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.
InterItem 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  
InterItem 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 
InterItem 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 ItemTotal 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 Matrix^{a}  
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 IProfessor and Assignments  Subscale IIConvenience  Subscale IIILogic  Subscale IVAccess 
TimelyResponse Care Effective Answer  Schedule AnyTime AnyLocation  Logical Order Understood  Computer Access Internet Access 
This table displays the remaining variables in their subscales.
TTest On Gender
Independent Samples Test  
Levene's Test for Equality of Variances  ttest for Equality of Means  
F  Sig.  t  df  Sig. (2tailed)  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 (IJ)  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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