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Synopsis Our research was to discover the attitudes of our target population, Indiana University of Pennsylvania (IUP) students, towards online...

Synopsis

Our research was to discover the attitudes of our target population, Indiana University of Pennsylvania (IUP) students, towards online courses. The questionnaire was distributed to 90 IUP students consisting males, females, and all the different years between Freshman and Senior status. Our goal is to analyze the results of the data collected to determine correlations, significant differences, and to find the best factor structure.

Introduction

The purpose of this research was to find out the correlations of different students about their attitude of online classes. The study aimed to identify students attitudes toward their online classes. We found out that understanding the students attitude toward online is essential to ensure that the students were able to take advantage of the benefits of taking an online class. A total of 90 participants were asked to answer a questionnaire.

 

Summary of Findings

Research Design

Research design is extremely important in understanding the purpose of the research itself. It is defined as “the framework or blueprint for conducting the marketing research project.” Research design gives details of the procedures necessary for obtaining the information needed to identify or solve marketing research problems. For this particular project the research design chosen is exploratory. Exploratory research provides insights and understandings of the problem confronting the researchers, which in this case, is attitudes and experiences with online classes.

Choosing a target population is vital in research design. If the target population is not precisely defined the research will result in ineffective or misleading results. The target population that is used for this research is the students of Indiana University of Pennsylvania. The research team chose this population because this sample possesses the information that is desired, therefore the generalizations and inferences made will be relevant in the findings. This target population was easy to reach since the researchers themselves are students of this university. In order to gather data the research team used convenience sampling, meaning our subjects were in the right place at the right time who are easy to research. We each distributed thirty copies of the questionnaire to students at random, for a total of ninety questionnaires. By doing this, the nonprobability sampling technique was implemented. This technique best suits this project because this creates minimal space for bias or tainted data.

Data Analysis

Mean, Median, Mode

Our gender distribution consisted of 42 males and 48 females, totalling to 90 people surveyed. These results accurately represent the Indiana University of Pennsylvania population because the student body is 55% female 45% male. Our results were 46.7% male and 53.3% female.

Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Male

42

46.7

46.7

46.7

Female

48

53.3

53.3

100.0

Total

90

100.0

100.0

StudentStatus1

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

FreshmanAndSophmore

30

33.3

33.3

33.3

Junior

27

30.0

30.0

63.3

Senior

33

36.7

36.7

100.0

Total

90

100.0

100.0

LogicalOrder

Understood

Easy

ComputerAccess

InternetAccess

N

Valid

90

90

90

90

90

Missing

0

0

0

0

0

Mean

3.84

3.81

3.53

4.69

4.57

Median

4.00

4.00

4.00

5.00

5.00

Mode

4

4

4

5

5

TimelyResponse

Care

EffectiveAnswer

Want

Schedule

AnyTime

N

Valid

90

90

90

90

90

90

Missing

0

0

0

0

0

0

Mean

3.64

2.97

3.22

3.52

4.20

4.16

Median

4.00

3.00

3.00

4.00

5.00

4.00

Mode

4

3

4

5

5

5

Statistics

AnyLocation

Interaction

EvaluateKnowledge

EnjoyedCourse

N

Valid

90

90

90

90

Missing

0

0

0

0

Mean

4.30

2.94

3.49

3.38

Median

5.00

3.00

4.00

4.00

Mode

5

4

4

4

Correlations

The Correlation Matrix shows the connections between the variables that evaluate the same subscales. For example, the variables LogicalOrder and Understood are on the same subscale and their correlation is .701, which is considered a strong linear relationship.

Inter-Item Correlation Matrix

LogicalOrder

Understood

Easy

ComputerAccess

InternetAccess

LogicalOrder

1.000

.701

.570

.154

.092

Understood

.701

1.000

.749

.152

.131

Easy

.570

.749

1.000

.013

.066

ComputerAccess

.154

.152

.013

1.000

.685

InternetAccess

.092

.131

.066

.685

1.000

TimelyResponse

.243

.294

.391

.140

.136

Care

.358

.418

.553

.097

-.031

EffectiveAnswer

.385

.391

.499

.044

-.123

Want

.246

.204

.193

.251

-.018

Schedule

.315

.256

.243

.183

.027

AnyTime

.405

.326

.216

.256

.070

AnyLocation

.164

.047

.089

.173

.066

Interaction

.370

.313

.328

.086

-.144

EvaluateKnowledge

.397

.482

.515

.111

-.041

EnjoyedCourse

.433

.580

.589

.020

-.076

Inter-Item Correlation Matrix

TimelyResponse

Care

EffectiveAnswer

Want

Schedule

AnyTime

LogicalOrder

.243

.358

.385

.246

.315

.405

Understood

.294

.418

.391

.204

.256

.326

Easy

.391

.553

.499

.193

.243

.216

ComputerAccess

.140

.097

.044

.251

.183

.256

InternetAccess

.136

-.031

-.123

-.018

.027

.070

TimelyResponse

1.000

.491

.529

.375

.167

.054

Care

.491

1.000

.827

.441

.345

.284

EffectiveAnswer

.529

.827

1.000

.454

.329

.314

Want

.375

.441

.454

1.000

.575

.434

Schedule

.167

.345

.329

.575

1.000

.611

AnyTime

.054

.284

.314

.434

.611

1.000

AnyLocation

.186

.307

.335

.353

.441

.618

Interaction

.326

.611

.572

.586

.405

.373

EvaluateKnowledge

.356

.494

.549

.434

.332

.396

EnjoyedCourse

.327

.628

.691

.454

.350

.348

Inter-Item Correlation Matrix

AnyLocation

Interaction

EvaluateKnowledge

EnjoyedCourse

LogicalOrder

.164

.370

.397

.433

Understood

.047

.313

.482

.580

Easy

.089

.328

.515

.589

ComputerAccess

.173

.086

.111

.020

InternetAccess

.066

-.144

-.041

-.076

TimelyResponse

.186

.326

.356

.327

Care

.307

.611

.494

.628

EffectiveAnswer

.335

.572

.549

.691

Want

.353

.586

.434

.454

Schedule

.441

.405

.332

.350

AnyTime

.618

.373

.396

.348

AnyLocation

1.000

.295

.228

.233

Interaction

.295

1.000

.555

.596

EvaluateKnowledge

.228

.555

1.000

.703

EnjoyedCourse

.233

.596

.703

1.000

Chronbach’s Alpha

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.888

.878

15

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item-Total Correlation

Squared Multiple Correlation

Cronbach's Alpha if Item Deleted

LogicalOrder

52.42

97.280

.562

.563

.880

Understood

52.46

96.408

.593

.729

.879

Easy

52.73

94.917

.601

.683

.879

ComputerAccess

51.58

106.539

.231

.585

.891

InternetAccess

51.70

108.954

.051

.575

.895

TimelyResponse

52.62

98.597

.477

.449

.884

Care

53.30

91.673

.720

.753

.873

EffectiveAnswer

53.04

91.414

.723

.780

.873

Want

52.74

90.777

.588

.568

.880

Schedule

52.07

97.658

.535

.506

.881

AnyTime

52.11

99.134

.537

.638

.882

AnyLocation

51.97

101.898

.400

.481

.886

Interaction

53.32

92.580

.655

.588

.876

EvaluateKnowledge

52.78

93.456

.675

.579

.875

EnjoyedCourse

52.89

89.583

.731

.720

.872

Factor Analysis

Rotated Component Matrixa

Component

1

2

3

4

LogicalOrder

.171

.210

.865

.056

Understood

.252

.064

.882

.092

ComputerAccess

.055

.188

.073

.885

InternetAccess

-.019

-.024

.058

.927

TimelyResponse

.812

-.040

.077

.192

Care

.827

.249

.236

-.052

EffectiveAnswer

.840

.272

.229

-.134

Schedule

.141

.756

.214

.028

AnyTime

.015

.863

.295

.087

AnyLocation

.233

.819

-.146

.085

Experience With Online Classes

Subscale I-Professor and Assignments

Subscale II-Convenience

Subscale III-Logic

Subscale IV-Access

TimelyResponse

Care

Effective Answer

Schedule

AnyTime

AnyLocation

Logical Order

Understood

Computer Access

Internet Access

This table displays the remaining variables in their subscales.

T-Test On Gender

Independent Samples Test

Levene's Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

LogicalOrder

Equal variances assumed

.925

.339

.903

88

.369

.202

.224

-.243

Equal variances not assumed

.911

87.995

.365

.202

.222

-.239

Understood

Equal variances assumed

.221

.640

1.163

88

.248

.265

.228

-.188

Equal variances not assumed

1.166

87.106

.247

.265

.227

-.187

Easy

Equal variances assumed

.121

.729

-.071

88

.943

-.018

.251

-.517

Equal variances not assumed

-.071

85.369

.944

-.018

.252

-.519

ComputerAccess

Equal variances assumed

.035

.851

-.295

88

.768

-.042

.141

-.322

Equal variances not assumed

-.296

87.352

.768

-.042

.141

-.321

InternetAccess

Equal variances assumed

2.046

.156

-.859

88

.393

-.125

.146

-.414

Equal variances not assumed

-.840

72.694

.404

-.125

.149

-.422

TimelyResponse

Equal variances assumed

.336

.563

-.397

88

.692

-.092

.232

-.554

Equal variances not assumed

-.400

87.917

.690

-.092

.231

-.551

Care

Equal variances assumed

3.386

.069

.068

88

.946

.018

.262

-.502

Equal variances not assumed

.069

87.817

.945

.018

.258

-.496

EffectiveAnswer

Equal variances assumed

.669

.416

.113

88

.911

.030

.264

-.495

Equal variances not assumed

.113

87.576

.910

.030

.263

-.493

Want

Equal variances assumed

9.193

.003

1.837

88

.070

.583

.318

-.048

Equal variances not assumed

1.866

87.091

.065

.583

.313

-.038

Schedule

Equal variances assumed

.311

.578

.905

88

.368

.205

.227

-.245

Equal variances not assumed

.910

87.786

.365

.205

.226

-.243

AnyTime

Equal variances assumed

.438

.510

.772

88

.442

.155

.201

-.244

Equal variances not assumed

.782

87.737

.437

.155

.198

-.239

AnyLocation

Equal variances assumed

.298

.587

1.248

88

.215

.241

.193

-.143

Equal variances not assumed

1.262

87.909

.210

.241

.191

-.139

Interaction

Equal variances assumed

4.431

.038

.889

88

.377

.238

.268

-.294

Equal variances not assumed

.902

87.361

.370

.238

.264

-.287

EvaluateKnowledge

Equal variances assumed

6.756

.011

.803

88

.424

.199

.248

-.294

Equal variances not assumed

.818

86.116

.416

.199

.244

-.285

EnjoyedCourse

Equal variances assumed

1.392

.241

1.595

88

.114

.452

.284

-.111

Equal variances not assumed

1.609

87.996

.111

.452

.281

-.106

ANOVA Test

Multiple Comparisons

Bonferroni

Dependent Variable

(I) StudentStatus1

(J) StudentStatus1

Mean Difference (I-J)

Std. Error

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

LogicalOrder

2.00

3.00

.037

.280

1.000

-.65

.72

4.00

.394

.266

.427

-.26

1.04

3.00

2.00

-.037

.280

1.000

-.72

.65

4.00

.357

.274

.587

-.31

1.03

4.00

2.00

-.394

.266

.427

-1.04

.26

3.00

-.357

.274

.587

-1.03

.31

Understood

2.00

3.00

-.207

.284

1.000

-.90

.49

4.00

.321

.270

.711

-.34

.98

3.00

2.00

.207

.284

1.000

-.49

.90

4.00

.529

.278

.180

-.15

1.21

4.00

2.00

-.321

.270

.711

-.98

.34

3.00

-.529

.278

.180

-1.21

.15

Easy

2.00

3.00

.089

.317

1.000

-.68

.86

4.00

-.073

.301

1.000

-.81

.66

3.00

2.00

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Introduction
The purpose of this research was to find out the correlations of different students about
their attitude of online classes. The study aimed to identify student’s attitudes toward...

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