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Chung, Self-esteem 1

Running head: SELF-ESTEEM OF INDIVIDUALS WITH SPEECH IMPAIRMENTS

Self-esteem Qualities of Individuals with Speech Impairments

Who Use the Internet and AAC Devices

Yoosun Chung

George Mason University

EDRS 811

December 4, 2001


Self-esteem Qualities of Individuals with Speech Impairments

Who Use the Internet and AAC Devices

I. Research Questions and Hypotheses

A. Rational for the Study

The topic I am interested in investigating for this research is the impact of using the Internet

and Augmentative and Alternative Communication (AAC) devices on individuals with speech

impairments. Based on my experience as an individual with cerebral palsy (C.P.), the advent of

the Internet has impacted my life in many positive ways. Specially, I have gained much self-

esteem by using the Internet. Additionally, my self-esteem has improved since I have been using

my AAC device for a year.

I wanted to ask myself why self-esteem is an important issue to study. Appell, Hoffman and

Speller (1999) address that self-esteem is an essential, powerful tool, equipping a child with a

disability to maneuver through challenging times. Daves (1999) states that children with

disabilities have even greater challenges in life and their self-esteem is a major influence on the

outcome of their lives. Based on these studies, I have learned that self-esteem is especially

important for people with disabilities who frequently have to face challenging realities.

There are numerous researchable issues regarding use of the Internet for individuals with

physical disabilities. Some of these issues include: self-esteem of the users, accessibility, how

the Internet could be further developed taking into account the perspectives of individuals with

disabilities, among others. From these issues, I chose to focus on self-esteem issues that people

with speech impairments face. Much research has dealt with the issue of self-esteem in people

with disabilities. However, questions about the actual effects of the Internet on these


individuals’ self-esteem are not fully understood. Thus, I wanted to concentrate on both the

Internet and the self-esteem of individuals with speech impairments.

Regarding the use of AAC devices, there is a continual dilemma regarding whether the use of

AAC is detrimental or beneficial to the development of speech for young users. In addition,

individuals with speech impairments, including myself, sometimes worry about possibility of

becoming too dependent on an AAC device enough so that they would eventually lose the

courage to speak using their own voice. However, in my own experiences using AAC, I have

had many more opportunities to participate in class discussions and to present at conferences in

educational settings. Additionally, I believe that I’ll be able to greatly excel in my professional

career, because I know that my AAC device will assist me in interacting with my peers. My

curiosity now shifts and I wonder whether others with speech impairments gain a greater sense

of self-esteem due to their own use of AAC.

It is common knowledge that self-esteem is of great importance for people with disabilities.

Therefore, if my research shows that using the Internet and AAC device results in gains of self-

esteem for individuals with speech impairments, more people will then understand the

importance of using the Internet and /or AAC within the disability communities.

B. Statement of the Research Questions

1. Is the self-esteem of individuals with speech impairments affected by their experiences of

using the Internet and / or an AAC device?

2. Is there a significant relationship between the self-esteem scores of individuals with speech

impairments and [1] the number of months they have used an AAC device and / or [2] the

number of months they have used the Internet?


C. Research Hypotheses and Null Hypotheses

Research Hypotheses

1. Individuals with speech impairments who have experiences using the Internet have greater

self-esteem scores than those who have less experience using the Internet.

2. Individuals with speech impairments who have experiences using of AAC have greater

self-esteem scores than those who have less experience using AAC.

3. The self-esteem scores of individuals with speech impairments can be predicted by [1] the

number of months of experiences using AAC and / or [2] by the number of months of

experience using the Internet.

Null H ypotheses

1. There will be no significant difference in the self-esteem scores of individuals with speech

impairments who have used the Internet for many years and those who used the Internet

for less time.

2. There will be no significant difference in the self-esteem scores of individuals with speech

impairments who have used AAC for more years and those who have used AAC for less

time.

3. The self-esteem scores of individuals with speech impairments cannot be predicted by [1]

the number of months of experiences using AAC and / or [12] by the number of months of

experience using the Internet.

II. Data Collection

A. Variables in the Study

The purpose of this study is to investigate different self-esteem issues of individuals with


speech impairments who use the Internet and AAC devices. More specifically, I would like to

find out how the self-esteem of individuals with speech impairments is affected through using

the Internet and through using an AAC device. In order to examine self-esteem, two

independent variables will be examined both in an analysis of variance, ANOVA, and in a

Multiple Linear Regression Analysis, MLR. The first variable examines the experiences of

individuals with speech impairments using the Internet (EXINT). The second variable looks at

the experiences of an individual with speech impairments using an AAC device (EXAAC). The

dependent variable to be examined is the self-esteem (ESTEEM) of individuals with speech

impairments.

B. Descriptions of Instruments

I used a Likert-style questionnaire instrument for this study. I followed closely the

questionnaire that was used by Grimaldi and Goette (1999, Appendix). The first part of my

questionnaire looked at the potential existence of a gain or loss in the degree of an individual’s

self-esteem (ESTEEM) due to his or her use of the Internet and use of an AAC device. Also

within this questionnaire, I asked the questions regarding barriers that people face using the

Internet and AAC and regarding any desires these peoples have when using these two mediums.

C. Data Collection Procedures

For this study, I subscribed to an AAC LISTERSEV. I chose this LISTSERV because it was

comprised of people who fit my target population --- individuals with speech impairments who

use AAC devices. In addition, I could conclude that people who were members of the AAC

LISTSERV were already Internet users, and this met the criteria for my target population too.

I posted a message on the LISTSERV stating my research need for participant who meet my

criteria. After I identified my participants (50 people), I emailed my questionnaire to them, and


they emailed it back to me.

* Note: I did not collect actual data. Rather, I used simulated data for this study.

D. Coding and Organization of the Data

For the dependent variable for both the ANOVA and MLR in this study, the scores from the

self-esteem (ESTEEM) questionnaire were used.

For the independent variables for the ANOVA, experiences with the Internet (EXINT) was

coded as 3 levels: Low (1), Medium (2), and High (3). The other independent variable for the

ANOVA, experiences with an AAC device (EXINT) was coded as 3 levels also: Low (1),

Medium (2), and High (3). In both cases, the low range was from 1 to 12 months, the medium

range was from 13 to 36 months, and the high range was for greater 37 months.

For the MLR analysis, this time, I used both experiences with the Internet (EXINT) and

experiences with the AAC device (EXAAC) as interval data. I measured these two independent

variables over a series of months. More specifically, for the EXINT variable, each number

corresponds to the number of months of experience that the user has had with the Internet. In

addition, for the EXAAC variable, each number corresponds to the number of months experience

with the AAC device. Please see the variable lists in table 1.

NAME TYPE PROCEDURE

Self-esteem (ESTEEM) Dependent, interval ANOVA & MLR

Experiences of the Internet

(EXINT)

Independent, categorical

Independent, interval

ANOVA

MLR

Experiences of the AAC

(EXAAC)

Independent, categorical

Independent, interval

ANOVA

MLR

Table 1


III. Data Analysis

A. Rationale for Analysis Technique

Descriptive statistics used in this study describe the population of individuals with speech

impairments by size, measures of central tendency, and measures of dispersion. These statistics

shows the characterization of similar populations of individuals with speech impairments. The

frequency table shows the list of self- esteem (ESTEEM) scores that people received and also

shows how many times each score appears. Standard deviation and variance measure the

dispersion for each of the dependent and independent variables used in the analyses.

A two-way ANOVA was used to test the null hypothesis: that there is no significant

difference in the self-esteem scores, a dependent variable (ESTEEM), of individuals with speech

impairments based on the two independent variables, the experiences with the Internet (EXINT)

and experiences with AAC devices (EXAAC). This design is a 3 X 3 factorial design with three

levels for the participant’s Internet experiences (low, medium, high) and three levels of

experiences using AAC (low, medium, high).

The MLR procedure was used to analyze whether the two predictor variables, the

participant’s experiences with the Internet (EXINT) and their experiences with AAC devices

(EXAAC), predicted the self-esteem scores (ESTEEM) of individuals with speech impairments.

The purpose of this analysis was to determine if one or both predictor variables could account for

the differences in variance in the self-esteem scores of participants. In addition, the resulting

coefficients indicated the strength of the relationship between the two predictor variables

(EXINT and EXAAC) and the dependent variable (ESTEEM).

B. Exploratory Analyses

Exploratory analyses were performed to determine if the assumptions for the ANOVA and


MLR were met. The alpha level for the analyses was set at .05.

For the ANOVA, several assumptions regarding the data had to be met. First, the individuals

in the groups had to be independent of one another. This assumption was obviously met because

selecting a person for the Internet group is not at all influenced by selecting a person for the

AAC group. The second assumption was that the samples selected were random samples. Since

I selected my samples from the AAC LISTSERV, my participants already used both the Internet

and AAC devices. Thus, my sample is not a true random sample. However, since I used the

participant’s degree of experiences starting from low, not interpreted as no experience at all, to

high, I can say that my samples are near to randomly selected in my study.

The next assumption is that scores from the dependent variable are normally distributed

within each group. The Kolmogorov-Smirnov Tests was used to test the distribution of self-

esteem scores for the independent variable, experiences of the Internet (EXINT) in Table 2 with

1-12 month-experiences (p = .081 > .05), 13-36 month-experiences (p = .200 > .05), and more

than 37 month-experiences (p = .200 > .05) all indicating a normal distribution.

In addition, the Kolmogorov-Smirnov Test for the independent variable, experiences with an

AAC device (EXAAC), also indicated normal distributions for the self-esteem scores with 1-12

month-experiences (p = .200 > .05), 13-36 month-experiences (p = .200 > .05), and more than 37

month-experiences (p = .200 > .05) in Table 3.


Table 2

Table 3

The last assumption for the ANOVA is that the variances of the groups are equal regarding

the dependent measure. The Levene Test was used to test this assumption for the independent

variable, experience with the Internet (EXINT), showing that this assumption was met (p = .877

> .05) in Table 4. In addition, for the other independent variable, experience with an AAC

device (EXAAC), also met this assumption with (p = .977 > .05) in Table 5.

Table 4: Levene Test of Independent Variable: experiences with the Internet (EXINT)

Test of Homogeneity of Variance

.131 2 47 .877Based on MeanSelf-esteem Scores

LeveneStatistic df1 df2 Sig.

Tests of Normality

.207 9 .200* .897 9 .300

.157 17 .200* .933 17 .308

.101 24 .200* .974 24 .743

levels of experienceswith an AAC device1-12 months ofexperience with AAC13-36 months ofexperiences withAACmore than 37 monthsof experiences withAAC

Self-esteemscores

Statistic df Sig. Statistic df Sig.Kolmogorov-Smirnova Shapiro-Wilk

This is a lower bound of the true significance.*. Lilliefors Significance Correctiona.

Tests of Normality

.260 9 .081 .926 9 .451

.143 15 .200* .945 15 .461

.107 26 .200* .969 26 .601

levels of experienceswith the Internet1-12 months ofexperiences with theInternet13-36 months ofexperiences with theInternetmore than 37 monthsof experiences withthe Internet

Self-esteem scoresStatistic df Sig. Statistic df Sig.

Kolmogorov-Smirnova Shapiro-Wilk



Table 5: Levene Test of Independent Variable: experiences with AAC (EXAAC)

The MLR analysis also required several assumptions prior to analyzing the data. As I

mentioned in ANOVA assumptions above, individuals in the groups is independent of one

another. And my samples are almost randomly selected. In addition, variables measured for the

MLR procedure are at least on an interval scale.

For the MLR procedure, it was assumed that the relationship between the dependent variable

and each independent variable is linear. A matrix scatterplot in figure 1 tested for this

assumption. This figure did not indicate a linear relationship. Fortunately, I found that my linear

regression model was appropriate, when I tested the linear relationship again after MLR analysis

was performed. That is, I performed a series of scatterplots - plotting the studentized deleted

residual on the Y-axis and each independent variable on the X-axis in the sequence. Since I did

not see patterns in Figure 2 and Figure 3, I can say that my linear regression model was

appropriate.

Test of Homogeneity of Variance

.024 2 47 .977Based on MeanSelf-esteem Scores

LeveneStatistic df1 df2 Sig.


Figure 1: Scatterplot matrix for all variables used in the MLR

Figure 2: Scatterplot for sdr_1 and EXAAC


Figure 3: Scatterplot for sdr_1 and EXINT

A Kolmogorov-Smirnov test was performed on the studentized deleted residuals to test for a

normal distribution of the dependent variable. The result (p = .200 > .05) in Table 6 indicates

that a normal distribution of the dependent variable (ESTEEM).

Table 6: Normality Test f or Dependent Variable (ESTEEM)

Lastly, a scatterplot was used to test for constant variance of the predicted and residual values

Tests of Normality

.069 50 .200* .974 50 .517StudentizedDeleted Residual

Statistic df Sig. Statistic df Sig.Kolmogorov-Smirnova Shapiro-Wilk



of the self-esteem scores. Since there is no particular pattern in the Figure 4, the variance is

constant.

Figure 4: Scatterplot of Studentized Deleted Residuals against Predicted Values for

the Dependent Variable (ESTEEM)

C. Description of Analyses

The ANOVA procedure answers this question: Is the self-esteem of individuals with speech

impairments affected by the experiences these individuals have using the Internet and by their

experiences using an AAC device? Using the ANOVA, I was able to determine if either the use

of the Internet or the use of the AAC devices has an effect on the self-esteem of the individuals

with speech impairments. A Bonferroni post hoc comparison was used to indicate the specific

differences between various groups considering that the ANOVA shows that a significant

difference exists for the independent variables.


The MLR procedure answers this research question: Is there a significant relationship

between the self-esteem scores of individuals with speech impairments and [1] the number of

months they have used an AAC device and / or [2] the number of months they have used the

Internet? Using the results of a stepwise regression model, I produced the R2 value to determine

the percent of variability accounted for by each of the predictor variables. In addition, I was able

to establish the regression equation using regression coefficients.

IV. Research Design

A. Description of the Sample

I chose 50 participants, with speech impairments, who subscribed to an AAC LISTSERV.

My ultimate target population included individuals with speech impairments, and the sample was

selected from an AAC LISTSERV because this afforded me the opportunity to find people with

speech impairments more easily. Thus, my sample was not a true random sample.

In addition, the purpose of this study was to investigate the relationship between self-esteem

issues and the experiences of individuals with the Internet and AAC devices. My sample used

both the Internet and AAC devices, since subscribing to a LISTSERV means that a person has

already used the Internet and they would not subscribe to this listserv unless they use AAC.

Thus, this further indicates that my sample is not a true random sample. However, since I am

using the participant’s degree of experience starting from low, not interpreted as no experience at

all, to high, I can say that my samples are almost randomly selected at least in my study.

B. Description of the Research Design

I did not use any treatment, pre-test or post-testing at all in this study. I simply designed a

data collection procedure for description, relationship (for ANOVA analysis), and prediction (for

MLR analysis). Table 7 shows the type of research design, the analysis methods, and variables

for this research.


TYPE OF RESEARCH DESIGN

ANALYSIS METHOD

INDEPENDENT / PREDICTOR VARIABLES

DEPENDENT VARIABLE

Relationship 3 X 3 Factorial ANOVA

A. The experiences of the Internet

B. The experiences of the AAC device

Self-esteem scores

Prediction Stepwise MLR A. The experiences of the Internet

B. The experiences of the AAC device

Self-esteem scores

Table 7

C. Design Validity

Sampling in this study is relatively small (external validity) --- this is a very obvious

external validity threat. To solve this generalization issue, I will increase the number of

participants in my future research studies.

My questionnaire may not be the best tool for measuring levels of self-esteem --- my

research conclusions could be wrong if my questionnaire is not an appropriate

measurement tool. Thus, I should put in the effort to create an appropriate questionnaire

so that my conclusions are more credible and generalizable.

D. Statistical Validity

The alpha level for all statistical analyses was set at .05. --- using a different alpha level

might result in different statistical results.

The results of various tests for the assumptions of the ANOVA and MLR show that most

of the data met the criteria for each assumption, except the linear relationship in the

scatterplots for each variable in the MLR analysis. --- transforming data before


performing any analysis may be result in better conclusions.

V. Results

A. Descriptive Findings

Tables and figures in Appendix show the size of the population, the measures of central

tendency, standard deviation, and frequencies for each of the independent and dependent

variables. The mean of self-esteem scores for individuals with speech impairments is 32.6647

with a standard deviation of 3.1008. The frequency table of dependent variable (ESTEEM)

shows the list of self-esteem scores that people received and also shows how many of each score

there is. In addition, the frequency table of two independent variables (EXINT and EXAAC)

also shows the list of each level of Internet or AAC usage that the people experienced and shows

how many of each level there is. I also included histograms of the dependent variable and two

independent variables for reference in Appendix.

B. Inferential Findings

ANOVA

The ANOVA in Table 8 showed that we fail to reject the null hypothesis that the effect of

experiences of the Internet on self-esteem scores is the same for the three level-experiences with

AAC in the population (F(4,41) = 1.148, p = .348 > .05). Thus, there is no interaction effect

between the experiences of the Internet and the experiences of the AAC devices on the

participants’ self-esteem scores. You can see this fact when you look at the interaction graph in

figure 5.


Table 8

Figure 5

Since I did not find an interaction between the experiences of the Internet and the

experiences of the AAC device, it made sense to test the hypotheses about main effects of these

two independent variables. An examination of the ANOVA in Table 8, revealed main effects at

Tests of Between-Subjects EffectsDependent Variable: self-esteem scores using RV:Normal, mean 32.80, sd 3.25

241.956a 8 30.244 5.411 .00034172.417 1 34172.417 6113.647 .000

106.477 2 53.238 9.525 .00093.121 2 46.560 8.330 .00125.658 4 6.415 1.148 .348

229.171 41 5.59053820.131 50

471.126 49

SourceCorrected ModelInterceptEXAACEXINTEXAAC * EXINTErrorTotalCorrected Total

Type III Sumof Squares df Mean Square F Sig.

R Squared = .514 (Adjusted R Squared = .419)a.


the alpha level of .05 for the two independent variables, experiences with the Internet and

experiences with an AAC device. In other words, we reject the null that there is no significant

difference in the self-esteem scores among the three levels of the Internet experiences (F(2,41) =

8.330, p = .001 < .05). Also, we reject the null that there is no significant difference in the self-

esteem scores among the three levels of the AAC experiences (F(2,41) = 9.525, p = .000 < .05).

Based on the ANOVA Table 8, I conclude that there are differences in the mean self-

esteem scores both among the individuals who have three different levels of Internet experiences

and among those who have three different levels of AAC experiences. Then which of the

Internet groups have significantly different self-esteem scores from one another? And which of

AAC users have significantly different self-esteem scores from one another? I used the

Bonferroni post hoc test to identify the groups that are statistically different from one another.

The mean self-esteem scores for individuals with 1-12 months of Internet experiences

(mean: 29.394) is significantly lower than the mean self-esteem scores for both those who have

13-36 months of Internet experiences (mean: 31.545) and those with more than 37 months of

Internet experiences (mean: 33.226). A Bonferroni post hoc test revealed that the mean

difference between 1-12 months of experiences with the Internet and 13-36 months of

experiences with the Internet is 2.7961 and is significant at the .05 level (p = .023 < .05). Also,

the mean difference between 1-12 months of experiences with the Internet and more than 37

months of experiences with the Internet is 3.9678 and is significant at the .05 level (p = .000

< .05). However, there is no significant difference between 13-36 months of experiences with

the Internet and more than 37 months of experiences with the Internet. Table 9 and Table 10

show the statistics for these findings.


Table 9

Table 10

Similarly, table 11 and 12 show at which level the AAC users is statistically different

from one another. Specifically, the mean self-esteem scores for individuals with more than 37

months of AAC experiences (mean: 33.580) is significantly higher than the mean self-esteem

scores for those who has 1-12 months of AAC experience (mean: 29.602). A Bonferroni post

hoc test revealed that the mean difference between 1-12 months of experiences with the AAC

levels of the Internet experiencesDependent Variable: self-esteem scores using RV:Normal, mean 32.80, sd 3.25

29.394 .820 27.737 31.051

31.545 .731 30.069 33.022

33.226 .493 32.231 34.221

levels of the Internetexperiences1-12 months ofexperiences with theInternet13-36 months ofexperiences with theInternetmore than 37 monthsof experiences withthe Internet

Mean Std. Error Lower Bound Upper Bound95% Confidence Interval

Multiple ComparisonsDependent Variable: self-esteem scores using RV:Normal, mean 32.80, sd 3.25Bonferroni

-2.7961* .9968 .023 -5.2844 -.3078

-3.9678* .9144 .000 -6.2502 -1.6854

2.7961* .9968 .023 .3078 5.2844

-1.1716 .7666 .402 -3.0851 .7418

3.9678* .9144 .000 1.6854 6.2502

1.1716 .7666 .402 -.7418 3.0851

(J) levels of theInternet experiences13-36 months ofexperiences with theInternetmore than 37 monthsof experiences withthe Internet1-12 months ofexperiences with theInternetmore than 37 monthsof experiences withthe Internet1-12 months ofexperiences with theInternet13-36 months ofexperiences with theInternet

(I) levels of theInternet experiences1-12 months ofexperiences with theInternet

13-36 months ofexperiences with theInternet

more than 37 monthsof experiences withthe Internet

MeanDifference

(I-J) Std. Error Sig. Lower Bound Upper Bound95% Confidence Interval

Based on observed means.The mean difference is significant at the .05 level.*.


and more than 37 months of experiences with the AAC is 3.9410 and is significant at the .05

level (p = .000 < .05). However, there is no significant difference between 13-36 months of

experiences with the AAC and 1-12 months of experiences with the AAC. Also, there is no

significant difference between 13-36 months of experiences of the AAC and more than 37

months of experiences with the AAC.

Table 11

Table 12

levels of the AAC device experiencesDependent Variable: self-esteem scores using RV:Normal, mean 32.80, sd 3.25

29.602 .863 27.859 31.346

30.983 .651 29.668 32.299

33.580 .530 32.510 34.651

levels of the AACdevice experiences1-12 months ofexperiences with anAAC device13-36 months ofexperiences with anAAC devicemore than 37 monthsof experiences of anAAC device

Mean Std. Error Lower Bound Upper Bound95% Confidence Interval

Multiple ComparisonsDependent Variable: self-esteem scores using RV:Normal, mean 32.80, sd 3.25Bonferroni

-2.1096 .9746 .109 -4.5424 .3232

-3.9410* .9241 .000 -6.2477 -1.6343

2.1096 .9746 .109 -.3232 4.5424

-1.8314 .7495 .057 -3.7022 3.940E-02

3.9410* .9241 .000 1.6343 6.2477

1.8314 .7495 .057 -3.9403E-02 3.7022

(J) levels of the AACdevice experiences13-36 months ofexperiences with anAAC devicemore than 37 monthsof experiences of anAAC device1-12 months ofexperiences with anAAC devicemore than 37 monthsof experiences of anAAC device1-12 months ofexperiences with anAAC device13-36 months ofexperiences with anAAC device

(I) levels of the AACdevice experiences1-12 months ofexperiences with anAAC device

13-36 months ofexperiences with anAAC device

more than 37 monthsof experiences of anAAC device

MeanDifference

(I-J) Std. Error Sig. Lower Bound Upper Bound95% Confidence Interval

Based on observed means.The mean difference is significant at the .05 level.*.


MRL

According to the regression Model Summary Table 13, the correlation of predicted self-

esteem scores with the actual scores (multiple correlation coefficient) was .763. And, 58.2 % of

the variance in self-esteem scores accounted for the experiences with the AAC device and for the

experiences with the Internet (R2 = .582).

Table 13

Since this model was statistically significant at the .05 alpha level (F = 32.687, p = .000 <

.05 in Table 14), we reject the null hypothesis that the self-esteem scores of individuals with

speech impairments cannot be predicted by the two variables, the number of months an

individual used an AAC device and the number of months he/she used the Internet.

Table 14

Model Summaryc

.598a .357 .344 2.5116

.763b .582 .564 2.0476

Model12

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), months of Internetexperiences

a.

Predictors: (Constant), months of Internetexperiences, months of AAC experiences

b.

Dependent Variable: self-esteem scoresc.

ANOVAc

168.341 1 168.341 26.687 .000a302.785 48 6.308471.126 49274.081 2 137.040 32.687 .000b197.046 47 4.192471.126 49

RegressionResidualTotalRegressionResidualTotal

Model1

2

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), months of Internet experiencesa. Predictors: (Constant), months of Internet experiences, months of AACexperiences

b.

Dependent Variable: self-esteem scoresc.


Using the coefficients in Table 15, the regression equation can be made: ESTEEM =

25.175 + (.124)*EXINT + (.117)*EXAAC. For model 2, with a constant of 25.175, the

unstandardized B (the partial regression coefficient for that variable) value for experiences of the

Internet is .124 and the unstandardized B value for experiences of the AAC device is .117.

Table 15

VI. Conclusions

A. Interpretations

The ANOVA results indicate that there was no interaction effect between the experiences of

the Internet and the experiences of the AAC devices on self-esteem scores of individuals with

speech impairments. In addition, I found that the participants who have more experiences with

the Internet received higher mean self-esteem scores than those with less experience. More

specifically, the mean self-esteem scores for individuals with more than 37 months of Internet

experiences are significantly higher than the mean self-esteem scores for those with 1-12 months

of Internet experience. In addition, individuals with 13-36 months of the Internet experiences

received higher mean self-esteem scores than those with 1-12 months of the Internet experiences.

Coefficientsa

28.341 .909 31.169 .000.139 .027 .598 5.166 .000

25.175 .973 25.872 .000.124 .022 .533 5.599 .000

.117 .023 .478 5.022 .000

(Constant)months of Internetexperiences(Constant)months of Internetexperiencesmonths of AACexperiences

Model1

2

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: self-esteem scoresa.


Similarly, the mean self-esteem scores for individuals with more than 37 months of AAC

experiences are significantly higher than the mean self-esteem scores for those with 1-12 months

of AAC experiences.

In the multiple regression analysis, I found that the self-esteem scores with speech

impairments could be predicted by the level of Internet experiences and by the level of AAC

device experience.

I expected these results demonstrated through ANOVA analysis and MLR analysis, based on

my own experiences and based on my common sense. As I stated in the beginning, the use of

Internet and / or the use of an AAC device could provide individuals with disabilities with more

opportunities to participate in educational settings or in a professional career; their self-esteem

will increase as they use the Internet and / or AAC devices.

B. Implications

I mentioned in the beginning of this paper that many people already know of the importance

of self-esteem for people with disabilities. Thus, people who have disabilities, their family, and

professionals should consider different way to improve their self-esteem. Specifically, my

research showed that using the Internet and / or using AAC results in an increase in self-esteem

for individuals with speech impairment. This fact suggests that more people need to be aware of

the importance of using the Internet and /or using AAC for people in disability communities.

I hope that our society will help to provide Internet opportunities for them, especially in the

case of individuals (or their families) who earn low incomes and who cannot easily access

computer equipment. Additionally, I hope that our society will help to get AAC devices for

those who really need them.

Even though I did not discuss the participants’ opinion of how the Internet and AAC can be


improved within this paper, I asked the questions in my questionnaire, regarding barriers that my

participants face using the Internet and AAC and regarding any desires they have about these

two mediums. For future research, whether I just list the barriers and desires or whether I

analyze them by a qualitative method, I hope that this information will help to expedite the

awareness of professionals in the technology field, such as web developers, adaptive technology

specialists, etc.

C. Recommendations

Further research in this area is needed regarding individuals with various disabilities, since I

only investigated individuals with speech impairments. Maybe individuals with different

disabilities have different perspectives depending on what kind of disability they have. In

addition, I want to suggest to future researchers that they select their samples from students at

different schools, because using participants subscribed to AAC LISTSERV may have biased the

results. Also, the sample must be much larger for valid and reliable results for this topic.

Lastly, I suggest that how the Internet affects the self-esteem of individuals with disabilities

can be investigated by a quasi-experimental research design. A researcher will need to have an

experimental group and a comparison group. Then, all participants of the study will take the pre-

test on self-esteem issues. Then, for the experimental group, they will learn how to use the

Internet for a certain period of time. The comparison group will not learn how to use the

Internet. After the designated time, all participants will take the post-test on self-esteem issues.

In this way, researchers will be able to investigate the self-esteem issues before and after the

participants begin using the Internet. These results will be more pure reflection of the influence

of the Internet on the self-esteem of people with disabilities than my study is.


References

Daves, J. (1999). Improving your child’s self-esteem. The Exceptional Parent , 29 (9), 52-54.

Appell D., Hoffman M., & Speller N. (1999). Get your daily dose of self-esteem. The Exceptional Parent, 29 (9), 55-56.

Grimaldi C., & Goette T. (1999). The Internet and the independence of individuals with disabilities. Internet Research: Electronic Networking Applications and Policy, 9 (4), 272-280

Appendix: Descriptive Statistics

Chung, Self-esteem 26Statistics

50 50 500 0 0

32.6647 2.30 2.34.4385 .11 .11

3.1008 .76 .779.6148 .58 .6014.01 2 225.08 1 139.09 3 3

ValidMissing

N

MeanStd. Error of MeanStd. DeviationVarianceRangeMinimumMaximum

Self-esteemScores

levels ofexperienceswith an AAC

device

levels ofexperiences

with theInternet

levels of experiences with an AAC device

9 18.0 18.0 18.0

17 34.0 34.0 52.0

24 48.0 48.0 100.0

50 100.0 100.0

1-12 months ofexperiences with anAAC device13-36 months ofexperiences with anAAC devicemore than 37 monthsof experiences of anAAC deviceTotal

ValidFrequency Percent Valid Percent

CumulativePercent

levels of experiences with the Internet

9 18.0 18.0 18.0

15 30.0 30.0 48.0

26 52.0 52.0 100.0

50 100.0 100.0

1-12 months ofexperiences with theInternet13-36 months ofexperiences with theInternetmore than 37 monthsof experiences withthe InternetTotal


CumulativePercent

Self-esteem scores

1 2.0 2.0 2.01 2.0 2.0 4.01 2.0 2.0 6.01 2.0 2.0 8.01 2.0 2.0 10.01 2.0 2.0 12.01 2.0 2.0 14.01 2.0 2.0 16.01 2.0 2.0 18.01 2.0 2.0 20.01 2.0 2.0 22.01 2.0 2.0 24.01 2.0 2.0 26.01 2.0 2.0 28.01 2.0 2.0 30.01 2.0 2.0 32.01 2.0 2.0 34.01 2.0 2.0 36.01 2.0 2.0 38.01 2.0 2.0 40.01 2.0 2.0 42.01 2.0 2.0 44.01 2.0 2.0 46.01 2.0 2.0 48.01 2.0 2.0 50.01 2.0 2.0 52.01 2.0 2.0 54.01 2.0 2.0 56.01 2.0 2.0 58.01 2.0 2.0 60.01 2.0 2.0 62.01 2.0 2.0 64.01 2.0 2.0 66.01 2.0 2.0 68.01 2.0 2.0 70.01 2.0 2.0 72.01 2.0 2.0 74.01 2.0 2.0 76.01 2.0 2.0 78.01 2.0 2.0 80.01 2.0 2.0 82.01 2.0 2.0 84.01 2.0 2.0 86.01 2.0 2.0 88.01 2.0 2.0 90.01 2.0 2.0 92.01 2.0 2.0 94.01 2.0 2.0 96.01 2.0 2.0 98.01 2.0 2.0 100.0

50 100.0 100.0

25.0827.4927.7227.7827.9328.6428.6928.7229.0429.5330.5230.9130.9831.1331.2231.2731.2831.6232.0232.0332.0932.2132.2732.3532.8732.9333.2033.5133.5333.5733.5833.6833.7333.8934.1234.1934.2134.5334.6235.0235.3735.5435.6336.2537.3137.3537.5237.7437.7539.09Total


CumulativePercent

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