Rural Low Income Women’s Struggles and Strength: A Call to Change

Juliana Plumb, B.S. in Family Studies
*Bonnie Braun, Ph.D. Faculty Advisor
Department of Family Studies
University of Maryland, College Park

Abstract

Although many scholars have studied issues concerning rural poverty, few have investigated the unique way in which gender impacts rural culture and poverty. Using data from the Rural Families Speak research study, the author investigated the effect of a gendered perspective on views regarding rural, low-income women. The mixed-methods study sought to identify qualitatively the ways in which each woman’s role as partner, mother, and female citizen impacted their lives and to corroborate the findings with a quantitative analysis. The most significant factors emerging from the analysis included: employment, child support, childcare, food security, and emotional well-being.

Rural Low Income Women’s Struggles and Strength: A Call to Change

Introduction

Feminist scholars have long studied the ways in which women’s lives are negatively impacted by gender roles, discrimination, and oppression within modern America. Connell describes the overall process by which men benefit from their gender status within society as the “gendered accumulation process” (Connell, 2002). This accumulation process may refer to power, health, time, money, and wealth.

The differential in gender is especially problematic for women in lower socio-economic levels. Yet, while many studies of poverty analyze income disparities among American households, few address the inequalities that exist within them (Ruspini, 2002). Specifically, few studies analyze the ways in which men and women individually benefit or suffer from gender oppression as a key to understanding poverty. The literature on gender demonstrates that women can be negatively impacted by their gender and thus their well-being is diminished. Gender could be a factor for consideration in public policy and programs affecting the well-being of women and, if those women are mothers, the well-being of their children.

The phenomenon of gendered accumulation was the focus of this research study that sought to answer the general research question: Is there evidence of impact of the gendered accumulation process within a sample of women from the Rural Families Speak (RFS) study? The study reported here employed the theory of gendered accumulation as a lens through which to understand rural women's poverty. The analysis was done to reveal ways in which the roles of the rural women in this sample, as mother, partner, and citizen, may negatively affect their well-being (and thus contribute to the gendered accumulation process). The sample for this experimental study was limited to women in the Appalachian counties of the RFS study.

Method

In 1998, a team of researchers from 15 land-grant universities were authorized by the USDA-affiliated Agricultural Experiment Stations to conduct a unique, multi-state, longitudinal study known as Rural Families Speak to understand factors affecting the well-being of rural, low- income families. Beginning in 2000, data were collected from mothers age 18 and older, with at least one child under age 13 and incomes below 200% of poverty, eligible for or receiving food stamps, were recruited through programs serving low-income families. The counties from which the sample came were identified as meeting a rurality criteria of 6-8 based on the Butler and Beale (1994) coding scheme.

Sample

Data for this study were confined to Appalachian counties, a designated economically- depressed geographical region where forty-two percent of the population is rural; many live in persistent poverty and face multiple challenges (Dyk, Braun, Swanson, & Seiling, 2002). For this study, the sample used for quantitative analysis differed from the sample used for qualitative analysis. Data for the quantitative analysis were collected from 208 mothers in seven Appalachian counties of five states (Kentucky, Maryland, New York, Ohio and West Virginia). Data used for the qualitative analysis were gathered from the sub-sample of 11 Maryland mothers who were interviewed all three years. 

Data Collection and Analysis

Data were collected by on-site interviews using common instruments and open-ended questions over a period of three years. Standardized quantitative measures of physical and mental health, food security, employment, and income, including child support, were collected, coded, and made available for analysis using SPSS XI TM. Qualitative data were transcribed verbatim by the multi-state team. This researcher coded for thematic factors using the principles of grounded theory and qualitative analysis techniques (Berg, 2003).

This study employed an inductive, mixed-methods analysis. Qualitative analysis was employed first to identify, from the women’s own words, factors related to their well-being that provided evidence of negative impact by their relational roles. A search was conducted for factors that were mentioned by at least one-third of the participants (or 4 of the 11 women). Once thematic factors were identified, quotes were selected to capture the women’s own words as a means of exemplifying and explaining the phenomenon.

The quantitative analysis followed and complemented the qualitative analysis. Simple t-tests were run on all of the demographics, child support, child care subsidy, food security, and depressive symptoms screening data collected from all 208 participants from the seven Appalachian counties (See Table 1). A separate simple t-test was conducted on the 11 participants that were the Maryland sub-sample to compare the two on a variety of variables. For this article, only a few of the findings are included. For more specifics on others, see Plumb, (2006).

Results

Results are presented from both the qualitative and quantitative analysis. Conclusions are drawn on the combination of the two.

Qualitative

Five major thematic factors were extracted from the interviews that appeared to negatively impact the well-being of these mothers. The five identified factors were:

1. Child Support

Child support was defined as the financial support received or not received by mothers from their children’s’ father. Six of the 11 participants cited child support a total of 21 times during the interviews. Most reported that they had not received any support at all, while the others reported that payments were often erratic and/or not the full amount ordered through the court.

As one mother explained, “He hasn’t paid nothing. He did pay five hundred, but that was six or seven years ago.” Another mother recalled, “The last check was like 16 [dollars] or something. It’s nothing.” One mother explained that when she did receive a check, it would only complicate her life because it would disrupt her normal allowance for food stamps and other benefits. Then, the next month (when she did not receive a check) she was forced to reapply for the programs.

Some of the women have a much larger problem, however. They simply don’t know where the fathers of their children were. To aggravate the problem, they found the local authorities unreliable in helping them track down the fathers. One woman explained that although her ex-husband was finally caught for failing to pay child support, she would not receive the full amount they collected from him at the time of his arrest because she had hired a private agency in order to track him down. “But see, I went through a child support network, so when they get it, [owed child support] they get a third.”

2. Childcare/Daycare/After school care

Childcare/Daycare/After school care was defined as the availability, quality, cost and type of care, and regulations surrounding care. Eight of the 11 participants cited the subject of care of children 25 times during their interviews.

The women spoke about their childcare situations, discussing the availability, quality, price, and logistics of the care in their area. Although most of the women were able to find childcare for their children at least some of the time, one explained her difficult situation with her young daughter, “There’s only certain people who will watch her because her seizures are deadly ones.”

The quality of care available was also a cause for concern among many of the women. “Having to be licensed doesn’t make them good providers.” More than one of the women shared horror stories they had heard about local providers abusing children. Others explained that some providers offered no structure and educational opportunities and simply let the children play all day.

Other mothers explained that the lack of after-school programs made it difficult to find care for their school-aged children. “There isn’t too many after school programs,” one mother explained. Another said outright that the schools her children attended had no after school care available at all, forcing her to either find a sitter or be home in the afternoons.

For some mothers, the issue was not finding quality care, but rather the hours and days they were able to get the care. “It’s more convenient to find a sitter for part-time,” one mother stated. This would no doubt make it very difficult to pursue salaried, full-time employment. Another mother explained that the work she was able to find in her area was mostly on the weekends, and that “It’s hard to find a sitter during that time.”

Not surprisingly, for many of the non-skilled mothers in the study, the cost of childcare made working outside the home financially impractical. If the only work available to the mothers is entry-level and/or minimum wage, practically all of their earnings (if not all) would go right to their care provider. One woman explained, “If I just went in a minimum wage job it wouldn’t be worth it [financially].”

3. Employment

Employment was defined as the mother’s ability to be employed, number of hours able to work, type of work available, and pay. Seven of the 11 participants cited it a total of 20 times during the interviews.

One common issue was women having to miss work because their children were sick and they were unable to find them childcare. The mothers explained that if their children had a fever, were throwing up, they were unable to leave them with their regular provider and were forced to stay home from their jobs. Another mother offered examples of when she had to suddenly leave work because her children were sick or hurt.

While most women did not state outright that they had been discriminated as mothers in the workplace, one mother speculated that the reason she didn’t get a job she had applied for was because the manager knew she had children. Still others spoke about the negative reactions their bosses had to their childcare and health emergencies.

4. Food Security

Food security was defined as the availability of food for the mother and her family, cost of food, and manner in which food was dispersed within the family. Four of the 11 participants cited being negatively impacted in terms of their food security 5 times.

“I know what it’s like not eat so my kids could.” This mother is not alone in her account of how food is distributed in her home. Many of the mothers admitted that, when money and food were tight, they were the first to go hungry [but only after asked directly). One mother admitted that she would not eat to “ease” her husband’s mind because he would be worried that they would not have enough to make it until they received next month’s food stamps. Another mother explained that, “Sometimes me and my husband would not eat lunch to make sure that the kids had lunch to eat on the weekends....” Yet another mother admitted to going whole days without eating anything at all in order to save food.

5. Emotional Well-being

Emotional well-being was defined as the mother’s state of being as related to stress, time for friends, time for enjoyable activities, and time alone Six of the 11 participants cited negative impacts to their emotional well-being 21 times.

Many of the mothers reported complicated mental health and well-being issues. Several made statements implying their low self-confidence and negative self-image was often directly linked to their partners. “[I told my husband that] if he didn’t straighten up I was going to go on a diet and get a boyfriend. But, he could care less… maybe if I lost the weight he might.” Another mother explained how her children respected her more now that they weren’t living with her husband, saying, “If he don’t care about anything I say or feel, why should they?”

More than one of the mothers recounted the horrific impact of domestic violence. “I went through a battered woman’s course, which I’m glad I did because I was really beaten down. I wouldn’t even get up and dress myself…I wouldn’t do nothing for myself.” Another mother explained the stress of running from an abusive ex-husband. “My ex-husband came looking for me, and I was terrified of him. He’s… I took some very bad beatings.”

Support for this research was provided by the United States Department of Agriculture (NRICGP2000-01759), American Association of Family and Consumer Sciences, Louisiana State University Agricultural Center, University of Maryland and University of Kentucky College of Agriculture. Data were collected in conjunction with the cooperative multi-state research project NC-223 Rural Low-Income Families: Monitoring Their Well-being and Functioning in the context of Welfare Reform. Cooperating states were: California, Colorado, Indiana, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Nebraska, New Hampshire, New York, Ohio, Oregon and Wyoming.

Quantitative

Simple t-tests confirmed that the all-Appalachian sample (Table 2) and the Maryland only sub-sample (Table 3) were similar in terms of the average number of children in the family, racial makeup, and annual income. The all-Appalachian sample had a higher rate of the participants having a partner, while the Maryland sample had a higher rate of employment than the larger sample. The average overall depression score and average food security score for the Maryland sample was also higher than that of the all-Appalachian sample.

The simple t-test run on the Maryland sub-sample data revealed important quantitative information to help explain the qualitative information previously collected and categorized from the same sub-sample interviews. These data, presented below, correspond to the five identified thematic factors.

1. Child Support

Although the quantitative data revealed that 83% of the eligible participants reported they were receiving some form of child or spousal support, this figure may be misleading. Closer examination of qualitative data revealed that while the majority of eligible women were “receiving support,” they were not likely receiving all of the child support they were owed for all of their children.

2. Childcare/After school care/Daycare

Another factor identified in the qualitative analysis was the issue of childcare/after school care/day care. Only one of the women had received childcare financial assistance. And while all of the women knew how to apply for welfare cash assistance, only 63.64% of the women knew how to apply for a childcare subsidy.

3. Employment

Many of the women (40%) were currently looking for work/more work. All of the 11 Maryland participants stated that they had worked for pay at some time in their lives. At the time of the study, 54.55% of the Maryland participants were employed, averaging 1.68 jobs, for an average total of 25.5 hours a week. This is in keeping with the statements that some of the women made about part-time work being easier for them.

4. Food Security

One of the less frequent but still identified factors was the issue of food security. The statistical analysis corroborated the statements made by many of the women. No less than 80% of the women reported that they ate less in order to make food stretch in their homes. While 40% reported they had gone hungry.

5. Emotional Well-being

The Maryland sample exhibited high rates of depressive symptoms related directly to emotional well-being. Sixty-three percent of the participants stated they had experienced depression and 36.36% had an eating disorder or were obese.

Discussion

More than anything, the study has enabled the words of the mothers to answer the question of whether or not there is evidence of a gendered accumulation process in the RFS study. The women frequently, and with some consistency, spoke to the ways in which their roles as mother, partner, and citizen influenced their lives. They discussed the ways that non-existent and erratic child support payments left them financially unstable and without state benefits. They explained how quality childcare was often difficult to secure for their children, and how the high cost and inflexible hours of childcare makes employment difficult for them. The mothers also spoke about their food security; often admitting that they would sacrifice their own food intake to make sure their family had enough. Finally, they talked about their emotional well-being; admitting to high levels of stress and suffering from psychological disorders. And, while not all of the quantitative data from the Maryland sub-sample, as analyzed, was able to corroborate and better explain the qualitative findings, several quantitative factors did reinforce the mothers’ statements.

For example, while 83% of the mother’s reported they received child support, the qualitative statements seemed to suggest otherwise. Upon closer examination, the quantitative evidence showed that perhaps the mothers were receiving child support, but not necessarily for all their children. The qualitative findings suggested that many mothers were burdened by the high cost and lack of flexibility of childcare, yet only 9% of them reported receiving childcare financial subsidizes. The average number of hours worked per week (only 25) was able to substantiate the statements made by many of the mothers that only part-time work was available to them. In keeping with the statements many mother’s made about stretching the food budget, 80% reported they had eaten less in order to make sure their families could eat. Confirming the statements by the participants regarding their lack of friends and stressful financial situations, 63% percent of the participants reported coping with depression and anxiety.

The mixed-methods approach enabled the qualitative data to explain and support the quantitative data, and vice versa. The quantitative statistics corroborated qualitative findings and offered a better understanding of the extent to which women may be experiencing conditions about which only a few explicitly spoke. Conversely, the qualitative findings helped to give explanations and a deeper meaning to the quantitative data. The mixed-methods design afforded a greater sense of understanding and accuracy to the lived conditions of these women.

As Aslanbeigui, Pressman, & Summerfield, (2003) discussed, gender equity can be addressed in the public policy arena. The findings from this study provide policy implications for those interested in better serving the needs of rural, low-income women. Policy suggestions related to the five thematic factors follow:

• Child Support : Although most of the women received some child support, many were not receiving all they were owed. State authorities need to work together to track down and collect from fathers who are not paying their court-ordered child support. Creating better record networks and extradition laws would help to better secure payments.

• Child Care: Many of the mothers spoke the difficulty in finding quality and affordable childcare/daycare. Many others complained about their local school system’s lack of after-school care. Making sure that registered childcare facilities meet high standards of care, creating comprehensive childcare subsidizes, and notifying women of their availability should support rural women in seeking and maintaining employment. Provision of after-school programs could also support mothers’ employment.

• Employment: More than one of the mothers testified that the prohibitive cost of childcare kept them from working. By making childcare/ daycare more affordable and accessible, mothers should be better able to work. Some explained how often their employers were unsympathetic when they were forced to leave work due to an emergency with their children. Educating employers to be more family-friendly and workers to stand up for their legal rights should create a better working environment for mothers.
• Food Security: Many of the women cut back on their food intake to ensure their children and partners would have enough. By supporting food security screening, food cooperatives, WIC programs, community/home gardens, and food banks, families could become more food secure. Encouraging use of food stamps and enrolling families in nutrition education programs as well as expanding the availability of, and access to, affordable food could further reduce food insecurity. If families are food secure, then mothers will no longer endanger their own health to feed their families.
• Emotional Well-Being: Many of the women complained of little or no time to themselves or with friends. Encouraging mothers to make time for themselves and their friends (and providing them with the childcare) may help mothers decrease the stress and avoid more serious mental health problems like depression and anxiety. More than one woman explained a history of physical abuse. Continuing to support domestic violence shelters and affordable therapy for victims should help these women be happier and healthier.

These, and other policy implications drawn from the wealth of information gathered, may be practically and financially difficult, yet they offer real solutions for better serving rural, low-income women. By taking a gendered perspective, the mothers’ own words can lead to solutions that target their often-ignored needs.

Ultimately, based on the qualitative and quantitative results, there is undoubtedly evidence that the mothers studied in the RFS sample were negatively impacted due to their relational roles. The results explain ways in which these women experience poverty. Further qualitative research on the Appalachian women, and ultimately on all women in the RFS study, would refute or confirm these exploratory findings.

The Maryland mothers shared stories of their personal sacrifices and familial struggles, as well as their isolation from resources and information. But they also exhibited drive to survive and succeed within their communities and families. It is both their struggles and their strength that inspires a call to change.

References

Aslanbeigui, N., Pressman, S. & Summerfield, G. (2003). Toward gender equity: policies and strategies. International Journal of Politics, Culture & Society, 16(3) 327.

Berg, B. L. (2003). Qualitative research methods for the social sciences (5th ed.). Boston: Allyn and Bacon.

Butler, M.A. and Beale, C.L. 1994. Rural-Urban continuum codes of metro and non- metrocounties, 1993. (Staff Report No. 9425), Agriculture and Rural Economy Division, Economic Research Service, Washington, DC: USDA.

Connell, R.W. (2002). Gender. Blackwell: Malden.

Dyk, P. H., Braun, B., Swanson, J. & Seiling, S. (2002). Appalachian rural family economic well-being in the context of public assistance. Presentationat the annual meeting of the Rural Sociological Society, August 14-18, 2002, Chicago IL.

Plumb, J. (2006). Rural Low-Income Women: Taking a Gendered Perspective. Undergraduate Honors Thesis. University of Maryland: College, Park.

Ruspini, E. (2002). The study of women’s deprivation: How to reveal the gender dimension of poverty. International Journal of Social Research Methodology, 4(2).

Table 1. Appalachian Sample & Maryland Sub-Sample

	Maryland Sub-Sample	All-Appalachian Sample
Avg. Age	29.54 years	28 years
Racial Makeup	Non-Hispanic White: 89.4% Hispanic/Latino: .5% African American: 6.7% Native American: 1.9% Multi-racial: 1.4%	Non-Hispanic White: 93.9% Hispanic/Latino: 0% African American: 0% Native American: .6% Multi-racial: 0%
Participant has partner	45.4%	63%
Avg. No. of Children	2.36	2.2
Employed Currently	54.55%	44%
Avg. Household Income	$12,545	$13,311
Avg. Knowledge of Community Services Score	19.18	18.33
Avg. depression score	23.18	19.47
Avg. Life Skill Assessment score	19.45	19.09
Avg. food security score	5.36	3.49
Amount of stress right now	1.0	1.786
Avg. parental confidence score:	28.27	31.08

 

Table 2. All Appalachian Sample T-Test Continued

	Test Value = 0
	t	df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference
					Lower	Upper
Participant's age at the time of interview	57.661	205	.000	28.21359	27.2489	29.1783
Does a participant have a partner?	18.766	207	.000	.62981	.5636	.6960
Partner's age at the time of interview	41.406	127	.000	32.82813	31.2593	34.3970
Number of children	27.166	207	.000	2.22115	2.0600	2.3823
Self-currently working?	12.813	207	.000	.44231	.3743	.5104
Self - currently looking for work?	7.815	113	.000	.35088	.2619	.4398
Self - ever worked for pay?	69.599	173	.000	.96552	.9381	.9929
Self - seasonal worker?	2.897	150	.004	.05298	.0168	.0891
Self - number of jobs	21.742	89	.000	1.20000	1.0903	1.3097
Self - Total hours worked per week	24.096	72	.000	29.61644	27.1663	32.0666
Partner - currently working?	17.672	129	.000	.70769	.6285	.7869
Partner - seasonal worker?	2.716	117	.008	.05932	.0161	.1026
Partner - number of jobs	29.968	91	.000	1.11957	1.0454	1.1938
Partner - Total hours worked per week	32.328	80	.000	44.16049	41.4420	46.8790
Self - number of childcare arrangements	11.605	204	.000	1.09756	.9111	1.2840
Self - Payment of childcare arrangement 1	25.861	112	.000	2.53982	2.3452	2.7344
Total annual income (not included food stamp)	19.403	207	.000	13310.67231	11958.2010	14663.1436
TANF ( Temporary assistance to needy families)/MFIP(Minnesota)	5.460	207	.000	46.20471	29.5203	62.8892
Child or spousal support	5.490	159	.000	73.14438	46.8319	99.4568
Food Stamps	14.824	204	.000	161.10244	139.6746	182.5303
School Lunch Program	17.089	202	.000	.59113	.5229	.6593
EIC(Earned Income Credit)	16.205	206	.000	.56039	.4922	.6286
Child care assistance	6.135	207	.000	.15385	.1044	.2033
Total scores of knowledge of community services	62.666	166	.000	18.33533	17.7577	18.9130
Participant - Depression/Anxiety	12.200	207	.000	.41827	.3507	.4859
Participant - Anger management	4.821	207	.000	.10096	.0597	.1422
Participant - Fatigue	9.700	207	.000	.31250	.2490	.3760
Participant - Eating disorder/obesity	7.237	207	.000	.20192	.1469	.2569
Participant - Emotional, physical, or sexual abuse	5.675	207	.000	.13462	.0878	.1814
Participant - Migraines/Headaches	13.192	207	.000	.45673	.3885	.5250
Cut the size of meal	11.437	109	.000	.54545	.4509	.6400
Eat less	10.110	108	.000	.48624	.3909	.5816
Hungry	6.563	107	.000	.28704	.2003	.3737
Not eat for a whole day	5.680	66	.000	.32836	.2129	.4438
How often not eat for a whole day?	7.071	20	.000	.71429	.5036	.9250
Food Security Score	11.993	172	.000	3.48555	2.9119	4.0592
Confidence that you know what is right for kid	67.763	200	.000	5.12438	4.9753	5.2735
Amount of stress right now	14.525	200	.000	1.78607	1.5436	2.0285
Ability to cope with stress	45.783	200	.000	4.48756	4.2943	4.6808
Total scores of parental confidence	89.258	198	.000	31.07538	30.3888	31.7619
Total scores of parental support	49.760	198	.000	26.83920	25.7755	27.9028

Table 3. Maryland Sub-Sample T-Test

	Test Value = 0
	t	df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference
					Lower	Upper
Participant's age at the time of interview	14.828	10	.000	29.54545	25.1058	33.9851
Does a participant have a partner?	2.887	10	.016	.45455	.1037	.8054
Partner's age at the time of interview	19.453	4	.000	32.20000	27.6042	36.7958
Number of children	8.480	10	.000	2.36364	1.7426	2.9847
Total number of family members	15.872	10	.000	4.18182	3.5947	4.7689
Self-currently working?	3.464	10	.006	.54545	.1946	.8963
Self - number of jobs	3.953	5	.011	1.66667	.5828	2.7505
Self - Total hours worked per week	5.251	3	.013	25.50000	10.0452	40.9548
Self - educational level	9.960	10	.000	3.36364	2.6112	4.1161
Total annual income (not included food stamp)	6.369	10	.000	12545.45455	8156.4946	16934.4145
Child or spousal support	2.452	5	.058	83.00000	-4.0209	170.0209
Food Stamps	4.138	10	.002	193.36364	89.2470	297.4803
School Lunch Program	6.708	10	.000	.81818	.5464	1.0899
Child care assistance	1.000	10	.341	.09091	-.1116	.2935
Total scores of knowledge of community services	13.162	10	.000	19.18182	15.9346	22.4291
Total scores of depression scales	6.064	10	.000	23.18182	14.6638	31.6999
Total scores of life skill assessment	18.886	10	.000	19.45455	17.1593	21.7498
Participant - High blood pressure	1.491	10	.167	.18182	-.0899	.4536
Participant - Depression/Anxiety	4.183	10	.002	.63636	.2974	.9753
Participant - Anger management	1.491	10	.167	.18182	-.0899	.4536
Participant - Fatigue	2.390	10	.038	.36364	.0247	.7026
Participant - Eating disorder/obesity	2.390	10	.038	.36364	.0247	.7026
Participant - Migraines/Headaches	2.887	10	.016	.45455	.1037	.8054
How often cut the size of meal?	2.121	6	.078	.42857	-.0658	.9229
Eat less	6.000	9	.000	.80000	.4984	1.1016
Hungry	2.449	9	.037	.40000	.0306	.7694
	Test Value = 0	6	.172	.28571	-.1656	.7370
	t	df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference	6.8531
					Lower	Upper
Confidence that you know what is right for kid	19.333	10	.000	5.27273	4.6651	5.8804
Ability to create safe home for kid	14.330	10	.000	4.90909	4.1458	5.6724
Success in teaching kid to behave	9.113	10	.000	4.27273	3.2280	5.3174
Find fun activities of interest to kid	11.423	10	.000	4.45455	3.5857	5.3234
Amount of stress right now	3.708	10	.004	1.00000	.3991	1.6009
Ability to cope with stress	7.262	10	.000	4.09091	2.8357	5.3461
Total scores of parental confidence	17.288	10	.000	28.27273	24.6289	31.9165
Total scores of parental support	6.203	10	.000	23.45455	15.0295	31.8796