Limitations and future study - UNIVERSITY OF AMSTERDAM

SECTION 5.................................................................................................................................. 20

7. Limitations and future study

It is important to mention that this research suffered from the three following limitations.

First, since convenience sampling was used for data collection, respondents’ integrity, loyalty and willingness to answer truthfully could have exerted an effect in their answer (Azman, et al., 2011).

Random sampling is advised in order to rule out personal biases. Second, there was a big difference between students from Ecuador and students from the University of Amsterdam. This affected the analysis of data given the unevenness of groups. In the same way, there were more women who participated than men, which reduced the possibility to analyze the difference between genders. It is recommended to ask more men to participate in surveys since they were the ones with the lowest response rate. Third, procrastination evaluation did not differentiate between passive and active procrastinators, being the latter positively related to academic performance (Kim, Fernandez, &

26 Terrier, 2017). Future research should take this difference into account to explore if the type of procrastination affects the moderation effect of task strategies, time management and online participation on academic performance. Nevertheless, the results from this study are still useful for university authorities to identify priority groups and focus strategies to help them deal with procrastination. These findings also raise awareness about the type of procrastination as active procrastinators are conscious about their decision to delay tasks while passive tend to be unaware and suffer the negative consequences.

8. Conclusions

Covid-19 forced the world to adapt to a new reality in which education became online.

This brought many benefits such as flexibility in time and space, but also challenges like academic procrastination. It is true procrastination has always been accompanied university students affecting their academic performance, however, online education seems to be the perfect environment to enhance this negative behavior. Previous researches have focused on analyzing academic procrastination in one specific place, while students that come from different regions might show stronger tendencies to procrastinate and therefore need more attention or support from the educational institutions. Thus, this study wanted to address this issue by comparing procrastination rates among four regions; Western Europe; Eastern Europe, South America and Asia. The results showed that the biggest difference in procrastination rates was between South America and Asia, being the latter the region that procrastinates the most followed by Western Europe and South America. There are several reasons why Asian students show higher procrastination than others. For instance, they face more challenges such as a new language,

27 culture, and discrimination. On the other hand, Western European students procrastinate more than South Americans as they pay lower tuition fees, can work without restriction and can extend their education if they need. These benefits are not available to international South American students, which force them to study harder and finish their bachelor on time. Last but not least, the economic background of students in Ecuador motivates them to study to be able to work and help their families. The second part of the study related to the moderation effect of task strategies, time management and online participation in the negative relationship between procrastination and academic performance. The results indicated that the three moderators could counteract the negative effect of procrastination. However, given the moderators were correlated with the independent variable, it is believed that students were active procrastinators. Being an active procrastinator is actually considered a positive type of procrastination which helps them to organize their time and tasks in their own time. Since this study did no distinguish between the two types of procrastination, future research should investigate this phenomenon in order to better understand students’ needs.

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35 10. Appendix

1. Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

What is your gender? 215 1 2 1.74 .440

What is your age? 215 17 56 24.62 7.528

Where are you from? 215 1 8 3.25 2.599

Which region are you from? 215 1 4 2.43 1.038

What is your highest level of education you are currently enrolled in?

215 1 4 2.87 .434

What is your employment status?

215 1 7 4.32 1.448

Valid N (listwise) 215

1.1 Where are you from?

Frequency Percent Valid Percent Cumulative %

Valid The Netherlands 49 22.8 22.8 22.8

Ecuador 102 47.4 47.4 70.2

India 3 1.4 1.4 71.6

South Korea 3 1.4 1.4 73.0

China 10 4.7 4.7 77.7

Germany 4 1.9 1.9 79.5

Belgium 2 .9 .9 80.5

Other (please list below) 42 19.5 19.5 100.0

Total 215 100.0 100.0

36 1.2 Other country

Frequency Percent Valid Percent Cumulative %

Valid 173 80.5 80.5 80.5

Albania 1 .5 .5 80.9

Argentina 1 .5 .5 81.4

Belarus 1 .5 .5 81.9

Brazil 1 .5 .5 82.3

Bulgaria 1 .5 .5 82.8

Colombia 2 .9 .9 83.7

Croatia 3 1.4 1.4 85.1

Czech Republic 1 .5 .5 85.6

Estonia 2 .9 .9 86.5

Finland 1 .5 .5 87.0

France 1 .5 .5 87.4

Greece 1 .5 .5 87.9

Hungary 2 .9 .9 88.8

Indonesia 1 .5 .5 89.3

Italy 1 .5 .5 89.8

Latvia 1 .5 .5 90.2

Lithuania 1 .5 .5 90.7

Luxembourg 1 .5 .5 91.2

Moldova 1 .5 .5 91.6

Pakistan 2 .9 .9 92.6

Peru 1 .5 .5 93.0

Portugal 1 .5 .5 93.5

Romania 2 .9 .9 94.4

Russia 1 .5 .5 94.9

Serbia 1 .5 .5 95.3

Slovakia 1 .5 .5 95.8

Spain 2 .9 .9 96.7

Sweden 1 .5 .5 97.2

Taiwan 1 .5 .5 97.7

The Philippines 1 .5 .5 98.1

Ukraine 2 .9 .9 99.1

Vietnam 2 .9 .9 100.0

Total 215 100.0 100.0

37 2. Separation of regions

Western Europe

Eastern Europe South America Asia

The Netherlands (49) Albania Ecuador (102) India (3)

Germany (4) Belarus Argentina South Korea (3)

Belgium (2) Bulgaria Brazil China (10)

Finland Croatia (3) Colombia (2) Indonesia

France Czech Republic Peru Pakistan (2)

Greece Estonia (2) Russia

Italy Hungary (2) Taiwan

Luxemburg Latvia The Philippines

Portugal Lithuania Vietnam (2)

Spain (2) Moldova

Sweden Romania (2)

Serbia

Slovakia

Ukraine (2)

64 20 107 24

2.1 Descriptive of regions

Frequency Percent Valid Percent

Cumulative Percent

Valid Western Europe 64 29.8 29.8 29.8

Eastern Europe 20 9.3 9.3 39.1

South America 107 49.8 49.8 88.8

Asia 24 11.2 11.2 100.0

Total 215 100.0 100.0

38 3. Kruskal Wallis One ANOVA test and post-hoc

4. Reliability test for variables

Academic Procrastination Cronbach's Alpha N of Items

.869 9

Time Management Cronbach's Alpha N of Items

.917 9

Task Strategies Cronbach's Alpha N of Items

.811 4

Online Participation Cronbach's Alpha N of Items

.837 7

1 5 Assumptions for Linear regression

1. Linearity

2. Normality of Residuals

3. . Homoscedasticity

4. Multicollinearity

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Collinearity Statistics B

Std.

Error Beta Tolerance VIF

1 (Constant) 81.536 5.088 16.025 0.000

P_X -3.505 1.581 -0.150 -2.217 0.028 1.000 1.000 a. Dependent Variable: Academic Performance

2 5: Outliers

DATA WITH OUTLIERS DATA WITHOUT OUTLIERS

Coefficients^a

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Std.

Error Beta

1 (Constant) 81.536 5.088 16.025 0.000

P_X -3.505 1.581 -0.150 -2.217 0.028

a. Dependent Variable: AP_Y

Coefficients^a

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Std.

Error Beta

1 (Constant) 88.049 3.238 27.196 0.000

P_X -4.293 1.003 -0.290 -4.279 0.000

a. Dependent Variable: AP_Y

Model Summary^b

Model R R Square

Adjusted R Square

Std. Error of the Estimate

1 .150^a 0.023 0.018 19.47418

a. Predictors: (Constant), P_X b. Dependent Variable: AP_Y

Model Summary^b

Model R R Square

Adjusted R Square

Std. Error of the Estimate

1 .290^a 0.084 0.080 12.13671

a. Predictors: (Constant), P_X b. Dependent Variable: AP_Y

1 Third Hypothesis – Linear regression Academic Procrastination and Academic Performance Model Summary

Model R R Square

Adjusted R Square

Std. Error of the Estimate

1 .150^a .023 .018 19.47418

NOTES: a. Predictors: (Constant), Academic Procrastination

ANOVA

Coefficients

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.

95.0% Confidence Interval for B

B Std. Error Beta

Lower Bound

Upper Bound 1 (Constant) 81.536 5.088 16.025 .000 71.507 91.565

AcadProc -3.505 1.581 -.150 -2.217 .028 -6.621 -.388 NOTES. Dependent Variable: Academic Performance

Model Sum of Squares df Mean Square F Sig.

1 Regression 1863.330 1 1863.330 4.913 .028^b Residual 80778.907 213 379.244

Total 82642.237 214

NOTES.

a. Dependent Variable: Academic Performance b. Predictors: (Constant), Academic Procrastination

2 HYPOTHESIS 4:

**************** PROCESS Procedure for SPSS Version 3.5.3 ****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

**************************************************************************

Model : 1 Y : AP_Y X : P_X W : TM_M Sample

Size: 215

**************************************************************************

OUTCOME VARIABLE:

AP_Y

Model Summary

R R-sq MSE F df1 df2 p .2641 .0697 364.3568 5.2723 3.0000 211.0000 .0016 Model

coeff se t p LLCI ULCI constant 11.3785 22.3770 .5085 .6116 -32.7327 55.4896 P_X 13.0087 5.7256 2.2720 .0241 1.7219 24.2955 TM_M 17.4975 5.6319 3.1068 .0022 6.3954 28.5995 Int_1 -4.0638 1.5182 -2.6767 .0080 -7.0566 -1.0710 Product terms key:

Int_1 : P_X x TM_M

Test(s) of highest order unconditional interaction(s):

R2-chng F df1 df2 p X*W .0316 7.1646 1.0000 211.0000 .0080 ---

Focal predict: P_X (X) Mod var: TM_M (W)

Conditional effects of the focal predictor at values of the moderator(s):

TM_M Effect se t p LLCI ULCI 2.5067 2.8222 2.4784 1.1387 .2561 -2.0633 7.7077 3.6667 -1.8918 1.9020 -.9946 .3211 -5.6412 1.8576 4.4444 -5.0525 2.3235 -2.1745 .0308 -9.6328 -.4721

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output:

95.0000

W values in conditional tables are the 16th, 50th, and 84th percentiles.

--- END MATRIX ---

3 HYPOTHESIS 5:

**************** PROCESS Procedure for SPSS Version 3.5.3 ****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

**************************************************************************

Model : 1 Y : AP_Y X : P_X W : TS_M Sample

Size: 215

**************************************************************************

OUTCOME VARIABLE:

AP_Y

Model Summary

R R-sq MSE F df1 df2 p .2760 .0762 361.8349 5.7992 3.0000 211.0000 .0008 Model

coeff se t p LLCI ULCI constant 33.3802 14.6616 2.2767 .0238 4.4783 62.2821 P_X 9.0462 4.0956 2.2088 .0283 .9728 17.1197 TS_M 15.0450 4.4240 3.4008 .0008 6.3241 23.7659 Int_1 -4.0109 1.3370 -2.9999 .0030 -6.6465 -1.3753 Product terms key:

Int_1 : P_X x TS_M

Test(s) of highest order unconditional interaction(s):

R2-chng F df1 df2 p X*W .0394 8.9993 1.0000 211.0000 .0030 ---

Focal predict: P_X (X) Mod var: TS_M (W)

Conditional effects of the focal predictor at values of the moderator(s):

TS_M Effect se t p LLCI ULCI 1.6400 2.4684 2.3042 1.0713 .2853 -2.0738 7.0105 2.7500 -1.9837 1.7339 -1.1440 .2539 -5.4017 1.4343 4.0000 -6.9973 2.3847 -2.9342 .0037 -11.6982 -2.2964

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output:

95.0000

W values in conditional tables are the 16th, 50th, and 84th percentiles.

--- END MATRIX ---

4 HYPOTHESIS 6:

**************** PROCESS Procedure for SPSS Version 3.5.3 ****************

Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3

**************************************************************************

Model : 1 Y : AP_Y X : P_X W : OP_M Sample

Size: 215

**************************************************************************

OUTCOME VARIABLE:

AP_Y

Model Summary

R R-sq MSE F df1 df2 p .2900 .0841 358.7307 6.4580 3.0000 211.0000 .0003 Model

coeff se t p LLCI ULCI constant 20.0313 17.0701 1.1735 .2419 -13.6185 53.6811 P_X 12.1728 4.6510 2.6172 .0095 3.0043 21.3412 OP_M 13.0440 3.5108 3.7154 .0003 6.1233 19.9646 Int_1 -3.3641 1.0226 -3.2897 .0012 -5.3799 -1.3482 Product terms key:

Int_1 : P_X x OP_M

Test(s) of highest order unconditional interaction(s):

R2-chng F df1 df2 p X*W .0470 10.8219 1.0000 211.0000 .0012 ---

Focal predict: P_X (X) Mod var: OP_M (W)

Conditional effects of the focal predictor at values of the moderator(s):

OP_M Effect se t p LLCI ULCI 3.2857 1.1194 1.9830 .5645 .5730 -2.7895 5.0284 4.2857 -2.2446 1.7398 -1.2902 .1984 -5.6743 1.1850 5.4914 -6.3007 2.1718 -2.9012 .0041 -10.5819 -2.0196

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output:

95.0000

W values in conditional tables are the 16th, 50th, and 84th percentiles.

-- END MATRIX

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English

Intro

Dear participant,

ank you for participating in our survey for our bachelor's thesis project on study behavior.

is survey should take you less than 15 minutes to ll in. We highly recommend to do it in the computer as it is easier to go through all the questions.

Please, feel free to share what you truly think, there are no right or wrong answers. Some questions look alike to ensure a consistent measurement.

Your responses are anonymous and will be handled con dentially, according to the rules set by the university.

By participating in this survey, you provide permission to use your answers for research and analysis purposes. Your participation is voluntary and you may decide to stop at any given moment.

If you have any questions about the research project, please email us at w.vaneerde@uva.nl (dr. Wendelien van Eerde)

We really appreciate your help!

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Demographics

What is your gender?

What is your age?

Where are you from?

Male Female Other

Not

Applicable

0 10 20 30 40 50 60 70 80 90 100

The Netherlands Ecuador

India

South Korea China Germany Belgium

Other (please list below)

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In what country do you currently live?

What is your highest level of education you are currently enrolled in?

What is your employment status?

The Netherlands Ecuador

India

South Korea China Germany Belgium

Other (please list below)

Less than a high school diploma Master's degree (e.g. MA, MS, MEd) High school degree or equivalent Doctorate (e.g. PhD, edD)

Bachelor's degree (e.g. BA, BS)

Employed full-time (40+hours a week) Student Employed part-time (less than 40 hours a week) Retired

Unemployed (currently looking for work) Self-employed Unemployed (not currently looking for work) Unable to work

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Intro vignettes

In the following, you will read about a person named Kim. You will be

asked to imagine the situation Kim is in and then answer several questions about it. Please note that after you have read this description, you can not return to it.

V1

Kim really wants to pass an important exam this semester. Kim has made a plan for studying. Each day, Kim will read one chapter in the textbook and make notes. Further, Kim has planned to have one week le for memorizing everything. Kim intends to read one chapter today, too.

Today is a great day for going swimming. Kim’s friends call asking Kim to join them for an all-day swimming trip. Kim decides to put oﬀ studying un l tomorrow to go swimming with the friends knowing that this will jeopardize the plan because there will be no me to read two chapters in the textbook on any other day, and thus, there will be less than one week le for memorizing everything.

Please indicate what you think of Kim

Does not apply at all Does not apply Does apply Does totally apply

Kim puts o reading the chapter in the textbook despite knowing to be worse o for putting o .

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.

V2

Today, Kim’s friend calls to tell Kim that her boyfriend broke up with her. Kim decides to put oﬀ studying un l tomorrow to help the friend knowing that this will not jeopardize the plan because there will be me to read two chapters in the textbook on any other day.

Does not apply at all Does not apply Does apply Does totally apply

Kim’s putting o of reading the chapter in the textbook is caused by situational circumstances.

Kim is responsible for putting o reading the

chapter in the textbook.

Kim’s putting o of reading the chapter in the textbook is strategic in nature.

Not at all

acceptable Not

acceptable Acceptable Totally acceptable To what extent is it acceptable that Kim puts o reading the chapter

in the textbook?

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Please indicate what you think of Kim

.

V3

Does not apply at all Does not apply Does apply Does totally apply

Kim puts o reading the chapter in the textbook despite knowing to be worse o for putting o .

Kim’s putting o of reading the chapter in the textbook is caused by situational circumstances.

Kim is responsible for putting o reading the

chapter in the textbook.

Kim’s putting o of reading the chapter in the textbook is strategic in nature.

Not at all

acceptable Not

acceptable Acceptable Totally acceptable To what extent is it acceptable that Kim puts reading the chapter in

the textbook o ?

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Today, the internet is not working. Thus, Kim cannot access the online textbook and there is no other way to get access to the textbook. Kim decides to put oﬀ studying un l tomorrow, knowing that this will jeopardize the plan because there will be no me to read two chapters in the textbook on any other day, and thus, there will be less than one week le for memorizing everything.

Please indicate what you think of Kim

.

Does not apply at all Does not apply Does apply Does totally apply

Kim puts o reading the chapter in the textbook despite knowing to be worse o for putting o .

Kim’s putting o of reading the chapter in the textbook is caused by situational circumstances.

Kim is responsible for putting o reading the

chapter in the textbook.

Kim’s putting o of reading the chapter in the textbook is strategic in nature.

Not at all

acceptable Not

acceptable Acceptable Totally acceptable To what extent is it acceptable that Kim puts reading the chapter in

the textbook o ?

In document UNIVERSITY OF AMSTERDAM (pagina 32-81)