# The impact of education on income（use spss do some data analysis）

The impact of education on income（use spss do some data analysis）

OVERVIEW Following is a road map that briefly outlines the contents of an entire thesis for which you will model your final paper. Reminders

• Your paper must adhere to my requirements for final papers for my class. Any papers that are not formatted to my specifications will receive a failing grade.

• The entire paper must be APA formatted and double spaced (per APA specifications). See the APA guide I sent you for help.

• Your study may be qualitative, quantitative or a stand-alone comprehensive literature review. • If using quantitative data, you must also have the questionnaire and/or code book included in the Appendix.

You need do a research plan like this example.

Research Plan example

PLEASE NOTE: This is just an example or template to help you write your own plan. Do not copy.

Research Topic

Investigation of the Effectual Instructional Method in High

Research Question

What is an effective instructional method that could be used in the classroom environment to improve

Mathematics scores

of Grade 10 learners in NY.

Research Purpose.

The purpose of this study is to determine whether gamification or online methodologies are suitable for use as classroom instructional alternatives to a traditional pedagogy to improve Mathematics scores of Grade 10 learners in NY public schools?

Research Theory

Cognitive information processing theory.

The theory is based on the idea that humans processes information they receive, rather than just responding to stimuli. Cognitive information processing is used when the learner play an active role in seeking ways to understand and process information that he receives and relate it to what is already known and stored within memory (Piaget,1970)

Hypothesis

Gamification is the effective classroom instructional method to positively improve Grade 10 learners’ Mathematics grades.

Variables

Independent Variables

Gamification, Online and Traditional groupings.

Data type: Categorical data (Nominal)

Dependent Variables:

Mathematics scores

Data Type: Continuous Data (Ratio)

Target Population

NY Public Schools from all districts.

Sampling Plan

Sample size – 30 participants Within group design (i.e. participants are related (same) in each group)

Cluster Random Sampling – These participants will be selected from all NY Districts using cluster random sampling method.

Research Design

Within Group Experimental Design

Short design Description

I am going to recruit 30 Grade 10 Mathematics learners from NY public schools using cluster random sampling as participants in this study. The participants will receive treatment of a classroom instructional method each week i.e. Gamification, Online and Traditional method respectively. After each instructional exposure, the participants will be served with a standardised Mathematics test. In each iteration, the test administered will be different but the level of difficulty will not vary.

Test Statistics

Repeated Measures ANOVA

PAPER CHAPTERS ,

1. Introduction(4pages)

Introduction requirement

Review Literature only from 2010 unless it is a landmark study (Only two Landmark studies before 2010 can be reviewed or cited).

You must review papers published in English

Review a minimum of 15 studies that are relevant to your topic and the core theory in your research.

Review at least two studies that are contradictory to each other.

Review only the work that is directly related to the hypotheses, core theories and, the topic.

Write a minimum of 3 pages but not more than 4 pages. (Introduction +/- 3 pages excluding reference pages).

Include the References.

Be sure to include all the content as was discussed in class: i.e. The importance of the study topic; Theories that your topic is based on; Background studies on the topic; Existing gaps on the topic in the body of knowledge; The gaps that going to be filled in by your research; Purpose statement and Hypotheses.

2. Method and Results （10pages）

Research method requirement

Collect data

Perform reliability test on instruments used (If self-designed).

Write the Method section exactly as discussed in class. Remember to include subsections: Participants/Samples, Materials and Procedure.

Finally, write the Results section.

4. Discussion (3pages)

This chapter synthesizes and discusses the results considering the study’s research questions, literature review, and conceptual framework. Finding patterns and themes is one result of analysis. Finding ambiguities and inconsistencies is another. Overall, this chapter offers the researcher an opportunity to reflect thoroughly on the study’s findings, and the practical and theoretical implications thereof. This chapter also presents a set of concluding statements and recommendations. Conclusions are assertions based on findings and must therefore be warranted by the findings. With respect to each finding, you are asking yourself, “Knowing what I now know, what conclusion can I draw?” Recommendations are the application of those conclusions. In other words, you are now saying to yourself, “Knowing what I now know to be true, I recommend that

Method

Sections

The Method section has three main subsections

Participants

Materials

Procedure

Each subsection can have its own heading

Participants

How many?

How were they selected (e.g. from Academic Writing and Reading Class).

Essential demographics information: percentage female (or male), age range and average age, and the percentage of participants belonging to various ethnic groups if any. Include what is relevant only.

Discuss rules used to include other participants if any. However, it is not necessary to include if no data were excluded.

Did the participates receive any form of incentive

State your sampling procedure, (Random, convinience e.t.c.), for example “We randomly selected 100 children from high schools near UCASS main campus.”

Example 1

Example 2

Example 3

Materials

In this section, you should provide a description of any equipment or physical settings that were important aspects of your study.

Example:

Measuring participant’s response speed to a stimulus on a computer screen.

You have to describe: software you are using, important characteristics of the monitor (size, refresh rate, contrast, etc. but not always necessary), and distance of participants from the monitor.

Discuss variables, control variables, and any extraneous variables that might influence your results.

Explain whether your experiment uses a within-groups or between-groups design.

Example: The experiment used a 3×2 between-subjects design. The independent variables were age and understanding of second-order beliefs.

Questionnaire

The source of the questionnaire. Cite. Include the tool full name followed by abbreviation and citation of original author Occupational Stress Indicator (OSI; Cooper, 1997), after that, you can call it the OSI.

What is the questionnaire measuring? Note: questionnaire is an instrument to measure variables not to test hypothesis.

For example, your questionnaire cannot directly measure “whether Chinese boys have different attitudes toward basketball.” Your questionnaire can only measure attitudes.

The number of items in your questionnaire

If you are creating a new questionnaire, place the full set of items in an Appendix and refer the reader to the Appendix:

Any reliability estimates (e.g., Cronbach’s alpha, test-retest reliability) that might be available from previous research.

Example 1

Example 2

Procedure

The researcher provides a step-by-step description of the participants’

experience.

Don’t Describe:

Any data analysis

Actions taken by the researcher that do not directly involve the participants.

Procedure

Step-by-step of how you collected the data

Were surveys filled out in a classroom? The mall? Was the researcher present?

Procedure

Write in order which events occurred

usually starts with informed consent procedure

Discuss experimental design

Describe each condition, how students were assigned to each condition (randomly assigned), counterbalancing, etc.

When? (during regular class time?)

Instructions to participants

Where? (each person in own room or group format?)

How? (paper and pencil? Internet? All in one packet? Order of measures?)

Debriefing

Things to include

Instructions to participants. What were they told the study was about? “Participants were briefed that the study was designed to decribe male behaviour toward speeding”.

Informed consent. Did the researcher administer informed consent?

Assignment to conditions. How was this done? Were participants randomly assigned?

Experimental manipulations. How were participants treated across conditions?

Duration. How long did the procedure take? “Participants generally completed the questionnaire within 10 minutes.”

Debriefing. Were participants debriefed? Were they given a written debriefing or did the researcher conduct an oral debriefing?

Dismissal. This is a concise way to conclude the Procedure section, e.g., “Participants were given a written debriefing, thanked for their participation, and were dismissed.”

Homework

Write the method section:

Collect your data

Perform reliability and validity tests on your samples and instruments used(If self designed).

Write your Method exactly as discussed in class.

Submit Next Week.

Guidelines for Results Section

APA Style

APA Results Section Details

The Results section is where you summarize the data you collected and present the main findings (even those that are counter to your hypotheses).

You should also explain what analyses were used (e.g., oneway ANOVA, t-test).

The results section should be organized in some fashion.

Don’t start a new page for this section, continue from the Method Section

Center the word “Results” and continue typing on the very next double-spaced line

Basics of Results Section

Before writing your results, look at your results carefully

It may help to have any tables or figures you plan to use created before writing this section

Briefly state the main findings in words. That is, first give a general description, then go into the details.

A common way to report results is to

Restate your hypothesis for the reader.

Summarize the results for each of the statistical tests you completed for that hypothesis.

Repeat steps 1 and 2 for each subsequent hypothesis.

Writing Up Statistical Analyses

When presenting the results of statistical tests, give descriptive statistics before the corresponding inferential statistics. In other words, give means and/or percentages (perhaps referring to a table or figure), before writing about the results of any statistical tests you performed.

When presenting means, it is reasonable to use one additional digit of accuracy than what is contained in the raw data. In other words, if the raw data consisted of whole numbers, then the means should contain one decimal place.

When presenting nominal or ordinal data, give the percents rather than frequencies (since percents are independent of the sample size).

Writing Up Statistical Analyses

The general format for presenting an inferential statistic is: Statistic(df) = value, probability = value.

Note that exact p values are preferred.

For our paper, stating if the results is greater than or less than .05 is sufficient.

An example of this would be F (1, 149) = 107.31, p<.001.
In general, any p-value less than or equal to .05 is considered significant and you should be sure to point out to your reader that there was an effect.
P-values greater than .05 are not significant and are considered uninterpretable.
Writing Up Statistical Analyses
When actually presenting the results, try to emphasize the meaning of the statistics. That is, clearly describe what it is you are testing and what significance means for the variables involved.
Do not mention individual scores (raw data) except as an example; instead report means and standard deviations.
It is not appropriate to discuss what you think these findings mean, including the implication of the results! Save this for the Discussion section.
Writing Up Statistical Analyses
Examples of how to report common statistical analyses:
An analysis using Pearson's correlation coefficient supported this observation, r(58) = .63, p < .001.
The control group (M = 14.1) remembered more words on the memory test than the drugged group (M = 12.3). This difference was tested using an independent groups t test, and was shown to be nonsignificant, t(18) = 1.23, p = .283. Thus, the data fail to support the notion of a drug effect on memory.
The mean scores for the short, medium, and long retention intervals were 5.9, 10.3, and 14.2, respectively. A one way analysis of variance revealed a significant effect of retention interval, F(2, 34) = 123.07, p < .001.
Additional Considerations
In cases where the reader would expect something to be significant and it is not, you should address the issue.
Be careful with the word "prove". Since statistical tests are based on probability and can be in error, they do not really prove anything.
Do not talk about the meaning of the alpha level or the null hypothesis, and what chance factors have to do with it. Since you are writing for the scientific community, you can assume the reader will have a working knowledge of statistics.
Tables and figures
After the Reference section (unless you have footnote or authors notes pages) come:
any tables
any figures
Each belongs on a separate page (multiple figure captions can appear on one page however).
Note that figures are not typed, so pages with the actual figure and tables do not have a manuscript page header and page number.
Tables and figures
Tables and figures should be able to stand alone (i.e., you should not have to read the manuscript to be able to understand a table or figure).
A big help in this regard is the table title or the figure caption. Use these wisely to explain what is going on in the table or figure. In other words, do not be afraid to be a little bit verbose in your table titles and figure captions.
Tables and figures should not duplicate the same information. Likewise, you should not repeat the data point values in a table or figure in the text of the manuscript.
Tables and figures
If you only have a few data points to present, do it in the text of the manuscript rather than in a table or figure.
Tables and figures are most often used to present results, but may also be used to present other information, such as the design or a theoretical schema.
If you include a table or figure, you must introduce it in the text of the results section (e.g., Table 1 displays the...) and describe to the reader what should be seen in it.
DO NOT reference tables and figures in text like this:
(see Table 1)
Instead ….. Table 1 details the mean scores, etc.
Tables
Type the table number and then (on the next double spaced line) type the table title flush left and italicized. Note that there are no periods used after the table number or title.
There are different ways to format tables. Your best bet is to set the tabs for the table or to use your word processor's table generating ability.
When using columns with decimal numbers, make the decimal points line up. Be sure to use clear descriptors and labels in the table.
APA style tables do not contain any vertical lines, so do not draw them in or use your word processor to generate them.
Figures
'Figures' is the technical term for graphs, charts, drawings and pictures.
Figures (other than pictures) may printed in black and white only (keeping it in two dimensions).
If the figure is a chart or graph, verbally label the axes (do not use "X" and "Y") and provide a key if necessary (e.g., explaining what open vs. filled circles are).
Figure
Figure Captions
Start on a new page. Center the phrase Figure Captions at the top. Double space everything!
Each figure caption is typed flush left in block format.
The word 'figure' and the number are italicized while the title is not.
For example, Figure 1. The effects of...
Figures
Center each figure on the page vertically as well as horizontally and arrange for the figure to use the bulk of the page.
Do not have manuscript page header for the page the actual figure is on.
Reporting: Single Sample t-test
A single sample t-test was used to determine if participants (N = 40) WIFI speed satisfactory levels were different to the Beijing City levels as it was studied by China Mobile on this topic (µ = 4.12). No outliers were found when examining a boxplot. The participants in this study rated their WIFI speed satisfactory level at UCASS campus as M = 4.80 (SD = 1.22). The assumption of normality was not met, as assessed by Shapiro-Wilk's test (p = .003). The participants rated their WIFI speed satisfactory level at UCASS as significantly different than the City of Beijing, t(39) = 3.52, p = .001, d = 0.56.
Reporting: paired-samples t-test
A paired-samples t-test was used to determine whether there was a statistically significant mean difference between the distance ran when participants consumed a carbohydrate-protein drink compared to a carbohydrate-only drink. No outliers were detected that were more than 1.5 box-lengths from the edge of the box in a boxplot. The assumption of normality was not violated, as assessed by Shapiro-Wilk's test (p = .780). Participants ran further when having the carbohydrate-protein drink (M = 11.30, SD = 0.72 km) as opposed to the carbohydrate only drink (M = 11.17, SD = 0.73 km), a statistically significant mean increase of 0.14 km, 95% CI [0.09, 0.18], t(19) = 6.35, p < .001, d = 1.42.
Reporting: Independent-samples t-test
There were 20 male and 20 female participants. An independent-samples t-test was analyzed to determine if there were differences in engagement to an advertisement between males and females. There were no outliers in the data, as assessed by inspection of a boxplot. Engagement scores for each level of gender were normally distributed, as assessed by Shapiro-Wilk's test (Male p = .970, Female p = .560), and there was homogeneity of variances, as assessed by Levene's test for equality of variances (p = .174). The advertisement was more engaging to male viewers (M = 5.56, SD = 0.35) than female viewers (M = 5.30, SD = 0.35). This was indicated by a large and significant difference between groups, t(38) = 2.37, p = .023, d = 0.75.
Reporting: Repeated measures ANOVA
A repeated measures ANOVA was conducted to determine whether there were statistically significant differences in CRP concentration over the course of a 6-month exercise intervention. There was one outlier and the data was normally distributed for each group, as assessed by boxplot and Shapiro-Wilk test (ps = .350, .903, .372), respectively. The assumption of sphericity was violated, as assessed by Mauchly's Test of Sphericity, p = .043. Therefore, a Greenhouse-Geisser correction was applied (ε = 0.648). The exercise intervention elicited statistically significant changes in CRP concentration over time, F(1.30, 11.66) = 26.94, p < .001, η2 = .75, with CRP concentration decreasing from pre-intervention (M = 4.33, SD = 0.64 mg/mL) to 3 months (M = 3.94, SD = 0.57 mg/mL) to 6 months (post-intervention) (M = 3.65, SD = 0.43 mg/mL). Post-hoc analysis with a Bonferroni adjustment revealed that CRP concentration was statistically significantly decreased from pre-intervention to 3-months (M = 0.39 mg/mL, p < .001, d = 2.45), and from pre-intervention to post-intervention (M = 0.68 mg/mL, p = .001, d = 1.87) but not from 3 months to post-intervention (M = 0.29 mg/mL, p = .054, d = 0.91).
Reporting: One-way ANOVA
A one-way ANOVA was conducted to determine if the ability to cope with workplace-related stress (CWWS score) was different for groups with different physical activity levels. Participants were classified into four groups: sedentary (n = 7), low (n = 9), moderate (n = 8) and high levels of physical activity (n = 7). There were no outliers, as assessed by boxplot; data was normally distributed for each group, as assessed by Shapiro-Wilk test (p = .215); and there was homogeneity of variances, as assessed by Levene's test of homogeneity of variances (p = .120). Data is presented as mean ± standard deviation. CWWS score was statistically significantly different between different physical activity groups, F(3, 27) = 8.32, p < .001, ω2 = 0.42. CWWS score increased from the sedentary (M = 4.15, SD = 0.77) to the low (M = 5.88, SD = 1.69), moderate (M = 7.12, SD = 1.57) and high (M = 7.51, SD = 1.24) physical activity groups, in that order. Tukey post hoc analysis revealed that the mean increase from sedentary to was statistically significant (p = .002, d = 2.34), as well as the increase from sedentary to high (p < .001, d = 3.24), but no other group differences were statistically significant.
Reporting: chi-square test
A chi-square test for association was conducted between gender and preference for performing competitive sport. All expected cell frequencies were greater than five. There was a statistically significant association between gender and preference for performing competitive sport, χ2(1) = 5.20, p = .023. There was a moderately strong association between gender and preference for performing competitive sport, ɸ = .322.
Reporting: Correlation
A Pearson's product-moment correlation was run to assess the relationship between cholesterol concentration and daily time spent watching TV in males aged 45 to 65 years. Preliminary analyses showed the relationship to be linear with both variables normally distributed, as assessed by Shapiro-Wilk's test (TV time p = .130, Cholesterol p = .064), and there were no outliers. There was a moderate positive correlation between daily time spent watching TV and cholesterol concentration, r = .371, p < .001, with time spent watching TV explaining 14% of the variation in cholesterol concentration.
Reporting: multiple regression
A multiple regression was run to predict VO2max from age, weight and heart rate. The data was screened for assumptions and outliers, and no outliers were found. All assumptions of linearity, normality, homoscedasticty, and multicollinearity were found to be met. The multiple regression model statistically significantly predicted VO2max, F(3,96) = 5.52, p = .002, adj. R2 = .15. All three variables added statistically significantly to the prediction. Regression coefficients and standard errors can be found in Table 1 (Appendix). Heart rate was not related to VO2max, while age and weight both negatively predicted VO2max, such as increasing age and weight lowered the VO2max.
Example
It was hypothesized that dead individuals in the therapy group would have lower depression scores and lower passivity scores compared to participants in the no-therapy group. A series of t-tests were computed to test this prediction. Consistent with the present study’s hypotheses, the therapy group had significantly lower depression scores than the no-therapy group, t (98) = 3.49, p < .001, with means (SD) of 37.67 (3.56) and 25.78 (2.99), respectively. However, the difference in passivity scores for the treatment and no-treatment groups were not significant, t (98) = 1.04, p > .05, with means (SD) of 29.81 (2.23) and 29.31 (2.01), respectively. Further, it was hypothesized that the psychotherapy group would have fewer individuals classified as having significant sexual dysfunction, compared to the no-therapy group. A χ2 analysis revealed that participants who did not receive therapy were over-represented among those classified as having significant sexual dysfunction, and participants who received therapy were under-represented among those classified as having significant sexual dysfunction, χ2 (1, N = 100) = 3.85, p < .05. Table 2 shows cross-tabulation matrix.

Validity and Reliability

Cronbach’s alpha

Cronbach’s alpha is a convenient test used to estimate the reliability, or internal consistency, of a composite score.

Reliability

Say an individual takes a Happiness Survey. Your happiness score would be highly reliable (consistent) if it produces the same or similar results when the same individual re-takes your survey, under the same conditions.

Cronbach’s alpha

Cronbach’s alpha gives us a simple way to measure whether or not a score is reliable.

Cronbach’s alpha

It is used under the assumption that you have multiple items measuring the same underlying construct

Cronbach’s alpha

For the Happiness Survey, you might have five questions all asking different things, but when combined, could be said to measure overall happiness.

Cronbach’s alpha Analysis

Cronbach’s alpha results should give you a number from 0 to 1

Cronbach’s alpha Analysis

The general rule of thumb is that a Cronbach’s alpha of .70 and above is good, .80 and above is better, and .90 and above is best.

Reporting

A measure of sexual knowledge was obtained using an author constructed questionnaire titled the Sexual Hygiene Anxiety and Gripes For Educational and Academic Research (SHAGFEAR). Attached in the Appendix is the questionnaire with a 12 item measure that asked participants to rate the descriptiveness of a series of statements about sexual hygiene on a numerical rating scale of 1 (not at all like me) to 7 (very much like me). Ratings are averaged across all 12 items. Mean for the total sample was 3.00, SD = 2.12, Cronbach’s alpha was .99. Participants scoring greater than 2.44 were classified as having significant sexual dysfunction.

Literature Questionnaire

In the present study, a measure of depression was obtained using revised version of the Beck’s Depression Inventory (BDI-2, Beck, Steer, & Brown, 1996). This 21 item measure asks participants to select statements that are descriptive of how they have been feeling for the past week, including that day. Responses are coded on a scale of 0-4 for level of depressiveness, α = 0.9.

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