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Universitas Gajah Mada

Fakultas Psikologi

A Meta-Analytic For the Relationship between Birth

Order and Marital Adjustment

Presented to: Dr. Moordiningsih

By student: Fakher Nabeel Mouhammad Khalili

November 2011

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A Meta-Analytic For the Relationship between BirthOrder and Marital Adjustment

Fakher Nabeel Mouhammad Khalili

Fakultas PsikologiUniversitas Gadjah MadaThis article presents a meta-analysis of the relationship between

complementarity birth order and marital adjustment. The quantitative

review of 21 studies from 14 articles, 3 thesis and 4 dissertations.Summary analysis provided not support the Toman's assumptions very

well, that there is a weak relationship between complementarity birthorder and marital adjustment, the correlation coefficient after all

procedures of correction was (0.06).

Key words: meta-analysis, complementarity, birth order and maritaladjustment.

Introduction and background of study:

Adler introduced the study of birth order in the early 1900's with his focus onfamily constellation (1928). From that point forward, Toman (1959; 1962), Anderson(1987), Hoopes and Harper (1981) and other researchers have continued to study anddevelop the birth order theory. However, the relative lack of empirical researchsuggests that further analysis of the marital interaction of various birth ordercombinations is needed.

Many researches exploring the nature of marital relationshipsexistent, and it isproposed that the oldest question in the history of the study of marriage is how to

distinguish happy from unhappy marriages (Gottman & Krokoff, 1989). Theimportance of this question is thought to lie in the central role that marriage plays inthe development and overall well-being of individuals and families (Bradbury,Fincham, & Beach, 2000; Whitehead, 2004) and, consequently, in the need to developempirically supported interventions for couples that prevent or reduce marital distressand divorce (Bradbury et al, 2000). Marriage researchers have focused mostly onmarital outcomes (e.g., marital adjustment, satisfaction or dissatisfaction, conflict, anddivorce) but this approach has been criticized as it provides no clarification about howcouples arrive at such outcomes; it is suggested that marital research insteademphasize the prediction of outcomes (Karney & Bradbury, 1995).

Birth order and sibling position have been found to impact personalitycharacteristics, social development, and behaviors and attitudes in social relationships(Anderson, 1987; Lawson & Brossart, 2004; Nyman, 1995; Pollet & Nettle, 2007).While some literature has focused on marital adjustment, satisfaction, intimacy, andconflict resolution (Billings, 1979; Koren, Carlton & Shaw, 1980; Margolin &Wambolt, 1981) relatively little literature has addressed these factors in conjunctionwith the birth order of married couples. In fact, there is little research that examinesthe impact of birth order on any marital variables (Anderson, 1987; Hardman, 1984).

Toman (1993) was the first theorist to maintain that individuals seek out andare most satisfied in marital relationships in which sibling rank is replicated (e.g., anindividual who is the oldest sibling in his/her family seeks out and is more compatible

with an individual who is a younger sibling), and he termed this the duplicationtheorem of social relationships.

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Relationships such as these are proposed to be successful because they areinteractive: each partner desires to perform tasks that the other does not, and both

partners have slightly different interests and preferences (Toman, 1993). Thisproposition reflects the idea of complementarity.

Toman's notion of complementarity is operationalized according to birth rank

and sibling gender. Birth rank refers to the order in which an individual was born intoa family, which results in varying levels of individual power among siblings (i.e., dueto differences in age and/or size) and responsibility imparted by parents (Leman,1998). Both Toman (1993) and Adler (1956) asserted that each birth position has itsown set of learned behaviors (i.e., roles), but Toman extended this idea and proposedthat these behaviors create a system of cooperation (i.e., complementarity) amongsiblings.

Oldest siblings assume the roles of leader, director, and caretaker, whileyounger siblings look to be led, directed, and cared for, and it is assumed that botholdest and younger siblings are comfortable in such positions. Middle siblings are

purported to be the most flexible in their roles because they have been both older

siblings and younger siblings; therefore, it is proposed that they are more able toadjust their roles according to each individual with whom they interact based on that

person's birth rank (Adler, 1956; Leman, 1998; Toman, 1993).When both spouses hold the same birth rank in their respective families a rank

conflict occurs wherein each partner may attempt to perform the same roles in therelationship. For example, conflict and lower levels of satisfaction or adjustment mayoccur in a marriage if a husband and wife who are both oldest children in theirfamilies attempt to lead in the relationship and expect the other to follow. Equally, ahusband and wife who are younger siblings in their family may look to each other tolead and make decisions in the relationship, which in turn may result in tasks not

being addressed or resolved (Toman, 1993). These two examples, albeit a bit extreme,demonstrate rank conflict in its simplest form.

Toman believed that individuals learn about intimacy and cooperation withinthe sibling relationship, and the second component of complementarity considerssibling sex. Toman proposed that individuals with opposite-sex siblings learn to liveclosely with members of the opposite sex (i.e., brothers learn about females throughinteractions with their sisters and sisters learn about males from their interactions withmale siblings). A sex conflict occurs when at least one spouse in a marriage wasreared in a monosexual sibling group (e.g., a woman who only had sisters or a manwho only had brothers). Toman asserted that individuals from monosexual siblingconfigurations are the least prepared to live closely with an opposite-sex spouse and

may initially have trouble adjusting to the new relationship, but if the other spouse hasat least one opposite-sex sibling, he/she is able to help ease the adjustment of livingclosely with an individual of the opposite sex (Toman, 1993).

In sum, according to Toman 's duplication theorem of social relationships,romantic relationships are considered complementary when neither a rank nor a sexconflict exists. Relationships are partially complementary when either a rank or a sexconflict occurs in the relationship, but not both. Finally, non-complementaryrelationships occur when both a rank and a sex conflict in the relationship. Rankconflicts always involve both individuals while sex conflicts can involve one or bothspouse(s) (Toman, 1993).

While Toman never differentiated the importance of rank or sex conflict in

marital satisfaction or adjustment, but Stanley (2009) believes that rank conflict ismore detrimental to relationships than sex conflicts due to the power differentials

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proposed to be inherent in birth rank (Adler, 1956; Kerr & Bowen, 1988; Toman,1993). This notion is supported by a small body of research exploring differences in

behaviors among siblings of different birth ranks (e.g., Mize & Pinjala, 2002;Stoneman et al., 1986).

As previously stated, Toman proposed that couples in which each spouse has a

different birth rank are proposed to have higher levels of marital adjustment due totheir role complementarity (Toman, 1993). But, birth rank alone does not ensure thatspecific roles will be clear (e.g., that an oldest child will be more dominant and ayoungest child will be more submissive). In fact, Toman discussed various factors thatimpact birth order roles apart from rank. For example, Toman proposed that thegender of each child of a particular birth rank may impact his/her role in a family:oldest girls may be given more responsibility for younger siblings than would oldestmales, which may result in an woman who is the oldest in a family exhibiting moredirecting or caregiving behaviors in a marriage relationship than would a man who isthe oldest (Toman, 1993).

In addition to the gender of an individual of a particular birth rank, Toman

also proposed that spacing (in terms of years) between siblings has an impactinterpersonal role development. Toman proposed that personality (i.e., interpersonalrole) is set by five years of age and that when the spacing between children is five ormore years, birth rank characteristics reset. Thus, a second child who is 6 yearsyounger than his/her sibling may actually be similar to a firstborn in terms ofinterpersonal role more than would a second child only 2 years younger than his/hersibling. Spouses who have different birth ranks. Therefore, simply exploring birthorder complementarity among spouses may not provide a full representation of theunderlying interpersonal mechanisms that operate in a relationship.

Problem of the study:Some theorists and researchers (Bank & Kahn, 1982; Kemper, 1966; Toman

and Gray, 1961; Weller, Natan & Hazi, 1974) agree with Toman that the partners bring to the marriage from their family of origin an indisputable of siblingconstellation. Other researchers and sociologists (Birtchnell & Mayhew, 1977; Ernst& Angst, 1983; Forer, 1976; Kelly & Conley, 1987; Levinger & Sonnheim, 1965;Pinsky, 1974; Sutton-Smith & Rosenberg, 1970; Terman, 1938; Vos & Hayden,1985) maintain that there is no basis for marital adjustment in sibling birth order.

Therefore, the purpose of this study was to attempt to resolve the conflict in theliterature about the association between marital adjustment and birth order focusingon complementarity birth order for spouses .

In addition, mass of the research that has tested Toman's theory, includingresearch conducted by Toman himself, has only explored birth rank complementarityamong individuals. Thus, the current study will explore the impact of rankcomplementarity on marital adjustment among spouses. Thus, this study will test aspecific theory of marital adjustment that predicts marital outcomes based on thecomplementarity of birth positions of spouses. Moreover the purpose of this study isto test whether the degree of interpersonal complementarity provides an explanationthe proposed relationship between birth rank complementarity and marital adjustment.

The hope is that this research will provide further insight into the interpersonalmechanisms that underlie marriage relationships.

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Definition of the terms: Birth order: Birth order is defined as the actual sequential order inwhich each child is born (Adler, 1928; 1956).

Marital Adjustment: the degree to which individual adjust and adapt tothe many facets of their marriages. Marital adjustment is used to refer to those

processes necessary to achieve a harmonious and functional maritalrelationship (Sabatelli, 1988, p.894).

The study hypothesis:The current study will test the Toman 's Theory which suggested there is a

relationship between birth order and marital adjustment.

The method:

This study relating to meta-analysis methodology, the researcher obtained thestudies from databases websites (EBSCO, PROQUEST, SAGE, SPRINGERLINK &JSTOR) which included articles, thesis, dissertations, that trying to discover therelationship between birth order and marital adjustment, these studies were (21); 14articles, 3 thesis and 4 dissertations.Selection criteria:

The researcher selected the studies which included birth order as independentvariable and marital adjustment as dependent variables, also the current study interestwith complementarity birth order and it relation with marital adjustment, so this studywill not include that studies which interest with similarity in birth order, Theresearcher captured the values of size samples, F values, and r values, F values were

converted to t, d, and r values.

Summary of meta-analysis procedures:Analysis of data using meta-analysis techniques (Hunter-Schmidt, 1990) carried

out with step-by-step analysis as follows:1. Converting the algebraic equation of the F values to become the valueof t, d and r.2. Bare Bones Meta analysis: correcting for the sampling error.3. Artifacts other than sampling errors for correction of measurementerror.

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Data analysis:1. Characteristics of study sample:

The samples that were examined in this study of meta-analysis had thecharacteristics as listed in table 1.

Table.1 Characteristics of study sample

The Year The Author The Sample Size The Gender

1982 Berta Elisa Ortiz 160 Male1982 Berta Elisa Ortiz 160 Female

1974 Leonard Weller, Orah Natan,Ophrah Hazi 236 Both

1998 Michelle Laurel Vliet Brubaker 61 Both2001 Renee M. Schilling 91 Both1974 Stephen Bruce Gold 150 Male1974 Stephen Bruce Gold 150 Female

2002 Michelle Laurel 58 Both2001 Megan E. Zuchowski 49 Both2009 Krystal L. Stanley 61 Both1995 Michelle Laurel 160 Both1986 Suzanne Little Dastrup 80 Male1986 Suzanne Little Dastrup 80 Female1993 Bloser 254 Both1987 Toman 94 Both2008 Amanda Elizabeth Majors Beal 79 Male2008 Amanda Elizabeth Majors Beal 79 Female1937 Clifford Kirkpatrick 348 Both1976 Vassar College 62 Male1976 Vassar College 62 Female1977 Birtchnell & Mayhew 1097 Both

TOTAL 3571Mean 170.0476SD 226.1171

2. Transformation F values to t, d, and r values:The researcher used the following equation to suitable transformation

t = F

d = 2 t/N

d = 2r/ (1- r2)r = d/(4 + d2 )..equation (1)

Table.2 Transformation F values to t, d, and r values

# TheYear The Author N F t D r xy

1. 1982 Berta Elisa Ortiz 160 - - - 0.4628

2. 1982 Berta Elisa Ortiz 160 - - - 0.4058

3. 1974 Leonard Weller, Orah Natan,Ophrah Hazi 236

103.68

10.1823

1.32563 0.12775

4 1998 Michelle Laurel Vliet Brubaker 61 1.165 1.0793 0.2763 0.12162

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# TheYear The Author N F t D r xy

5 9

5. 2001 Renee M. Schilling 91 10.092

3.17679

0.66604 0.17742

6. 1974 Stephen Bruce Gold 150 0.11 0.33166

0.05416 0.02672

7. 1974 Stephen Bruce Gold 150 1.01 1.00499

0.16411 0.07332

8. 2002 Michelle Laurel 58 2.55 1.59687

0.41936 0.16386

9. 2001 Megan E. Zuchowski 49 3.64 1.90788

0.54511 0.19721

1

0. 2009 Krystal L. Stanley 61 2.318 1.5225 0.38987 0.15511

11. 1995 Michelle Laurel 160 - - - 0.4628

12. 1986 Suzanne Little Dastrup 80 5.443

2.33302

0.52168 0.16977

13. 1986 Suzanne Little Dastrup 80 6.335

2.51694

0.56281 0.17507

14. 1993 Bloser 254 1.09

1.04403

0.13102 0.05807

15. 1987 Toman 94 4.91

2.21585 0.4571 0.15313

16. 2008 Amanda Elizabeth Majors Beal 79 - - - 0.02

17. 2008 Amanda Elizabeth Majors Beal 79 - - - 0.03

18. 1937 Clifford Kirkpatrick 348 0.093

0.30496 0.0327 0.01616

19. 1976 Vassar College 62 6.82

2.61151

0.66332 0.20166

20

.1976 Vassar College 62 12.1 3.4785

1

0.8835

40.2202

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# TheYear The Author N F t D r xy

21. 1977 Birtchnell & Mayhew

1097

13.445

3.66674

0.22142 0.05301

3. Sampling Error Correction (Bare Bone Meta Analysis):If the population is assumed as constant correlation between some studies, the

best estimation for the correlation is not a simple average of correlation across studies,but it is a weighted average (Hunter & Schmidt, 1990). The best estimation for thepopulation correlation by following equation:

a. The average of population correlation ():

= ( Ni ri )/ Ni equation (2)

The results of these calculations are in table.3

Table. 3 Sampling Error Correction

# TheYear N rxy N rxy

1. 1982 160 0.4628 74.0482. 1982 160 0.4058 64.9283. 1974 236 0.12775 30.149

4. 1998 61 0.1216

2

7.4186

5. 2001 91 0.17742 16.146

6. 1974 150 0.02672 4.0073

7. 1974 150 0.07332 10.998

8. 2002 58 0.16386 9.5037

9. 2001 49 0.19721 9.663410. 2009 61 0.15511 9.4615

11. 1995 160 0.4628 74.048

12. 1986 80 0.16977 13.581

13. 1986 80 0.17507 14.005

14. 1993 254 0.05807 14.75

1

5.1987 94 0.1531

3 14.394

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# TheYear N rxy N rxy

16. 2008 79 0.02 1.58

1

7. 2008 79 0.03 2.3718. 1937 348 0.01616 5.6239

19. 1976 62 0.20166 12.503

20. 1976 62 0.2202 13.652

21. 1977 1097 0.05301 58.154

Total 3571 3.4715 386.94Average 170.04

80.108

The estimation of population correlation average is 0.108.

b. Variance of correlations across studiesrxy (2r):

2r = [ Ni (ri - )2]/ Ni .equation (3)

The results of these calculations in table.4.

Table. 4 Variance of rxy (2r)

# TheYear N rxy (rxy - ) (r xy - )2 N (rxy - )2

1. 1982 160 0.4628 0.3544 0.1256 20.1012. 1982 160 0.4058 0.2974 0.0885 14.1563. 1974 236 0.12775 0.0194 0.0004 0.0888

4. 1998 61 0.12162 0.0133 0.0002 0.0107

5. 2001 91 0.17742 0.0691 0.0048 0.4341

6. 1974 150 0.02672 -0.082 0.0067 0.9998

7. 1974 150 0.07332 -0.035 0.0012 0.1841

8. 2002 58 0.16386 0.0555 0.0031 0.1787

9. 2001 49 0.19721 0.0889 0.0079 0.3869

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10. 2009 61 0.15511 0.0468 0.0022 0.1333

11. 1995 160 0.4628 0.3544 0.1256 20.101

12. 1986 80 0.16977 0.0614 0.0038 0.3017

13. 1986 80 0.17507 0.0667 0.0045 0.356

14. 1993 254 0.05807 -0.05 0.0025 0.6422

15. 1987 94 0.15313 0.0448 0.002 0.1885

16. 2008 79 0.02 -0.088 0.0078 0.6167

17. 2008 79 0.03 -0.078 0.0061 0.485

18. 1937 348 0.01616 -0.092 0.0085 2.9579

19. 1976 62 0.20166 0.0933 0.0087 0.5397

20. 1976 62 0.2202 0.1118 0.0125 0.7755

2

1. 1977 1097 0.05301 -0.055 0.0031 3.36Total 3571 3.4715 66.997

Average 170.048

0.0188

SD 0.137

The variance of correlations across studies rxy (2r) is 0.0188, so the standard

deviation = 0.0188 = 0.137.

c. Variance of sampling error (2e):

The variance of correlations across studies rxy (2r) contains two componentsthese are; the variance of correlations in population (2) and the variance ofcorrelations in samples due to sampling error (2e), estimation of populationcorrelation variance can be simply obtained by correcting the observed variance ( 2r)via removing variance of sampling error (Hunter & Schmidt, 1990). Sampling errorvariance can be calculated using the following equation:

2e = (1 2)2/( 1) .equation (4)

Thus the value of variance of correlation due to sampling error is:= (1 (0.108) 2) 2/ (170 1) = 0.0058.

So sampling error variance is 0.0058.

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d. Estimation of population correlation variance (2):We can estimate the population correlation variance (2) or true variance by

correcting observed variance or variance across studies (2r) via subtracting thevariance of sampling error (2e). Population correlation variance can be calculated

using the following equation:

2 = 2r - 2eequation (5)so the value of population correlation variance (2) is:0.0188 - 0.0058 = 0.013.Then the standard deviation of population correlation = 2 = 0.013 =

0.1139.

e. Confidence interval and nature of population correlation:

The confidence interval with = 0.108, = 0.1139 and confidence level = 0.95is: z = 0.108 + (1.96 X 0.1139) or 0.108 - (1.96 X 0.1139)so +0.33 0.115-

The corrected standard deviation of 0.1139 can be compared with the mean of0.108: 0.108/0.1139 = 0.95. That is, the mean correlation is nearly one standarddeviations above 0. Thus, if the study population correlations are normally distributed,the probability of a zero or below-zero correlation is existing. So the qualitative natureof the relationship is near zero or very week: so the relationship betweencomplementarity birth order and marital adjustment not strong.

f. The impact of sampling error:The impact of sampling error can be determined by using the following

equation:2/2r = 0.013/0.0188 = 0.69.thus the study reliability is (0.69), so the percentage of variance refer to

sampling error is:1 0.69 = 0.31 = 31%.

4. Measurement error correction:Correction of artifacts other than sampling error is measurement error. To make

estimation of measurement error, the following table presentsmeasurement error estimation worksheet including reliabilities of independentvariable (rxx) which it is the birth order and dependent variable (ryy) which it is themarital adjustment.

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Table. 5 Measurement Error Estimation Worksheet

# N rxy rxx ryy a = rxx b = ryy N x rxy

1. 160 0.4628 0.87 0.76 0.9327 0.8718 74.048

2. 160 0.4058 0.83 0.76 0.911 0.8718 64.928

3. 236 0.12775 0.85 0.78 0.922 0.8832 30.149

4. 61 0.12162 0.87 0.80 0.9327 0.8944 7.4186

5. 91 0.17742 0.76 0.69 0.8741 0.8331 16.146

6. 150 0.02672 0.88 0.96 0.9381 0.9798 4.0073

7. 150 0.07332 0.87 0.96 0.9327 0.9798 10.998

8. 58 0.16386 0.89 0.82 0.9434 0.9055 9.5037

9. 49 0.19721 0.71 0.64 0.8426 0.8 9.6634

10

.61 0.15511 0.76 0.97 0.8718 0.9849 9.4615

11. 160 0.4628 0.83 0.76 0.911 0.8718 74.048

12. 80 0.16977 0.94 0.87 0.9695 0.9327 13.581

13. 80 0.17507 0.90 0.83 0.9487 0.911 14.005

14. 254 0.05807 0.90 0.93 0.9487 0.9644 14.75

15. 94 0.15313 0.97 0.90 0.9849 0.9487 14.394

16. 79 0.02 0.79 0.72 0.8888 0.8485 1.58

1

7. 79 0.03 0.97 0.90 0.9849 0.9487 2.37

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# N rxy rxx ryy a = rxx b = ryy N x rxy

18. 348 0.01616 0.88 0.66 0.9381 0.8124 5.6239

19. 62 0.20166 0.94 0.87 0.9695 0.9327 12.503

20. 62 0.2202 0.84 0.75 0.9165 0.866 13.652

21. 1097 0.05301 0.76 0.77 0.8718 0.8775 58.154

Total 3571 19.434 18.919Mean 170.048 0.92541 0.90089

SD 0.0386936 0.054895

a. Average of attenuation factor ():To correct for the artifacts, we first compute the mean compound artifact

attenuation factor, by the following equation: = Ave (a) Ave (b) equation (6) = 0.92541 x 0.90089 = 0.8337

b. Population correlation after correcting by measurement error ():

Calculation of the true population correlation after the correction ofmeasurement errors was performed by the following equations.

= Ave (i) = / equation (7)

= 0.108/0.8337 = 0.1295 = 0.13.

Therefore, the actual population correlation when corrected by measurementerror in both dependent and independent variables is 0.13.

c. The sum of the squared coefficients of variation (V):This performed by the following equations:

V= SD2 (a)/Ave2(a) + SD2 (b)/ Ave2(b).equation (8)=(0.0386936)2/( 0.92541)2 + (0.054895)2/ (0.90089)2 = 0.0055

d. The variance due to artifact variation (S22):

This computed by the following equations:S22 = 22 V equation (9)

Therefore, S22 = (0.13)2 (0.8337)2 (0.0055) = 0.0000646054 = 0.00006.e. The variance in true score correlations (Var()):

Var () = Var (xy) - 2

2

V/..equation (10)Var () = (0.013) [(0.13)2 (0.8337)2 (0.0055)/(0.8337)] = 0.0129

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SD = 0.0129 = 0.114.Thus the real population correlation () was estimated to be 0.13 and the

standard deviation (SD) was 0.114.

f. Confidence interval and nature of population correlation:

The confidence interval with = 0.13, = 0.114 and confidence level = 0.95is:

z = 0.13 + (1.96 X 0.114) or 0.13 - (1.96 X 0.114)so +0.35 0.09-

The corrected standard deviation of 0.114 can be compared with the mean of0.13: 0.13/0.114 = 1.14. That is, the mean correlation is near one standard deviationsabove 0. Thus, if the study population correlations are normally distributed, the

probability of a zero or below-zero correlation is existing. So the qualitative nature ofthe relationship is near zero or very week: so the relationship betweencomplementarity birth order and marital adjustment not strong.

g. The impact of measurement error:The impact of measurement error can be determined by using the following

equation:22 V/ 2 (xy) x100 % ...equation (12)

= 0.00006/0.013 x 100% = 0.0046154 x 100% = 0.46%

5. Direct range restriction correction:

To obtain the value of population correlation (rp) after removing the effect ofdirect range restriction, we will use the following equation:

rp = [(U2 + 2)(1 - U2)] . equation (13) (Card, 2011, p. 141)but U = / s = 0.114/0.137 = 0.832.so rp = 0.13 [((0.832)2 + (0.108)2) (1 - (0.832)2)] = 0.06

The results:

The value of () is existence between the accepted area of null hypothesis andaccepted area of alternative hypothesis, so the relationship is very week (0.06):therefore the relationship between complementarity birth order and marital adjustmentnot strong.

Sampling error:The value of sampling error variance showed that the percentage of variance

due to sampling error is big, which was 31%. This percentage suggests the big

possibility of bias due to error in sampling.

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Measurement error:The value of measurement error variance in both independent and dependent

variables is equal to (0.00006), and the value of population variance was estimated to(0.013), thus when the variance of measurement error compared with the populationvariance due to measurement error variance it will be small (0.46%), and smaller than

the impact of sampling error (31%), but although this percentage (0.46%) is verysmall it suggests the possibility of bias due to measurement error.

Direct range restriction artifacts:The value of population correlation before direct range restriction correction

was (0.13), and after correction was (0.06), so the percentage of direct rangerestriction artifacts (46%), it is a big value, so we can consider the relationship

between complementarity birth order and marital adjustment is very weak.

Discussion:The purpose of this study was to examine the relationship between

complementarity birth order and marital adjustment. Meta analysis for (21) studiedFindings indicate that there is a weak relation between these variables, or not strong.So this study somewhat support the assumption of Toman theory, but the currentevidence by meta analysis is not enough, because there is a probably that; thesampling error still work and this study may be could not removing the bias due tosampling error which lead the value of correlation between complementarity birthorder and marital adjustment not clear very well or misty. Any way the findings ofstudies in this article were heterogeneous, we can notice that the range of r was from(0.02) to (0.46), may be the research should conduct chi square test (2) beforecompleting the rest meta analysis steps, so to make a sure the researcher computedthe (2); it was (68.48) with df (20) this value was significant the p-value was (0.000),so the studies in this article were heterogeneous, that lead us to repeat meta analysis inanother occasion take in account dividing the studies according to cultural or socio-economic settings.References:

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