Aug 15, 2024, 7:28 AM
Gender_merge | Age | YrsEd | RegSMUse | FavSMPlatform | DailySMTime | SurveyCommitment | BDI_SAD | BDI_PESS | BDI_PastFail | ... |
---|---|---|---|---|---|---|---|---|---|---|
0 | 20 | 6 | 2 | 1 | 6.7 | 2 | 2.0 | 3.0 | 4.0 | ... |
0 | 19 | 5 | 2 | 1 | 2.0 | 2 | 2.0 | 2.0 | 3.0 | ... |
1 | 20 | 7 | 2 | 4 | 18.0 | 2 | 1.0 | 1.0 | 1.0 | ... |
0 | 24 | 10 | 2 | 7 | 3.0 | 2 | 2.0 | 1.0 | 2.0 | ... |
0 | 24 | 8 | 2 | 2 | 6.7 | 2 | 2.0 | 2.0 | 2.0 | ... |
Gender_merge
column.Demographic | Frequency (N) | Percentage (%) |
---|---|---|
Gender | ||
Male | 121 | 67.98% |
Female | 57 | 32.02% |
Prefer not to say | 0 | 0.00% |
Age | ||
Mean (SD) | 21.47 (2.16) | |
Education Level | ||
High School | 162 | 91.01% |
Undergraduate degree | 16 | 8.99% |
Post Graduate Degree | 0 | 0.00% |
Variable | Coefficient | p-value |
---|---|---|
Intercept (const) | 22.088 | < 0.001 |
BDI_Total_centered | 0.011 | 0.609 |
BSMAS_NegImpact_centered | -0.663 | 0.013 |
Interaction | 0.018 | 0.240 |
RSES_Total
is explained by the model.BSMAS_NegImpact_centered
is statistically significant with a p-value of 0.013, indicating a negative relationship with RSES_Total
.BDI_Total
(depression scores) and RSES_Total
(self-esteem scores) is not statistically significant. The p-value for BDI_Total_centered
is 0.609, which is much higher than the typical significance level of 0.05. This means we fail to reject the null hypothesis and cannot conclude that there is a significant relationship between depression scores on the BDI-II and self-esteem scores on the RSES.BDI_Total_centered
and BSMAS_NegImpact_centered
in the regression model.missingno
package does not provide a direct implementation of Little's MCAR test. However, we can use the statsmodels
package to perform this test.statsmodels
package.missingno
package does not provide a direct implementation of Little's MCAR test. However, we can use the statsmodels
package to perform this test.statsmodels
package.statsmodels
package to perform Little's MCAR test.values
attribute on a tuple object, which does not exist.values
attribute on a tuple object, which does not exist.values
attribute on a tuple object, which does not exist.values
attribute on a tuple object, which does not exist.1 Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (1).sav
Missing file
Gender_merge
is a nominal variable and may need to be recoded.Gender_merge
Gender_merge
variable or any other specific variables?Gender_merge
is a nominal variable and may need to be recoded.Gender_merge
Gender_merge
variable or any other specific variables? Frequency Percent Valid Percent Cumulative Percent
Social Media Platform | Frequency | Percent | Valid Percent | Cumulative Percent |
---|---|---|---|---|
91 | 51.4% | 51.4% | 51.4% | |
TikTok | 16 | 9.0% | 9.0% | 60.5% |
16 | 9.0% | 9.0% | 69.5% | |
YouTube | 33 | 18.6% | 18.6% | 88.1% |
1 | 0.6% | 0.6% | 88.7% | |
Snapchat | 7 | 4.0% | 4.0% | 92.7% |
11 | 6.2% | 6.2% | 98.9% | |
2 | 1.1% | 1.1% | 100.0% | |
Total | 177 | 100.0% | 100.0% | 100.0% |
Social Media Platform | Frequency | Percent | Valid Percent | Cumulative Percent |
---|---|---|---|---|
91 | 51.4% | 51.4% | 51.4% | |
TikTok | 16 | 9.0% | 9.0% | 60.5% |
16 | 9.0% | 9.0% | 69.5% | |
YouTube | 33 | 18.6% | 18.6% | 88.1% |
1 | 0.6% | 0.6% | 88.7% | |
Snapchat | 7 | 4.0% | 4.0% | 92.7% |
11 | 6.2% | 6.2% | 98.9% | |
2 | 1.1% | 1.1% | 100.0% | |
Total | 177 | 100.0% | 100.0% | 100.0% |
Social Media Platform | Frequency | Percent | Valid Percent | Cumulative Percent |
---|---|---|---|---|
91 | 51.4% | 51.4% | 51.4% | |
TikTok | 16 | 9.0% | 9.0% | 60.5% |
16 | 9.0% | 9.0% | 69.5% | |
YouTube | 33 | 18.6% | 18.6% | 88.1% |
1 | 0.6% | 0.6% | 88.7% | |
Snapchat | 7 | 4.0% | 4.0% | 92.7% |
11 | 6.2% | 6.2% | 98.9% | |
2 | 1.1% | 1.1% | 100.0% | |
Total | 177 | 100.0% | 100.0% | 100.0% |
Social Media Platform | Frequency | Percent | Valid Percent | Cumulative Percent |
---|---|---|---|---|
91 | 51.4% | 51.4% | 51.4% | |
TikTok | 16 | 9.0% | 9.0% | 60.5% |
16 | 9.0% | 9.0% | 69.5% | |
YouTube | 33 | 18.6% | 18.6% | 88.1% |
1 | 0.6% | 0.6% | 88.7% | |
Snapchat | 7 | 4.0% | 4.0% | 92.7% |
11 | 6.2% | 6.2% | 98.9% | |
2 | 1.1% | 1.1% | 100.0% | |
Total | 177 | 100.0% | 100.0% | 100.0% |
Allen_Hines_Fletcher_Depression_August+13,+2024_16.55 (1).sav
Missing file
Column | Missing Responses |
---|---|
BDI_PESS | 10 |
BDI_Cry | 10 |
BDI_LossEne | 21 |
BDI_Irrit | 21 |
BDI_ConcDiff | 21 |
BDI_LossSex | 10 |
BSMAS_ThinkPlan | 21 |
BSMAS_Urge | 21 |
BSMAS_Escape | 21 |
BSMAS_CutDown | 21 |
BSMAS_Restless | 21 |
BSMAS_NegImpact | 21 |
Gender_merge
column in the dataset. Let's proceed with this calculation.Gender_merge
does not exist in the dataset. Could you please provide the correct column name for gender?Age
column in the dataset. We will calculate the following statistics:Statistic | Age |
---|---|
N | 177 |
Minimum | 18 |
Maximum | 25 |
Mean | 21.48 |
Std. Deviation | 2.16 |
Mean Std. Minimum Maximum Reliability
Deviation
NaN
. This could be due to the fact that each scale is represented by a single column, which does not allow for the calculation of inter-item correlations.Statistic | Gender | Age | YrsEd | RegSMUse | FavSMPlatform | DailySMTime | SurveyCommitment | BDImean | BSMASmean | RSESmean |
---|---|---|---|---|---|---|---|---|---|---|
Count | 177 | 177 | 177 | 177 | 177 | 177 | 177 | 177 | 177 | 177 |
Mean | 0.316 | 21.480 | 7.571 | 2.0 | 2.503 | 6.099 | 1.994 | 1.842 | 2.608 | 2.218 |
Std. Deviation | 0.466 | 2.156 | 3.247 | 0.0 | 1.940 | 4.579 | 0.130 | 0.795 | 1.008 | 0.464 |
Minimum | 0 | 18.000 | 1.000 | 2.0 | 1.000 | 0.200 | 1.000 | 1.000 | 1.000 | 1.000 |
25th Percentile | 0 | 20.000 | 6.000 | 2.0 | 1.000 | 2.800 | 2.000 | 1.158 | 1.800 | 2.000 |
50th Percentile | 0 | 22.000 | 6.000 | 2.0 | 1.000 | 5.000 | 2.000 | 1.700 | 2.600 | 2.300 |
75th Percentile | 1 | 23.000 | 10.000 | 2.0 | 4.000 | 8.400 | 2.000 | 2.300 | 3.200 | 2.500 |
Maximum | 1 | 25.000 | 16.000 | 2.0 | 8.000 | 18.000 | 3.000 | 4.222 | 5.000 | 4.000 |
Statistic | Gender | Age | YrsEd | RegSMUse | FavSMPlatform | DailySMTime | SurveyCommitment | BDImean | BSMASmean | RSESmean |
---|---|---|---|---|---|---|---|---|---|---|
Count | 177 | 177 | 177 | 177 | 177 | 177 | 177 | 177 | 177 | 177 |
Mean | 0.316 | 21.480 | 7.571 | 2.0 | 2.503 | 6.099 | 1.994 | 1.842 | 2.608 | 2.218 |
Std. Deviation | 0.466 | 2.156 | 3.247 | 0.0 | 1.940 | 4.579 | 0.130 | 0.795 | 1.008 | 0.464 |
Minimum | 0 | 18.000 | 1.000 | 2.0 | 1.000 | 0.200 | 1.000 | 1.000 | 1.000 | 1.000 |
25th Percentile | 0 | 20.000 | 6.000 | 2.0 | 1.000 | 2.800 | 2.000 | 1.158 | 1.800 | 2.000 |
50th Percentile | 0 | 22.000 | 6.000 | 2.0 | 1.000 | 5.000 | 2.000 | 1.700 | 2.600 | 2.300 |
75th Percentile | 1 | 23.000 | 10.000 | 2.0 | 4.000 | 8.400 | 2.000 | 2.300 | 3.200 | 2.500 |
Maximum | 1 | 25.000 | 16.000 | 2.0 | 8.000 | 18.000 | 3.000 | 4.222 | 5.000 | 4.000 |
BDImean
, BSMASmean
, and RSESmean
columns.BDImean
, BSMASmean
, and RSESmean
columns:Statistic | BDImean | BSMASmean | RSESmean |
---|---|---|---|
Count | 177 | 177 | 177 |
Mean | 1.842 | 2.608 | 2.218 |
Std. Deviation | 0.795 | 1.008 | 0.464 |
Minimum | 1.000 | 1.000 | 1.000 |
25th Percentile | 1.158 | 1.800 | 2.000 |
50th Percentile | 1.700 | 2.600 | 2.300 |
75th Percentile | 2.300 | 3.200 | 2.500 |
Maximum | 4.222 | 5.000 | 4.000 |
BDImean
, `BSMASmean 6.70
Category | Frequency | Valid Percent |
---|---|---|
Less than 10 minutes | 16 | 12.4 |
10-30 minutes | 30 | 23.3 |
31-60 minutes | 25 | 19.4 |
1-2 hours | 29 | 22.5 |
2-3 hours | 21 | 16.3 |
More than 3 hours | 8 | 6.2 |
Total | 129 | 100.0 |
Category | Frequency | Valid Percent |
---|---|---|
Less than 10 minutes | 16 | 12.4 |
10-30 minutes | 30 | 23.3 |
31-60 minutes | 25 | 19.4 |
1-2 hours | 29 | 22.5 |
2-3 hours | 21 | 16.3 |
More than 3 hours | 8 | 6.2 |
Total | 129 | 100.0 |
Category | Frequency | Valid Percent |
---|---|---|
Less than 10 minutes | 141 | 79.66 |
10-30 minutes | 36 | 20.34 |
31-60 minutes | 0 | 0.00 |
1-2 hours | 0 | 0.00 |
2-3 hours | 0 | 0.00 |
More than 3 hours | 0 | 0.00 |
Total | 177 | 100.0 |
Category | Frequency | Valid Percent |
---|---|---|
Less than 10 minutes | 141 | 79.66 |
10-30 minutes | 36 | 20.34 |
31-60 minutes | 0 | 0.00 |
1-2 hours | 0 | 0.00 |
2-3 hours | 0 | 0.00 |
More than 3 hours | 0 | 0.00 |
Total | 177 | 100.0 |
Category | Frequency | Valid Percent |
---|---|---|
Less than 10 minutes | 141 | 79.66 |
10-30 minutes | 36 | 20.34 |
31-60 minutes | 0 | 0.00 |
1-2 hours | 0 | 0.00 |
2-3 hours | 0 | 0.00 |
More than 3 hours | 0 | 0.00 |
Total | 177 | 100.0 |
Time Spent on Social Media (hours) | Frequency | Valid Percent |
---|---|---|
Less than 10 minutes | 16 | 12.4% |
10 - 30 minutes | 25 | 19.4% |
31 - 60 minutes | 22 | 17.1% |
1 - 2 hours | 29 | 22.6% |
2 - 3 hours | 18 | 14.0% |
More than 3 hours | 13 | 10.0% |
Total | 123 | 100.0% |
Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (3).xlsx
Missing file
BDI_II
represents depression scores and RSES_Sum
represents self-esteem scores. However, we need to identify the column that represents social media use.BSMAS
column to represent social media use, BDI_II
for depression scores, and RSES_Sum
for self-esteem scores.BSMAS
is not present in the dataset. Let's list all the columns in the dataset to identify the correct column name for social media use.BSMAS_Sum
. We will use BSMAS_Sum
for social media use, BDI_II
for depression scores, and RSES_Sum
for self-esteem scores.BDI_II
column, we can proceed with the Pearson Correlation analysis. We will handle the missing value by removing the corresponding row.BSMAS_Sum
) and depression scores (BDI_II
).BSMAS_Sum
) and self-esteem scores (RSES_Sum
).BDI_II
) and self-esteem scores (RSES_Sum
).BSMAS_Sum
column to determine these thresholds and then categorize the users accordingly.BSMAS_Sum
column.BSMAS_Sum
score, is a scale variable. This means it is a quantitative variable that can take on a range of numerical values and has meaningful intervals between values. In this case, the BSMAS_Sum
score represents the sum of responses to a set of questions designed to measure social media use, and higher scores indicate greater use.BSMAS_Sum
column to determine the thresholds. Here are the steps:1 Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (2).xlsx
Missing file
Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (2).xlsx
is not found in the current directory. Could you please upload the file so I can proceed with the analysis?Dailysmtime
does not exist in the dataset. Could you please confirm the correct column name for daily social media time?DailySM Time
does not exist in the dataset. Could you please confirm the correct column name for daily social media time?Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (2).xlsx
is not found in the current directory. Could you please upload the file so I can proceed with the analysis?Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (2).xlsx
is still not found in the current directory. Could you please upload the file so I can proceed with the analysis?Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (2).xlsx
so I can proceed with the analysis?Doc1.docx
Missing file
.docx
file, which is not the expected Excel file. To identify the outliers in the DailySMTime
column, I need the Excel file Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (2).xlsx
.Allen_Hines_Fletcher_Depression_August+13,+2024_14.26 (3).sav
Missing file
.sav
). To identify the outliers in the DailySMTime
column and perform a Box-Cox transformation, I will first need to load the data from this file.StartDate EndDate IPAddress \
0 2024-07-04 23:39:25 2024-07-04 23:41:48 174.251.164.34
1 2024-07-03 14:58:30 2024-07-03 15:00:22 172.56.41.227
2 2024-07-07 00:05:53 2024-07-07 00:08:56 72.217.1.24
3 2024-07-07 00:09:35 2024-07-07 00:12:27 73.41.80.155
4 2024-07-07 11:20:19 2024-07-07 11:22:57 174.244.146.65
Duration__in_seconds_ RecordedDate Q6 Q8_6 Q8_2 Q8_7 Q8_3 \
0 142.0 2024-07-04 23:41:49 187.0 NaN 1.0 NaN NaN
1 111.0 2024-07-03 15:00:23 187.0 NaN 1.0 NaN NaN
2 183.0 2024-07-07 00:08:57 187.0 NaN NaN NaN NaN
3 172.0 2024-07-07 00:12:27 187.0 NaN NaN NaN NaN
4 158.0 2024-07-07 11:22:58 187.0 NaN 1.0 NaN NaN
... Q63_3_1 Q63_4_1 Q63_5_1 Q63_6_1 Q63_7_1 Q63_8_1 Q63_9_1 \
0 ... 4.0 4.0 4.0 4.0 4.0 4.0 4.0
1 ... 2.0 4.0 4.0 3.0 4.0 2.0 2.0
2 ... 4.0 1.0 4.0 4.0 4.0 4.0 4.0
3 ... 4.0 3.0 2.0 3.0 4.0 3.0 4.0
4 ... 2.0 2.0 3.0 4.0 2.0 3.0 3.0
Q63_10_1 RSES_Sum bow_cox
0 4.0 40.0 NaN
1 4.0 32.0 NaN
2 1.0 31.0 NaN
3 3.0 29.0 NaN
4 3.0 29.0 NaN
[5 rows x 94 columns]
DailySMTime
column and perform a Box-Cox transformation.DailySMTime
column are:0 4.0
2 4.0
3 4.0
48 4.0
49 4.0
Name: Q63_3_1, dtype: float64
DailySMTime
data:ZScore_BDI
and ZScore_RSES
columns. I will start by calculating the Z-scores for these columns and then visualize the results.Q6
and RSES_Sum
contain missing values, resulting in an empty DataFrame after dropping NaNs.BDI
and RSES
.BDI
and RSES
have been calculated and added to the dataframe. Here are the first few rows: Q6 RSES_Sum ZScore_BDI ZScore_RSES
0 187.0 40.0 NaN 3.883400
1 187.0 32.0 NaN 2.144894
2 187.0 31.0 NaN 1.927581
3 187.0 29.0 NaN 1.492954
4 187.0 29.0 NaN 1.492954
BDI
are not calculated correctly due to missing values. The Z-scores for RSES
have been calculated correctly.Q6
and RSES_Sum
contain missing values, resulting in an empty DataFrame after dropping NaNs.BDI
and RSES
.BDI
and RSES
have been calculated and added to the dataframe. Here are the first few rows: Q6 RSES_Sum ZScore_BDI ZScore_RSES
0 187.0 40.0 NaN 3.883400
1 187.0 32.0 NaN 2.144894
2 187.0 31.0 NaN 1.927581
3 187.0 29.0 NaN 1.492954
4 187.0 29.0 NaN 1.492954
BDI
are not calculated correctly due to missing values. The Z-scores for RSES
have been calculated correctly.ZScore_BDI
and ZScore_RSES
columns. I will visualize the distribution of these Z-scores.