neighborhood contexts during the middle school years, which has been described elsewhere (Hughes et al., 2008). The analytic sample consisted of 387 ado- lescents who identified as Black (n − 149; 38.5%). Dominican American (n − 114; 29.5%), and Chinese American (n =124; 32.0%; see Supporting Information for exclusionary criteria). Table 1 presents the demo- graphic characteristics for each ethnic-racial group by sex, maternal education, school, and immigrant status. There were no differences in the gender composition of adolescents from the three ethnic-racial groups, z(2)-247, p = ns. Relative to Black and Dominican American youth, Chinese American youth were more likely to come from households with mothers who were less well educated, F(2, 388)-9.38, p<.001. Black and Dominican American youth in the sample were represented at all six schools, and Chinese American youth were represented at three of the six schools. Black youth were significantly more likely to identify as native-origin (third generation) than were their Dominican American and Chinese American peers, (2)=165.74, p<.001. Procedure The procedure for the present study is described in detail elsewhere (Hughes et al., 2008). Principal investigators first identified public middle schools in which at least three of the four ethnic-racial Table 1 Demographic Characteristics in Percent of the Analytic sample (n-387) Presented by Ethnicity-Race Demographic characteris Black Dominican Chinese tics (n-149) (n-114) (n-124) Gender %Girls %Boys Schools (diversity score) %School 1 (0.74) %School 2 (0.69) %School 3 (0.44) %School 4 (0.33) %School 5 (0.70) %School 6 (0.50) Materal education % Less than high school %High school %Some college %Bachelors or beyond Generation status %Native-origin %Immigrant-origin 56.4 43.6 20 45.0 25.5 9.4 10.7 7.4 4.0 23.5 19.5 53.0 66.4 33.6 ******** **** ** 50.0 50.0 0.9 36.8 5.3 8.8 19.3 28.9 11.4 24.6 14.0 50.0 6.1 93.9 52.4 15.3 8.1 0.0 75.8 0.8 0.0 177 34.7 10.5 37.1 4.8 95.2 Ethnic-Racial Identity and Discrimination elll groups initially targeted for the larger study (ie., Black, Dominican American, Chinese American, and White) constituted 20% or more of the student population. Each of the six schools we initially approached agreed to participate in the study and all had a sixth-through-eighth-grade structure. We recruited students in all non-English as a second language sixth grade classrooms at the first assess- ment. For the seventh and eighth grade assess- ments, we permitted non-participating students to enter the study. Research assistants distributed and collected consent forms for a 2- to 3-week period in students' homeroom classes. The principal investi- gators provided students with a small non-mone- tary incentive for their participation. Overall, 77% of recruited adolescents returned parental consent forms and 78% of those had affirmative parental consent. We administered surveys in the spring of sixth, seventh, and eighth grades during two class periods that the school principal and teachers deemed appropriate. We collected data from two cohorts of adolescents; Cohort 1 was recruited in 2005 when students were sixth graders (n = 188), and Cohort 2 included adolescents recruited as sixth graders in 2006 (n – 199). Measures Ethnic-Racial Identity Exploration We used a four-item measure, derived from the Multigroup Ethnic Identity Measure (MEIM; Phin- ney, 1992), to assess ethnic-racial identity explo- ration. Adolescents indicated the extent to which they questioned or sought information about their ethnicity-race using a 5-point Likert scale (e.g., "In order to learn more about my ethnic/racial back- ground, I have often talked to other people about my ethnic/racial group"; 1- strongly disagree, 5-strongly agree). Internal consistency/reliability of the four-item measure was adequate across the three waves of study for each ethnic-racial group (time-range Black = 66-84; ime-range Domini- can = .69-75; imerange Chinese - 70-81). A con- firmatory factor analysis of the four items across the three waves indicated configural invariance as indicated by the acceptable fit indices, comparative fit index (CFI) 97; root mean square error of approximation (RMSEA) - .04, 90% CI 1.03, .06]. A chi-square difference test indicated that constraining the factor loadings to be equivalent across time did not diminish model fit, indicating metric invariance, Ax (6) 10.18, p=ns. We assessed exploration using an observed mean score across the four items, el12 Del Toro, Hughes, and Way which were coded such that higher values indicated Ax(4)-5.97, p=ns. The resulting measure was an more exploration. observed mean score of the three items, which were coded such that higher values indicated positive evaluations toward one's ethnic-racial group. Ethnic-Racial Identity Commitment We assessed commitment using a four-item mea- sure derived from the MEIM identity achievement subscale, which assessed ethnic-racial identity com- mitment and affirmation (Phinney, 1992). We omit- ted the three affirmation items from the original seven-item achievement measure because they were redundant with items that assessed private regard (e.g., "I am happy that I am a member of the eth- nic/racial group I belong to"). Thus, the items cap- tured the construct of commitment only (e.g., "I have a strong sense of belonging to my own eth- nic/racial group"). Students rated each item on a 5- point Likert scale (1 strongly disagree, 5-strongly agree). The internal consistency/reliability was ade- quate across the three waves of study for each eth- nic-racial group (merange Black - 83-85; ime range Dominican = 88-89; time-range Chi- nese - 77-85). A confirmatory factor analysis of these items across the three assessments indicated configural invariance, CFI .98; RMSEA .06, 90% CI [.04, 07]. A chi-square difference test indicated metric invariance, as constraining the factor load- ings to be equivalent across time did not result in a significant decrement in model fit, Ax²(6) - 6.44, p= ns. The measure was a unit-weighted mean score across the four items, which were coded such that higher values indicated higher commitment. Private Regard We used the private regard subscale of the Mul- tidimensional Inventory of Black Identity (MIBI)- Teen (Scottham, Sellers, & Nguyên 2008) which consisted of three items, with minor revisions such that references to "Black" were re-worded as refer- ences to "my ethnic/racial group." Students rated items on a 5-point Likert scale (eg, "I feel good about people from my ethnic/racial group"; 1-strongly disagree, 5-strongly agree). The internal consistency/reliability was acceptable across the three assessments for each ethnic-racial group (time range Black-76-78; imerange Domini- can = 81-85; time range Chinese - .79-87). A con- firmatory factor analysis indicated configural invariance across the three assessments, CFI- .99; RMSEA - .04, 90% CI [.02, .06]. A chi-square differ- ence test indicated metric invariance as factor load- ings constrained to be equivalent across time did not result in a significant decrement in model fit, Public Regard We assessed public regard using three items from the MIBI-Teen (Scottham et al., 2008). Adoles- cents indicated the extent to which they felt others value their group on a 5-point Likert scale (e.g., "A lot of people don't expect my ethnic/racial group to do well in life"; 1- strongly disagree, 5- strongly agree). The internal consistency was adequate across the three waves of the study and across the three ethnic-racial groups (me-range Black - 76-87; ime range Dominican - 74-0.86; imerange Chi- nese - 79-85). Fit indices from a confirmatory fac- tor analysis met the criteria for configural invariance across the three assessments, CFI = .99; RMSEA = .02, 90% CI [.00, .04]. A chi-square differ- ence test indicated metric invariance, as constrain- ing the factor loadings to be equivalent across time did not result in a significant decrement in model fit, Az(4)-1.67, p=ns. The resulting measure was a unit-weighted average of the three items, which were coded such that higher values indicated ado- lescents' perceived positive evaluations of others toward one's ethnic-racial group. Ethnic-Racial Discrimination Items assessing perceived ethnic-racial discrimi- nation were adapted from measures used in prior studies (Greene, Way, & Pahl, 2006; Hughes, Del Toro, Harding, Way, & Rarick, 2016; Hughes & Johnson, 2001; Williams, Neighbors, & Jackson, 2003). Adolescents responded to items that assessed varied manifestations of covert and overt discrimi- nation. We used the term covert discrimination to refer to perceptions that one has been the target of often unconscious negative attitudes and stereo- types pertaining to one's ethnic-racial group (e.g. others seeming uncomfortable around or afraid of you because of race or ethnicity), whereas we use the term overt discrimination to refer to instances of concrete and visible discrimination (e.g., name call- ing, bullying). The wording of items explicitly spec- ified the source of ethnic-racial discrimination (peers, adults in school, adults outside of school), but items regarding different sources appeared in separate parts of the survey. The measure of dis- crimination from adults in school had substantial missing data in sixth grade as well as a low mean

Ciccarelli: Psychology_5 (5th Edition)
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Author:Saundra K. Ciccarelli, J. Noland White
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Chapter1: The Science Of Psychology
Section: Chapter Questions
Problem 1TY
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Related questions
Question

Who were the participants and what were the measures? What was the research design?

neighborhood contexts during the middle school
years, which has been described elsewhere (Hughes
et al., 2008). The analytic sample consisted of 387 ado-
lescents who identified as Black (n = 149; 38.5%),
Dominican American (n y = 114; 29.5%), and Chinese
American (n = 124; 32.0%; see Supporting Information
for exclusionary criteria). Table 1 presents the demo-
graphic characteristics for each ethnic-racial group by
sex, maternal education, school, and immigrant status.
There were no differences in the gender composition
of adolescents from the three ethnic-racial groups,
x²(2) = 2.47, p = ns. Relative to Black and Dominican
American youth, Chinese American youth were more
likely to come from households with mothers who
were less well educated, F(2, 388) = 9.38, p <.001.
Black and Dominican American youth in the sample
were represented at all six schools, and Chinese
American youth were represented at three of the six
schools. Black youth were significantly more likely to
identify as native-origin (third generation) than were
their Dominican American and Chinese American
peers, x²(2) = 165.74, p <.001.
Procedure
The procedure for the present study is described
in detail elsewhere (Hughes et al., 2008). Principal
investigators first identified public middle schools
in which at least three of the four ethnic-racial
Table 1
Demographic Characteristics in Percent of the Analytic sample
(n = 387) Presented by Ethnicity-Race
Demographic characteris-
tics
Gender
%Girls
%Boys
Schools (diversity score)
%School 1 (0.74)
%School 2 (0.69)
%School 3 (0.44)
%School 4 (0.33)
%School 5 (0.70)
%School 6 (0.50)
Maternal education
%Less than high school
%High school
%Some college
%Bachelors or beyond
Generation status
%Native-origin
%Immigrant-origin
Black
(n = 149)
56.4
43.6
2.0
45.0
25.5
9.4
10.7
7.4
4.0
23.5
19.5
53.0
66.4
33.6
Dominican
(n = 114)
50.0
50.0
0.9
36.8
5.3
8.8
19.3
28.9
11.4
24.6
14.0
50.0
6.1
93.9
Chinese
(n = 124)
47.6
52.4
15.3
8.1
0.0
75.8
0.8
0.0
17.7
34.7
10.5
37.1
4.8
95.2
Ethnic-Racial Identity and Discrimination e111
groups initially targeted for the larger study (i.e.,
Black, Dominican American, Chinese American,
and White) constituted 20% or more of the student
population. Each of the six schools we initially
approached agreed to participate in the study and
all had a sixth-through-eighth-grade structure. We
recruited students in all non-English as a second
language sixth grade classrooms at the first assess-
ment. For the seventh and eighth grade assess-
ments, we permitted non-participating students to
enter the study. Research assistants distributed and
collected consent forms for a 2- to 3-week period in
students' homeroom classes. The principal investi-
gators provided students with a small non-mone-
tary incentive for their participation. Overall, 77%
of recruited adolescents returned parental consent
forms and 78% of those had affirmative parental
consent. We administered surveys in the spring of
sixth, seventh, and eighth grades during two class
periods that the school principal and teachers
deemed appropriate. We collected data from two
cohorts of adolescents; Cohort 1 was recruited in
2005 when students were sixth graders (n
188),
and Cohort 2 included adolescents recruited as
sixth graders in 2006 (n = 199).
Measures
Ethnic-Racial Identity Exploration
We used a four-item measure, derived from the
Multigroup Ethnic Identity Measure (MEIM; Phin-
ney, 1992), to assess ethnic-racial identity explo-
ration. Adolescents indicated the extent to which
they questioned or sought information about their
ethnicity-race using a 5-point Likert scale (e.g., "In
order to learn more about my ethnic/racial back-
ground, I have often talked to other people about
my ethnic/racial group"; 1= strongly disagree,
5= strongly agree). Internal consistency/reliability of
the four-item measure was adequate across the
three waves of study for each ethnic-racial group
(time-range Black = .66-.84; time-range Domini-
can = .69-.75; αtime-range Chinese = .70-81). A con-
firmatory factor analysis of the four items across
the three waves indicated configural invariance as
indicated by the acceptable fit indices, comparative
fit index (CFI) = .97; root mean square error of
approximation (RMSEA) = .04, 90% CI [.03, .06]. A
chi-square difference test indicated that constraining
the factor loadings to be equivalent across time did
not diminish model fit, indicating metric invariance,
Ax²(6) 10.18, p= ns. We assessed exploration
using an observed mean score across the four items,
e112 Del Toro, Hughes, and Way
which were coded such that higher values indicated
more exploration.
Ethnic-Racial Identity Commitment
We assessed commitment using a four-item mea-
sure derived from the MEIM identity achievement
subscale, which assessed ethnic-racial identity com-
mitment and affirmation (Phinney, 1992). We omit-
ted the three affirmation items from the original
seven-item achievement measure because they were
redundant with items that assessed private regard
(e.g., "I am happy that I am a member of the eth-
nic/racial group I belong to"). Thus, the items cap-
tured the construct of commitment only (e.g., “I
have a strong sense of belonging to my own eth-
nic/racial group"). Students rated each item on a 5-
point Likert scale (1 = strongly disagree, 5 = strongly
agree). The internal consistency/reliability was ade-
quate across the three waves of study for each eth-
nic-racial group (time-range Black = .83-.85; time™
range
Dominican = .88-.89;
time-range Chi-
nese = .77-.85). A confirmatory factor analysis of
these items across the three assessments indicated
configural invariance, CFI= .98; RMSEA = .06, 90%
CI [.04, .07]. A chi-square difference test indicated
metric invariance, as constraining the factor load-
ings to be equivalent across time did not result in a
significant decrement in model fit, Ax²(6) = 6.44,
p = ns. The measure was a unit-weighted mean
score across the four items, which were coded such
that higher values indicated higher commitment.
Private Regard
We used the private regard subscale of the Mul-
tidimensional Inventory of Black Identity (MIBI)-
Teen (Scottham, Sellers, & Nguyên, 2008) which
consisted of three items, with minor revisions such
that references to "Black" were re-worded as refer-
ences to "my ethnic/racial group." Students rated
items on a 5-point Likert scale (e.g., "I feel good
about people from my ethnic/racial group";
1 = strongly disagree, 5 = strongly agree). The internal
consistency/reliability was acceptable across the
three assessments for each ethnic-racial group
(time-range Black = .76-.78; time-range Domini-
can = .81-.85; otime-range Chinese = .79-87). A con-
firmatory factor analysis indicated configural
invariance across the three assessments, CFI = .99;
RMSEA = .04, 90% CI [.02, .06]. A chi-square differ-
ence test indicated metric invariance as factor load-
ings constrained to be equivalent across time did
not result in a significant decrement in model fit,
Ax² (4) = 5.97, p = ns. The resulting measure was an
observed mean score of the three items, which were
coded such that higher values indicated positive
evaluations toward one's ethnic-racial group.
Public Regard
We assessed public regard using three items
from the MIBI-Teen (Scottham et al., 2008). Adoles-
cents indicated the extent to which they felt others
value their group on a 5-point Likert scale (e.g., "A
lot of people don't expect my ethnic/racial group
to do well in life"; 1 = strongly disagree, 5 = strongly
agree). The internal consistency was adequate across
the three waves of the study and across the three
ethnic-racial groups (time-range Black = .76-87;
time-range Dominican = .74-0.86; time-range Chi-
nese = .79-.85). Fit indices from a confirmatory fac-
tor analysis met the criteria for configural
invariance across the three assessments, CFI = .99;
RMSEA = .02, 90% CI [.00, .04]. A chi-square differ-
ence test indicated metric invariance, as constrain-
ing the factor loadings to be equivalent across time
did not result in a significant decrement in model
fit, Ax²(4) = 1.67, p = ns. The resulting measure was
a unit-weighted average of the three items, which
were coded such that higher values indicated ado-
lescents' perceived positive evaluations of others
toward one's ethnic-racial group.
Ethnic-Racial Discrimination
Items assessing perceived ethnic-racial discrimi-
nation were adapted from measures used in prior
studies (Greene, Way, & Pahl, 2006; Hughes, Del
Toro, Harding, Way, & Rarick, 2016; Hughes &
Johnson, 2001; Williams, Neighbors, & Jackson,
2003). Adolescents responded to items that assessed
varied manifestations of covert and overt discrimi-
nation. We used the term covert discrimination to
refer to perceptions that one has been the target of
often unconscious negative attitudes and stereo-
types pertaining to one's ethnic-racial group (e.g.,
others seeming uncomfortable around or afraid of
you because of race or ethnicity), whereas we use
the term overt discrimination to refer to instances of
concrete and visible discrimination (e.g., name call-
ing, bullying). The wording of items explicitly spec-
ified the source of ethnic-racial discrimination
(peers, adults in school, adults outside of school),
but items regarding different sources appeared in
separate parts of the survey. The measure of dis-
crimination from adults in school had substantial
missing data in sixth grade as well as a low mean
Transcribed Image Text:neighborhood contexts during the middle school years, which has been described elsewhere (Hughes et al., 2008). The analytic sample consisted of 387 ado- lescents who identified as Black (n = 149; 38.5%), Dominican American (n y = 114; 29.5%), and Chinese American (n = 124; 32.0%; see Supporting Information for exclusionary criteria). Table 1 presents the demo- graphic characteristics for each ethnic-racial group by sex, maternal education, school, and immigrant status. There were no differences in the gender composition of adolescents from the three ethnic-racial groups, x²(2) = 2.47, p = ns. Relative to Black and Dominican American youth, Chinese American youth were more likely to come from households with mothers who were less well educated, F(2, 388) = 9.38, p <.001. Black and Dominican American youth in the sample were represented at all six schools, and Chinese American youth were represented at three of the six schools. Black youth were significantly more likely to identify as native-origin (third generation) than were their Dominican American and Chinese American peers, x²(2) = 165.74, p <.001. Procedure The procedure for the present study is described in detail elsewhere (Hughes et al., 2008). Principal investigators first identified public middle schools in which at least three of the four ethnic-racial Table 1 Demographic Characteristics in Percent of the Analytic sample (n = 387) Presented by Ethnicity-Race Demographic characteris- tics Gender %Girls %Boys Schools (diversity score) %School 1 (0.74) %School 2 (0.69) %School 3 (0.44) %School 4 (0.33) %School 5 (0.70) %School 6 (0.50) Maternal education %Less than high school %High school %Some college %Bachelors or beyond Generation status %Native-origin %Immigrant-origin Black (n = 149) 56.4 43.6 2.0 45.0 25.5 9.4 10.7 7.4 4.0 23.5 19.5 53.0 66.4 33.6 Dominican (n = 114) 50.0 50.0 0.9 36.8 5.3 8.8 19.3 28.9 11.4 24.6 14.0 50.0 6.1 93.9 Chinese (n = 124) 47.6 52.4 15.3 8.1 0.0 75.8 0.8 0.0 17.7 34.7 10.5 37.1 4.8 95.2 Ethnic-Racial Identity and Discrimination e111 groups initially targeted for the larger study (i.e., Black, Dominican American, Chinese American, and White) constituted 20% or more of the student population. Each of the six schools we initially approached agreed to participate in the study and all had a sixth-through-eighth-grade structure. We recruited students in all non-English as a second language sixth grade classrooms at the first assess- ment. For the seventh and eighth grade assess- ments, we permitted non-participating students to enter the study. Research assistants distributed and collected consent forms for a 2- to 3-week period in students' homeroom classes. The principal investi- gators provided students with a small non-mone- tary incentive for their participation. Overall, 77% of recruited adolescents returned parental consent forms and 78% of those had affirmative parental consent. We administered surveys in the spring of sixth, seventh, and eighth grades during two class periods that the school principal and teachers deemed appropriate. We collected data from two cohorts of adolescents; Cohort 1 was recruited in 2005 when students were sixth graders (n 188), and Cohort 2 included adolescents recruited as sixth graders in 2006 (n = 199). Measures Ethnic-Racial Identity Exploration We used a four-item measure, derived from the Multigroup Ethnic Identity Measure (MEIM; Phin- ney, 1992), to assess ethnic-racial identity explo- ration. Adolescents indicated the extent to which they questioned or sought information about their ethnicity-race using a 5-point Likert scale (e.g., "In order to learn more about my ethnic/racial back- ground, I have often talked to other people about my ethnic/racial group"; 1= strongly disagree, 5= strongly agree). Internal consistency/reliability of the four-item measure was adequate across the three waves of study for each ethnic-racial group (time-range Black = .66-.84; time-range Domini- can = .69-.75; αtime-range Chinese = .70-81). A con- firmatory factor analysis of the four items across the three waves indicated configural invariance as indicated by the acceptable fit indices, comparative fit index (CFI) = .97; root mean square error of approximation (RMSEA) = .04, 90% CI [.03, .06]. A chi-square difference test indicated that constraining the factor loadings to be equivalent across time did not diminish model fit, indicating metric invariance, Ax²(6) 10.18, p= ns. We assessed exploration using an observed mean score across the four items, e112 Del Toro, Hughes, and Way which were coded such that higher values indicated more exploration. Ethnic-Racial Identity Commitment We assessed commitment using a four-item mea- sure derived from the MEIM identity achievement subscale, which assessed ethnic-racial identity com- mitment and affirmation (Phinney, 1992). We omit- ted the three affirmation items from the original seven-item achievement measure because they were redundant with items that assessed private regard (e.g., "I am happy that I am a member of the eth- nic/racial group I belong to"). Thus, the items cap- tured the construct of commitment only (e.g., “I have a strong sense of belonging to my own eth- nic/racial group"). Students rated each item on a 5- point Likert scale (1 = strongly disagree, 5 = strongly agree). The internal consistency/reliability was ade- quate across the three waves of study for each eth- nic-racial group (time-range Black = .83-.85; time™ range Dominican = .88-.89; time-range Chi- nese = .77-.85). A confirmatory factor analysis of these items across the three assessments indicated configural invariance, CFI= .98; RMSEA = .06, 90% CI [.04, .07]. A chi-square difference test indicated metric invariance, as constraining the factor load- ings to be equivalent across time did not result in a significant decrement in model fit, Ax²(6) = 6.44, p = ns. The measure was a unit-weighted mean score across the four items, which were coded such that higher values indicated higher commitment. Private Regard We used the private regard subscale of the Mul- tidimensional Inventory of Black Identity (MIBI)- Teen (Scottham, Sellers, & Nguyên, 2008) which consisted of three items, with minor revisions such that references to "Black" were re-worded as refer- ences to "my ethnic/racial group." Students rated items on a 5-point Likert scale (e.g., "I feel good about people from my ethnic/racial group"; 1 = strongly disagree, 5 = strongly agree). The internal consistency/reliability was acceptable across the three assessments for each ethnic-racial group (time-range Black = .76-.78; time-range Domini- can = .81-.85; otime-range Chinese = .79-87). A con- firmatory factor analysis indicated configural invariance across the three assessments, CFI = .99; RMSEA = .04, 90% CI [.02, .06]. A chi-square differ- ence test indicated metric invariance as factor load- ings constrained to be equivalent across time did not result in a significant decrement in model fit, Ax² (4) = 5.97, p = ns. The resulting measure was an observed mean score of the three items, which were coded such that higher values indicated positive evaluations toward one's ethnic-racial group. Public Regard We assessed public regard using three items from the MIBI-Teen (Scottham et al., 2008). Adoles- cents indicated the extent to which they felt others value their group on a 5-point Likert scale (e.g., "A lot of people don't expect my ethnic/racial group to do well in life"; 1 = strongly disagree, 5 = strongly agree). The internal consistency was adequate across the three waves of the study and across the three ethnic-racial groups (time-range Black = .76-87; time-range Dominican = .74-0.86; time-range Chi- nese = .79-.85). Fit indices from a confirmatory fac- tor analysis met the criteria for configural invariance across the three assessments, CFI = .99; RMSEA = .02, 90% CI [.00, .04]. A chi-square differ- ence test indicated metric invariance, as constrain- ing the factor loadings to be equivalent across time did not result in a significant decrement in model fit, Ax²(4) = 1.67, p = ns. The resulting measure was a unit-weighted average of the three items, which were coded such that higher values indicated ado- lescents' perceived positive evaluations of others toward one's ethnic-racial group. Ethnic-Racial Discrimination Items assessing perceived ethnic-racial discrimi- nation were adapted from measures used in prior studies (Greene, Way, & Pahl, 2006; Hughes, Del Toro, Harding, Way, & Rarick, 2016; Hughes & Johnson, 2001; Williams, Neighbors, & Jackson, 2003). Adolescents responded to items that assessed varied manifestations of covert and overt discrimi- nation. We used the term covert discrimination to refer to perceptions that one has been the target of often unconscious negative attitudes and stereo- types pertaining to one's ethnic-racial group (e.g., others seeming uncomfortable around or afraid of you because of race or ethnicity), whereas we use the term overt discrimination to refer to instances of concrete and visible discrimination (e.g., name call- ing, bullying). The wording of items explicitly spec- ified the source of ethnic-racial discrimination (peers, adults in school, adults outside of school), but items regarding different sources appeared in separate parts of the survey. The measure of dis- crimination from adults in school had substantial missing data in sixth grade as well as a low mean
and variance across waves, and thus we excluded it
from the analysis. Missingness stemmed from
instances in which (a) a teacher remained present in
the classroom during survey administration or (b)
research assistants ran out of time during survey
administration and did not complete all measures
in the protocol. For these reasons, we used items
pertaining to ethnic-racial discrimination from peers
and non-school adults in the present analysis. For
each measure, adolescents rated items on a 5-point
Likert scale (0 = never; 4 = all the time). An explora-
tory factor analysis using the sixth-grade assess-
ment indicated that a three-factor solution best
represented the data, with 16 items pertaining to
non-school adults loading on a single factor and 18
items pertaining to peers loading on two separate
factors representing overt (nine items) and covert
(nine items) ethnic-racial discrimination, CFI = .99;
RMSEA = .03, 90% CI [.02, .03]. However, measures
of covert and overt types of peer ethnic-racial dis-
crimination were highly correlated at each wave (r-
range = .75-.82). Moreover, preliminary analyses
revealed that the final results did not vary by type
of peer ethnic-racial discrimination. Thus, we com-
bined items pertaining to overt and covert peer eth-
nic-racial discrimination into a single measure.
Internal consistency/reliability across the three
ethnic-racial groups was adequate for the measure
of discrimination from peers (αtime-range Black =
.93-98; dtime-range Dominican = .93-97; time-range
Chinese = 95-97) and for the measure of ethnic-
racial discrimination from non-school adults
(time-range Black = .95-96; time-range Domini-
can 94-.95; time-range Chinese = .92-.97). High
scores on each measure indicated more frequent
perceptions of ethnic-racial discrimination. In prior
work, trajectories of these measures of discrimina-
tion predicted academic, behavioral, and psycholog-
ical adjustment (Hughes, Del Toro et al., 2016).
Ethnicity-Race
Adolescents indicated their ethnicity-race multi-
ple times throughout the survey in each of the
3 years using both open-ended (e.g., "Please write
down the ethnic-racial group you identify with
most often") and closed-ended formats (e.g., "Are
you... White, Black or African American, Domini-
can or Dominican American, Puerto Rican, Mexican
or Mexican American, Chinese or Chinese Ameri-
can,
Other ethnicity-race"). The majority of
responses were consistent across time, but coders
resolved inconsistencies by categorizing adolescents
according to the self-label they used most often. As
Ethnic-Racial Identity and Discrimination e113
an example, Dominican American adolescent's
responses ranged from D.R., Dominican Republic,
Dominican American but all were coded as
Dominican American. One adolescent self-identified
as "African American" and "Dominican" in varied
waves but was coded as "Dominican" in accor-
dance with the mothers' identification of her child
as being "Dominican."
Covariates
In all primary analyses, we adjusted for demo-
graphic variables that have been associated with
measures of ethnic-racial identity, ethnic-racial dis-
crimination, or both in prior studies. Including sta-
tistical controls for these variables in the
autoregressive models reduced the possibility that
the ethnic-racial discrimination-identity relations
were due to an unmeasured third variable. Demo-
graphic controls included sex (0 = girl; 1= boy),
cohort (1 = Cohort 1; 2= Cohort 2), immigration sta-
tus (0 = both biological parents and the adolescent were
US born; 1 = at least one biological parent or the adoles-
cent was born abroad), and maternal education
(1= less than a high school degree; 4= a bachelor's
degree or more advanced). We also included the eth-
nic-racial diversity index (Benner & Graham, 2011),
which represents the probability of youth interact-
ing with student-peers of different ethnic-racial
groups
(0 = greater ethnic-racial homogeneity,
1 = greater ethnic-racial heterogeneity). Notably, due
to the fact that school records only provided infor-
mation on whether students were Black, Asian,
White, or Latino, the diversity index was based on
those pan-ethnic categories. Finally, we included
Rosenberg (1965)'s measure of self-esteem as a
covariate in all analyses, due to the fact that self-es-
teem has been associated with both perceived dis-
crimination (Harris-Britt, Valrie, Kurtz-Costes, &
Rowley, 2007; Verkuyten, 1998) and with compo-
nents of ethnic-racial identity (Umaña-Taylor, Var-
gas-Chanes, Garcia, & Gonzales-Backen, 2008). We
used self-esteem as measured in the sixth grade
because, on average, adolescents showed no change
in self-esteem across the three waves.
Missing Data
Missing data is a common challenge in many
longitudinal studies, including the present study.
Among the analytic sample of 387, 240 adolescents
(61.5%) contributed data at all three waves, whereas
147 (28.5%) contributed data for only two waves.
Among those with two waves of data, 77 (52%)
were recruited in seventh grade and returned to the
e114 Del Toro, Hughes, and Way
study in eighth grade. An additional 21 adolescents
(15%) participated in sixth grade, did not partici-
pate in seventh grade, but returned in eighth grade.
In all, 49 adolescents participated in sixth and sev-
enth grades but did not return to the study in
eighth grade. These two groups, one with all data
and the other with two waves of data, were
retained in the study as they were able to con-
tribute to the longitudinal parameter estimates.
Independent samples t tests comparing the 240
early adolescents with complete data to the 147
early adolescents with two waves of data on all
major constructs at each wave plus covariates indi-
cated that the two groups of students differed reli-
ably in one of the 25 independent samples t tests:
students with complete data reported greater explo-
ration in the eighth-grade (M = 2.79, SE = .06) than
their peers with incomplete data at the same assess-
ment (M = 2.57, SE= .08), t(332) = 2.13, p < .05.
According to Baraldi and Enders (2010), multiple
imputation and full information maximum likeli-
hood (FIML) perform better than other missing data
approaches (e.g., listwise deletion of cases with
missing values, or singly imputing missing values)
in the context of missing at random. Results using
FIML were similar to those using multiple imputa-
tion; thus, FIML results were retained and pre-
sented. Specifically, we retained all 387 using
maximum likelihood with robust standard errors,
which simultaneously estimates parameters using
all available data and estimates robust standard
errors in the context of non-normally distributed
Adult Ethnic-Racial
Discrimination
Sixth Grade
Ethnic-Racial Identity
Exploration
Sixth Grade
Peer Ethnic-Racial
Discrimination
Sixth Grade
data (Asparouhov & Muthén, 2010), which is the
case for adolescents' self-reports of ethnic-racial dis-
crimination in the present study.
Analytic Approach
We conducted all analyses in Mplus Version 8.3
(Muthén & Muthén, 1998-2019). We first examined
descriptive data on all key study variables at each
assessment, including means, standard deviations,
zero-order correlations, and ethnic-racial group dif-
ferences. To examine the longitudinal relations
between peer versus adult ethnic-racial discrimina-
tion and each component of ethnic-racial identity,
we estimated four cross-lagged and autoregressive
path models, one for each identity component
(Maxwell, Cole, & Mitchell, 2011; see Figure 1 for a
visual depiction). Separate models were estimated
to maintain model parsimony and because we had
insufficient statistical power to simultaneously
account for all components of identity in a single
equation. Each model included autoregressive paths
for each construct, covariation among the three con-
structs within each wave, and cross-lagged paths
among peer ethnic-racial discrimination, non-school
adult ethnic-racial discrimination, and each identity
component. In Supporting Information, we reported
our approach in model construction prior to testing
the primary research questions. To examine the
temporal ordering between discrimination and
identity, we tested whether constraining the cross-
lagged paths between two constructs to be
Adult Ethnic-Racial
Discrimination
Seventh Grade
Ethnic-Racial Identity
Exploration
Seventh Grade
Peer Ethnic-Racial
Discrimination
Seventh Grade
Adult Ethnic-Racial
Discrimination
Eighth Grade
Ethnic-Racial Identity
Exploration
Eighth Grade
Peer Ethnic-Racial
Discrimination
Eighth Grade
Figure 1. A visual depiction of a cross-lagged and autoregressive path model that examines the inter-relations among adult ethnic-racial
discrimination, ethnic-racial identity exploration, and peer ethnic-racial discrimination.
Transcribed Image Text:and variance across waves, and thus we excluded it from the analysis. Missingness stemmed from instances in which (a) a teacher remained present in the classroom during survey administration or (b) research assistants ran out of time during survey administration and did not complete all measures in the protocol. For these reasons, we used items pertaining to ethnic-racial discrimination from peers and non-school adults in the present analysis. For each measure, adolescents rated items on a 5-point Likert scale (0 = never; 4 = all the time). An explora- tory factor analysis using the sixth-grade assess- ment indicated that a three-factor solution best represented the data, with 16 items pertaining to non-school adults loading on a single factor and 18 items pertaining to peers loading on two separate factors representing overt (nine items) and covert (nine items) ethnic-racial discrimination, CFI = .99; RMSEA = .03, 90% CI [.02, .03]. However, measures of covert and overt types of peer ethnic-racial dis- crimination were highly correlated at each wave (r- range = .75-.82). Moreover, preliminary analyses revealed that the final results did not vary by type of peer ethnic-racial discrimination. Thus, we com- bined items pertaining to overt and covert peer eth- nic-racial discrimination into a single measure. Internal consistency/reliability across the three ethnic-racial groups was adequate for the measure of discrimination from peers (αtime-range Black = .93-98; dtime-range Dominican = .93-97; time-range Chinese = 95-97) and for the measure of ethnic- racial discrimination from non-school adults (time-range Black = .95-96; time-range Domini- can 94-.95; time-range Chinese = .92-.97). High scores on each measure indicated more frequent perceptions of ethnic-racial discrimination. In prior work, trajectories of these measures of discrimina- tion predicted academic, behavioral, and psycholog- ical adjustment (Hughes, Del Toro et al., 2016). Ethnicity-Race Adolescents indicated their ethnicity-race multi- ple times throughout the survey in each of the 3 years using both open-ended (e.g., "Please write down the ethnic-racial group you identify with most often") and closed-ended formats (e.g., "Are you... White, Black or African American, Domini- can or Dominican American, Puerto Rican, Mexican or Mexican American, Chinese or Chinese Ameri- can, Other ethnicity-race"). The majority of responses were consistent across time, but coders resolved inconsistencies by categorizing adolescents according to the self-label they used most often. As Ethnic-Racial Identity and Discrimination e113 an example, Dominican American adolescent's responses ranged from D.R., Dominican Republic, Dominican American but all were coded as Dominican American. One adolescent self-identified as "African American" and "Dominican" in varied waves but was coded as "Dominican" in accor- dance with the mothers' identification of her child as being "Dominican." Covariates In all primary analyses, we adjusted for demo- graphic variables that have been associated with measures of ethnic-racial identity, ethnic-racial dis- crimination, or both in prior studies. Including sta- tistical controls for these variables in the autoregressive models reduced the possibility that the ethnic-racial discrimination-identity relations were due to an unmeasured third variable. Demo- graphic controls included sex (0 = girl; 1= boy), cohort (1 = Cohort 1; 2= Cohort 2), immigration sta- tus (0 = both biological parents and the adolescent were US born; 1 = at least one biological parent or the adoles- cent was born abroad), and maternal education (1= less than a high school degree; 4= a bachelor's degree or more advanced). We also included the eth- nic-racial diversity index (Benner & Graham, 2011), which represents the probability of youth interact- ing with student-peers of different ethnic-racial groups (0 = greater ethnic-racial homogeneity, 1 = greater ethnic-racial heterogeneity). Notably, due to the fact that school records only provided infor- mation on whether students were Black, Asian, White, or Latino, the diversity index was based on those pan-ethnic categories. Finally, we included Rosenberg (1965)'s measure of self-esteem as a covariate in all analyses, due to the fact that self-es- teem has been associated with both perceived dis- crimination (Harris-Britt, Valrie, Kurtz-Costes, & Rowley, 2007; Verkuyten, 1998) and with compo- nents of ethnic-racial identity (Umaña-Taylor, Var- gas-Chanes, Garcia, & Gonzales-Backen, 2008). We used self-esteem as measured in the sixth grade because, on average, adolescents showed no change in self-esteem across the three waves. Missing Data Missing data is a common challenge in many longitudinal studies, including the present study. Among the analytic sample of 387, 240 adolescents (61.5%) contributed data at all three waves, whereas 147 (28.5%) contributed data for only two waves. Among those with two waves of data, 77 (52%) were recruited in seventh grade and returned to the e114 Del Toro, Hughes, and Way study in eighth grade. An additional 21 adolescents (15%) participated in sixth grade, did not partici- pate in seventh grade, but returned in eighth grade. In all, 49 adolescents participated in sixth and sev- enth grades but did not return to the study in eighth grade. These two groups, one with all data and the other with two waves of data, were retained in the study as they were able to con- tribute to the longitudinal parameter estimates. Independent samples t tests comparing the 240 early adolescents with complete data to the 147 early adolescents with two waves of data on all major constructs at each wave plus covariates indi- cated that the two groups of students differed reli- ably in one of the 25 independent samples t tests: students with complete data reported greater explo- ration in the eighth-grade (M = 2.79, SE = .06) than their peers with incomplete data at the same assess- ment (M = 2.57, SE= .08), t(332) = 2.13, p < .05. According to Baraldi and Enders (2010), multiple imputation and full information maximum likeli- hood (FIML) perform better than other missing data approaches (e.g., listwise deletion of cases with missing values, or singly imputing missing values) in the context of missing at random. Results using FIML were similar to those using multiple imputa- tion; thus, FIML results were retained and pre- sented. Specifically, we retained all 387 using maximum likelihood with robust standard errors, which simultaneously estimates parameters using all available data and estimates robust standard errors in the context of non-normally distributed Adult Ethnic-Racial Discrimination Sixth Grade Ethnic-Racial Identity Exploration Sixth Grade Peer Ethnic-Racial Discrimination Sixth Grade data (Asparouhov & Muthén, 2010), which is the case for adolescents' self-reports of ethnic-racial dis- crimination in the present study. Analytic Approach We conducted all analyses in Mplus Version 8.3 (Muthén & Muthén, 1998-2019). We first examined descriptive data on all key study variables at each assessment, including means, standard deviations, zero-order correlations, and ethnic-racial group dif- ferences. To examine the longitudinal relations between peer versus adult ethnic-racial discrimina- tion and each component of ethnic-racial identity, we estimated four cross-lagged and autoregressive path models, one for each identity component (Maxwell, Cole, & Mitchell, 2011; see Figure 1 for a visual depiction). Separate models were estimated to maintain model parsimony and because we had insufficient statistical power to simultaneously account for all components of identity in a single equation. Each model included autoregressive paths for each construct, covariation among the three con- structs within each wave, and cross-lagged paths among peer ethnic-racial discrimination, non-school adult ethnic-racial discrimination, and each identity component. In Supporting Information, we reported our approach in model construction prior to testing the primary research questions. To examine the temporal ordering between discrimination and identity, we tested whether constraining the cross- lagged paths between two constructs to be Adult Ethnic-Racial Discrimination Seventh Grade Ethnic-Racial Identity Exploration Seventh Grade Peer Ethnic-Racial Discrimination Seventh Grade Adult Ethnic-Racial Discrimination Eighth Grade Ethnic-Racial Identity Exploration Eighth Grade Peer Ethnic-Racial Discrimination Eighth Grade Figure 1. A visual depiction of a cross-lagged and autoregressive path model that examines the inter-relations among adult ethnic-racial discrimination, ethnic-racial identity exploration, and peer ethnic-racial discrimination.
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Step 1: Introducing Research and significance of measures in research

Research design refers to the structured plan or blueprint that researchers follow to investigate a specific research question or hypothesis. It encompasses the overall strategy for collecting, analysing, and interpreting data. The choice of research design depends on the nature of the research question and the goals of the study. Researchers use samples because it is often impractical or impossible to study an entire population. Sampling methods are employed to ensure that the selected sample is representative and provides reliable information about the population. The size and characteristics of the sample are crucial for the generalizability of research findings to a broader population. Measures in research refer to the tools, instruments, or methods used to collect data on variables of interest. These measures can be surveys, questionnaires, observations, physiological tests, or any other means of data collection. Measures are essential because they allow researchers to quantify and analyse variables systematically.

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