vCase Study: Desert Viejo Elementary School

  1. Case Study: Joshua
  2. Case Study: Desert Viejo Elementary School

Create one 7-10-slide PowerPoint presentation outlining two interventions for the selected case study. One of the interventions must include Critical Incident Stress Debriefing (CISD). It is up to you to decide which type of intervention is best suited for each scenario. Include the following in your interventions:

  1. Step-by-step description of both interventions
  2. Rationale for choosing each intervention
  3. Community resources that are available in your local community that you would include as part of an intervention for the scenario.

Include a minimum of three scholarly reference

Case Study: Desert Viejo (DV) Elementary School

Demographics

DV Elementary School is situated in a working class neighborhood. It is a K-6 school and has student body of 1,200 children. The population of students is diverse in culture and race.

Disposition

This morning as the children were preparing to go to lunch, a distraught father entered the school yard. Earlier in the day, the distraught father “Ben” had had another argument about custody and visitation with his ex-wife. Ben’s daughter Sierra is an 8-year-old 4th grader at DV Elementary School.

When Ben entered the school grounds, he was carrying an M-16 assault rifle. Ben is a retired veteran who likes to collect guns. His M-16 was equipped with an extended clip, allowing him to carry over 50 rounds of ammunition in the clip attached to the gun. Ben eventually made his way into the school, then the front administrative office and emptied all 50 rounds on the occupants.

A group of students were passing by the front office as the assault began. One child was injured, but not fatally. When emergency services arrive and secure the locked down school, they count 5 fatalities. One of the fatalities was Ben, who took his own life with a hand gun after his killing spree.

The small group of children passing by the office at the time of the assault was the only children who witnessed the horror. The remainder of the student body and teachers report hearing “pops,” crashing, and “breaking stuff” from their classrooms.

© 2014. Grand Canyon University. All Rights Reserved.

Your employer has asked to prepare a presentation on intercultural communication and the importance of considering diversity in communication.

Your employer has asked to prepare a presentation on intercultural communication and the importance of considering diversity in communication. You must prepare a PowerPoint presentation, creating one slide for each of the topics below. Be sure to include your speaker notes in the Notes section below each slide. Include a minimum of two relevant citations throughout your presentation.

Slide 1: Title Slide

Slide 2: Explain how diverse contexts impact and intercultural communication.

Slide 3: Provide three reasons to consider intercultural communication.

Slide 4: Identify two examples of intercultural communication that should be considered.

Slide 5: Identify a diverse context issue that can be addressed using intercultural communication strategies.

Slide 6: Provide two intercultural guidelines for how to address the issues identified in Slide 5, while still demonstrating respect for those differences.

Slide 7: References

Writing Requirements and Assignment Guidelines

Your Assignment should be at least five slides, not including the Title and Reference slides, and should include the following elements:

  • Title slide
  • Body
  • Reference slide: Include at least two sources listed in APA format
  • Use Arial or Times New Roman 24-point font
  • Use APA Formatting and Citation style
    • assignment must be written in Standard English and demonstrate superior organization, including a highly developed viewpoint and purpose. Your notes below each slide in the body of your presentation should be written in complete sentences and paragraphs, supporting the content on each slide. Presentations are visual; therefore, use clear, concise bullet points and avoid crowded slides. Additionally, images and diagrams are wonderful presentation tools, but be sure to include all required citations.

explain whether these coping mechanisms were effective in reducing the level of stress you experienced.

a description of two coping mechanisms you have used (problem-focused, emotion-focused, or biology-focused) to reduce stress. Then explain whether these coping mechanisms were effective in reducing the level of stress you experienced. Why or why not? Finally, propose two other coping mechanisms you might employ that also may be effective in reducing stress levels and explain why. Be specific.

create an annotated bibliography and an outline, two documents that will help you create your research paper due in week 7.

Attached Files:

  • File Annotated Bibliography APA Style.pdf (145.483 KB)

Your major project (due week 7) will be a literature review on a topic related to your psychology program.  You will develop this paper in stages during Weeks 5, 6, and 7.

According to the APA manual (p. 10),

“Literature reviews, including research syntheses and meta-analyses, are critical evaluations of material that has already been published. In meta-analyses, authors use quantitative procedures to statistically combine the results of studies. By organizing, integrating,and evaluating previously published material, authors of literature reviews consider the progress of research toward clarifying a problem. In a sense, literature reviews are tutorials, in that authors

  • define and clarify the problem;
  • summarize previous investigations to inform the reader of the state of research;
  • identify relations, contradictions, gaps, and inconsistencies in the literature; and
  • suggest the next step or steps in solving the problem.

The components of literature reviews can be arranged in various ways (e.g., by grouping research based on similarity in the concepts or theories of interest, methodological similarities among the studies reviewed, or the historical development of the field).”

For this assignment, you will create an annotated bibliography and an outline, two documents that will help you create your research paper due in week 7.

  • The annotated bibliography should be formatted using the example provided.  For this week you are only required to have 5 peer-reviewed articles in your annotated bibliography, but you will need a total of 10 articles for your first draft in Week 6 and your final paper in Week 7.
  • The outline should include your paper topic and the anticipated sections for your paper (no more than 3 main sections). Under each area include detailed information, including the purpose of that section in relation to the paper. This outline is not complete or final, but should provide enough detailed information so that you may receive helpful feedback for this paper.

    ORIGINAL PAPER

    Anxiety Symptoms in African American Children: Relations with Ethnic Pride, Anxiety Sensitivity, and Parenting

    Calonie M. K. Gray • Rona Carter •

    Wendy K. Silverman

    Published online: 2 October 2010

    � Springer Science+Business Media, LLC 2010

    Abstract This cross-sectional study examined the rela-

    tions among children’s ethnic pride, perceived parenting

    behavior (i.e., parental control, parental acceptance), anx-

    iety sensitivity, and child anxiety symptoms (i.e., physical

    symptoms, social anxiety symptoms, separation anxiety

    symptoms, and harm avoidance symptoms) in 266 African

    American school children (M = 9.98 years old; 55% girls).

    Structural equation modeling results indicated that high

    ethnic pride was associated with high parental acceptance.

    High perceived parental acceptance, in turn, was related to

    children reporting low levels of social anxiety symptoms

    and high levels of harm avoidance. In addition, high

    parental control was related to high anxiety sensitivity.

    Anxiety sensitivity partially mediated the relation between

    parental control and separation anxiety symptoms, such

    that parental control was both directly and indirectly rela-

    ted to separation anxiety symptoms. Parental control was

    indirectly related to physical symptoms, social anxiety

    symptoms, and harm avoidance symptoms through its

    direct link to anxiety sensitivity. The study’s results

    increment knowledge about factors influencing specific

    dimensions of anxiety in African American children.

    Keywords Child anxiety � Anxiety sensitivity � African American children � Parenting behaviors � Ethnic pride

    Introduction

    Research has shown that African American and European

    American children have similar prevalence rates of anxiety

    symptoms (Angold et al. 2002); yet African Americans are

    less likely to present for treatment than European Ameri-

    cans (Neal and Ward-Brown 1994). Due to this differential

    in treatment seeking, African American children are not

    well represented in clinical research studies on anxiety.

    The primary aim of the present study was to broaden

    understanding of factors that are likely related to anxiety

    symptoms in African American children, but have not been

    studied within the same study and within the framework of

    a conceptual model. These factors were: children’s ethnic

    pride, children’s perceived parental acceptance and control,

    and children’s anxiety sensitivity and anxiety symptoms.

    We begin by first providing definitions and brief back-

    ground of the factors that were of interest in this study.

    We follow this with an explanation of how we view these

    variables relating to anxiety symptoms within our con-

    ceptual model.

    Ethnic pride refers to having positive attitudes toward

    one’s ethnic group, along with a feeling of belonging to,

    and affiliating with, one’s ethnic group as a central part of

    one’s ethnic identity (McCreary et al. 1996; Valk and Karu

    2001). Ethnic pride is a salient construct among African

    Americans and has been shown to demonstrate protective

    properties with youth psychosocial factors (e.g., Gaylord-

    Harden et al. 2007; Marsiglia et al. 2001).

    With respect to parenting, research has identified broad

    dimensions of parenting, acceptance versus rejection and

    C. M. K. Gray (&) Research, Analysis, and Planning, Children’s Services Council

    of Broward County, 6600 West Commercial Boulevard,

    Lauderhill, FL 33319, USA

    e-mail: cgray@cscbroward.org

    R. Carter

    Department of Psychology, University of Michigan, Ann Arbor,

    MI, USA

    W. K. Silverman

    Department of Psychology, Florida International University,

    Miami, FL, USA

    123

    J Child Fam Stud (2011) 20:205–213

    DOI 10.1007/s10826-010-9422-3

    granting of autonomy versus control, as important in the

    development and maintenance of anxiety and its disorders

    (e.g., Chorpita et al. 1998; Craske 1999; Rapee 1997).

    Parental acceptance is defined as parents’ expression of

    warmth and responsiveness to children’s emotions and

    behavior, and is viewed as promoting children’s emotion

    regulation and willingness to explore their environment

    and learn, thereby decreasing their anxiety (Gottman et al.

    1997). Parental control is defined as low levels of parental

    encouragement of children’s autonomy and independence,

    and is viewed as decreasing children’s self-efficacy,

    thereby increasing their anxiety (Barber 1996; Chorpita

    and Barlow 1998).

    Anxiety sensitivity refers to the fear that symptoms of

    anxiety (e.g., racing heart) are uncontrollable and have

    harmful somatic, psychological, or social consequences

    (Reiss 1991). Evidence suggests that anxiety sensitivity,

    including in children, is a risk factor for the development

    and maintenance of anxiety symptoms (e.g., Reiss 1991;

    Silverman and Weems 1998).

    With respect to anxiety symptoms, we selected to study

    the above variables in relation to specific anxiety dimen-

    sions (not total anxiety scores). The anxiety dimensions we

    studied represent the subscales on the Multidimensional

    Anxiety Scale for Children (MASC; March et al. 1997),

    which include: social phobia, separation anxiety, harm

    avoidance, and physical symptoms. Our decision to use the

    MASC subscales was guided by research that specific

    anxiety symptoms or dimensions are manifested differently

    across ethnic groups (Compton et al. 2000). For example,

    Compton et al. (2000) found that African American chil-

    dren scored high on separation anxiety and low on social

    phobia; the opposite pattern was found with White children.

    The conceptual model we formulated to guide this study

    is shown in Fig. 1. Our model shows directionality of

    predicted relations between variables based on extant the-

    oretical and empirical research work; however, given the

    cross-sectional nature of the data, the model does not

    assume causality between any of the constructs. As shown

    in the model, we predicted children’s ethnic pride would be

    related to perceived parental acceptance and perceived

    parental control (see Paths a and b). Specifically, we pre-

    dicted that children who report high ethnic pride would

    perceive their parents as high on parental acceptance. We

    also predicted that children who report low ethnic pride

    would perceive their parents as high on parental control.

    These predictions are based on research findings showing

    that high ethnic pride is associated with low internalizing

    symptoms, including anxiety symptoms in African Amer-

    ican youth (Gaylord-Harden et al. 2007), and is associated

    with high parental acceptance (Wills et al. 2007).

    The relation between perceived parenting behavior and

    anxiety has been found to vary across ethnic groups. For

    example, in a clinic-referred sample of urban adolescent

    Children’s Ethnic Pride

    Perceived Parental Control

    Perceived Parental

    Acceptance

    Children’s Anxiety Sensitivity

    Harm Avoidance Symptoms

    Separation Anxiety Symptoms

    Social Anxiety Symptoms

    Physical Symptoms

    a

    e

    d

    c

    b

    h

    g

    f

    Fig. 1 Theoretical model. This figure does not show all

    estimated paths

    206 J Child Fam Stud (2011) 20:205–213

    123

    girls, Finkelstein et al. (2001) found that youth perceived

    maternal control was significantly and positively related to

    youths’ anxious-depressed affect in the White and Latina

    girls. In contrast, youth perceived maternal control was

    significantly and negatively related to youths’ anxious-

    depressed affect in the African American girls. Such

    variations may be due in part ethnic variations in children’s

    perceptions of appropriate childrearing (e.g., Finkelstein

    et al. 2001; Greenfield et al. 2003; Mason et al. 2004). We

    were interested in further examining the pattern of relations

    among ethnic pride, perceived parenting behavior, and

    anxiety symptoms in African American children given the

    possibility that ethnic variations in children’s perceptions

    of their parents’ behavior may present differential risk for

    the development and maintenance of anxiety symptoms

    (Finkelstein et al. 2001).

    As the model shows, we also predicted that children’s

    perceived parental control and perceived parental accep-

    tance would be related to children’s anxiety sensitivity (see

    Paths c and d). Specifically, we predicted that children who

    perceive their parents as high in parental control would

    report high levels of anxiety sensitivity, and children who

    perceive their parents high in parental acceptance would

    report low levels of anxiety sensitivity. These predictions

    are based on research showing links between certain

    aspects of parenting behavior and child anxiety sensitivity

    (e.g., retrospective reports of parental control in childhood

    linked to high anxiety sensitivity; Scher and Stein 2003).

    We were further interested in examining anxiety sensi-

    tivity in African American children given research showing

    that African Americans, in general report more somatiza-

    tion symptoms (i.e., symptoms of physiological complaints

    that are the result of psychological distress) compared to

    other ethnic groups (e.g., Heurtin-Roberts et al. 1997; Neal

    and Turner 1991). Even though anxiety sensitivity is likely

    to be relevant to African American children, few studies

    have examined anxiety sensitivity using samples of African

    American children. The studies that have been conducted

    have focused mainly on the psychometric properties of

    anxiety sensitivity measures (Lambert et al. 2004).

    Lastly, we predicted that children’s anxiety sensitivity

    would mediate the relation between their perceived

    parental acceptance and perceived parental control, as well

    as their anxiety symptoms (see Paths e–h). Specifically, we

    expected the children’s perceived parental acceptance and

    parental control would be differentially related to chil-

    dren’s anxiety sensitivity and that anxiety sensitivity

    would, in turn, be positively related to anxiety across all

    symptom dimensions. Although no study to date has

    examined these mediated relations, our prediction is based

    on research indicating links between parenting behavior

    and child anxiety sensitivity (Scher and Stein 2003; Watt

    et al. 1998), and research indicating links between child

    anxiety sensitivity and child anxiety symptoms (Olatunji

    and Wolitzky-Taylor 2009).

    Though parenting behavior (e.g., Wood et al. 2003) and

    anxiety sensitivity (e.g., Reiss 1991) are each identifiable

    risk factors for the development and maintenance of anx-

    iety and its disorders in children, questions remain.

    Research has only focused, however, on how these vari-

    ables operate independently without considering the more

    likely complex, multivariate dynamics among these vari-

    ables. Moreover, this research has used predominantly

    White samples of children, which may or may not be

    generalizable to African American child samples.

    In sum, we examined the relations among ethnic pride,

    parental acceptance, parental control, anxiety sensitivity

    and anxiety symptoms across all dimensions. Specifically,

    we examined these relations in a sample of African

    American school children, a subgroup not often studied in

    child anxiety research. Examining such relations in this

    sample can increment the existing literature.

    Method

    Participants

    Participants were involved in a larger study on expressions

    of anxiety in minority school children. All parents were

    asked to sign an informed consent form if they gave per-

    mission for their child’s participation, or to indicate if they

    declined child’s participation. Children also were asked to

    sign the form to provide their informed assent. Contingency

    table and analysis of variance results indicated no signifi-

    cant differences on sociodemographic characteristics (i.e.,

    child age, recipient of free or reduced lunch status) among

    children with consent/assent, children without consent/

    assent, and children who did not return consent forms.

    Only the data of the African American children were

    included in the analyses. This led to the exclusion of data

    from 63 non-African American families. The final sample

    comprised 266 African American school children ages

    8–13 years (M = 9.88 years, SD = 1.10; 55% girls).

    Thirty-three percent (n = 88) were in third grade, 29%

    (n = 76) in fourth grade, and 38% (n = 102) in fifth grade.

    According to school records, approximately 74% (n = 198)

    of the children received free or reduced lunch.

    Measures

    To reduce participant burden and to assist teachers by

    maximizing instructional time, we extracted items with

    highest factor loadings from previously validated mea-

    sures. This method has been used in widely published

    national studies (e.g., Add Health; Udry 1998), and

    J Child Fam Stud (2011) 20:205–213 207

    123

    represents a psychometrically sound approach, while

    reducing participant burden (e.g., Volpe et al. 2009).

    For all measures, reliability estimates were found to be

    comparable to estimates found in past research utilizing the

    full measures.

    Child Anxiety Symptoms

    The MASC is a 39-item self-rating scale that measures a

    range of anxiety symptoms in youth that are aligned with

    the DSM IV diagnostic categories for anxiety disorders

    (March et al. 1997). Four scale scores can be derived from

    the MASC: physical symptoms, social anxiety symptoms,

    separation anxiety symptoms, and harm avoidance symp-

    toms. Children responded to each item on a 4-point scale: 1

    (never), 2 (rarely), 3 (sometimes), or 4 (often), to yield four

    subscale scores (physical symptoms, social anxiety symp-

    toms, separation anxiety symptoms, harm avoidance

    symptoms) with higher numbers indicating more symp-

    toms. The internal consistency estimates for subscales in

    this study ranged from .71 to .85.

    Child Anxiety Sensitivity

    A brief version of the Childhood Anxiety Sensitivity Index

    (CASI; Silverman et al. 1991) was used to assess the extent

    to which children believe the experience of anxiety will

    result in negative consequences. The brief version of the

    CASI consisted of eight items: items 9 and 18 (for physi-

    ological concerns); 4 and 10 (for control concerns); 2 and

    15 (for mental incapacitation concerns); and 1 and 17 (for

    social concerns). Children responded to these questions on

    a 3-point scale, 1 (none), 2 (some), or 3 (a lot), to yield a

    total score with higher numbers indicating more anxiety

    sensitivity. In this study, the CASI demonstrated accept-

    able internal consistency (a = .75).

    Perceived Parental Behavior

    A brief version of the Children’s Report of Parent Behavior

    Inventory acceptance and control subscales (CRPBI; Sch-

    ludermann and Schludermann 1988) was used to assess the

    extent to which children perceive their parents’ (mother

    and father) childrearing practices as accepting and/or

    controlling. The brief version of the acceptance subscale

    consists of two items: ‘‘My mother (father) is a person who

    enjoys doing things with me,’’ and ‘‘My mother (father) is a

    person who often praises me.’’ The brief version of the

    control subscale consists of two items: ‘‘My mother (father)

    is a person who is always telling me how I should behave,’’

    and ‘‘My mother (father) is a person who wants to control

    whatever I do’’. A separate rating was obtained for the

    mother and the father; and these ratings were then

    combined to create a parental acceptance score and a

    parental control score. Children responded to these ques-

    tions on a 3-point scale [1 (not like), 2 (somewhat like), or 3

    (a lot)], with higher numbers indicating more parental

    acceptance and more parental control. The psychometric

    properties for the CRPBI have been documented in over

    one hundred studies; estimates for internal consistency

    have been reported in previous research (a C .65; Gaylord- Harden et al. 2007; Schludermann and Schludermann

    1988) (a C .57 in this study).

    Ethnic Pride

    Children’s ethnic pride was evaluated using a measure

    adapted from the Multigroup Ethnic Identity Measure

    (MEIM; Phinney 1992), which assesses ethnic identity

    development, affirmation, belonging, and commitment.

    Children responded to three items: ‘‘I have spent time

    trying to find out more about my ethnic group, such as its

    history, traditions, and customs,’’ ‘‘I am active in organi-

    zations or social groups that include mostly members of my

    own ethnic group;’’ and ‘‘I have a strong sense of

    belonging to my own ethnic group.’’ Children responded to

    these questions on a 3-point scale from 1 (none) to 3 (a lot).

    Items were summed to yield a total score with higher

    numbers indicating higher ethnic pride. Previous studies

    with samples of school-age children reported internal

    consistencies ranging from .30 to .72 (Reese et al. 1998)

    (a = .41 in this study).

    Procedure

    Students with signed parent consent and child assent forms

    were administered several questionnaires about the child’s

    emotional functioning including the measures used in this

    study. The questionnaires were completed during school

    hours in a small group setting containing 5–8 students per

    group. Each questionnaire was read aloud by two African

    American female researchers while the children followed

    along, indicating their responses on a separate answer

    sheet. Total administration time of questionnaires was

    approximately 30 min. The data were collected over a

    3-week period.

    Results

    Preliminary and Descriptive Analyses

    There were small amounts of missing data in this study.

    The amount never exceeded more than 1% of the cases for

    any given variable. There was no systematic pattern found

    in the missing data. The minimal missing data was imputed

    208 J Child Fam Stud (2011) 20:205–213

    123

    using the SPSS 16.0 missing value analysis, which utilizes

    Expectation–Maximization (EM) method with importance

    re-sampling as described in King et al. (2001). Outlier

    analyses included both non-model based and model based

    evaluations of variable data. No outliers were found in

    these data.

    Table 1 presents the means and standard deviations for

    the study variables. There were some statistically signifi-

    cant sex differences on measures of children’s anxiety

    sensitivity and childhood anxiety (see Table 1). The mean

    score for girls on anxiety sensitivity as measured by the

    CASI was 2.43 units higher than the mean score for boys.

    Further, the mean scores along all dimensions of anxiety

    were significantly higher for girls as compared to boys.

    On average, for physical, social anxiety, separation anxi-

    ety, and harm avoidance symptoms girls scored 3.3, 2.93,

    3.14, and 1.74 units higher than their male counterparts,

    respectively.

    Table 2 shows the correlation estimates among study

    variables. There were a few noteworthy significant corre-

    lations. Anxiety sensitivity was correlated with all study

    variables except parental control. Perceived parental con-

    trol was correlated with social anxiety and separation

    anxiety symptoms. Parental acceptance was positively

    correlated with social anxiety, but was negatively corre-

    lated with harm avoidance.

    SEM Analyses

    Structural equation modeling (SEM) analysis was used to

    examine the hypothesized relations among child ethnic

    pride, perceived parenting behavior, anxiety sensitivity and

    child anxiety symptoms using AMOS 7.0 (Arbuckle 2006)

    with a single indicator path analytic approach and a robust

    weighted least squares solution. SEM was selected over

    ordinary least squares regression because SEM allows for

    more accurate path estimates between variables by utilizing

    a measurement modeling technique that accounts for

    measurement error when estimating the paths among

    variables in the analysis (Byrne 2001). To evaluate

    hypothesized mediated relations (i.e., whether anxiety

    sensitivity mediated the effects of parenting behavior on

    child anxiety), the joint significance test recommended by

    MacKinnon et al. (2002) was used. This method simulta-

    neously tests whether the independent variable is related to

    the hypothesized mediators and whether the hypothesized

    Table 1 Means and standard deviations for study variables

    Variables Total Sample (N = 266) Boys (n = 121) Girls (n = 145) t (264)

    M SD M SD M SD

    Ethnic pride 6.30 1.45 6.14 1.45 6.44 1.45 -1.68

    Anxiety sensitivity 14.36 3.68 13.03 3.41 15.46 3.54 -5.66**

    Perceived parental control 8.38 1.77 8.42 1.88 8.34 1.68 .36

    Perceived parental acceptance 10.16 1.79 10.25 1.72 10.09 1.85 .72

    Physical symptoms 12.45 7.14 10.65 6.97 13.95 6.97 -3.85**

    Social anxiety symptoms 10.63 6.21 9.03 5.99 11.96 6.10 -3.93**

    Separation anxiety symptoms 9.06 5.11 7.35 4.64 10.49 5.05 -5.22**

    Harm avoidance symptoms 17.78 4.50 16.83 4.20 18.57 4.61 -3.20**

    * p \ .05; ** p \ .01

    Table 2 Intercorrelations for study variables

    Variable 1 2 3 4 5 6 7 8

    1. Ethnic pride – 17** .10 .14* .08 .00 .08 .20**

    2. Anxiety sensitivity – .13** .05 .58** .55** .57** .27**

    3. Perceived parental control – .02 .10 .13* .19** .06

    4. Perceived parental acceptance – .00 -.16** -.09 .24**

    5. Physical symptoms – .60** .53** .29**

    6. Social anxiety symptoms – .64** .24**

    7. Separation anxiety symptoms – .38**

    8. Harm avoidance symptoms –

    * p \ .05; ** p \ .01

    J Child Fam Stud (2011) 20:205–213 209

    123

    mediators are related to the dependent variable. The joint

    significance test has improved statistical power than other

    tests of mediation while retaining adequate control over

    Type I error rates (MacKinnon et al. 2002). Because little

    empirical work has been done to identify factors that

    impact anxiety in African American children, we did not

    want to limit ourselves when modeling potential relations

    among variables. Thus, we modeled both indirect and

    direct effects to test for mediation (see Jaccard and Jacoby

    2009).

    To reduce clutter in the figure (see Fig. 2), not all details

    of analyses are apparent. These include: (1) students’

    biological sex (0 = male,1 = female), age in years, and

    school attended (0 = school one,1 = school two) were

    included as covariates for all endogenous variables; (2)

    direct causal paths were included from children’s ethnic

    pride, perceived parental acceptance and perceived paren-

    tal control to each outcome (i.e., children’s anxiety sensi-

    tivity and perceived parental control and acceptance were

    modeled as only partial mediators, not complete mediators

    of these effects); and (3) the path model included correlated

    errors where it was reasonable to assume that factors other

    than the common cause are influencing the correlation

    between variables. For example, factors other than anxiety

    sensitivity and perceived parenting behavior (e.g., child

    temperament and perceptions of control) can contribute to

    the correlation between these variables and the outcome

    variables.

    The overall fit of the model was acceptable as demon-

    strated by the statistically non-significant chi square test of

    model fit (X2 (4) = 5.61, p = 0.23). The Root Mean

    Square Error of Approximation (RMSEA) was .04. The

    p value for the test of close fit was 0.52. The Tucker-Lewis

    index (TLI) was .98. Taken together, these global fit

    indices (i.e., X2, RMSEA, p value for close fit, and TLI) all

    pointed towards good model fit. More focused tests of fit

    revealed no theoretically meaningful or sizeable modifi-

    cation indices, nor were any of the absolute standardized

    residuals larger than 1.96.

    Children’s ethnic pride was associated with perceived

    parental acceptance such that as children’s ethnic pride

    increased, perceived parental acceptance increased (B =

    .18, 95% CI = .02, .32, p \ .05). Further, as children’s ethnic pride increased, perceived parental acceptance

    Children’s Ethnic Pride

    Perceived Parental Control

    Perceived Parental

    Acceptance

    Children’s Anxiety Sensitivity

    Harm Avoidance Symptoms

    Separation Anxiety Symptoms

    Social Anxiety Symptoms

    Physical Symptoms

    .1 8

    (.1 5)

    *

    1. 09

    (. 56

    ) * *

    .2 8

    (.1 3)

    *

    .24 (.19) *

    .71 (.51) **

    .93 (.5

    5)* *

    – .61 (-.17)**

    .39 ( .14)

    *

    .56 (.22)**

    .37 (.12)*

    Fig. 2 SEM model showing significant relations among

    children’s ethnic pride,

    perceived parenting behavior,

    anxiety sensitivity and child

    anxiety symptoms. Note Standardized path coefficients

    are in parentheses. * p \ .05. ** p \ .01. Students’ age in years, students’ biological sex,

    and school attended are

    included as covariates although

    not shown. Students’ age in

    years, students’ biological sex,

    and school attended are

    correlated with all exogenous

    variables although curved arrows are not shown. Error variances for perceived parental

    acceptance and perceived

    parental control are correlated

    although curved arrows are not shown

    210 J Child Fam Stud (2011) 20:205–213

    123

    increased which, in turn, was related to a decrease in social

    anxiety symptoms (B = -.61, 95% CI = -.94, -.28,

    p \ .001) and an increase in harm avoidance symptoms (B = .56, 95% CI = .28, .83, p \ .001). Children’s ethnic pride was not significantly related to perceived parental

    control.

    Children’s ethnic pride had a significant direct effect on

    harm avoidance symptoms, such that as children’s ethnic

    pride increased, levels of harm avoidance symptoms

    increased (B = .37, 95% CI = .02, .72, p \ .05). Chil- dren’s ethnic pride did not have a significant direct effect

    on any other dimensions of anxiety symptoms.

    Perceived parental control was significantly related to

    children’s anxiety sensitivity (B = .28, 95% CI = .04, .51,

    p \ .05) indicating that as children’s report of perceived parental control increased their reports of anxiety sensi-

    tivity symptoms increased. Perceived parental acceptance

    was not significantly related to children’s anxiety sensi-

    tivity in this sample. Both perceived parental acceptance

    and control accounted for approximately 16% of the vari-

    ance in anxiety sensitivity.

    Perceived parental control also had a direct effect on

    childhood anxiety such that as, perceived parental control

    increased, symptoms of separation anxiety increased

    (B = .39, 95% CI = .11, .67, p \ .01). Thus, children’s anxiety sensitivity partially mediated the relation between

    perceived parental control and separation anxiety symp-

    toms (i.e., perceived parental control was both directly and

    indirectly related to separation anxiety symptoms). Chil-

    dren’s anxiety sensitivity accounted for 24% of the vari-

    ance in the prediction of separation anxiety symptoms.

    Conversely, perceived parental control was indirectly

    related to physical symptoms (B = 1.09, 95% CI = .87,

    1.28, p \ .001), social anxiety symptoms (B = .93, 95% CI = .75, 1.11, p \ .001), and harm avoidance symptoms (B = .24, 95% CI = .09, .39, p \ .01). All of the endog- enous variables in the model accounted for a good portion

    of the variance in children’s reports of physical (33%),

    social anxiety (36%), separation anxiety (37%), and harm

    avoidance symptoms (15%).

    Discussion

    Our study investigated factors related to the domains of

    anxiety symptoms in a sample of African American school

    children. Specifically, we evaluated the relations between

    ethnic pride, anxiety sensitivity, perceived parental control

    and acceptance, and specific dimensions of child anxiety

    symptoms in our sample. Although we did not find evi-

    dence for full support of our theoretical model, our findings

    underscore the nuances in the manifestation of anxiety

    symptoms among African American children.

    Overall, our findings suggest that children’s ethnic

    pride, parental behaviors and anxiety sensitivity are all

    factors that may impact anxiety symptoms in African

    American children. Specifically, we found that children

    who reported high ethnic pride perceived their parent’s

    child rearing behavior as high in parental acceptance.

    Higher perceived parental acceptance, in turn, predicted

    children reporting lower levels of social anxiety symptoms

    and higher levels of harm avoidance. In addition, children

    with perceptions of high parental control reported high

    anxiety sensitivity. Anxiety sensitivity partially mediated

    the relation between perceived parental control and sepa-

    ration anxiety symptoms. However, parental control was

    indirectly related to physical symptoms, social anxiety

    symptoms, and harm avoidance symptoms through its

    direct link to anxiety sensitivity.

    The results of our study add to what is known about how

    parental behavior contributes to the dimensions of anxiety

    symptoms in children, particularly in African American

    children. Children who reported high ethnic pride per-

    ceived their parents as high in parental acceptance. Parental

    acceptance was, in turn, significantly, yet differentially

    linked with social anxiety and harm avoidance symptoms.

    Based on previous research documenting negative relations

    between parental acceptance and anxiety in children

    (e.g., Craske 1999), we expected and found that as parental

    acceptance increased children’s social anxiety symptoms

    decreased. Contrary to our expectations, however, was the

    finding that as parental acceptance increased children’s

    harm avoidance symptoms (i.e., behavioral response to

    threatening situations, March, 1997) also increased.

    Research with African Americans has shown that chil-

    dren’s high ethnic pride is associated with high parental

    acceptance (Wills et al. 2007), and parents who exhibit

    warm and accepting parenting practices tend to integrate

    messages regarding ethnic pride and racial socialization

    into their child rearing practices, including messages on

    potential discrimination (Caughy et al. 2002; McHale et al.

    2006). Using the CRPBI, McHale et al. (2006) found

    perceived parental acceptance to be positively associated

    with preparation for bias, an increased awareness for an

    African American child to develop coping strategies for

    prejudices and discrimination within their larger socioeth-

    nic milieu; this preparation for bias may increase children’s

    symptoms related to harm avoidance. To advance under-

    standing of the relation between parental acceptance and

    harm avoidance symptoms in African American children,

    additional work is needed and should likely include

    research on parent’s racial socialization practices.

    Perceived parental control, unlike parental acceptance,

    was found to be significantly positively associated with

    anxiety sensitivity in children. This finding is inconsistent

    with the work of Scher and Stein (2003), who found that

    J Child Fam Stud (2011) 20:205–213 211

    123

    children who reported their parents as hostile and rejecting

    (converse of accepting) had higher anxiety sensitivity. It is

    likely that the discrepant findings lie in the methodology.

    Scher and Stein used a sample of college students who

    reported on their current levels of anxiety and retrospec-

    tively reported their perceptions of their parents’ behavior.

    The age of the sample (college students vs. children); the

    temporal lag in reporting parenting behavior (retrospective

    vs. current); and the inclusion of certain aspects of par-

    enting behavior (acceptance/hostile and control/granting

    autonomy) all may help to explain the inconsistencies in

    findings. More work is needed in this area to fully explicate

    the impact of parenting behavior on anxiety sensitivity.

    Our study also extends research on anxiety sensitivity in

    African American children. Noteworthy was the mediating

    role of anxiety sensitivity in the relation between perceived

    parental control and anxiety symptoms. In our study, as

    parental control increased, youth-reported anxiety sensi-

    tivity increased, which, in turn, was associated with the

    manifestation of anxiety symptoms across all dimensions of

    anxiety. This was not the case with parental acceptance.

    Parents who are controlling increase their child’s risk of

    experiencing feelings of fear of anxiety symptoms, which in

    turn lead to anxiety symptoms. Because African Americans

    have been shown to exhibit elevated somatization symp-

    toms (Heurtin-Roberts et al. 1997) and heightened atten-

    tiveness to physiological symptoms is associated with

    anxiety sensitivity, an understanding of factors that put

    African American children at risk for anxiety sensitivity

    is increasingly important. Children with high anxiety

    sensitivity tend to associate their somatic symptoms

    (e.g., accelerated heart beat) as distressing, which, in turn,

    increase their overall anxiety (Ginsburg and Drake 2002);

    these processes are cyclical (Reiss 1991) and likely per-

    petuate until there is clinical intervention. Albeit clinical

    intervention with the child may be warranted, if parental

    control is a risk factor for anxiety sensitivity, intervening at

    the parent-level may also be an appropriate applied strategy.

    Limitations of the present study should be noted. First,

    we utilized children’s reports as the sole method for cap-

    turing data. Although children are the most reliable

    reporting source for their anxiety symptoms and anxiety

    sensitivity (Silverman and Eisen 1992), data from parents

    on their parenting behavior, including racial and ethnic

    socialization practices should be used in future studies to

    examine convergence and divergence with children’s

    reports. Additionally, this study utilized a cross-sectional

    sample. Future research investigating the relations we

    examined in our study would benefit from prospective

    research designs to better understand potential causal

    relations among the study’s variables.

    Another study limitation was the somewhat low reli-

    ability of the ethnic pride measure used in this study. Reese

    et al. (1998) suggested there may be other mediating

    mechanisms that impact the reliability of reports of ethnic

    pride among African American children (e.g., cognitive

    processes, socioeconomic factors) and have discussed the

    challenges in fully disentangling this construct in children.

    Nevertheless, this study is the first to call attention to the

    potential importance of ethnic pride in trying to understand

    anxiety problems in samples of African American children.

    Future research should continue to examine ethnic pride as

    a relevant and reliable construct among African American

    children.

    Finally, other factors not measured in this study could

    have accounted for the variance in domains of anxiety

    (e.g., parents’ anxiety symptoms). Although, our theoreti-

    cal framework accounted for a good portion of the variance

    in the domains of anxiety, more research is needed to

    consider other factors that may place African American

    youth at risk for or protect from anxiety problems.

    We suggest that future researchers continue to extend our

    work so that knowledge on the domains of childhood anx-

    iety symptoms in African American children is advanced.

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Indicate the possible etiologies in a general way and the steps you would take to diagnose, treat, and manage these cases, with consideration of pharmacological intervention

All assignments MUST be typed, double-spaced, in APA style, and must be written at graduate level English.
 

Your responses must reflect your own understanding of the text material in direct and specific context to the case studies below. Cite your work according to APA format.

You are required to discuss case-specific cultural factors as well as legal and ethical issues present in each case.

Your responses must be 2 pages in length per case.  
Entire assignment must be 6 pages plus a title and reference page.

Considering the indicated symptoms for all three cases below you are required to:

  1. Indicate the possible etiologies in a general way and the steps you would take to diagnose, treat, and manage these cases, with consideration of pharmacological intervention
  2. You don’t need to discuss every detail of your treatment – just write from the standpoint of a clinical psychotherapist who does not prescribe medication, but coordinates care with physicians who do.

Case Studies:  Respond to all three (3):

1. Bob is a 47-year-old Native-American man who comes to you with his wife of 15 years. He appears agitated and complains that he feels anxious. His wife reports that Bob has had a lot of trouble sitting still, frequently jumping up and pacing. While he has been this way throughout their marriage, it is getting increasingly worse. Bob tells you that he is a lot like his abusive father, who died of alcoholism.  Bob feels that if he drank, he’d be able to relax, but swore he would never be like his father. The wife tells you that the children are frightened when Bob gets agitated as he has punched holes in walls. You notice the wife looks frightened when you ask her if she feels the children are safe in the home.

2. Steven is a 7-year-old Asian-American first grade student brought to treatment by his mother. He has been disruptive in class. His teacher and the school counselor are strongly urging that Steven be put on medication for ADHD, but his mother is reluctant to do so.  Steven is described as exceptionally gifted and appears bored in the classroom.  His mother tells you that Steven is impatient and rageful to his younger sisters at home. She is afraid Steven is taking after his abusive father, who no longer lives in the home due to domestic violence. She tells you that their father is allowed to visit the children in the home under her supervision.

3. Melinda is an exceptionally bright, 17-year-old African-American young woman, in her first year at a state college. Both parents accompany her to your office. When she came home for spring break, she started talking nonsense, saying that the college’s biology department is using a high-powered laser to alter the DNA in her brain. She has always been an “A” student and has never shown any sign of psychiatric disturbance prior to this.

By this point, your prospectus should be complete and should include revisions made based on feedback from your instructor.

By this point, your prospectus should be complete and should include revisions made based on feedback from your instructor. Submitting the prospectus will assist you and your instructor/chair in preparing a plan for the remainder of your dissertation phase.

General Guidelines:

Use the following information to ensure successful completion of the assignment:

· Ensure to follow the professor’s comments from your previous submission.

· Instructors will be using a grading rubric to grade the assignments. It is recommended that learners strictly follow the instructions for assignment criteria and expectations for successful completion of the assignment.

· Doctoral learners are required to use APA style for their writing assignments.

· You are required to submit this assignment to LopesWrite. Refer to the directions in the Student Success Center. Only Word documents can be submitted to LopesWrite.

Directions:

Compile all of the revisions to your prospectus into a final and complete prospectus document using the most current version of the “Prospectus Template.” This includes completing Table 1 or Table 2 (whichever is applicable to the study methodology) in Appendix B of the prospectus template.

Create a table to hold the intended demographic information that will be collected in your study. Insert the completed table into the final prospectus as Appendix C.

Verify that revisions have been made according to the instructor/chair comments and rubric as well as the “Academic Quality Review (AQR) Prospectus Review Checklist.”

COMMENTS:

Usually, we do not include the actual name of the county or state. Just say mental health services in one county located in a southern state. You need to make sure you establish that this problem exists outside of this one county in Texas. You need to include the sample in the problem, purpose and RQs. Cite the theory you are using from the seminal source, which would be Bandura or Vygotsky. I provided a problem, purpose and RQ. You need at least 2 questions. See comments.

Be sure and read about the data collection and management section criteria. What you are discussing in your answer belongs in the method and design and sources of data section. (Yes, I have the template memorized). 🙂 In the data collection section, you need to tell us each and every step that you will use to get site authorization and IRB approval, doing a field test of the interview guide, make initial contact with potential participants to share the purpose of the study and get informed consent, to collecting and preparing data for analysis. I would strongly recommend looking at other GCU dissertations. Start this section off by writing these steps off in number format… Then, remember back to when we were in school and wrote out how to make a peanut butter and jelly sandwich? Go through the steps yourself to see what is missing. After you get that down, then put in paragraph format. Often, as a chair, I have my learners keep this in number format (like the steps of a recipe) until we get it down pat. It is easier to provide feedback if

Describe ways in which your boundaries may have been tested this week. How did you react?

1. Self-care is very important to those that work in helping professionals. It can be easy to be over-worked by doing “too much work” while working with others. What does it mean to do “too much work” as a counselor in a counseling session? How will you guard against doing “too much” work in future sessions?

2. Why are boundaries important in counseling sessions? Describe ways in which your boundaries may have been tested this week. How did you react? What did you learn?

3. How do boundaries affect your relationship during clinical supervision? What is the potential for a dual relationship or boundary crossing during clinical supervision?

each questions must be answered with 150-200 words.

Read the following scenario and explain what power issues may arise. What factors influence statistical power?

Part1- Due Thursday 

Respond to the following in a minimum of 175 words:

Read the following scenario and explain what power issues may arise. What factors influence statistical power?

A researcher is exploring differences between men and women on ‘number of different recreational drugs used.’ The researcher collects data on a sample of 50 men and 50 women between the ages of 18-25. Each participant is asked ‘how many different recreational drugs have you tried in your life?’ The IV is gender (male/female) and the DV is ‘number of reported drugs.’

Part2-PLEASE SEE ATTACHMENT

PART3-PLEASE SEE ATTACHMENT…THIS IS A GROUP ASSIGNMENT I ONLY HAVE TO COMPLETE A PART OF THE TABLE. I WILL POST MY PART ON TUESDAY

REFERENCE

CHAPTER 13

LEARNING OBJECTIVES

  • Explain how researchers use inferential statistics to evaluate sample data.
  • Distinguish between the null hypothesis and the research hypothesis.
  • Discuss probability in statistical inference, including the meaning of statistical significance.
  • Describe the t test and explain the difference between one-tailed and two-tailed tests.
  • Describe the F test, including systematic variance and error variance.
  • Describe what a confidence interval tells you about your data.
  • Distinguish between Type I and Type II errors.
  • Discuss the factors that influence the probability of a Type II error.
  • Discuss the reasons a researcher may obtain nonsignificant results.
  • Define power of a statistical test.
  • Describe the criteria for selecting an appropriate statistical test.

Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.

SAMPLES AND POPULATIONS

Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?

In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.

Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred the Democratic candidate would be between 60% and 54% and the percentage preferring the Republican would be between 46% and 40%. In this case, the researcher could predict with a great deal of certainty that the Democratic candidate will win because there is no overlap in the projected population values. Note, however, that even when we are very (in this case, 95%) sure, we still have a 5% chance of being wrong.

Inferential statistics allow us to arrive at such conclusions on the basis of sample data. In our study with the model and no-model conditions, are we confident that the means are sufficiently different to infer that the difference would be obtained in an entire population?

Page 268

INFERENTIAL STATISTICS

Much of the previous discussion of experimental design centered on the importance of ensuring that the groups are equivalent in every way except the independent variable manipulation. Equivalence of groups is achieved by experimentally controlling all other variables or by randomization. The assumption is that if the groups are equivalent, any differences in the dependent variable must be due to the effect of the independent variable.

This assumption is usually valid. However, it is also true that the difference between any two groups will almost never be zero. In other words, there will be some difference in the sample means, even when all of the principles of experimental design are rigorously followed. This happens because we are dealing with samples, rather than populations. Random or chance error will be responsible for some difference in the means, even if the independent variable had no effect on the dependent variable.

Therefore, the difference in the sample means does show any true difference in the population means (i.e., the effect of the independent variable) plus any random error. Inferential statistics allow researchers to make inferences about the true difference in the population on the basis of the sample data. Specifically, inferential statistics give the probability that the difference between means reflects random error rather than a real difference.

NULL AND RESEARCH HYPOTHESES

Statistical inference begins with a statement of the null hypothesis and a research (or alternative) hypothesis. The null hypothesis is simply that the population means are equal—the observed difference is due to random error. The research hypothesis is that the population means are, in fact, not equal. The null hypothesis states that the independent variable had no effect; the research hypothesis states that the independent variable did have an effect. In the aggression modeling experiment, the null and research hypotheses are:

H0 (null hypothesis): The population mean of the no-model group is equal to the population mean of the model group.

H1 (research hypothesis): The population mean of the no-model group is not equal to the population mean of the model group.

The logic of the null hypothesis is this: If we can determine that the null hypothesis is incorrect, then we accept the research hypothesis as correct. Acceptance of the research hypothesis means that the independent variable had an effect on the dependent variable.

The null hypothesis is used because it is a very precise statement—the population means are exactly equal. This permits us to know precisely the Page 269probability of obtaining our results if the null hypothesis is correct. Such precision is not possible with the research hypothesis, so we infer that the research hypothesis is correct only by rejecting the null hypothesis. We reject the null hypothesis when we find a very low probability that the obtained results could be due to random error. This is what is meant by statistical significance: A significant result is one that has a very low probability of occurring if the population means are equal. More simply, significance indicates that there is a low probability that the difference between the obtained sample means was due to random error. Significance, then, is a matter of probability.

PROBABILITY AND SAMPLING DISTRIBUTIONS

Probability is the likelihood of the occurrence of some event or outcome. We all use probabilities frequently in everyday life. For example, if you say that there is a high probability that you will get an A in this course, you mean that this outcome is likely to occur. Your probability statement is based on specific information, such as your grades on examinations. The weather forecaster says there is a 10% chance of rain today; this means that the likelihood of rain is very low. A gambler gauges the probability that a particular horse will win a race on the basis of the past records of that horse.

Probability in statistical inference is used in much the same way. We want to specify the probability that an event (in this case, a difference between means in the sample) will occur if there is no difference in the population. The question is: What is the probability of obtaining this result if only random error is operating? If this probability is very low, we reject the possibility that only random or chance error is responsible for the obtained difference in means.

Probability: The Case of ESP

The use of probability in statistical inference can be understood intuitively from a simple example. Suppose that a friend claims to have ESP (extrasensory perception) ability. You decide to test your friend with a set of five cards commonly used in ESP research; a different symbol is presented on each card. In the ESP test, you look at each card and think about the symbol, and your friend tells you which symbol you are thinking about. In your actual experiment, you have 10 trials; each of the five cards is presented two times in a random order. Your task is to know whether your friend’s answers reflect random error (guessing) or whether they indicate that something more than random error is occurring. The null hypothesis in your study is that only random error is operating. In this case, the research hypothesis is that the number of correct answers shows more than random or chance guessing. (Note, however, that accepting the research hypothesis could mean that your friend has ESP ability, but it could also mean that the cards were marked, that you had somehow cued your friend when thinking about the symbols, and so on.)

Page 270You can easily determine the number of correct answers to expect if the null hypothesis is correct. Just by guessing, 1 out of 5 answers (20%) should be correct. On 10 trials, 2 correct answers are expected under the null hypothesis. If, in the actual experiment, more (or less) than 2 correct answers are obtained, would you conclude that the obtained data reflect random error or something more than merely random guessing?

Suppose that your friend gets 3 correct. Then you would probably conclude that only guessing is involved, because you would recognize that there is a high probability that there would be 3 correct answers even though only 2 correct are expected under the null hypothesis. You expect that exactly 2 answers in 10 trials would be correct in the long run, if you conducted this experiment with this subject over and over again. However, small deviations away from the expected 2 are highly likely in a sample of 10 trials.

Suppose, though, that your friend gets 7 correct. You might conclude that the results indicate more than random error in this one sample of 10 observations. This conclusion would be based on your intuitive judgment that an outcome of 70% correct when only 20% is expected is very unlikely. At this point, you would decide to reject the null hypothesis and state that the result is significant. A significant result is one that is very unlikely if the null hypothesis is correct.

A key question then becomes: How unlikely does a result have to be before we decide it is significant? A decision rule is determined prior to collecting the data. The probability required for significance is called the alpha level. The most common alpha level probability used is .05. The outcome of the study is considered significant when there is a .05 or less probability of obtaining the results; that is, there are only 5 chances out of 100 that the results were due to random error in one sample from the population. If it is very unlikely that random error is responsible for the obtained results, the null hypothesis is rejected.

Sampling Distributions

You may have been able to judge intuitively that obtaining 7 correct on the 10 trials is very unlikely. Fortunately, we do not have to rely on intuition to determine the probabilities of different outcomes. Table 13.1 shows the probability of actually obtaining each of the possible outcomes in the ESP experiment with 10 trials and a null hypothesis expectation of 20% correct. An outcome of 2 correct answers has the highest probability of occurrence. Also, as intuition would suggest, an outcome of 3 correct is highly probable, but an outcome of 7 correct is highly unlikely.

The probabilities shown in Table 13.1 were derived from a probability distribution called the binomial distribution; all statistical significance decisions are based on probability distributions such as this one. Such distributions are called sampling distributions. The sampling distribution is based on the assumption that the null hypothesis is true; in the ESP example, the null hypothesis is that the person is only guessing and should therefore get 20% correct. Such a distribution assumes that if you were to conduct the study with the same number of observations over and over again, the most frequent finding would be 20%. However, because of the random error possible in each sample, there is a certain probability associated with other outcomes. Outcomes that are close to the expected null hypothesis value of 20% are very likely. However, outcomes farther from the expected result are less and less likely if the null hypothesis is correct. When your obtained results are highly unlikely if you are, in fact, sampling from the distribution specified by the null hypothesis, you conclude that the null hypothesis is incorrect. Instead of concluding that your sample results reflect a random deviation from the long-run expectation of 20%, you decide that the null hypothesis is incorrect. That is, you conclude that you have not sampled from the sampling distribution specified by the null hypothesis. Instead, in the case of the ESP example, you decide that your data are from a different sampling distribution in which, if you were to test the person repeatedly, most of the outcomes would be near your obtained result of 7 correct answers.

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TABLE 13.1 Exact probability of each possible outcome of the ESP experiment with 10 trials

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All statistical tests rely on sampling distributions to determine the probability that the results are consistent with the null hypothesis. When the obtained data are very unlikely according to null hypothesis expectations (usually a .05 probability or less), the researcher decides to reject the null hypothesis and therefore to accept the research hypothesis.

Sample Size

The ESP example also illustrates the impact of sample size—the total number of observations—on determinations of statistical significance. Suppose you had tested your friend on 100 trials instead of 10 and had observed 30 correct answers. Just as you had expected 2 correct answers in 10 trials, you would now expect 20 of 100 answers to be correct. However, 30 out of 100 has a much Page 272lower likelihood of occurrence than 3 out of 10. This is because, with more observations sampled, you are more likely to obtain an accurate estimate of the true population value. Thus, as the size of your sample increases, you are more confident that your outcome is actually different from the null hypothesis expectation.

EXAMPLE: THE t AND F TESTS

Different statistical tests allow us to use probability to decide whether to reject the null hypothesis. In this section, we will examine the t test and the F test. The t test is commonly used to examine whether two groups are significantly different from each other. In the hypothetical experiment on the effect of a model on aggression, a t test is appropriate because we are asking whether the mean of the no-model group differs from the mean of the model group. The F test is a more general statistical test that can be used to ask whether there is a difference among three or more groups or to evaluate the results of factorial designs (discussed in Chapter 10).

To use a statistical test, you must first specify the null hypothesis and the research hypothesis that you are evaluating. The null and research hypotheses for the modeling experiment were described previously. You must also specify the significance level that you will use to decide whether to reject the null hypothesis; this is the alpha level. As noted, researchers generally use a significance level of .05.

t Test

The sampling distribution of all possible values of t is shown in Figure 13.1. (This particular distribution is for the sample size we used in the hypothetical experiment on modeling and aggression; the sample size was 20 with 10 participants in each group.) This sampling distribution has a mean of 0 and a standard deviation of 1. It reflects all the possible outcomes we could expect if we compare the means of two groups and the null hypothesis is correct.

To use this distribution to evaluate our data, we need to calculate a value of t from the obtained data and evaluate the obtained t in terms of the sampling distribution of t that is based on the null hypothesis. If the obtained t has a low probability of occurrence (.05 or less), then the null hypothesis is rejected.

The t value is a ratio of two aspects of the data, the difference between the group means and the variability within groups. The ratio may be described as follows:

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The group difference is simply the difference between your obtained means; under the null hypothesis, you expect this difference to be zero. The value of t increases as the difference between your obtained sample means increases. Note that the sampling distribution of t assumes that there is no difference in the population means; thus, the expected value of t under the null hypothesis is zero. The within-group variability is the amount of variability of scores about the mean. The denominator of the t formula is essentially an indicator of the amount of random error in your sample. Recall from Chapter 12 that s, the standard deviation, and s2, the variance, are indicators of how much scores deviate from the group mean.

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FIGURE 13.1

Sampling distributions of t values with 18 degrees of freedom

A concrete example of a calculation of a t test should help clarify these concepts. The formula for the t test for two groups with equal numbers of participants in each group is:

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Page 274The numerator of the formula is simply the difference between the means of the two groups. In the denominator, we first divide the variance (images and images) of each group by the number of subjects in that group (n1 and n2) and add these together. We then find the square root of the result; this converts the number from a squared score (the variance) to a standard deviation. Finally, we calculate our obtained t value by dividing the mean difference by this standard deviation. When the formula is applied to the data in Table 12.1, we find:

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Thus, the t value calculated from the data is 4.02. Is this a significant result? A computer program analyzing the results would immediately tell you the probability of obtaining a t value of this size with a total sample size of 20. Without such a program, there are Internet resources to find a table of “critical values” of t (http://www.statisticsmentor.com/category/statstables/) or to calculate the probability for you (http://vassarstats.net/tabs.html). Before going any farther, you should know that the obtained result is significant. Using a significance level of .05, the critical value from the sampling distribution of t is 2.101. Any t value greater than or equal to 2.101 has a .05 or less probability of occurring under the assumptions of the null hypothesis. Because our obtained value is larger than the critical value, we can reject the null hypothesis and conclude that the difference in means obtained in the sample reflects a true difference in the population.

Degrees of Freedom

You are probably wondering how the critical value was selected from the table. To use the table, you must first determine the degrees of freedom for the test (the term degrees of freedom is abbreviated as df). When comparing two means, you assume that the degrees of freedom are equal to n1 + n2 − 2, or the total number of participants in the groups minus the number of groups. In our experiment, the degrees of freedom would be 10 + 10 − 2 = 18. The degrees of freedom are the number of scores free to vary once the means are known. For example, if the mean of a group is 6.0 and there are five scores in the group, there are 4 degrees of freedom; once you have any four scores, the fifth score is known because the mean must remain 6.0.

One-Tailed Versus Two-Tailed Tests

In the table, you must choose a critical t for the situation in which your research hypothesis either (1) specified a direction of difference between the Page 275groups (e.g., group 1 will be greater than group 2) or (2) did not specify a predicted direction of difference (e.g., group 1 will differ from group 2). Somewhat different critical values of t are used in the two situations: The first situation is called a one-tailed test, and the second situation is called a two-tailed test.

The issue can be visualized by looking at the sampling distribution of t values for 18 degrees of freedom, as shown in Figure 13.1. As you can see, a value of 0.00 is expected most frequently. Values greater than or less than zero are less likely to occur. The first distribution shows the logic of a two-tailed test. We used the value of 2.101 for the critical value of t with a .05 significance level because a direction of difference was not predicted. This critical value is the point beyond which 2.5% of the positive values and 2.5% of the negative values of t lie (hence, a total probability of .05 combined from the two “tails” of the sampling distribution). The second distribution illustrates a one-tailed test. If a directional difference had been predicted, the critical value would have been 1.734. This is the value beyond which 5% of the values lie in only one “tail” of the distribution. Whether to specify a one-tailed or two-tailed test will depend on whether you originally designed your study to test a directional hypothesis.

F Test

The analysis of variance, or F test, is an extension of the t test. The analysis of variance is a more general statistical procedure than the t test. When a study has only one independent variable with two groups, F and t are virtually identical—the value of F equals t2 in this situation. However, analysis of variance is also used when there are more than two levels of an independent variable and when a factorial design with two or more independent variables has been used. Thus, the F test is appropriate for the simplest experimental design, as well as for the more complex designs discussed in Chapter 10. The t test was presented first because the formula allows us to demonstrate easily the relationship of the group difference and the within-group variability to the outcome of the statistical test. However, in practice, analysis of variance is the more common procedure. The calculations necessary to conduct an F test are provided in Appendix C.

The F statistic is a ratio of two types of variance: systematic variance and error variance (hence the term analysis of variance). Systematic variance is the deviation of the group means from the grand mean, or the mean score of all individuals in all groups. Systematic variance is small when the difference between group means is small and increases as the group mean differences increase. Error variance is the deviation of the individual scores in each group from their respective group means. Terms that you may see in research instead of systematic and error variance are between-group variance and within-group variance. Systematic variance is the variability of scores between groups, and error variance is the variability of scores within groups. The larger the F ratio is, the more likely it is that the results are significant.

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Calculating Effect Size

The concept of effect size was discussed in Chapter 12. After determining that there was a statistically significant effect of the independent variable, researchers will want to know the magnitude of the effect. Therefore, we want to calculate an estimate of effect size. For a t test, the calculation is

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where df is the degrees of freedom. Thus, using the obtained value of t, 4.02, and 18 degrees of freedom, we find:

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This value is a type of correlation coefficient that can range from 0.00 to 1.00; as mentioned in Chapter 12, .69 is considered a large effect size. For additional information on effect size calculation, see Rosenthal (1991). The same distinction between r and r2 that was made in Chapter 12 applies here as well.

Another effect size estimate used when comparing two means is called Cohen’s d. Cohen’s d expresses effect size in terms of standard deviation units. A d value of 1.0 tells you that the means are 1 standard deviation apart; a d of .2 indicates that the means are separated by .2 standard deviation.

You can calculate the value of Cohen’s d using the means (M) and standard deviations (SD) of the two groups:

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Note that the formula uses M and SD instead of images and s. These abbreviations are used in APA style (see Appendix A).

The value of d is larger than the corresponding value of r, but it is easy to convert d to a value of r. Both statistics provide information on the size of the relationship between the variables studied. You might note that both effect size estimates have a value of 0.00 when there is no relationship. The value of r has a maximum value of 1.00, but d has no maximum value.

Confidence Intervals and Statistical Significance

Confidence intervals were described in Chapter 7. After obtaining a sample value, we can calculate a confidence interval. An interval of values defines the most likely range of actual population values. The interval has an associated confidence interval: A 95% confidence interval indicates that we are 95% sure that the population value lies within the range; a 99% interval would provide greater certainty but the range of values would be larger.

Page 277A confidence interval can be obtained for each of the means in the aggression experiment. The 95% confidence intervals for the two conditions are:

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A bar graph that includes a visual depiction of the confidence interval can be very useful. The means from the aggression experiment are shown in Figure 13.2. The shaded bars represent the mean aggression scores in the two conditions. The confidence interval for each group is shown with a vertical I-shaped line that is bounded by the upper and lower limits of the 95% confidence interval. It is important to examine confidence intervals to obtain a greater understanding of the meaning of your obtained data. Although the obtained sample means provide the best estimate of the population values, you are able to see the likely range of possible values. The size of the interval is related to both the size of the sample and the confidence level. As the sample size increases, the confidence interval narrows. This is because sample means obtained with larger sample sizes are more likely to reflect the population mean. Second, higher confidence is associated with a larger interval. If you want to be almost certain that the interval contains the true population mean (e.g., a 99% confidence interval), you will need to include more possibilities. Note that the 95% confidence intervals for the two means do not overlap. This should be a clue to you that the difference is statistically significant. Indeed, examining confidence intervals is an alternative way of thinking about statistical significance. The null hypothesis is that the difference in population means is 0.00. However, if you were to subtract all the means in the 95% confidence interval for the no-model condition from all the means in the model condition, none of these differences would include the value of 0.00. We can be very confident that the null hypothesis should be rejected.

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FIGURE 13.2

Mean aggression scores from the hypothetical modeling experiment including the 95% confidence intervals

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Statistical Significance: An Overview

The logic underlying the use of statistical tests rests on statistical theory. There are some general concepts, however, that should help you understand what you are doing when you conduct a statistical test. First, the goal of the test is to allow you to make a decision about whether your obtained results are reliable; you want to be confident that you would obtain similar results if you conducted the study over and over again. Second, the significance level (alpha level) you choose indicates how confident you wish to be when making the decision. A .05 significance level says that you are 95% sure of the reliability of your findings; however, there is a 5% chance that you could be wrong. There are few certainties in life! Third, you are most likely to obtain significant results when you have a large sample size because larger sample sizes provide better estimates of true population values. Finally, you are most likely to obtain significant results when the effect size is large, i.e., when differences between groups are large and variability of scores within groups is small.

In the remainder of the chapter, we will expand on these issues. We will examine the implications of making a decision about whether results are significant, the way to determine a significance level, and the way to interpret nonsignificant results. We will then provide some guidelines for selecting the appropriate statistical test in various research designs.

TYPE I AND TYPE II ERRORS

The decision to reject the null hypothesis is based on probabilities rather than on certainties. That is, the decision is made without direct knowledge of the true state of affairs in the population. Thus, the decision might not be correct; errors may result from the use of inferential statistics.

A decision matrix is shown in Figure 13.3. Notice that there are two possible decisions: (1) Reject the null hypothesis or (2) accept the null hypothesis. There are also two possible truths about the population: (1) The null hypothesis is true or (2) the null hypothesis is false. In sum, as the decision matrix shows, there are two kinds of correct decisions and two kinds of errors.

Correct Decisions

One correct decision occurs when we reject the null hypothesis and the research hypothesis is true in the population. Here, our decision is that the population means are not equal, and in fact, this is true in the population. This is the decision you hope to make when you begin your study.

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FIGURE 13.3

Decision matrix for Type I and Type II errors

The other correct decision is to accept the null hypothesis, and the null hypothesis is true in the population: The population means are in fact equal.

Type I Errors

Type I error is made when we reject the null hypothesis but the null hypothesis is actually true. Our decision is that the population means are not equal when they actually are equal. Type I errors occur when, simply by chance, we obtain a large value of t or F. For example, even though a t value of 4.025 is highly improbable if the population means are indeed equal (less than 5 chances out of 100), this can happen. When we do obtain such a large t value by chance, we incorrectly decide that the independent variable had an effect.

The probability of making a Type I error is determined by the choice of significance or alpha level (alpha may be shown as the Greek letter alpha—α). When the significance level for deciding whether to reject the null hypothesis is .05, the probability of a Type I error (alpha) is .05. If the null hypothesis is rejected, there are 5 chances out of 100 that the decision is wrong. The probability of making a Type I error can be changed by either decreasing or increasing the significance level. If we use a lower alpha level of .01, for example, there is less chance of making a Type I error. With a .01 significance level, the null hypothesis is rejected only when the probability of obtaining the results is .01 or less if the null hypothesis is correct.

Type II Errors

Type II error occurs when the null hypothesis is accepted although in the population the research hypothesis is true. The population means are not equal, but the results of the experiment do not lead to a decision to reject the null hypothesis.

Research should be designed so that the probability of a Type II error (this probability is called beta, or β) is relatively low. The probability of making a Page 280Type II error is related to three factors. The first is the significance (alpha) level. If we set a very low significance level to decrease the chances of a Type I error, we increase the chances of a Type II error. In other words, if we make it very difficult to reject the null hypothesis, the probability of incorrectly accepting the null hypothesis increases. The second factor is sample size. True differences are more likely to be detected if the sample size is large. The third factor is effect size. If the effect size is large, a Type II error is unlikely. However, a small effect size may not be significant with a small sample.

The Everyday Context of Type I and Type II Errors

The decision matrix used in statistical analyses can be applied to the kinds of decisions people frequently must make in everyday life. For example, consider the decision made by a juror in a criminal trial. As is the case with statistics, a decision must be made on the basis of evidence: Is the defendant innocent or guilty? However, the decision rests with individual jurors and does not necessarily reflect the true state of affairs: that the person really is innocent or guilty.

The juror’s decision matrix is illustrated in Figure 13.4. To continue the parallel to the statistical decision, assume that the null hypothesis is the defendant is innocent (i.e., the dictum that a person is innocent until proven guilty). Thus, rejection of the null hypothesis means deciding that the defendant is guilty, and acceptance of the null hypothesis means deciding that the defendant is innocent. The decision matrix also shows that the null hypothesis may actually be true or false. There are two kinds of correct decisions and two kinds of errors like those described in statistical decisions. A Type I error is finding the defendant guilty when the person really is innocent; a Type II error is finding the defendant innocent when the person actually is guilty. In our society, Type I errors by jurors generally are considered to be more serious than Type II errors. Thus, before finding someone guilty, the juror is asked to make sure that the person is guilty “beyond a reasonable doubt” or to consider that “it is better to have a hundred guilty persons go free than to find one innocent person guilty.”

The decision that a doctor makes to operate or not operate on a patient provides another illustration of how a decision matrix works. The matrix is shown in Figure 13.5. Here, the null hypothesis is that no operation is necessary. The decision is whether to reject the null hypothesis and perform the operation or to accept the null hypothesis and not perform surgery. In reality, the surgeon is faced with two possibilities: Either the surgery is unnecessary (the null hypothesis is true) or the patient will die without the operation (a dramatic case of the null hypothesis being false). Which error is more serious in this case? Most doctors would believe that not operating on a patient who really needs the operation—making a Type II error—is more serious than making the Type I error of performing surgery on someone who does not really need it.

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FIGURE 13.4

Decision matrix for a juror

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FIGURE 13.5

Decision matrix for a doctor

One final illustration of the use of a decision matrix involves the important decision to marry someone. If the null hypothesis is that the person is “wrong” for you, and the true state is that the person is either “wrong” or “right,” you must decide whether to go ahead and marry the person. You might try to construct a decision matrix for this particular problem. Which error is more costly: a Type I error or a Type II error?

CHOOSING A SIGNIFICANCE LEVEL

Researchers traditionally have used either a .05 or a .01 significance level in the decision to reject the null hypothesis. If there is less than a .05 or a .01 probability that the results occurred because of random error, the results are said to be significant. However, there is nothing magical about a .05 or a .01 significance level. The significance level chosen merely specifies the probability of a Type I error if the null hypothesis is rejected. The significance level chosen by the researcher usually is dependent on the consequences of making a Type I versus a Type II error. As previously noted, for a juror, a Type I error is more serious than a Type II error; for a doctor, however, a Type II error may be more serious.

Researchers generally believe that the consequences of making a Type I error are more serious than those associated with a Type II error. If the null hypothesis is rejected, the researcher might publish the results in a journal, and the results might be reported by others in textbooks or in newspaper or magazine articles. Page 282Researchers do not want to mislead people or risk damaging their reputations by publishing results that are not reliable and so cannot be replicated. Thus, they want to guard against the possibility of making a Type I error by using a very low significance level (.05 or .01). In contrast to the consequences of publishing false results, the consequences of a Type II error are not seen as being very serious.

Thus, researchers want to be very careful to avoid Type I errors when their results may be published. However, in certain circumstances, a Type I error is not serious. For example, if you were engaged in pilot or exploratory research, your results would be used primarily to decide whether your research ideas were worth pursuing. In this situation, it would be a mistake to overlook potentially important data by using a very conservative significance level. In exploratory research, a significance level of .25 may be more appropriate for deciding whether to do more research. Remember that the significance level chosen and the consequences of a Type I or a Type II error are determined by what the results will be used for.

INTERPRETING NONSIGNIFICANT RESULTS

Although “accepting the null hypothesis” is convenient terminology, it is important to recognize that researchers are not generally interested in accepting the null hypothesis. Research is designed to show that a relationship between variables does exist, not to demonstrate that variables are unrelated.

More important, a decision to accept the null hypothesis when a single study does not show significant results is problematic, because negative or nonsignificant results are difficult to interpret. For this reason, researchers often say that they simply “fail to reject” or “do not reject” the null hypothesis. The results of a single study might be nonsignificant even when a relationship between the variables in the population does in fact exist. This is a Type II error. Sometimes, the reasons for a Type II error lie in the procedures used in the experiment. For example, a researcher might obtain nonsignificant results by providing incomprehensible instructions to the participants, by having a very weak manipulation of the independent variable, or by using a dependent measure that is unreliable and insensitive. Rather than concluding that the variables are not related, researchers may decide that a more carefully conducted study would find that the variables are related.

We should also consider the statistical reasons for a Type II error. Recall that the probability of a Type II error is influenced by the significance (alpha) level, sample size, and effect size. Thus, nonsignificant results are more likely to be found if the researcher is very cautious in choosing the alpha level. If the researcher uses a significance level of .001 rather than .05, it is more difficult to reject the null hypothesis (there is not much chance of a Type I error). However, that also means that there is a greater chance of accepting an incorrect null hypothesis (i.e., a Type II error is more likely). In other words, a meaningful result is more likely to be overlooked when the significance level is very low.

Page 283A Type II error may also result from a sample size that is too small to detect a real relationship between variables. A general principle is that the larger the sample size is, the greater the likelihood of obtaining a significant result. This is because large sample sizes give more accurate estimates of the actual population than do small sample sizes. In any given study, the sample size may be too small to permit detection of a significant result.

A third reason for a nonsignificant finding is that the effect size is small. Very small effects are difficult to detect without a large sample size. In general, the sample size should be large enough to find a real effect, even if it is a small one.

The fact that it is possible for a very small effect to be statistically significant raises another issue. A very large sample size might enable the researcher to find a significant difference between means; however, this difference, even though statistically significant, might have very little practical significance. For example, if an expensive new psychiatric treatment technique significantly reduces the average hospital stay from 60 to 59 days, it might not be practical to use the technique despite the evidence for its effectiveness. The additional day of hospitalization costs less than the treatment. There are other circumstances, however, in which a treatment with a very small effect size has considerable practical significance. Usually this occurs when a very large population is affected by a fairly inexpensive treatment. Suppose a simple flextime policy for employees reduces employee turnover by 1% per year. This does not sound like a large effect. However, if a company normally has a turnover of 2,000 employees each year and the cost of training a new employee is $10,000, the company saves $200,000 per year with the new procedure. This amount may have practical significance for the company.

The key point here is that you should not accept the null hypothesis just because the results are nonsignificant. Nonsignificant results do not necessarily indicate that the null hypothesis is correct. However, there must be circumstances in which we can accept the null hypothesis and conclude that two variables are, in fact, not related. Frick (1995) describes several criteria that can be used in a decision to accept the null hypothesis. For example, we should look for well-designed studies with sensitive dependent measures and evidence from a manipulation check that the independent variable manipulation had its intended effect. In addition, the research should have a reasonably large sample to rule out the possibility that the sample was too small. Further, evidence that the variables are not related should come from multiple studies. Under such circumstances, you are justified in concluding that there is in fact no relationship.

CHOOSING A SAMPLE SIZE: POWER ANALYSIS

We noted in Chapter 9 that researchers often select a sample size based on what is typical in a particular area of research. An alternative approach is to select a sample size on the basis of a desired probability of correctly rejecting the null hypothesis. This probability is called the power of the statistical test. It is obviously related to the probability of a Type II error:

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TABLE 13.2 Total sample size needed to detect a significant difference for a t test

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We previously indicated that the probability of a Type II error is related to significance level (alpha), sample size, and effect size. Statisticians such as Cohen (1988) have developed procedures for determining sample size based on these factors. Table 13.2 shows the total sample size needed for an experiment with two groups and a significance level of .05. In the table, effect sizes range from .10 to .50, and the desired power is shown at .80 and .90. Smaller effect sizes require larger samples to be significant at the .05 level. Higher desired power demands a greater sample size; this is because you want a more certain “guarantee” that your results will be statistically significant. Researchers usually use a power between .70 and .90 when using this method to determine sample size. Several computer programs have been developed to allow researchers to easily make the calculations necessary to determine sample size based on effect size estimates, significance level, and desired power.

You may never need to perform a power analysis. However, you should recognize the importance of this concept. If a researcher is studying a relationship with an effect size correlation of .20, a fairly large sample size is needed for statistical significance at the .05 level. An inappropriately low sample size in this situation is likely to produce a nonsignificant finding.

THE IMPORTANCE OF REPLICATIONS

Throughout this discussion of statistical analysis, the focus has been on the results of a single research investigation. What were the means and standard deviations? Was the mean difference statistically significant? If the results are significant, you conclude that they would likely be obtained over and over again if the study were repeated. We now have a framework for understanding the results of the study. Be aware, however, that scientists do not attach Page 285too much importance to the results of a single study. A rich understanding of any phenomenon comes from the results of numerous studies investigating the same variables. Instead of inferring population values on the basis of a single investigation, we can look at the results of several studies that replicate previous investigations (see Cohen, 1994). The importance of replications is a central concept in Chapter 14.

SIGNIFICANCE OF A PEARSON r CORRELATION COEFFICIENT

Recall from Chapter 12 that the Pearson r correlation coefficient is used to describe the strength of the relationship between two variables when both variables have interval or ratio scale properties. However, there remains the issue of whether the correlation is statistically significant. The null hypothesis in this case is that the true population correlation is 0.00—the two variables are not related. What if you obtain a correlation of .27 (plus or minus)? A statistical significance test will allow you to decide whether to reject the null hypothesis and conclude that the true population correlation is, in fact, greater than 0.00. The technical way to do this is to perform a t test that compares the obtained coefficient with the null hypothesis correlation of 0.00. The procedures for calculating a Pearson r and determining significance are provided in Appendix C.

COMPUTER ANALYSIS OF DATA

Although you can calculate statistics with a calculator using the formulas provided in this chapter, Chapter 12, and Appendix C, most data analysis is carried out via computer programs. Sophisticated statistical analysis software packages make it easy to calculate statistics for any data set. Descriptive and inferential statistics are obtained quickly, the calculations are accurate, and information on statistical significance is provided in the output. Computers also facilitate graphic displays of data.

Some of the major statistical programs include SPSS, SAS, SYSTAT, and freely available R and MYSTAT. Other programs may be used on your campus. Many people do most of their statistical analyses using a spreadsheet program such as Microsoft Excel. You will need to learn the specific details of the computer system used at your college or university. No one program is better than another; they all differ in the appearance of the output and the specific procedures needed to input data and have the program perform the test. However, the general procedures for doing analyses are quite similar in all of the statistics programs.

The first step in doing the analysis is to input the data. Suppose you want to input the data in Table 12.1, the modeling and aggression experiment. Data Page 286are entered into columns. It is easiest to think of data for computer analysis as a matrix with rows and columns. Data for each research participant are the rows of the matrix. The columns contain each participant’s scores on one or more measures, and an additional column may be needed to indicate a code to identify which condition the individual was in (e.g., Group 1 or Group 2). A data matrix in SPSS for Windows is shown in Figure 13.6. The numbers in the “group” column indicate whether the individual is in Group 1 (model) or Group 2 (no model), and the numbers in the “aggscore” column are the aggression scores from Table 12.1.

Other programs may require somewhat different methods of data input. For example, in Excel, it is usually easiest to set up a separate column for each group, as shown in Figure 13.6.

The next step is to provide instructions for the statistical analysis. Again, each program uses somewhat different steps to perform the analysis; most require you to choose from various menu options. When the analysis is completed, you are provided with the output that shows the results of the statistical procedure you performed. You will need to learn how to interpret the output. Figure 13.6 shows the output for a t test using Excel.

When you are first learning to use a statistical analysis program, it is a good idea to practice with some data from a statistics text to make sure that you get the same results. This will ensure that you know how to properly input the data and request the statistical analysis.

SELECTING THE APPROPRIATE STATISTICAL TEST

We have covered several types of designs and the variables that we study may have nominal, ordinal, interval, or ratio scale properties. How do you choose the appropriate statistical test for analyzing your data? Fortunately, there are a number of online guides and tutorials such as http://www.socialresearch-methods.net/selstat/ssstart.htm and http://wise.cgu.edu/choosemod/opening.htm; SPSS even has its own Statistics Coach to help with the decision.

We cannot cover every possible analysis. Our focus will be on variables that have either (1) nominal scale properties—two or more discrete values such as male and female or (2) interval/ratio scale properties with many values such as reaction time or rating scales (also called continuous variables). We will not address variables with ordinal scale values.

Research Studying Two Variables (Bivariate Research)

In these cases, the researcher is studying whether two variables are related. In general, we would refer to the first variable as the independent variable (IV) and the second variable as the dependent variable (DV). However, because it does not matter whether we are doing experimental or nonexperimental research, we could just as easily refer to the two variables as Variable X and Variable Y or Variable A and Variable B.

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FIGURE 13.6

Sample computer input and output using data from Table 12.1 (modeling experiment)

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Research with Multiple Independent Variables

In the following situations, we have more complex research designs with two or more independent variables that are studied with a single outcome or dependent variable.

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These research design situations have been described in previous chapters. There are of course many other types of designs. Designs with multiple variables (multivariate statistics) are described in detail by Tabachnick and Fidell (2007). Procedures for research using ordinal level measurement may be found in a book by Siegel and Castellan (1988).

You have now considered how to generate research ideas, conduct research to test your ideas, and evaluate the statistical significance of your results. In the final chapter, we will examine issues of generalizing research findings beyond the specific circumstances in which the research was conducted.

Study Terms

Alpha level (p. 270)

Analysis of variance (F test) (p. 275)

Confidence interval (p. 276)

Degrees of freedom (p. 274)

Page 289Error variance (p. 275)

Inferential statistics (p. 267)

Null hypothesis (p. 268)

Power (p. 284)

Probability (p. 269)

Research hypothesis (p. 268)

Sampling distribution (p. 270)

Statistical significance (p. 269)

Systematic variance (p. 275)

t test (p. 272)

Type I error (p. 279)

Type II error (p. 279

What do you think about this, and what safeguards do you think should be put into place in order to ensure that behavioral therapy is ethical?

Week 14 Discussion: Discussion on Children and Older Adults

Presentation on Therapeutic Techniques for Children and Older Adults

James O’Hara, Karen Watson, Carinna Wilmot, Emily Ziniel

Regis College

Presentation on Therapeutic Techniques for Children and Older Adults

Introduction

            This discussion will focus on therapeutic techniques that can be used for children and older adults. The first technique consists of behavioral therapy. Behavioral therapy focuses on the way that behaviors are learned through associations with positive or negative effects associated with the behaviors, and how it is thus possible to form new associations and thus change problematic behaviors (Gotter, 2016). This is an appropriate approach for children because children may not yet have developed the self-critical faculties required to reflect on their own beliefs, which is the foundation of popular interventions such as cognitive-behavioral therapy. Likewise, older adults may also be more set in their ways and thus responsive to direct consequences of their behaviors rather than overarching criticisms of their broader worldviews. There are some ethical concerns associated with behavioral therapy, due to the fact that the process of re-forming positive and negative associations could be seen by some as inherently manipulative (Graham, 2019). Care must thus be taken in order to have the full consent of subjects and/or guardians.

A second approach that would be appropriate for children and older adults consists of family therapy. Family therapy begins from the basic premise that most people are embedded within family networks that contribute a great deal to their emotional states as well as to the resources at their disposal (LoBiondo-Wood, 2008). This is especially important for children, given that children are by definition embedded in relations with their families or guardians. it could also be very important for older adults insofar as they may no longer be working and may thus find themselves dependent once again on their family networks as they retire and enter into the elderly phase of their lifecycles. For example, in some situations, elder abuse may be a problem that needs to be addressed through the implementation of family therapy (National Institute on Aging, 2019).

Discussion Prompts

1.         There are some ethical disputes about behavioral therapy, due to the fact that behaviorism was developed through research on animals and seems to suggest that humans can be easily manipulated. What do you think about this, and what safeguards do you think should be put into place in order to ensure that behavioral therapy is ethical?

2.         To what extent does the family shape the individual, and to what extent does the individual shape the family. You are welcome to draw on personal experiences in order to more effectively reflect on and respond to this prompt.

How can we use learning and memory theories and research to memorize important information and create more effective study techniques?

STRATEGIES FOR ENHANCING LEARNING AND MEMORY

We have explored strategies for enhancing learning and memory in several ways, answering questions like:

  • How can we use learning and memory theories and research to memorize important information and create more effective study techniques?
  • What is the best way to store information in long-term memory?
  • What are some strategies we can use to help us get information out of long-term memory when we need it?

Using course readings, supplemental scholarly literature, and other relevant sources as a basis, develop a presentation on strategies for improving learning and memory.

Assignment Instructions

  1. Develop your presentation using Power Point, Prezi, or a similar alternative file format.
  2. Your presentation will be in two parts:
    • Part 1: Focus on three techniques for enhancing learning and memory. These are general techniques that can be used with anyone, including yourself!
    • Part 2: Focus on memory techniques for a specific population you plan to work with (such as children, older adults, autistic individuals or another special needs population).
  3. The length of your presentation should be 10–15 slides (not including the title page and references).
    • Slides should contain bullet points or brief phrases as well as images (topic-related pictures or clip art) on select slides. Optionally, you may also insert short video clips.
    • You are also required to use the notes section to include expanded details that elaborate on the slides.
  4. Use your course resources and at least three peer-reviewed and scholarly resources (no more than five years old) to help support your presentation.
  5. Review the Strategies for Enhancing Learning and Memory Scoring Guide as you work through this assignment to ensure that you complete all required elements.

The following resources will help you locate sources to support your ideas and cite them appropriately using APA style:

Self-Evaluation

When you have finished your presentation, write a separate, one-page self-evaluation of your work compared to the scoring guide criteria.

  1. Ensure that you have completed all assignment requirements (ideally at the distinguished level).
  2. Evaluate your performance using the criteria in the scoring guide.
    • Compare and contrast your self-evaluation from the Week 5 assignment with the feedback provided by your instructor to align your personal evaluation with faculty expectations.
  3. Indicate the proficiency level you met for each criterion.
  4. Include the scoring guide (including comments) with your self-evaluation.
  5. Submit as a separate attachment when you submit your presentation.

Assignment Requirements

  • Written communication: Written communication is free of errors that detract from the overall message.
  • Sources: A minimum of three scholarly sources published within the past five years is required.
  • APA formatting: Resources and citations are formatted according to current APA style and formatting guidelines.
  • Length: 10–15 slides (not including the title and references slides).