Researchin Nursing& Health, 1992, 15, 313-317
Focus on Psychometrics Aggregating Family Data Sandra L. Ferketich and Ramona T. Mercer
Instruments to collect data about families are often administered to all or some individuals within the family. Researchers may wish to use these individual scores to describe the family. The purpose of this article is to describe the special issues with aggregation of data when only a small number of family members are used as respondents. A refinement of the definition of aggregation for family researchers is proposed to assist researchers to focus on specific issues when data are to reflect subgroups within the family. A few examples of changes in findings are reported to illustrate the effectsof different aggregation schemes when two members of the family are used as respondents. 0 1992 John Wiley 8 Sons, Inc.
Measurement of the individual’s perception of phenomena is critical to much of nursing research. When the focus of the research is on the group rather than the individual, several measurement issues arise about whether to use individual or group data. Researchers studying the family have suggested various approaches to the measurement of family phenomena to solve the problems of data from multiple sources. When instruments designed to measure individual perceptions of self, others in the family, or the family as a unit are used, the researcher is faced with a key question of how the data will be used in the analysis. Will data be kept at the individual level or aggregated with all other or selected other member’s scores? The choice undoubtedly will be driven by a number of factors, including the underlying philosophic orientation to family study and the research questions. The purpose of this article is to discuss aggregation of data when the family is the unit of study and to illustrate how choices may affect the analysis results when two respondents provide data. When the unit of analysis is the entire system, it is appropriate to observe how the system adapts to change or deals with crisis to remain stable and how the system displays patterns of behavior following specific events. In such research, the
conceptualization, measurement, and analysis all indicate the family as a unit or system. Thus, relationships are transactional and nonlinear and hypotheses need to be stated as sequences of actions or configurations of behaviors (Feetham, 1991). Feetham (1991) elucidates the similarities and differences between family-related and family research. In either type of study, however, the critical points for the researcher are that the conceptualization and definition of the family are made explicit and that the measurement approach is logically consistent with the theoretical framework and family definition. As long as the above criteria are met, there may be additional reasons that specific instruments and methods of treating data are selected. There are a number of reasons that measurement at the individual level is selected as an index of the group. Lack of reliable and valid instruments, cost of measurement, and complexity of measurement and analyses at the family level may be deterrents. Just as in organizational research, responses of individuals in the family, under certain circumstances, can provide information about the family and the phenomena of interest. Under these circumstances, investigators have been interested in ways to present family data that take into account as much information as is available from the family.
Sandra Ferketich, PhD, RN, is an associate professor in the College of Nursing and the Director of the Predoctoral and Postdoctoral Clinical Nursing Research Instrumentation Fellowship Program (NRSA Institutional Grant No. 5T32 NR07029) at the University of Arizona. Ramona T. Mercer, PhD, RN, is a professor emeritus, Department of Family Health Care Nursing, School of Nursing, University of California, San Francisco. This article is part of the ongoing series, Focus on Psychometrics, edited or contributed by Dr. Ferketich. Requests for reprints can be addressed to Dr. Sandra Ferketich, College of Nursing, University of Arizona, Tuscon, AZ 85721. 0 1992 John Wiley & Sons, Inc. CCC 0160-6891/92/040313-05$04.00
314
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Thus, researchers have proposed various ways that data from individual family members could be combined to be more reflective of the family as a whole. When undertaking to obtain data from several members of the family or group, the researcher must make decisions about how to:(a) appropriately select informants and data sources, (b) obtain the sample and (c) generalize findings (Thomas, 1987). If the decision is to include more than one person from each family, then a plan for handling multiple scores needs to be made. Organizationalresearchers have some ways of handling data when a number of members from an organization provide data (Verran, Mark, & Lamb, 1992). However, when only two or possibly three members of the family report on the family’s response, this is a special case that needs further elucidation.
Another strategy that is suggested is to conduct interviews of family members. However, Thomas (1987) argued that a single, mutually agreed upon view of the family does not exist, and different approaches cannot be expected to yield congruent perspectives of the family. She allotted time for single family member interviews in addition to her interview with the total family present. In all instances data offered by one family member when interviewed alone was different than when the family was together. In conclusion, other methods of obtaining data may have problems or the choice of research question may cause the researcher to collect data from two to three similar individuals in the family, based on age, roles, or other defining characteristics. If this is done, it is a special case of aggregating data that seems especially relevant to researchers studying the family.
Selection of Respondents
Analyses of Two Respondents’ Data
Although there is some support for using either an individual report, data from all the members in the family, or data obtained from the family as a group, these strategies may not work well or only work under certain circumstances. It is appropriate to measure an individual family member when the basic focus is on family as the environment for growth, development, and individual member well-being or when the individual is thought to reflect family behavior. Additionally, for some research problems, one family member may have the most accurate and/or unique knowledge about a problem. For example, in a study of a child’s eating habits, the person who prepares and serves the meals may be the single best informant depending on the research question. Another strategy that is suggested is to obtain data from all members of the family. Since families may be composed of members of different age and developmental groups, it may be difficult to find instruments that have been standardized across the groups under study. For example, a measure of a particular phenomena may be available for adults but not for children. Or instruments that work well for adults of one developmental stage may not work for adults of another developmental stage. For some problems concerning health care, persons outside the family may be selected as informants. Thomas (1987) cautioned that an outside informant’s data may not capture the information sought. She found opposite views of family functioning that reflected two realities when she interviewed health care providers and family members .
Currently, family researchers tend to treat issues of aggregation as the same, no matter how many data sources are used. In order to help focus on the special problems when scores from a subgroup, such as two or three individuals, rather than scores from a larger group of individuals are combined, a more refined classification is suggested. The categories proposed are: IW aggregation, first level; family subgroup aggregation, second level; and family level aggregation, third level. None of the levels are being proposed for any specific research questions but instead are suggested as ways to assist researchers in more precisely addressing aggregation. At the first level of the proposed categorization, no aggregation occurs and is termed individual level data meaning that data are collected from the individual and analyzed as such. This level is applicable whether the questions asked focus on the individual’s perception of self, other members, or the family as a whole. The second level is family subgroup level aggregation. Data are aggregated from selected individuals within the group. For example, data from the adults in the family may be aggregated to create a composite score. Again, the focus of the question is not being defined by the level. Thus, a couple may be each asked questions about one of the partner’s level of physical ability, the partner’s response to an aversive event, or another family member’s response to the partner’s level of ability. Alternatively, each individual may be asked for their perception of the family’s response to a member’s health problem, behavior, or an event affecting the family. In this case, for example,
AGGREGATING FAMILY DATA / FERKETICH AND MERCER
if the nonaffected member views the problem as less severe than the affected member does, the magnitude of the difference and its direction can be easily described and treated in an analysis. The third level is the family level aggregation. Data are aggregated from all members of the family. The number of members will vary with family size and composition. As with the other levels, the focus of the question is not the defining characteristic. When data are collected from a number of individuals, description of differences within the family may be more difficult. For example a measure of variance may be needed rather than a discrepancy measure that might be appropriate for family subgroup level aggregation. When treating data from a small number of subjects in a farmly, there are a variety of proposed methods for aggregation. Data collected from two family members may be kept at Level 1 to compare the findingsfrom the analysis for congruence. The researcher might use only the extreme score from the respondents, based on the assumption that a family can function only to the level of the leastfunctioning family member (Uphold & Strickland, 1989).When couple and family scores are derived from individual responses, a careful defense explaining both the rationale and utility is important (Larsen & Olson, 1990). Summing the scores of family members can be done if the same number of members of each family will be included in the study. Except under extreme circumstances, the sum of the scores will exceed the expected scale range; therefore, the new scores may be difficult to interpret in a meaningful way. A possible solution to this dilemma is to do an average score of the respondents. When one family member is more extreme than the other, this may be lost in creating the average. When scores are summed or averaged, the assumption is made that both members’ perceptions are equally valid and contribute equally to the family’s response. It can be argued that the strength of one member will support or ameliorate the weakness of the other member. As a functioning dyad then, the two can be stronger than the weakest but not as strong as the strongest. Major problems in using mean scores are that mean scores do not reflect differences based on age or developmental stage; the ensuing reduction of score variance also reduces the influence of a deviant member within the family; the mean does not account for the differences between (or among) the contributing scores; and the mean does not take into account the order of the scores (Fisher, Kokes, Ransom, Phillips, & Rudd, 1985). The mean score will give an accurate picture of a couple’s position on a scale if their
315
scores are similar; however, when their scores are very different, a mean score eliminates those differences, giving a distorted picture of differences (Larsen & Olson, 1990). Discrepancy scores can be used, but they highlight differences as opposed to the mean score which conceals differences (Larsen & Olson, 1990). Although differences between scores or discrepancy scores have been associated with less satisfaction, greater conflict, and stress, they also raise problems (Fisher et al., 1985). Importantly, discrepancy scores do not reflect score level or position on a scale as the mean scores do, and may be conceptually misleading depending on the conceptualization underlying the instrument. For example, on a scale of 0 to 100, one couple might have a score of 80 and 90;whereas, another couple may have scores of 40 and 50. However, both couples would receive a discrepancy score of 10. Discrepancy scores tend to be less reliable than the contributing scores, with distributions having less variance along with some attenuation of distribution, reducing the possibility for reaching statistical levels of significance (Fisheret al., 1985). Another possibility is to create a number of categories based on the magnitude and/or direction of the discrepancy. This may be helpful if not many respondents are used. However, when the number of respondents increases, the number of ways of categorizing also increases. For example, if two subjects were asked to rate the level of disability of one of the subjects, three categories might be logically developed: (a) complete agreement, (b) disabled views disability as more than other family member does, and (c) disabled views disability as less than other family member does. If three subjects are asked the same question, the number of categories increases. For example, there could be (a) complete agreement, (b) disabled and one other view disability as equal, but subject three views disability as less, (c) disabled and one other view disability as equal, but subject three views disability as greater, (d) disabled views disability as greater than subject two but less than subject one, etc. These permutations may have relevance in a study if the difference in expectations of the two members (who might be parents) are in disagreement with each other and with the child. In summary, investigators have choices in using instrument scores and in analyzing data. It may be of assistance to researchers to be aware of these possibilities and use analysis strategies to test one approach against another. The findings, however, cannot be evaluated solely on the outcome of the analysis but must fit with the conceptualization of the family and with the research questions.
316
RESEARCH IN NURSING & HEALTH
variables, method, instrument properties, and sample size were minimized. All reported standardized betas were significant at p d .05. As noted earlier, there may be a number of sources The independent variables were perceived social of data in a family study. In this portion of the support, received social support, social support article, we provide a sample set of analyses to network size, self-esteem, mastery, anxiety, illustrate differences in results with different depression, and negative life events. The dependent methods of treating the two scores obtained on variable was family function. Although there are each variable from the adult partners in a family. many permutations of how data can be treated, There are many ways that data could have been for the sake of space, we selected to modify the treated; just a few are used here for illustration. dependent variable of family function by leaving The data are analyzed at Level 1, no aggregation, the score as an individual report of the family, as and at Level 2, family subgroup aggregation; the a discrepancy score between the two partners, and data obtained from each level of aggregation are as an average score of the two partners. Independent shown in Table 1. The same set of variables and variables were treated in the same manner keeping procedure, regression analysis, were used. Thus, the independent variables as individual reports, differences due to variable set and/or method were as averages between the two partners, and as disminimized. With the exception of one instrument, crepancy scores between the two partners. Furall others were established instruments with known and acceptable psychometric properties ( N u ~ a l l y , thermore, equations were run to illustrate a consistent and not consistent approach to aggregation. 1978). The instrument measuring social support Since predictor variables were measured for network size was devised for the study from which both men and women, regressions were done first the data were taken. The sample contained 200 with women and men with their respective meacouples with complete data on each of the measures. sures, on mean scores of the women and men Thus, instability of findings due to changes in
Flndings
Table 1. Regression Results for Exploratory Analytms Dependent
Independent
Variable
Variables
P
Cumulative R2
Women, indiv. level
Family func.
Men, indiv. level
Family func.
Family func. discrepancy Family func. discrepancy Family func. average
.23 - .24 -.14 .13 .10 .36 - .22 -.21 - .25 .15 -.17
.13 .22 .24 .25 .26 .23 .31 .34
Women, indiv. level
Perc. SOC.support Anxiety Neg. life events Mastery Rec. SOC.support Perc. SOC. support Depression Neg. life events Neg. life events Self-esteem Depression Neg. life events Depression Perc. SOC. support Perc. SOC.support Depression Neg. life events Neg. life events Self-esteem Neg. life events Perc. SOC.support Depression Self-esteem SOC.support size
- .32 - .32 .15 .33 - .20 -.15 - .25 .16 - .30 .23 - .28 -20 .11
.21 .31 .33 .17 .23 .25
Equation
Men, indiv. level Women, indiv. level
Men, indiv. level
Family func. average
Womenlmen, discrepancy Womenlmen, average
Family func. discrepancy Family func. average
.08 .10
.03
.08 .10 * 20 .33 .40 .43 .44
Note. Perc. SOC. support = Perceived social support. Rec. SOC. support = Received social support. SOC.support size = Social support network size. Neg. life events = Negative life events.
AGGREGATING FAMILY DATA / FERKETICH AND MERCER
with both women’s scores, then with the men’s scores, then with women’s and men’s predictor scores both entered. As can be seen in Table 1, the regression model accounting for the greatest variance in family functioning was the mean score of the women and men that included both the women’s and men’s predictor scores, with 44% of the variance accounted for. In this model scores on both the independent and dependent measures were averaged. Using the women’s data alone, 26% of the variance was accounted for, and using the men’s data alone, 34% was explained. Thus, in this sample the mean scores of the pregnant woman and her partner were more explanatory with both of their predictor scores entered. Not only was the amount of explained variance different across the separate approaches, but the variables that entered as well as the magnitude of their effect was different. Thus, the researcher confronted with these differences needs to evaluate the findings within the context of the conceptualization of the family. Perhaps what is even more exciting is the consistency of some variables for affecting the outcome variable no matter which aggregation strategy was used. Thus, as a means to examine the effect of different methods of aggregating scores and which scores to aggregate, it may be useful to explore some of the variations in order to refine approaches to measurement of data from families and their members.
REFERENCES Feetham, S.L. (1991). Conceptual and methodological issues in research of families. In A. Whall & J. Fawcett (Eds.), Theory development in
317
nursing (pp. 55-68). Philadelphia: F.A. Davis c o. Feetham, S.L., & Humenick, S.S. (1982). The Feetham Family Functioning Survey. In S.S. Humenick (Ed.), Analysis of current assessment strategies in the health care of young children and childbearing families (pp. 259-268). Nor-
walk, CT: Appleton-Century-Crofts. Fisher, L., Kokes, R.F., Ransom, D.C., Phillips, S.L., & Rudd, P. (1985). Alternative strategies for creating “relational” family data. Family Process, 2 4 , 213-224.
Fisher, L., Terry, H.E., Ransom, D.C. (1990). Advancing a family perspective in health research: Models and methods. Family Process, 2 9 , 177189.
Larson, A., & Olson, D.H. (1990). Capturing the complexity of family systems: Integrating family theory, family scores, and family analysis. In T.W. Draper & A.C. Marcos (Eds.), Family variables: Conceptualization, measurement, and use (pp. 19-47). Newbury Park, CA: Sage. Nunnally, J. (1978). Psychometric theory. New
York: McGraw-Hill. Schumm, W.R., Barnes, H.L., Bollman, S.R., Jurich, A.P., & Milliken, G.A. (1985). Approaches to the statistical analysis of family data. Home Economics Research Journal, 14, 112122. Thomas, R.B. (1987). Methodological issues and problems in family health care research. Journal of Marriage and the Family, 4 9 , 65-70. Uphold, C.R., & Strickland, O.L. (1989). Issues related to the unit of analysis in family nursing research. Western Journal of Nursing Research, 1 1 , 405-417. Verran, J.A., Mark, B.A., & Lamb, G. (1992). Psychometric examination of instruments using aggregated data. Research in Nursing & Health, I S . 237-240.
Focus on Psychometrics Aggregating Family Data Sandra L. Ferketich and Ramona T. Mercer
Instruments to collect data about families are often administered to all or some individuals within the family. Researchers may wish to use these individual scores to describe the family. The purpose of this article is to describe the special issues with aggregation of data when only a small number of family members are used as respondents. A refinement of the definition of aggregation for family researchers is proposed to assist researchers to focus on specific issues when data are to reflect subgroups within the family. A few examples of changes in findings are reported to illustrate the effectsof different aggregation schemes when two members of the family are used as respondents. 0 1992 John Wiley 8 Sons, Inc.
Measurement of the individual’s perception of phenomena is critical to much of nursing research. When the focus of the research is on the group rather than the individual, several measurement issues arise about whether to use individual or group data. Researchers studying the family have suggested various approaches to the measurement of family phenomena to solve the problems of data from multiple sources. When instruments designed to measure individual perceptions of self, others in the family, or the family as a unit are used, the researcher is faced with a key question of how the data will be used in the analysis. Will data be kept at the individual level or aggregated with all other or selected other member’s scores? The choice undoubtedly will be driven by a number of factors, including the underlying philosophic orientation to family study and the research questions. The purpose of this article is to discuss aggregation of data when the family is the unit of study and to illustrate how choices may affect the analysis results when two respondents provide data. When the unit of analysis is the entire system, it is appropriate to observe how the system adapts to change or deals with crisis to remain stable and how the system displays patterns of behavior following specific events. In such research, the
conceptualization, measurement, and analysis all indicate the family as a unit or system. Thus, relationships are transactional and nonlinear and hypotheses need to be stated as sequences of actions or configurations of behaviors (Feetham, 1991). Feetham (1991) elucidates the similarities and differences between family-related and family research. In either type of study, however, the critical points for the researcher are that the conceptualization and definition of the family are made explicit and that the measurement approach is logically consistent with the theoretical framework and family definition. As long as the above criteria are met, there may be additional reasons that specific instruments and methods of treating data are selected. There are a number of reasons that measurement at the individual level is selected as an index of the group. Lack of reliable and valid instruments, cost of measurement, and complexity of measurement and analyses at the family level may be deterrents. Just as in organizational research, responses of individuals in the family, under certain circumstances, can provide information about the family and the phenomena of interest. Under these circumstances, investigators have been interested in ways to present family data that take into account as much information as is available from the family.
Sandra Ferketich, PhD, RN, is an associate professor in the College of Nursing and the Director of the Predoctoral and Postdoctoral Clinical Nursing Research Instrumentation Fellowship Program (NRSA Institutional Grant No. 5T32 NR07029) at the University of Arizona. Ramona T. Mercer, PhD, RN, is a professor emeritus, Department of Family Health Care Nursing, School of Nursing, University of California, San Francisco. This article is part of the ongoing series, Focus on Psychometrics, edited or contributed by Dr. Ferketich. Requests for reprints can be addressed to Dr. Sandra Ferketich, College of Nursing, University of Arizona, Tuscon, AZ 85721. 0 1992 John Wiley & Sons, Inc. CCC 0160-6891/92/040313-05$04.00
314
RESEARCH IN NURSING 8 HEALTH
Thus, researchers have proposed various ways that data from individual family members could be combined to be more reflective of the family as a whole. When undertaking to obtain data from several members of the family or group, the researcher must make decisions about how to:(a) appropriately select informants and data sources, (b) obtain the sample and (c) generalize findings (Thomas, 1987). If the decision is to include more than one person from each family, then a plan for handling multiple scores needs to be made. Organizationalresearchers have some ways of handling data when a number of members from an organization provide data (Verran, Mark, & Lamb, 1992). However, when only two or possibly three members of the family report on the family’s response, this is a special case that needs further elucidation.
Another strategy that is suggested is to conduct interviews of family members. However, Thomas (1987) argued that a single, mutually agreed upon view of the family does not exist, and different approaches cannot be expected to yield congruent perspectives of the family. She allotted time for single family member interviews in addition to her interview with the total family present. In all instances data offered by one family member when interviewed alone was different than when the family was together. In conclusion, other methods of obtaining data may have problems or the choice of research question may cause the researcher to collect data from two to three similar individuals in the family, based on age, roles, or other defining characteristics. If this is done, it is a special case of aggregating data that seems especially relevant to researchers studying the family.
Selection of Respondents
Analyses of Two Respondents’ Data
Although there is some support for using either an individual report, data from all the members in the family, or data obtained from the family as a group, these strategies may not work well or only work under certain circumstances. It is appropriate to measure an individual family member when the basic focus is on family as the environment for growth, development, and individual member well-being or when the individual is thought to reflect family behavior. Additionally, for some research problems, one family member may have the most accurate and/or unique knowledge about a problem. For example, in a study of a child’s eating habits, the person who prepares and serves the meals may be the single best informant depending on the research question. Another strategy that is suggested is to obtain data from all members of the family. Since families may be composed of members of different age and developmental groups, it may be difficult to find instruments that have been standardized across the groups under study. For example, a measure of a particular phenomena may be available for adults but not for children. Or instruments that work well for adults of one developmental stage may not work for adults of another developmental stage. For some problems concerning health care, persons outside the family may be selected as informants. Thomas (1987) cautioned that an outside informant’s data may not capture the information sought. She found opposite views of family functioning that reflected two realities when she interviewed health care providers and family members .
Currently, family researchers tend to treat issues of aggregation as the same, no matter how many data sources are used. In order to help focus on the special problems when scores from a subgroup, such as two or three individuals, rather than scores from a larger group of individuals are combined, a more refined classification is suggested. The categories proposed are: IW aggregation, first level; family subgroup aggregation, second level; and family level aggregation, third level. None of the levels are being proposed for any specific research questions but instead are suggested as ways to assist researchers in more precisely addressing aggregation. At the first level of the proposed categorization, no aggregation occurs and is termed individual level data meaning that data are collected from the individual and analyzed as such. This level is applicable whether the questions asked focus on the individual’s perception of self, other members, or the family as a whole. The second level is family subgroup level aggregation. Data are aggregated from selected individuals within the group. For example, data from the adults in the family may be aggregated to create a composite score. Again, the focus of the question is not being defined by the level. Thus, a couple may be each asked questions about one of the partner’s level of physical ability, the partner’s response to an aversive event, or another family member’s response to the partner’s level of ability. Alternatively, each individual may be asked for their perception of the family’s response to a member’s health problem, behavior, or an event affecting the family. In this case, for example,
AGGREGATING FAMILY DATA / FERKETICH AND MERCER
if the nonaffected member views the problem as less severe than the affected member does, the magnitude of the difference and its direction can be easily described and treated in an analysis. The third level is the family level aggregation. Data are aggregated from all members of the family. The number of members will vary with family size and composition. As with the other levels, the focus of the question is not the defining characteristic. When data are collected from a number of individuals, description of differences within the family may be more difficult. For example a measure of variance may be needed rather than a discrepancy measure that might be appropriate for family subgroup level aggregation. When treating data from a small number of subjects in a farmly, there are a variety of proposed methods for aggregation. Data collected from two family members may be kept at Level 1 to compare the findingsfrom the analysis for congruence. The researcher might use only the extreme score from the respondents, based on the assumption that a family can function only to the level of the leastfunctioning family member (Uphold & Strickland, 1989).When couple and family scores are derived from individual responses, a careful defense explaining both the rationale and utility is important (Larsen & Olson, 1990). Summing the scores of family members can be done if the same number of members of each family will be included in the study. Except under extreme circumstances, the sum of the scores will exceed the expected scale range; therefore, the new scores may be difficult to interpret in a meaningful way. A possible solution to this dilemma is to do an average score of the respondents. When one family member is more extreme than the other, this may be lost in creating the average. When scores are summed or averaged, the assumption is made that both members’ perceptions are equally valid and contribute equally to the family’s response. It can be argued that the strength of one member will support or ameliorate the weakness of the other member. As a functioning dyad then, the two can be stronger than the weakest but not as strong as the strongest. Major problems in using mean scores are that mean scores do not reflect differences based on age or developmental stage; the ensuing reduction of score variance also reduces the influence of a deviant member within the family; the mean does not account for the differences between (or among) the contributing scores; and the mean does not take into account the order of the scores (Fisher, Kokes, Ransom, Phillips, & Rudd, 1985). The mean score will give an accurate picture of a couple’s position on a scale if their
315
scores are similar; however, when their scores are very different, a mean score eliminates those differences, giving a distorted picture of differences (Larsen & Olson, 1990). Discrepancy scores can be used, but they highlight differences as opposed to the mean score which conceals differences (Larsen & Olson, 1990). Although differences between scores or discrepancy scores have been associated with less satisfaction, greater conflict, and stress, they also raise problems (Fisher et al., 1985). Importantly, discrepancy scores do not reflect score level or position on a scale as the mean scores do, and may be conceptually misleading depending on the conceptualization underlying the instrument. For example, on a scale of 0 to 100, one couple might have a score of 80 and 90;whereas, another couple may have scores of 40 and 50. However, both couples would receive a discrepancy score of 10. Discrepancy scores tend to be less reliable than the contributing scores, with distributions having less variance along with some attenuation of distribution, reducing the possibility for reaching statistical levels of significance (Fisheret al., 1985). Another possibility is to create a number of categories based on the magnitude and/or direction of the discrepancy. This may be helpful if not many respondents are used. However, when the number of respondents increases, the number of ways of categorizing also increases. For example, if two subjects were asked to rate the level of disability of one of the subjects, three categories might be logically developed: (a) complete agreement, (b) disabled views disability as more than other family member does, and (c) disabled views disability as less than other family member does. If three subjects are asked the same question, the number of categories increases. For example, there could be (a) complete agreement, (b) disabled and one other view disability as equal, but subject three views disability as less, (c) disabled and one other view disability as equal, but subject three views disability as greater, (d) disabled views disability as greater than subject two but less than subject one, etc. These permutations may have relevance in a study if the difference in expectations of the two members (who might be parents) are in disagreement with each other and with the child. In summary, investigators have choices in using instrument scores and in analyzing data. It may be of assistance to researchers to be aware of these possibilities and use analysis strategies to test one approach against another. The findings, however, cannot be evaluated solely on the outcome of the analysis but must fit with the conceptualization of the family and with the research questions.
316
RESEARCH IN NURSING & HEALTH
variables, method, instrument properties, and sample size were minimized. All reported standardized betas were significant at p d .05. As noted earlier, there may be a number of sources The independent variables were perceived social of data in a family study. In this portion of the support, received social support, social support article, we provide a sample set of analyses to network size, self-esteem, mastery, anxiety, illustrate differences in results with different depression, and negative life events. The dependent methods of treating the two scores obtained on variable was family function. Although there are each variable from the adult partners in a family. many permutations of how data can be treated, There are many ways that data could have been for the sake of space, we selected to modify the treated; just a few are used here for illustration. dependent variable of family function by leaving The data are analyzed at Level 1, no aggregation, the score as an individual report of the family, as and at Level 2, family subgroup aggregation; the a discrepancy score between the two partners, and data obtained from each level of aggregation are as an average score of the two partners. Independent shown in Table 1. The same set of variables and variables were treated in the same manner keeping procedure, regression analysis, were used. Thus, the independent variables as individual reports, differences due to variable set and/or method were as averages between the two partners, and as disminimized. With the exception of one instrument, crepancy scores between the two partners. Furall others were established instruments with known and acceptable psychometric properties ( N u ~ a l l y , thermore, equations were run to illustrate a consistent and not consistent approach to aggregation. 1978). The instrument measuring social support Since predictor variables were measured for network size was devised for the study from which both men and women, regressions were done first the data were taken. The sample contained 200 with women and men with their respective meacouples with complete data on each of the measures. sures, on mean scores of the women and men Thus, instability of findings due to changes in
Flndings
Table 1. Regression Results for Exploratory Analytms Dependent
Independent
Variable
Variables
P
Cumulative R2
Women, indiv. level
Family func.
Men, indiv. level
Family func.
Family func. discrepancy Family func. discrepancy Family func. average
.23 - .24 -.14 .13 .10 .36 - .22 -.21 - .25 .15 -.17
.13 .22 .24 .25 .26 .23 .31 .34
Women, indiv. level
Perc. SOC.support Anxiety Neg. life events Mastery Rec. SOC.support Perc. SOC. support Depression Neg. life events Neg. life events Self-esteem Depression Neg. life events Depression Perc. SOC. support Perc. SOC.support Depression Neg. life events Neg. life events Self-esteem Neg. life events Perc. SOC.support Depression Self-esteem SOC.support size
- .32 - .32 .15 .33 - .20 -.15 - .25 .16 - .30 .23 - .28 -20 .11
.21 .31 .33 .17 .23 .25
Equation
Men, indiv. level Women, indiv. level
Men, indiv. level
Family func. average
Womenlmen, discrepancy Womenlmen, average
Family func. discrepancy Family func. average
.08 .10
.03
.08 .10 * 20 .33 .40 .43 .44
Note. Perc. SOC. support = Perceived social support. Rec. SOC. support = Received social support. SOC.support size = Social support network size. Neg. life events = Negative life events.
AGGREGATING FAMILY DATA / FERKETICH AND MERCER
with both women’s scores, then with the men’s scores, then with women’s and men’s predictor scores both entered. As can be seen in Table 1, the regression model accounting for the greatest variance in family functioning was the mean score of the women and men that included both the women’s and men’s predictor scores, with 44% of the variance accounted for. In this model scores on both the independent and dependent measures were averaged. Using the women’s data alone, 26% of the variance was accounted for, and using the men’s data alone, 34% was explained. Thus, in this sample the mean scores of the pregnant woman and her partner were more explanatory with both of their predictor scores entered. Not only was the amount of explained variance different across the separate approaches, but the variables that entered as well as the magnitude of their effect was different. Thus, the researcher confronted with these differences needs to evaluate the findings within the context of the conceptualization of the family. Perhaps what is even more exciting is the consistency of some variables for affecting the outcome variable no matter which aggregation strategy was used. Thus, as a means to examine the effect of different methods of aggregating scores and which scores to aggregate, it may be useful to explore some of the variations in order to refine approaches to measurement of data from families and their members.
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