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# Advanced Behavioral Research Design & Analysis: Demographic Variables (Research Proposal Sample)

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The study aims to establish how the demographic variables impact two of the Big Five variables that explain personality such as the conscientiousness (consc1-consc5), extroversion (extro1-extro6) and also the general self-efficiency (gse1-gse8).

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Advanced Behavioral Research Design & Analysis
Student name
Institution
Advanced Behavioral Research Design & Analysis
The study aims to establish how the demographic variables impact two of the Big Five variables that explain personality such as the conscientiousness (consc1-consc5), extroversion (extro1-extro6) and also the general self-efficiency (gse1-gse8). Quantitative analysis usually delves into measuring the objects and the analysis of the numerical data which is collected through the use of the questionnaires, polls and surveys. The computational techniques are employed in manipulating the data (Costello, & Osborne, 2005).
The data was tested if it has a missing values, the following were the result from the analysis.
Table 1
Summary of Univariate Analysis
Univariate Statistics
N
Mean
Std. Deviation
Missing
No. of Extremesa
Count
Percent
Low
High
consc
412
3.6214
.86505
36
8.0
5
0
extro
411
3.1350
.93651
37
8.3
0
0
gse
407
3.5811
.73948
41
9.2
3
0
Note. Number of cases outside the range (Q1 - 1.5*IQR, Q3 + 1.5*IQR).
From the above output, it is evident that the variables have missing values. This is because the total number of observations are not the same in all the three variables. The general self-efficiency ha the highest number of missing value at 9.2 percent of the total observations, followed closely by the extroversion at 8.3 percent and lastly conscientiousness has the least number of observation. The missing data was addressed by performing data imputation. So that to make the data suitable for data analysis to give the desired results.
Further, reverse scoring an item implies that changing the score or the numerical arrangement of a variable in such a way that the scale runs in the opposite direction depending on the questions that are asked and the response of the participants. Often the reverse scoring is employed on the negative question (Hinton, 2014). The positive scoring will not be similar or is always different from the negative scoring, for this reason, scoring needed to be reversed from the score to be easy and added horizontally. In this study, there is no variable that needs to be reverse scored.
The data was quantitative both the descriptive and inferential techniques were employed to get the meaning of the data. Descriptive statistics were employed to get the general meaning and describe the data that is under study (Hinton, 2014). The following is the descriptive statistics table.
Table 2
Summary of descriptive table
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
consc
412
1.00
5.00
3.6214
.86505
extro
412
1.00
5.00
3.1350
.93651
gse
412
1.00
5.00
3.5811
.73948
Valid N (listwise)
412
Note. N is the sample size
Before performing any test, it is appropriate to test the assumptions of the study. Assumptions are vital since if they are not followed, it makes it difficult for the researcher to perform the analysis to extract important information from the data. When performing the analysis, the following assumptions should not be violated by the variables. The assumption that is concerned with the scale of measurement. It is assumed that that data which is collected follows a continuous or ordinal scale. This assumption is not violated since the data that is collected for the study is measured under the continuous and ordinal scale (Tabachnick & Fidell, 2007). For example, gender follows an ordinal scale since it can be male or female, extroversion is also measured in six levels that range from 1 to 6, and this shows that this variable is nominal.
The normality assumption, which explains that the data is distributed normally. To test this assumption, we employ the Shapiro-Wilk normality test.
Table 3
Shapiro Test
Tests of Normality
Are you male or female?
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic
Df
Sig.
Statistic
df
Sig.
Consc
Male
.089
213
.000
.967
213
.000
Female
.113
199
.000
.954
199
.000
Note. The level of significance is 5%, a. Lilliefors Significance Correction
From the Shapiro-Wilk test above, the statistic (W) is 0.967, and the p-value is 0.000. Since the p-value is less than 1, then we say that the data is normally distributed. Therefore the assumption of normality is not violated by the variables.
The assumption that the dependent variable should be continuous. The dependent variable of the study is conscientiousness which is measured on the continuous scale. This confirms that the assumption of the continuous dependent variable is not violated by the variables (Hinton, 2014). Third, the independent variable(s) should be either continuous or categorical. This means that the variables can be measured by whether the continuous scale or the ordinal/nominal scale (for example the Likert scale). Gender is a categorical variable that is measured on a nominal scale (male or female). Hence, the assumption is not violated by the variables.
The variables should be independent. This assumption is applied in both the T-test and the ANOVA test (Tabachnick & Fidell, 2007). Independence means that there is no connection between the groups or the data. The critical value is 0.096. Since the critical value is more than the level of significance, we make a conclusion that there the variables or groups in the study are independent. Hence the assumption is not violated.
Lastly the assumption of equal variance. This test is used in the T-test, ANOVA and correlation analysis (Pierce, Block, & Aguinis, 2004).
Table 4: Levente test
The Levente Test for homogeneity of the variance
Test of Homogeneity of Variances
Levene Statistic
df1
df2
Sig.
consc
1.728
25
370
.017
extro
1.478
25
370
.067
Note. 5 % is the level of significance
From the above output, the Levente test statistic is 1.78 with critical value 0.017. Since th p-vale is smaller compared to alpha=0.05, then we conclude that the data obeys the assumption of homogeneity or equality of the variance. All these assumptions conform that the assumptions have not been violated.
Factor Analysis
Factor analysis is an analysis that brings together the inter-correlated variables under the general underlying variables. It aims at explaining the variance of the observed variables in terms of the concealed latent factors (Garson, 2013). Hence, providing a probability of attaining a vivid description of the data also using the output in succeeding analyses. The analysis starts with the inter-correlation between the variables of interest.
Table 5
KMO and the Bartlett's Test
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.899
Bartlett's Test of Sphericity
Approx. Chi-Square
4001.132
Df
171
Sig.
.000
Note. The level of significance is 0.05.
The above output is a vital part of factor analysis. Kaiser-Meyer-Olkin (KMO) is a measure that describes how suitable the data is for factor analysis. The KMO statistics range from zero to one. When the value is zero, it explains the total of partial correlations is relatively larger to the total associations. When the KMO is nearing to 1, then it indicates that the order of correlation are relatively impacted thus the factor analysis is definite and reliable (Costello, & Osborne, 2005). From the above output, the KMO is 0.899; this means the total of partial correlations is relatively larger to the total of associations showing the suitability of the factor analysis for the data. Besides, there is Bartlett’s test that investigates the null hypothesis that the initial correlation matrix is an identity matrix. If the results from the test is remarkable, it illustrates that the correlation matrix is not an identity matrix hence there is an association linking the variables we need to include in the analysis. The three variables that need are conscientiousness, general self-efficiency and extroversion. Since the p-value is less than alpha=0.05, then it shows that there is a high significance hence the factor analysis is suitable. Therefore, factor analysis is needed in this analysis.
Factor extraction
Table 6: factor extraction
Summary of the Factor Extraction
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
7.495
39.445
39.445
7.495
39.445
39.445
5.119
26.944
26.944
2
1.938
10.199
49.644
1.938
10.199
49.644
2.808
14.781
41.725
3
1.587
8.352
57.996
1.587
8.352
57.996
2.789
14.678
56.403
4
1.297
6.825
64.822
1.297
6.825
64.822
1.600
8.419
64.822
5
.954
5.020
69.842
6
.784
4.124
73.966
7
.604
3.180
77.145
8
.589
3.101
80.247
9
.541
2.849
83.096
10
.476
2.507
85.603
11
.441
2.320
87.923
12
.374
1.967
89.890
13
.355
1.870
91.760
14
.327
1.723
93.482
15
.301
1.582
95.064
16
.284
1.496
96.561
17
.254
1.334
97.895
...

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