men in Sub-Saharan Africa: a multinational analysis of demographic and health survey data. J Public Health. 2021;29:1433–1441. doi: 10.1007/s10389-020-01260-9 [ DOI ] [ Google Scholar ] [25]. Lomazzi V. The cultural roots of violence against women: individual and institutional gender norms in 12 countries. Soc Sci. 2023;12:117. doi: 10.3390/socsci12030117 [ DOI ] [ Google Scholar ] [26]. Glick P, Sakallı-Uğurlu N, Akbaş G, et al. Why do women endorse honor beliefs? Ambivalent sexism and religiosity as predictors. Sex Roles. 2016;75:543–554. doi: 10.1007/s11199-015-0550-5 [ DOI ] [ Google Scholar ] [27]. Oyediran KA. Explaining trends and patterns in attitudes towards wife-beating among women in Nigeria: analysis of 2003, 2008, and 2013 demographic and health survey data. Genus [Internet]. 2016;72:11. doi: 10.1186/s41118-016-0016-9 [ DOI ] [ Google Scholar ] [28]. Cislaghi B, Heise L. Gender norms and social norms: differences, similarities and why they matter in prevention science. Sociol Health Illn. 2020;42:407–422. doi: 10.1111/1467-9566.13008 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [29]. Dickson KS, Boateng EN, Adzrago D, et al. Silent suffering: unveiling factors associated with women’s inability to seek help for intimate partner violence in Sub-Saharan Africa (SSA). Reprod Health. 2023;20:110. doi: 10.1186/s12978-023-01651-7 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [30]. Agde ZD, Magnus JH, Assefa N, et al. Knowledge and attitude toward intimate partner violence among couples: a baseline findings from cluster randomized controlled trial in rural Ethiopia. Front Public Health. 2024;12:1467299. doi: 10.3389/fpubh.2024.1467299 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [31]. Adebowale AS. Spousal age difference and associated predictors of intimate partner violence in Nigeria. BMC Public Health [Internet]. 2018;18:212. doi: 10.1186/s12889-018-5118-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [32]. Ghoshal R, Douard A-C, Sikder S, et al. Risk and protective factors for IPV in low-and middle-income countries: a systematic review. J Aggression Maltreat Trauma. 2023;32:505–522. doi: 10.1080/10926771.2022.2154185 [ DOI ] [ Google Scholar ] [33]. Conroy AA. Gender, power, and intimate partner violence: a study on couples from rural Malawi. J Interpers Violence. 2014;29:866–888. doi: 10.1177/0886260513505907 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [34]. Ito TA, Miller N, Pollock VE. Alcohol and aggression: a meta-analysis on the moderating effects of inhibitory cues, triggering events, and self-focused attention. American Psychological Association; 1997. [ DOI ] [ PubMed ] [ Google Scholar ] [35]. Brevers D, Bechara A, Cleeremans A, et al. Impaired decision-making under risk in individuals with alcohol dependence. Alcohol Clin Exp Res. 2014;38:1924–1931. doi: 10.1111/acer.12447 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] [36]. Muluneh MD, Alemu YW, Meazaw MW. Geographic variation and determinants of help seeking behaviour among married women subjected to intimate partner violence: evidence from national population survey. Int J Equity Health. 2021;20:1–14. doi: 10.1186/s12939-020-01355-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials STROBE checklist Male Justification of IPV.docx ZGHA_A_2656563_SM1705.docx (35.3KB, docx) Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com . Articles from Global Health Action are provided here courtesy of Taylor & Francis ACTIONS View on publisher site PDF (909.9 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top