Overview

Dataset statistics

Number of variables13
Number of observations981
Missing cells2387
Missing cells (%)18.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.7 KiB
Average record size in memory106.1 B

Variable types

Numeric2
Text10
Boolean1

Dataset

DescriptionMDIS_연구정보 마이크로데이터 통합서비스 국가 주요정책 수립, 기업 경영전략 수립, 학술논문 등 심층 연구·분석에 활용되는 마이크로데이터의 수요가 지속적으로 증가하고 있습니다. 이에 통계청은 자체 작성하는 마이크로데이터뿐만 아니라 정부 각 부처, 지자체, 연구기관 등 타 통계작성기관의 마이크로데이터를 한 곳에 모아 MDIS (MicroData Integrated Service)를 통해 국민들이 다양한 통계자료를 편리하게 이용할 수 있도록 서비스하고 있습니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15089847/fileData.do

Alerts

공개여부 has constant value ""Constant
연구관리번호 is highly overall correlated with 연구연도High correlation
연구연도 is highly overall correlated with 연구관리번호High correlation
공동연구자 has 450 (45.9%) missing valuesMissing
키워드 has 15 (1.5%) missing valuesMissing
이용항목 has 760 (77.5%) missing valuesMissing
분석항목 has 737 (75.1%) missing valuesMissing
학술지 has 420 (42.8%) missing valuesMissing
연구관리번호 has unique valuesUnique

Reproduction

Analysis started2024-04-17 16:16:15.682731
Analysis finished2024-04-17 16:16:17.790459
Duration2.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연구관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct981
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531.73293
Minimum1
Maximum1078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-18T01:16:17.848639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51
Q1250
median497
Q3821
95-th percentile1029
Maximum1078
Range1077
Interquartile range (IQR)571

Descriptive statistics

Standard deviation321.99823
Coefficient of variation (CV)0.60556384
Kurtosis-1.2983192
Mean531.73293
Median Absolute Deviation (MAD)286
Skewness0.064689752
Sum521630
Variance103682.86
MonotonicityNot monotonic
2024-04-18T01:16:17.960838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 1
 
0.1%
755 1
 
0.1%
648 1
 
0.1%
645 1
 
0.1%
649 1
 
0.1%
651 1
 
0.1%
660 1
 
0.1%
667 1
 
0.1%
669 1
 
0.1%
696 1
 
0.1%
Other values (971) 971
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1078 1
0.1%
1077 1
0.1%
1076 1
0.1%
1075 1
0.1%
1074 1
0.1%
1073 1
0.1%
1072 1
0.1%
1071 1
0.1%
1070 1
0.1%
1069 1
0.1%
Distinct976
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2024-04-18T01:16:18.236449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length162
Median length97
Mean length35.991845
Min length7

Characters and Unicode

Total characters35308
Distinct characters578
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique971 ?
Unique (%)99.0%

Sample

1st row한국사회의 계층귀속감과 상향이동의식 변화: 연령(Age), 기간(Period) 및 코호트(Cohort) 효과를 중심으로
2nd rowLong-Term Changes in the Economic Activity of Older Males in Korea
3rd row1996~2000년 한국의 가구소득불평등 확대 - 임금, 노동공급, 가구구조 변화의 영향
4th row경기침체는 건강에 이로운가 1991년~2009년 한국의 실업률과 사망률
5th row공적보조금이 지역내 지역간 농가소득불평등에 미치는 영향 분석
ValueCountFrequency (%)
미치는 205
 
2.6%
영향 172
 
2.2%
연구 166
 
2.1%
분석 164
 
2.1%
중심으로 157
 
2.0%
150
 
1.9%
관한 107
 
1.4%
106
 
1.4%
대한 69
 
0.9%
따른 62
 
0.8%
Other values (3571) 6493
82.7%
2024-04-18T01:16:18.653697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6873
 
19.5%
908
 
2.6%
520
 
1.5%
463
 
1.3%
455
 
1.3%
434
 
1.2%
423
 
1.2%
409
 
1.2%
389
 
1.1%
388
 
1.1%
Other values (568) 24046
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24000
68.0%
Space Separator 6873
 
19.5%
Lowercase Letter 2596
 
7.4%
Decimal Number 650
 
1.8%
Uppercase Letter 472
 
1.3%
Other Punctuation 392
 
1.1%
Dash Punctuation 162
 
0.5%
Close Punctuation 55
 
0.2%
Open Punctuation 55
 
0.2%
Math Symbol 32
 
0.1%
Other values (4) 21
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
908
 
3.8%
520
 
2.2%
463
 
1.9%
455
 
1.9%
434
 
1.8%
423
 
1.8%
409
 
1.7%
389
 
1.6%
388
 
1.6%
374
 
1.6%
Other values (479) 19237
80.2%
Lowercase Letter
ValueCountFrequency (%)
e 309
11.9%
n 262
10.1%
i 232
 
8.9%
a 230
 
8.9%
o 206
 
7.9%
t 203
 
7.8%
r 181
 
7.0%
s 127
 
4.9%
l 123
 
4.7%
d 94
 
3.6%
Other values (16) 629
24.2%
Uppercase Letter
ValueCountFrequency (%)
I 50
10.6%
C 50
10.6%
S 43
 
9.1%
A 36
 
7.6%
D 34
 
7.2%
E 31
 
6.6%
K 30
 
6.4%
P 28
 
5.9%
M 24
 
5.1%
R 22
 
4.7%
Other values (15) 124
26.3%
Decimal Number
ValueCountFrequency (%)
1 165
25.4%
0 147
22.6%
2 138
21.2%
9 81
12.5%
8 37
 
5.7%
5 22
 
3.4%
6 20
 
3.1%
4 15
 
2.3%
7 15
 
2.3%
3 10
 
1.5%
Other Punctuation
ValueCountFrequency (%)
: 232
59.2%
, 86
 
21.9%
· 46
 
11.7%
/ 11
 
2.8%
& 8
 
2.0%
. 4
 
1.0%
* 4
 
1.0%
' 1
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 18
56.2%
= 8
25.0%
< 2
 
6.2%
2
 
6.2%
> 2
 
6.2%
Close Punctuation
ValueCountFrequency (%)
) 51
92.7%
2
 
3.6%
] 2
 
3.6%
Open Punctuation
ValueCountFrequency (%)
( 51
92.7%
2
 
3.6%
[ 2
 
3.6%
Final Punctuation
ValueCountFrequency (%)
5
62.5%
3
37.5%
Initial Punctuation
ValueCountFrequency (%)
4
57.1%
3
42.9%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
6873
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 162
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24000
68.0%
Common 8238
 
23.3%
Latin 3070
 
8.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
908
 
3.8%
520
 
2.2%
463
 
1.9%
455
 
1.9%
434
 
1.8%
423
 
1.8%
409
 
1.7%
389
 
1.6%
388
 
1.6%
374
 
1.6%
Other values (479) 19237
80.2%
Latin
ValueCountFrequency (%)
e 309
 
10.1%
n 262
 
8.5%
i 232
 
7.6%
a 230
 
7.5%
o 206
 
6.7%
t 203
 
6.6%
r 181
 
5.9%
s 127
 
4.1%
l 123
 
4.0%
d 94
 
3.1%
Other values (43) 1103
35.9%
Common
ValueCountFrequency (%)
6873
83.4%
: 232
 
2.8%
1 165
 
2.0%
- 162
 
2.0%
0 147
 
1.8%
2 138
 
1.7%
, 86
 
1.0%
9 81
 
1.0%
) 51
 
0.6%
( 51
 
0.6%
Other values (26) 252
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23993
68.0%
ASCII 11239
31.8%
None 50
 
0.1%
Punctuation 15
 
< 0.1%
Compat Jamo 7
 
< 0.1%
Math Operators 2
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6873
61.2%
e 309
 
2.7%
n 262
 
2.3%
: 232
 
2.1%
i 232
 
2.1%
a 230
 
2.0%
o 206
 
1.8%
t 203
 
1.8%
r 181
 
1.6%
1 165
 
1.5%
Other values (69) 2346
 
20.9%
Hangul
ValueCountFrequency (%)
908
 
3.8%
520
 
2.2%
463
 
1.9%
455
 
1.9%
434
 
1.8%
423
 
1.8%
409
 
1.7%
389
 
1.6%
388
 
1.6%
374
 
1.6%
Other values (478) 19230
80.1%
None
ValueCountFrequency (%)
· 46
92.0%
2
 
4.0%
2
 
4.0%
Compat Jamo
ValueCountFrequency (%)
7
100.0%
Punctuation
ValueCountFrequency (%)
5
33.3%
4
26.7%
3
20.0%
3
20.0%
Math Operators
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct760
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2024-04-18T01:16:18.938065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length3
Mean length3.1427115
Min length2

Characters and Unicode

Total characters3083
Distinct characters225
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique624 ?
Unique (%)63.6%

Sample

1st row이왕원
2nd row이철희
3rd row이철희
4th row이철희
5th row김태이
ValueCountFrequency (%)
이철희 16
 
1.6%
주익현 7
 
0.7%
기은광 6
 
0.6%
김영민 5
 
0.5%
정성훈 5
 
0.5%
문영만 5
 
0.5%
강창희 4
 
0.4%
엄진영 4
 
0.4%
김민정 4
 
0.4%
홍민기 4
 
0.4%
Other values (761) 945
94.0%
2024-04-18T01:16:19.301904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
 
6.7%
165
 
5.4%
111
 
3.6%
103
 
3.3%
78
 
2.5%
77
 
2.5%
70
 
2.3%
69
 
2.2%
68
 
2.2%
67
 
2.2%
Other values (215) 2069
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3006
97.5%
Space Separator 24
 
0.8%
Other Punctuation 24
 
0.8%
Uppercase Letter 19
 
0.6%
Lowercase Letter 7
 
0.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
206
 
6.9%
165
 
5.5%
111
 
3.7%
103
 
3.4%
78
 
2.6%
77
 
2.6%
70
 
2.3%
69
 
2.3%
68
 
2.3%
67
 
2.2%
Other values (190) 1992
66.3%
Uppercase Letter
ValueCountFrequency (%)
S 2
10.5%
O 2
10.5%
D 2
10.5%
K 2
10.5%
E 2
10.5%
R 1
 
5.3%
B 1
 
5.3%
Y 1
 
5.3%
I 1
 
5.3%
N 1
 
5.3%
Other values (4) 4
21.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
28.6%
n 1
14.3%
u 1
14.3%
m 1
14.3%
h 1
14.3%
i 1
14.3%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3006
97.5%
Common 51
 
1.7%
Latin 26
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
206
 
6.9%
165
 
5.5%
111
 
3.7%
103
 
3.4%
78
 
2.6%
77
 
2.6%
70
 
2.3%
69
 
2.3%
68
 
2.3%
67
 
2.2%
Other values (190) 1992
66.3%
Latin
ValueCountFrequency (%)
S 2
 
7.7%
e 2
 
7.7%
O 2
 
7.7%
D 2
 
7.7%
K 2
 
7.7%
E 2
 
7.7%
n 1
 
3.8%
u 1
 
3.8%
m 1
 
3.8%
h 1
 
3.8%
Other values (10) 10
38.5%
Common
ValueCountFrequency (%)
24
47.1%
, 24
47.1%
) 1
 
2.0%
( 1
 
2.0%
- 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3006
97.5%
ASCII 77
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
206
 
6.9%
165
 
5.5%
111
 
3.7%
103
 
3.4%
78
 
2.6%
77
 
2.6%
70
 
2.3%
69
 
2.3%
68
 
2.3%
67
 
2.2%
Other values (190) 1992
66.3%
ASCII
ValueCountFrequency (%)
24
31.2%
, 24
31.2%
S 2
 
2.6%
e 2
 
2.6%
O 2
 
2.6%
D 2
 
2.6%
K 2
 
2.6%
E 2
 
2.6%
) 1
 
1.3%
n 1
 
1.3%
Other values (15) 15
19.5%

공동연구자
Text

MISSING 

Distinct495
Distinct (%)93.2%
Missing450
Missing (%)45.9%
Memory size7.8 KiB
2024-04-18T01:16:19.561118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length3
Mean length8.3879473
Min length1

Characters and Unicode

Total characters4454
Distinct characters258
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique468 ?
Unique (%)88.1%

Sample

1st row김문조, 최율
2nd row김태훈
3rd row임정빈, 안동환
4th rowRyan Sullivan
5th row이재원
ValueCountFrequency (%)
허정 9
 
0.8%
8
 
0.7%
박지연 6
 
0.6%
김재덕 5
 
0.5%
김현경 5
 
0.5%
김영민 5
 
0.5%
김인철 5
 
0.5%
전현배 5
 
0.5%
박진 5
 
0.5%
조장희 4
 
0.4%
Other values (867) 1029
94.8%
2024-04-18T01:16:19.925915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
560
 
12.6%
, 528
 
11.9%
232
 
5.2%
136
 
3.1%
118
 
2.6%
94
 
2.1%
80
 
1.8%
75
 
1.7%
70
 
1.6%
69
 
1.5%
Other values (248) 2492
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3095
69.5%
Space Separator 560
 
12.6%
Other Punctuation 533
 
12.0%
Lowercase Letter 217
 
4.9%
Uppercase Letter 49
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
232
 
7.5%
136
 
4.4%
118
 
3.8%
94
 
3.0%
80
 
2.6%
75
 
2.4%
70
 
2.3%
69
 
2.2%
68
 
2.2%
64
 
2.1%
Other values (207) 2089
67.5%
Lowercase Letter
ValueCountFrequency (%)
n 30
13.8%
o 29
13.4%
e 22
10.1%
a 20
9.2%
u 16
 
7.4%
i 15
 
6.9%
g 13
 
6.0%
h 13
 
6.0%
m 10
 
4.6%
r 8
 
3.7%
Other values (11) 41
18.9%
Uppercase Letter
ValueCountFrequency (%)
S 8
16.3%
K 7
14.3%
T 4
8.2%
J 4
8.2%
G 3
 
6.1%
W 3
 
6.1%
P 3
 
6.1%
N 3
 
6.1%
B 3
 
6.1%
R 2
 
4.1%
Other values (6) 9
18.4%
Other Punctuation
ValueCountFrequency (%)
, 528
99.1%
. 3
 
0.6%
· 2
 
0.4%
Space Separator
ValueCountFrequency (%)
560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3095
69.5%
Common 1093
 
24.5%
Latin 266
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
232
 
7.5%
136
 
4.4%
118
 
3.8%
94
 
3.0%
80
 
2.6%
75
 
2.4%
70
 
2.3%
69
 
2.2%
68
 
2.2%
64
 
2.1%
Other values (207) 2089
67.5%
Latin
ValueCountFrequency (%)
n 30
 
11.3%
o 29
 
10.9%
e 22
 
8.3%
a 20
 
7.5%
u 16
 
6.0%
i 15
 
5.6%
g 13
 
4.9%
h 13
 
4.9%
m 10
 
3.8%
r 8
 
3.0%
Other values (27) 90
33.8%
Common
ValueCountFrequency (%)
560
51.2%
, 528
48.3%
. 3
 
0.3%
· 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3095
69.5%
ASCII 1357
30.5%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
560
41.3%
, 528
38.9%
n 30
 
2.2%
o 29
 
2.1%
e 22
 
1.6%
a 20
 
1.5%
u 16
 
1.2%
i 15
 
1.1%
g 13
 
1.0%
h 13
 
1.0%
Other values (30) 111
 
8.2%
Hangul
ValueCountFrequency (%)
232
 
7.5%
136
 
4.4%
118
 
3.8%
94
 
3.0%
80
 
2.6%
75
 
2.4%
70
 
2.3%
69
 
2.2%
68
 
2.2%
64
 
2.1%
Other values (207) 2089
67.5%
None
ValueCountFrequency (%)
· 2
100.0%

소속
Text

Distinct542
Distinct (%)55.4%
Missing3
Missing (%)0.3%
Memory size7.8 KiB
2024-04-18T01:16:20.136933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length101
Median length50
Mean length11.190184
Min length3

Characters and Unicode

Total characters10944
Distinct characters334
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)43.1%

Sample

1st row고려대학교 사회학과
2nd row서울대학교 경제학부
3rd row서울대학교 경제학부
4th row서울대학교 경제학부
5th row서울대학교 농업자원경제학과
ValueCountFrequency (%)
서울대학교 78
 
4.7%
경제학부 58
 
3.5%
한국보건사회연구원 48
 
2.9%
산업연구원 45
 
2.7%
한국농촌경제연구원 40
 
2.4%
서강대학교 35
 
2.1%
고려대학교 30
 
1.8%
사회복지학과 29
 
1.7%
대학원 29
 
1.7%
경제학과 28
 
1.7%
Other values (576) 1250
74.9%
2024-04-18T01:16:20.693932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1190
 
10.9%
847
 
7.7%
747
 
6.8%
694
 
6.3%
428
 
3.9%
360
 
3.3%
333
 
3.0%
325
 
3.0%
312
 
2.9%
299
 
2.7%
Other values (324) 5409
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9791
89.5%
Space Separator 694
 
6.3%
Other Punctuation 147
 
1.3%
Open Punctuation 82
 
0.7%
Close Punctuation 82
 
0.7%
Uppercase Letter 82
 
0.7%
Lowercase Letter 53
 
0.5%
Decimal Number 8
 
0.1%
Math Symbol 4
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1190
 
12.2%
847
 
8.7%
747
 
7.6%
428
 
4.4%
360
 
3.7%
333
 
3.4%
325
 
3.3%
312
 
3.2%
299
 
3.1%
297
 
3.0%
Other values (273) 4653
47.5%
Uppercase Letter
ValueCountFrequency (%)
K 23
28.0%
B 13
15.9%
S 12
14.6%
I 7
 
8.5%
D 6
 
7.3%
H 3
 
3.7%
C 3
 
3.7%
U 3
 
3.7%
R 2
 
2.4%
G 1
 
1.2%
Other values (9) 9
 
11.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
15.1%
l 7
13.2%
a 6
11.3%
s 5
9.4%
i 4
7.5%
n 4
7.5%
t 3
 
5.7%
o 3
 
5.7%
v 2
 
3.8%
f 2
 
3.8%
Other values (8) 9
17.0%
Other Punctuation
ValueCountFrequency (%)
, 136
92.5%
: 7
 
4.8%
· 2
 
1.4%
/ 1
 
0.7%
. 1
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 2
50.0%
> 1
25.0%
< 1
25.0%
Decimal Number
ValueCountFrequency (%)
1 4
50.0%
2 4
50.0%
Space Separator
ValueCountFrequency (%)
694
100.0%
Open Punctuation
ValueCountFrequency (%)
( 82
100.0%
Close Punctuation
ValueCountFrequency (%)
) 82
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9791
89.5%
Common 1018
 
9.3%
Latin 135
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1190
 
12.2%
847
 
8.7%
747
 
7.6%
428
 
4.4%
360
 
3.7%
333
 
3.4%
325
 
3.3%
312
 
3.2%
299
 
3.1%
297
 
3.0%
Other values (273) 4653
47.5%
Latin
ValueCountFrequency (%)
K 23
17.0%
B 13
 
9.6%
S 12
 
8.9%
e 8
 
5.9%
l 7
 
5.2%
I 7
 
5.2%
a 6
 
4.4%
D 6
 
4.4%
s 5
 
3.7%
i 4
 
3.0%
Other values (27) 44
32.6%
Common
ValueCountFrequency (%)
694
68.2%
, 136
 
13.4%
( 82
 
8.1%
) 82
 
8.1%
: 7
 
0.7%
1 4
 
0.4%
2 4
 
0.4%
+ 2
 
0.2%
· 2
 
0.2%
- 1
 
0.1%
Other values (4) 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9791
89.5%
ASCII 1151
 
10.5%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1190
 
12.2%
847
 
8.7%
747
 
7.6%
428
 
4.4%
360
 
3.7%
333
 
3.4%
325
 
3.3%
312
 
3.2%
299
 
3.1%
297
 
3.0%
Other values (273) 4653
47.5%
ASCII
ValueCountFrequency (%)
694
60.3%
, 136
 
11.8%
( 82
 
7.1%
) 82
 
7.1%
K 23
 
2.0%
B 13
 
1.1%
S 12
 
1.0%
e 8
 
0.7%
l 7
 
0.6%
: 7
 
0.6%
Other values (40) 87
 
7.6%
None
ValueCountFrequency (%)
· 2
100.0%

연구연도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5984
Minimum2007
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-18T01:16:20.784304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2016
Q12018
median2020
Q32021
95-th percentile2022
Maximum2023
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1470809
Coefficient of variation (CV)0.0010631227
Kurtosis2.3550613
Mean2019.5984
Median Absolute Deviation (MAD)2
Skewness-0.93830878
Sum1981226
Variance4.6099565
MonotonicityNot monotonic
2024-04-18T01:16:20.869722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2022 190
19.4%
2021 167
17.0%
2019 155
15.8%
2020 146
14.9%
2018 126
12.8%
2017 91
9.3%
2016 52
 
5.3%
2023 32
 
3.3%
2015 12
 
1.2%
2012 3
 
0.3%
Other values (5) 7
 
0.7%
ValueCountFrequency (%)
2007 1
 
0.1%
2008 2
 
0.2%
2011 1
 
0.1%
2012 3
 
0.3%
2013 1
 
0.1%
2014 2
 
0.2%
2015 12
 
1.2%
2016 52
5.3%
2017 91
9.3%
2018 126
12.8%
ValueCountFrequency (%)
2023 32
 
3.3%
2022 190
19.4%
2021 167
17.0%
2020 146
14.9%
2019 155
15.8%
2018 126
12.8%
2017 91
9.3%
2016 52
 
5.3%
2015 12
 
1.2%
2014 2
 
0.2%
Distinct973
Distinct (%)99.3%
Missing1
Missing (%)0.1%
Memory size7.8 KiB
2024-04-18T01:16:21.089736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length329
Median length151
Mean length82.021429
Min length17

Characters and Unicode

Total characters80381
Distinct characters699
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique966 ?
Unique (%)98.6%

Sample

1st rowIMF 환란 이후 사회 불평등 의식에 관한 담론이 사회 각계에서 널리 움트고 있다는 점에 착안해, 경제적 위기 국면에서 새로 출현하는 사회 불평등 의식을 보다 정치하게 분석해보자는 동기에서 출발한 것으로, 주관적 계층귀속감(subjective class identification)과 상향이동의식(upward mobility consciousness)을 집중적으로 다룸
2nd row20세기 후반 한국의 노년남성층의 경제활동참가율을 분석하여, 장기적 관점으로 이들의 은퇴행동의 변화와 패턴 및 원인을 규명하고자 함
3rd row가구주와 배우자의 임금, 고용, 근로시간, 그리고 기타소득과 가구구조 등 가구소득을 구성하는 각 요인들이 1996년과 2000년 사이 가구소득불평등도의 증가에 미친 효과를 분석
4th row1991년에서 2009년 한국의 광역시도별 시계열 자료를 이용하여 한국에 있어서의 경기변동과 사망률 간의 관계를 분석함
5th row농가소득 불평등의 심화요인과 정책효과를 살펴보기 위해 지니계수를 이용하여 공적보조금이 농가소득의 불평등도에 미치는 영향을 분석하고 농가소득 불평등도의 지역간 차이를 비교 분석
ValueCountFrequency (%)
696
 
3.9%
영향을 281
 
1.6%
미치는 257
 
1.4%
223
 
1.2%
분석하고자 149
 
0.8%
대한 141
 
0.8%
위한 111
 
0.6%
있는 107
 
0.6%
통해 81
 
0.4%
따른 73
 
0.4%
Other values (7412) 15913
88.2%
2024-04-18T01:16:21.466798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17078
 
21.2%
1901
 
2.4%
1631
 
2.0%
1441
 
1.8%
1389
 
1.7%
1341
 
1.7%
1218
 
1.5%
1054
 
1.3%
949
 
1.2%
901
 
1.1%
Other values (689) 51478
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58581
72.9%
Space Separator 17078
 
21.2%
Lowercase Letter 2063
 
2.6%
Decimal Number 1051
 
1.3%
Other Punctuation 853
 
1.1%
Uppercase Letter 328
 
0.4%
Open Punctuation 131
 
0.2%
Close Punctuation 131
 
0.2%
Dash Punctuation 65
 
0.1%
Final Punctuation 39
 
< 0.1%
Other values (3) 61
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1901
 
3.2%
1631
 
2.8%
1441
 
2.5%
1389
 
2.4%
1341
 
2.3%
1218
 
2.1%
1054
 
1.8%
949
 
1.6%
901
 
1.5%
898
 
1.5%
Other values (602) 45858
78.3%
Lowercase Letter
ValueCountFrequency (%)
e 241
11.7%
t 202
 
9.8%
i 176
 
8.5%
a 169
 
8.2%
o 157
 
7.6%
n 143
 
6.9%
s 137
 
6.6%
r 136
 
6.6%
l 89
 
4.3%
d 76
 
3.7%
Other values (15) 537
26.0%
Uppercase Letter
ValueCountFrequency (%)
D 43
13.1%
I 30
 
9.1%
R 27
 
8.2%
C 26
 
7.9%
O 25
 
7.6%
S 24
 
7.3%
E 19
 
5.8%
M 17
 
5.2%
P 15
 
4.6%
K 14
 
4.3%
Other values (14) 88
26.8%
Decimal Number
ValueCountFrequency (%)
0 268
25.5%
1 243
23.1%
2 225
21.4%
9 111
10.6%
6 43
 
4.1%
4 42
 
4.0%
5 40
 
3.8%
8 36
 
3.4%
7 22
 
2.1%
3 21
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 614
72.0%
. 118
 
13.8%
· 70
 
8.2%
/ 26
 
3.0%
& 14
 
1.6%
' 5
 
0.6%
: 3
 
0.4%
" 2
 
0.2%
% 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 124
94.7%
5
 
3.8%
1
 
0.8%
[ 1
 
0.8%
Close Punctuation
ValueCountFrequency (%)
) 124
94.7%
5
 
3.8%
1
 
0.8%
] 1
 
0.8%
Math Symbol
ValueCountFrequency (%)
~ 19
73.1%
3
 
11.5%
> 2
 
7.7%
2
 
7.7%
Final Punctuation
ValueCountFrequency (%)
36
92.3%
3
 
7.7%
Initial Punctuation
ValueCountFrequency (%)
31
91.2%
3
 
8.8%
Space Separator
ValueCountFrequency (%)
17078
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Control
ValueCountFrequency (%)
 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58579
72.9%
Common 19409
 
24.1%
Latin 2391
 
3.0%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1901
 
3.2%
1631
 
2.8%
1441
 
2.5%
1389
 
2.4%
1341
 
2.3%
1218
 
2.1%
1054
 
1.8%
949
 
1.6%
901
 
1.5%
898
 
1.5%
Other values (600) 45856
78.3%
Latin
ValueCountFrequency (%)
e 241
 
10.1%
t 202
 
8.4%
i 176
 
7.4%
a 169
 
7.1%
o 157
 
6.6%
n 143
 
6.0%
s 137
 
5.7%
r 136
 
5.7%
l 89
 
3.7%
d 76
 
3.2%
Other values (39) 865
36.2%
Common
ValueCountFrequency (%)
17078
88.0%
, 614
 
3.2%
0 268
 
1.4%
1 243
 
1.3%
2 225
 
1.2%
( 124
 
0.6%
) 124
 
0.6%
. 118
 
0.6%
9 111
 
0.6%
· 70
 
0.4%
Other values (28) 434
 
2.2%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58572
72.9%
ASCII 21640
 
26.9%
None 85
 
0.1%
Punctuation 73
 
0.1%
Compat Jamo 7
 
< 0.1%
Math Operators 2
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17078
78.9%
, 614
 
2.8%
0 268
 
1.2%
1 243
 
1.1%
e 241
 
1.1%
2 225
 
1.0%
t 202
 
0.9%
i 176
 
0.8%
a 169
 
0.8%
o 157
 
0.7%
Other values (66) 2267
 
10.5%
Hangul
ValueCountFrequency (%)
1901
 
3.2%
1631
 
2.8%
1441
 
2.5%
1389
 
2.4%
1341
 
2.3%
1218
 
2.1%
1054
 
1.8%
949
 
1.6%
901
 
1.5%
898
 
1.5%
Other values (599) 45849
78.3%
None
ValueCountFrequency (%)
· 70
82.4%
5
 
5.9%
5
 
5.9%
3
 
3.5%
1
 
1.2%
1
 
1.2%
Punctuation
ValueCountFrequency (%)
36
49.3%
31
42.5%
3
 
4.1%
3
 
4.1%
Compat Jamo
ValueCountFrequency (%)
7
100.0%
Math Operators
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

키워드
Text

MISSING 

Distinct959
Distinct (%)99.3%
Missing15
Missing (%)1.5%
Memory size7.8 KiB
2024-04-18T01:16:21.758820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length235
Median length139
Mean length45.845756
Min length5

Characters and Unicode

Total characters44287
Distinct characters591
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique952 ?
Unique (%)98.6%

Sample

1st row#불평등, #계층귀속감, #중상층, #상향이동, #코호트, #APC모형
2nd row#labor force participationrate, #older males, #Early retirement, #경제활동참가율, #취업자
3rd row#가구소득불평등, #임금, #고용, #근로시간, #가구구조, #외환위기
4th row#경기변동, #실업, #건강, #사망, #환경오염, #근로환경, #스트레스, #음주, #흡연
5th row#농가소득 불평등, #지역간 불평등,#지역내 불평등, #지니계수, #공적보조금
ValueCountFrequency (%)
분석 53
 
0.7%
회귀분석 52
 
0.7%
모형 52
 
0.7%
만족도 39
 
0.5%
삶의 38
 
0.5%
38
 
0.5%
청소년 36
 
0.5%
여성 35
 
0.5%
고용 35
 
0.5%
사회적 30
 
0.4%
Other values (3715) 6826
94.4%
2024-04-18T01:16:22.172685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6330
 
14.3%
# 5091
 
11.5%
, 4064
 
9.2%
e 627
 
1.4%
i 520
 
1.2%
475
 
1.1%
a 463
 
1.0%
450
 
1.0%
t 447
 
1.0%
419
 
0.9%
Other values (581) 25401
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22171
50.1%
Other Punctuation 9214
20.8%
Space Separator 6330
 
14.3%
Lowercase Letter 5261
 
11.9%
Uppercase Letter 889
 
2.0%
Decimal Number 190
 
0.4%
Dash Punctuation 93
 
0.2%
Close Punctuation 63
 
0.1%
Open Punctuation 63
 
0.1%
Final Punctuation 7
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
475
 
2.1%
450
 
2.0%
419
 
1.9%
411
 
1.9%
405
 
1.8%
402
 
1.8%
379
 
1.7%
379
 
1.7%
376
 
1.7%
339
 
1.5%
Other values (504) 18136
81.8%
Lowercase Letter
ValueCountFrequency (%)
e 627
11.9%
i 520
9.9%
a 463
 
8.8%
t 447
 
8.5%
n 410
 
7.8%
o 390
 
7.4%
r 362
 
6.9%
s 303
 
5.8%
l 286
 
5.4%
c 236
 
4.5%
Other values (16) 1217
23.1%
Uppercase Letter
ValueCountFrequency (%)
S 84
 
9.4%
D 82
 
9.2%
C 66
 
7.4%
A 64
 
7.2%
M 63
 
7.1%
P 63
 
7.1%
E 60
 
6.7%
I 57
 
6.4%
R 47
 
5.3%
O 41
 
4.6%
Other values (15) 262
29.5%
Decimal Number
ValueCountFrequency (%)
1 76
40.0%
9 41
21.6%
2 20
 
10.5%
4 17
 
8.9%
0 12
 
6.3%
5 9
 
4.7%
8 6
 
3.2%
6 5
 
2.6%
7 2
 
1.1%
3 2
 
1.1%
Other Punctuation
ValueCountFrequency (%)
# 5091
55.3%
, 4064
44.1%
& 20
 
0.2%
. 11
 
0.1%
· 10
 
0.1%
; 8
 
0.1%
: 4
 
< 0.1%
/ 4
 
< 0.1%
' 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
6330
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%
Final Punctuation
ValueCountFrequency (%)
7
100.0%
Initial Punctuation
ValueCountFrequency (%)
4
100.0%
Math Symbol
ValueCountFrequency (%)
> 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22167
50.1%
Common 15966
36.1%
Latin 6150
 
13.9%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
475
 
2.1%
450
 
2.0%
419
 
1.9%
411
 
1.9%
405
 
1.8%
402
 
1.8%
379
 
1.7%
379
 
1.7%
376
 
1.7%
339
 
1.5%
Other values (502) 18132
81.8%
Latin
ValueCountFrequency (%)
e 627
 
10.2%
i 520
 
8.5%
a 463
 
7.5%
t 447
 
7.3%
n 410
 
6.7%
o 390
 
6.3%
r 362
 
5.9%
s 303
 
4.9%
l 286
 
4.7%
c 236
 
3.8%
Other values (41) 2106
34.2%
Common
ValueCountFrequency (%)
6330
39.6%
# 5091
31.9%
, 4064
25.5%
- 93
 
0.6%
1 76
 
0.5%
) 63
 
0.4%
( 63
 
0.4%
9 41
 
0.3%
2 20
 
0.1%
& 20
 
0.1%
Other values (16) 105
 
0.7%
Han
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22167
50.1%
ASCII 22095
49.9%
Punctuation 11
 
< 0.1%
None 10
 
< 0.1%
CJK 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6330
28.6%
# 5091
23.0%
, 4064
18.4%
e 627
 
2.8%
i 520
 
2.4%
a 463
 
2.1%
t 447
 
2.0%
n 410
 
1.9%
o 390
 
1.8%
r 362
 
1.6%
Other values (64) 3391
15.3%
Hangul
ValueCountFrequency (%)
475
 
2.1%
450
 
2.0%
419
 
1.9%
411
 
1.9%
405
 
1.8%
402
 
1.8%
379
 
1.7%
379
 
1.7%
376
 
1.7%
339
 
1.5%
Other values (502) 18132
81.8%
None
ValueCountFrequency (%)
· 10
100.0%
Punctuation
ValueCountFrequency (%)
7
63.6%
4
36.4%
CJK
ValueCountFrequency (%)
2
50.0%
2
50.0%

공개여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
981 
ValueCountFrequency (%)
True 981
100.0%
2024-04-18T01:16:22.255071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

이용항목
Text

MISSING 

Distinct218
Distinct (%)98.6%
Missing760
Missing (%)77.5%
Memory size7.8 KiB
2024-04-18T01:16:22.451268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length445
Median length123
Mean length75.076923
Min length4

Characters and Unicode

Total characters16592
Distinct characters426
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)97.3%

Sample

1st row* 계층의식, 본인의 계층이동, 다음세대의 계층이동
2nd row* 연령, 성별, 취학여부, 일하였음, 구직, 이유, 산업, 학력, 혼인관계, 세대구성, 가족구성원수, 거주지
3rd row* 가구유형, 경상소득(임금근로, 사업, 이자배당, 연금, 기타), 비경상소득(퇴직금, 비경상보조금 등), 주된활동, 직업분류, 산업분류* 활동상태, 취업시간
4th row* 사망자주소, 사망년월일, 사망원인, 사망자 직업, 사망자 성별, 사망년령, 교육정도
5th row* 농가소득, 농업소득, 농외소득, 공적보조금, 사적보조금
ValueCountFrequency (%)
104
 
3.5%
성별 53
 
1.8%
산업분류 47
 
1.6%
연령 46
 
1.5%
종사자수 38
 
1.3%
33
 
1.1%
매출액 32
 
1.1%
가구주 30
 
1.0%
유형자산 20
 
0.7%
만나이 20
 
0.7%
Other values (1498) 2568
85.9%
2024-04-18T01:16:22.821191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2775
 
16.7%
, 1564
 
9.4%
339
 
2.0%
319
 
1.9%
318
 
1.9%
* 301
 
1.8%
258
 
1.6%
253
 
1.5%
221
 
1.3%
216
 
1.3%
Other values (416) 10028
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11003
66.3%
Space Separator 2775
 
16.7%
Other Punctuation 1947
 
11.7%
Lowercase Letter 412
 
2.5%
Connector Punctuation 174
 
1.0%
Decimal Number 83
 
0.5%
Open Punctuation 79
 
0.5%
Close Punctuation 78
 
0.5%
Uppercase Letter 34
 
0.2%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
339
 
3.1%
319
 
2.9%
318
 
2.9%
258
 
2.3%
253
 
2.3%
221
 
2.0%
216
 
2.0%
213
 
1.9%
211
 
1.9%
210
 
1.9%
Other values (358) 8445
76.8%
Lowercase Letter
ValueCountFrequency (%)
e 55
13.3%
t 47
11.4%
n 39
9.5%
r 36
 
8.7%
i 32
 
7.8%
a 29
 
7.0%
o 24
 
5.8%
s 22
 
5.3%
h 21
 
5.1%
m 17
 
4.1%
Other values (13) 90
21.8%
Uppercase Letter
ValueCountFrequency (%)
D 7
20.6%
I 6
17.6%
G 4
11.8%
P 4
11.8%
L 3
8.8%
R 3
8.8%
T 2
 
5.9%
A 2
 
5.9%
U 1
 
2.9%
H 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 1564
80.3%
* 301
 
15.5%
/ 33
 
1.7%
. 20
 
1.0%
· 13
 
0.7%
' 8
 
0.4%
: 3
 
0.2%
& 3
 
0.2%
# 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 23
27.7%
1 20
24.1%
2 15
18.1%
5 9
 
10.8%
3 8
 
9.6%
7 3
 
3.6%
9 2
 
2.4%
4 2
 
2.4%
8 1
 
1.2%
Math Symbol
ValueCountFrequency (%)
~ 5
71.4%
+ 2
 
28.6%
Space Separator
ValueCountFrequency (%)
2775
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 174
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11003
66.3%
Common 5143
31.0%
Latin 446
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
339
 
3.1%
319
 
2.9%
318
 
2.9%
258
 
2.3%
253
 
2.3%
221
 
2.0%
216
 
2.0%
213
 
1.9%
211
 
1.9%
210
 
1.9%
Other values (358) 8445
76.8%
Latin
ValueCountFrequency (%)
e 55
12.3%
t 47
 
10.5%
n 39
 
8.7%
r 36
 
8.1%
i 32
 
7.2%
a 29
 
6.5%
o 24
 
5.4%
s 22
 
4.9%
h 21
 
4.7%
m 17
 
3.8%
Other values (24) 124
27.8%
Common
ValueCountFrequency (%)
2775
54.0%
, 1564
30.4%
* 301
 
5.9%
_ 174
 
3.4%
( 79
 
1.5%
) 78
 
1.5%
/ 33
 
0.6%
0 23
 
0.4%
1 20
 
0.4%
. 20
 
0.4%
Other values (14) 76
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11000
66.3%
ASCII 5576
33.6%
None 13
 
0.1%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2775
49.8%
, 1564
28.0%
* 301
 
5.4%
_ 174
 
3.1%
( 79
 
1.4%
) 78
 
1.4%
e 55
 
1.0%
t 47
 
0.8%
n 39
 
0.7%
r 36
 
0.6%
Other values (47) 428
 
7.7%
Hangul
ValueCountFrequency (%)
339
 
3.1%
319
 
2.9%
318
 
2.9%
258
 
2.3%
253
 
2.3%
221
 
2.0%
216
 
2.0%
213
 
1.9%
211
 
1.9%
210
 
1.9%
Other values (357) 8442
76.7%
None
ValueCountFrequency (%)
· 13
100.0%
Compat Jamo
ValueCountFrequency (%)
3
100.0%

분석항목
Text

MISSING 

Distinct244
Distinct (%)100.0%
Missing737
Missing (%)75.1%
Memory size7.8 KiB
2024-04-18T01:16:23.080880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length201
Median length87
Mean length57.340164
Min length8

Characters and Unicode

Total characters13991
Distinct characters429
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique244 ?
Unique (%)100.0%

Sample

1st row*계층의식과 상향이동의식의 코호트 효과*계층의식과 상향의식의 연령효과*계층의식과 상향의식의 기간효과
2nd row*50세 이상 남성의 인구비중 및 경제활동참여율*연령증가에 따른 경제활동참여율 추이*도시, 농촌별 연도별 경제활동참여율 추이비교*노년기 남성의 취업 여부 추정모델
3rd row*'96과 '00의 소득 분위별 소득, 가구주 고용율, 배우자 고용율, 시간당 임금, 근로시간의 비교*최상, 최하 10분위 가구간 소득격차 확대요인 분해
4th row*사망률 추정*사망원인별 사망률 추정
5th row*지역별 소득불평등도와 공적보조금의 효과*지역내 지역간 소득불평등도 분해*소득원별 불평등도 분해
ValueCountFrequency (%)
73
 
2.6%
분석 63
 
2.2%
미치는 52
 
1.9%
영향 29
 
1.0%
따른 26
 
0.9%
대한 26
 
0.9%
결정요인 20
 
0.7%
효과 16
 
0.6%
변화 16
 
0.6%
관련 14
 
0.5%
Other values (1633) 2475
88.1%
2024-04-18T01:16:23.462714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2572
 
18.4%
* 679
 
4.9%
386
 
2.8%
229
 
1.6%
225
 
1.6%
220
 
1.6%
217
 
1.6%
170
 
1.2%
170
 
1.2%
154
 
1.1%
Other values (419) 8969
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9670
69.1%
Space Separator 2572
 
18.4%
Other Punctuation 800
 
5.7%
Lowercase Letter 655
 
4.7%
Uppercase Letter 213
 
1.5%
Decimal Number 66
 
0.5%
Close Punctuation 5
 
< 0.1%
Open Punctuation 5
 
< 0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
386
 
4.0%
229
 
2.4%
225
 
2.3%
220
 
2.3%
217
 
2.2%
170
 
1.8%
170
 
1.8%
154
 
1.6%
152
 
1.6%
149
 
1.5%
Other values (351) 7598
78.6%
Lowercase Letter
ValueCountFrequency (%)
e 83
12.7%
t 62
 
9.5%
o 54
 
8.2%
i 54
 
8.2%
n 49
 
7.5%
r 48
 
7.3%
a 44
 
6.7%
s 30
 
4.6%
c 26
 
4.0%
l 25
 
3.8%
Other values (14) 180
27.5%
Uppercase Letter
ValueCountFrequency (%)
E 26
12.2%
D 20
 
9.4%
I 16
 
7.5%
R 14
 
6.6%
F 14
 
6.6%
S 13
 
6.1%
P 12
 
5.6%
T 12
 
5.6%
O 12
 
5.6%
N 10
 
4.7%
Other values (12) 64
30.0%
Decimal Number
ValueCountFrequency (%)
1 24
36.4%
0 14
21.2%
2 9
 
13.6%
5 6
 
9.1%
3 5
 
7.6%
9 3
 
4.5%
4 2
 
3.0%
6 2
 
3.0%
7 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
* 679
84.9%
, 58
 
7.2%
· 31
 
3.9%
/ 12
 
1.5%
: 8
 
1.0%
' 6
 
0.8%
& 6
 
0.8%
Math Symbol
ValueCountFrequency (%)
~ 2
40.0%
> 2
40.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
2572
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9670
69.1%
Common 3453
 
24.7%
Latin 868
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
386
 
4.0%
229
 
2.4%
225
 
2.3%
220
 
2.3%
217
 
2.2%
170
 
1.8%
170
 
1.8%
154
 
1.6%
152
 
1.6%
149
 
1.5%
Other values (351) 7598
78.6%
Latin
ValueCountFrequency (%)
e 83
 
9.6%
t 62
 
7.1%
o 54
 
6.2%
i 54
 
6.2%
n 49
 
5.6%
r 48
 
5.5%
a 44
 
5.1%
s 30
 
3.5%
c 26
 
3.0%
E 26
 
3.0%
Other values (36) 392
45.2%
Common
ValueCountFrequency (%)
2572
74.5%
* 679
 
19.7%
, 58
 
1.7%
· 31
 
0.9%
1 24
 
0.7%
0 14
 
0.4%
/ 12
 
0.3%
2 9
 
0.3%
: 8
 
0.2%
' 6
 
0.2%
Other values (12) 40
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9669
69.1%
ASCII 4289
30.7%
None 31
 
0.2%
Math Operators 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2572
60.0%
* 679
 
15.8%
e 83
 
1.9%
t 62
 
1.4%
, 58
 
1.4%
o 54
 
1.3%
i 54
 
1.3%
n 49
 
1.1%
r 48
 
1.1%
a 44
 
1.0%
Other values (56) 586
 
13.7%
Hangul
ValueCountFrequency (%)
386
 
4.0%
229
 
2.4%
225
 
2.3%
220
 
2.3%
217
 
2.2%
170
 
1.8%
170
 
1.8%
154
 
1.6%
152
 
1.6%
149
 
1.5%
Other values (350) 7597
78.6%
None
ValueCountFrequency (%)
· 31
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

학술지
Text

MISSING 

Distinct475
Distinct (%)84.7%
Missing420
Missing (%)42.8%
Memory size7.8 KiB
2024-04-18T01:16:23.664448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length96
Median length66
Mean length31.26025
Min length4

Characters and Unicode

Total characters17537
Distinct characters296
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique435 ?
Unique (%)77.5%

Sample

1st row한국사회학(한국사회학회)
2nd rowEconomic Development and Cultural Changes(The university of Chicago Press Journals)
3rd row노동경제논집(한국노동경제학회)
4th row한국경제의 분석(한국경제의 분석패널)
5th row농업경제연구(한국농업경제학회)
ValueCountFrequency (%)
pages 244
 
9.5%
2022 133
 
5.2%
pp 96
 
3.7%
2021 75
 
2.9%
통권 68
 
2.6%
21 31
 
1.2%
no.1 30
 
1.2%
2020 30
 
1.2%
of 26
 
1.0%
2023 26
 
1.0%
Other values (1002) 1819
70.6%
2024-04-18T01:16:23.978712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2051
 
11.7%
2 1205
 
6.9%
1 793
 
4.5%
. 774
 
4.4%
p 750
 
4.3%
, 737
 
4.2%
o 667
 
3.8%
0 481
 
2.7%
3 432
 
2.5%
a 369
 
2.1%
Other values (286) 9278
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4516
25.8%
Decimal Number 4157
23.7%
Lowercase Letter 4091
23.3%
Space Separator 2051
11.7%
Other Punctuation 1796
 
10.2%
Close Punctuation 325
 
1.9%
Open Punctuation 325
 
1.9%
Uppercase Letter 274
 
1.6%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
286
 
6.3%
266
 
5.9%
262
 
5.8%
258
 
5.7%
248
 
5.5%
238
 
5.3%
162
 
3.6%
147
 
3.3%
145
 
3.2%
134
 
3.0%
Other values (218) 2370
52.5%
Lowercase Letter
ValueCountFrequency (%)
p 750
18.3%
o 667
16.3%
a 369
9.0%
e 365
8.9%
n 355
8.7%
l 323
7.9%
s 303
7.4%
g 258
 
6.3%
v 257
 
6.3%
i 82
 
2.0%
Other values (13) 362
8.8%
Uppercase Letter
ValueCountFrequency (%)
S 28
10.2%
K 28
10.2%
J 26
9.5%
T 24
8.8%
E 22
 
8.0%
D 21
 
7.7%
V 19
 
6.9%
N 19
 
6.9%
A 16
 
5.8%
I 13
 
4.7%
Other values (11) 58
21.2%
Decimal Number
ValueCountFrequency (%)
2 1205
29.0%
1 793
19.1%
0 481
 
11.6%
3 432
 
10.4%
4 272
 
6.5%
5 227
 
5.5%
7 194
 
4.7%
9 194
 
4.7%
6 184
 
4.4%
8 175
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 774
43.1%
, 737
41.0%
* 255
 
14.2%
· 12
 
0.7%
' 10
 
0.6%
: 6
 
0.3%
/ 1
 
0.1%
& 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 306
94.2%
] 19
 
5.8%
Open Punctuation
ValueCountFrequency (%)
( 306
94.2%
[ 19
 
5.8%
Space Separator
ValueCountFrequency (%)
2051
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8656
49.4%
Hangul 4511
25.7%
Latin 4365
24.9%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
286
 
6.3%
266
 
5.9%
262
 
5.8%
258
 
5.7%
248
 
5.5%
238
 
5.3%
162
 
3.6%
147
 
3.3%
145
 
3.2%
134
 
3.0%
Other values (214) 2365
52.4%
Latin
ValueCountFrequency (%)
p 750
17.2%
o 667
15.3%
a 369
8.5%
e 365
8.4%
n 355
8.1%
l 323
7.4%
s 303
6.9%
g 258
 
5.9%
v 257
 
5.9%
i 82
 
1.9%
Other values (34) 636
14.6%
Common
ValueCountFrequency (%)
2051
23.7%
2 1205
13.9%
1 793
 
9.2%
. 774
 
8.9%
, 737
 
8.5%
0 481
 
5.6%
3 432
 
5.0%
) 306
 
3.5%
( 306
 
3.5%
4 272
 
3.1%
Other values (14) 1299
15.0%
Han
ValueCountFrequency (%)
2
40.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13009
74.2%
Hangul 4511
 
25.7%
None 12
 
0.1%
CJK 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2051
15.8%
2 1205
 
9.3%
1 793
 
6.1%
. 774
 
5.9%
p 750
 
5.8%
, 737
 
5.7%
o 667
 
5.1%
0 481
 
3.7%
3 432
 
3.3%
a 369
 
2.8%
Other values (57) 4750
36.5%
Hangul
ValueCountFrequency (%)
286
 
6.3%
266
 
5.9%
262
 
5.8%
258
 
5.7%
248
 
5.5%
238
 
5.3%
162
 
3.6%
147
 
3.3%
145
 
3.2%
134
 
3.0%
Other values (214) 2365
52.4%
None
ValueCountFrequency (%)
· 12
100.0%
CJK
ValueCountFrequency (%)
2
40.0%
1
20.0%
1
20.0%
1
20.0%
Distinct979
Distinct (%)99.9%
Missing1
Missing (%)0.1%
Memory size7.8 KiB
2024-04-18T01:16:24.306720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9020408
Min length1

Characters and Unicode

Total characters2844
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique978 ?
Unique (%)99.8%

Sample

1st row24
2nd row1
3rd row2
4th row4
5th row5
ValueCountFrequency (%)
vol.21 2
 
0.2%
611 1
 
0.1%
673 1
 
0.1%
616 1
 
0.1%
622 1
 
0.1%
572 1
 
0.1%
573 1
 
0.1%
570 1
 
0.1%
574 1
 
0.1%
576 1
 
0.1%
Other values (969) 969
98.9%
2024-04-18T01:16:24.720842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 301
10.6%
1 301
10.6%
3 298
10.5%
7 297
10.4%
4 296
10.4%
6 293
10.3%
5 293
10.3%
8 291
10.2%
9 271
9.5%
0 188
6.6%
Other values (5) 15
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2829
99.5%
Lowercase Letter 9
 
0.3%
Space Separator 3
 
0.1%
Other Punctuation 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 301
10.6%
1 301
10.6%
3 298
10.5%
7 297
10.5%
4 296
10.5%
6 293
10.4%
5 293
10.4%
8 291
10.3%
9 271
9.6%
0 188
6.6%
Lowercase Letter
ValueCountFrequency (%)
v 3
33.3%
o 3
33.3%
l 3
33.3%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2835
99.7%
Latin 9
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 301
10.6%
1 301
10.6%
3 298
10.5%
7 297
10.5%
4 296
10.4%
6 293
10.3%
5 293
10.3%
8 291
10.3%
9 271
9.6%
0 188
6.6%
Other values (2) 6
 
0.2%
Latin
ValueCountFrequency (%)
v 3
33.3%
o 3
33.3%
l 3
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 301
10.6%
1 301
10.6%
3 298
10.5%
7 297
10.4%
4 296
10.4%
6 293
10.3%
5 293
10.3%
8 291
10.2%
9 271
9.5%
0 188
6.6%
Other values (5) 15
 
0.5%

Interactions

2024-04-18T01:16:17.316174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:16:17.169235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:16:17.391845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:16:17.239164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:16:24.797591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연구관리번호연구연도
연구관리번호1.0000.804
연구연도0.8041.000
2024-04-18T01:16:24.857717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연구관리번호연구연도
연구관리번호1.0000.965
연구연도0.9651.000

Missing values

2024-04-18T01:16:17.497574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:16:17.621848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-04-18T01:16:17.725492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연구관리번호연구제목연구책임자공동연구자소속연구연도연구목적키워드공개여부이용항목분석항목학술지연구번호
013한국사회의 계층귀속감과 상향이동의식 변화: 연령(Age), 기간(Period) 및 코호트(Cohort) 효과를 중심으로이왕원김문조, 최율고려대학교 사회학과2016IMF 환란 이후 사회 불평등 의식에 관한 담론이 사회 각계에서 널리 움트고 있다는 점에 착안해, 경제적 위기 국면에서 새로 출현하는 사회 불평등 의식을 보다 정치하게 분석해보자는 동기에서 출발한 것으로, 주관적 계층귀속감(subjective class identification)과 상향이동의식(upward mobility consciousness)을 집중적으로 다룸#불평등, #계층귀속감, #중상층, #상향이동, #코호트, #APC모형Y* 계층의식, 본인의 계층이동, 다음세대의 계층이동*계층의식과 상향이동의식의 코호트 효과*계층의식과 상향의식의 연령효과*계층의식과 상향의식의 기간효과한국사회학(한국사회학회)24
11Long-Term Changes in the Economic Activity of Older Males in Korea이철희<NA>서울대학교 경제학부200720세기 후반 한국의 노년남성층의 경제활동참가율을 분석하여, 장기적 관점으로 이들의 은퇴행동의 변화와 패턴 및 원인을 규명하고자 함#labor force participationrate, #older males, #Early retirement, #경제활동참가율, #취업자Y* 연령, 성별, 취학여부, 일하였음, 구직, 이유, 산업, 학력, 혼인관계, 세대구성, 가족구성원수, 거주지*50세 이상 남성의 인구비중 및 경제활동참여율*연령증가에 따른 경제활동참여율 추이*도시, 농촌별 연도별 경제활동참여율 추이비교*노년기 남성의 취업 여부 추정모델Economic Development and Cultural Changes(The university of Chicago Press Journals)1
221996~2000년 한국의 가구소득불평등 확대 - 임금, 노동공급, 가구구조 변화의 영향이철희<NA>서울대학교 경제학부2008가구주와 배우자의 임금, 고용, 근로시간, 그리고 기타소득과 가구구조 등 가구소득을 구성하는 각 요인들이 1996년과 2000년 사이 가구소득불평등도의 증가에 미친 효과를 분석#가구소득불평등, #임금, #고용, #근로시간, #가구구조, #외환위기Y* 가구유형, 경상소득(임금근로, 사업, 이자배당, 연금, 기타), 비경상소득(퇴직금, 비경상보조금 등), 주된활동, 직업분류, 산업분류* 활동상태, 취업시간*'96과 '00의 소득 분위별 소득, 가구주 고용율, 배우자 고용율, 시간당 임금, 근로시간의 비교*최상, 최하 10분위 가구간 소득격차 확대요인 분해노동경제논집(한국노동경제학회)2
33경기침체는 건강에 이로운가 1991년~2009년 한국의 실업률과 사망률이철희김태훈서울대학교 경제학부20111991년에서 2009년 한국의 광역시도별 시계열 자료를 이용하여 한국에 있어서의 경기변동과 사망률 간의 관계를 분석함#경기변동, #실업, #건강, #사망, #환경오염, #근로환경, #스트레스, #음주, #흡연Y* 사망자주소, 사망년월일, 사망원인, 사망자 직업, 사망자 성별, 사망년령, 교육정도*사망률 추정*사망원인별 사망률 추정한국경제의 분석(한국경제의 분석패널)4
44공적보조금이 지역내 지역간 농가소득불평등에 미치는 영향 분석김태이임정빈, 안동환서울대학교 농업자원경제학과2012농가소득 불평등의 심화요인과 정책효과를 살펴보기 위해 지니계수를 이용하여 공적보조금이 농가소득의 불평등도에 미치는 영향을 분석하고 농가소득 불평등도의 지역간 차이를 비교 분석#농가소득 불평등, #지역간 불평등,#지역내 불평등, #지니계수, #공적보조금Y* 농가소득, 농업소득, 농외소득, 공적보조금, 사적보조금*지역별 소득불평등도와 공적보조금의 효과*지역내 지역간 소득불평등도 분해*소득원별 불평등도 분해농업경제연구(한국농업경제학회)5
56경제적 변화와 아들선호:한국 여성의 노동시장성과와 출생성비이철희<NA>서울대학교 경제학부2013출생성비결정에 대한 경제학적 연구를 확장하여 여성의 노동시장성과의 상대적인 개선이 출생성비에 미친 영향을 분석#아들선호, #출생성비, #여성노동시장지위, #암금, #고용Y* 출생자 주소지(시군구), 모교육정도*시군구 수준 출생 성비 결정요인 분석*모친의 학력별 출생성비 결정요인 분석*도시화 정보별 출생성비 결정요인 분석응용경제(한국응용경제학회)8
67지역특화산업을 중심으로한 클러스터 집적화 분석조은설<NA>강원대학교 행정학과2014클러스터 형성의 기본이 되는 집적화가 어느정도 이루어졌는지 그리고 시간의 흐름에 따라 집적화 정도(집적성, 연계성, 경제성 측면으로 구분)가 어떻게 변화했는지를 분석#클러스터, #혁신클러스터, #집적화Y* 행정구역, 조사부문 종사자 합계, 광업제조업 부문 종사자 합계, 부가가치액*혁신클러스터 특화분야 고용 현황*혁신클러스터 종사자 LQ(입지상수)*혁신클러스터 단지별 특화분야 부가가치 규모 비중한국행정과 정책연구(강원행정학회)9
78Intergenerational health consequences of in utero exposure to maternal stress: Evidence from the 1980 Kwangju uprising (1980년 광주항쟁으로 인한 태아기 스트레스가 후속세대의 건강에 미친 효과)이철희<NA>서울대학교 경제학부2014광주항쟁 기간의 임산부의 스트레스가 아이들의 태아건강에 부정적인 영향을 미쳤으며, 심지어 차세대의 건강에 까지 악영향을 미쳤을 것이라는 가정에 대해 실증적으로 분석함#Stress in pregnancy, #Birth outcomes, #Low birth weight, #Preterm birth, #Intergenerational effect, #Kwangju uprisingY* 성별, 출생장소, 출생일, 임신주수, 출생아 체중, 다태아 출생순위, 부연령, 모연령, 부교육정도, 모교육정도, 부직업, 모직업, 모출생아수*Mother's in utero exposure to the Kwangju uprising and offspring birth weight *Mother's in utero exposure to the Kwangju uprising and birth outcomesSocial Science Medicine(ScienceDirect)10
89The effects of subsidies for childbearing on migration and fertility: Evidence from Korea홍성효Ryan Sullivan공주대학교 경제무역학과2015보조금의 규모가 지역에 따라 4천불에서 9천불에 이르는 한국내 특정 지역에 설립된 출산보조금의 효과를 분석함#fertility, #migration, #subsidy, #differnce-in-differences, #출산, #이주, #보조금, #이중차분법Y* 나이, 교육수준, 혼인상태, 5년전 거주지, 부인출생아수* 출산율 추정모형* 출산장려금이 거주지 이전에 미치는 영향The Singapore Economic Review11
911노후소득 수준의 장기적 변화: 코호트 분석 결과이철희이재원서울대학교 경제학부2015노후소득 보장관련 시사점을 얻기 위해, 장년 대비 노후소득대체율을 여러 출생 코호트에 대해 추정하고, 연령에 따른 노후소득 보장성 변화를 분석함#소득대체율, #노후소득 보장, #고령 빈곤, #코호트 분석, #소득원천, #소득계총Y* 임금근로소득, 사업소득, 임대소득, 연금, 공적연금, 재산소득, 퇴직금, 연금일시금, 사회보장 수혜*코호트별 장년소득 대비 노후소득 변화한국경제연구(한국경제연구학회)13
연구관리번호연구제목연구책임자공동연구자소속연구연도연구목적키워드공개여부이용항목분석항목학술지연구번호
9711072은퇴 부부는 여가시간을 어떻게 보내는가: 부부공유 여가시간과 여가만족도 및 기분상태의 관련성김혜중<NA>서울대학교2023은퇴한 부부가 여가시간을 어떻게 사용하고 어떤 활동에 배우자와 함께 참여하며, 배우자와 함께 공유하는 여가활동이 여가만족도 및 기분상태와 관련이 있는지를 살펴보는 것#은퇴부부, #여가시간, #부부공유여가, #여가만족도, #기분상태, #생활시간조사Y<NA><NA>한국가족관계학회지 28권 1호 159*181(23pages)976
9721073COVID-19 대유행 전·후 시기의 대한민국 노인의 여가 불만족에 관한 연구손재영문태영, 허중욱강원대학교2023대한민국의 노인을 대상으로 COVID-19 대유행 전후 시기에 노인의 여가 불만족 요인을 확인하는 것을 연구 목적으로 함#COVID-19 대유행, #노인, #여가 불만족, #여가제약, #생활만족Y<NA><NA>관광연구저널 제37권 제2호 71 * 86 (16page)977
9731076프로빗 모형을 이용한 2016년 가계금융복지조사 자료분석김민경이근백성균관대학교2023가계 금융투자의 주된 목적에 영향을 주는 인구통계학적 요인을 MNP 모형을 이용하여 분석하고자 함#가계금융·복지조사, #다항 프로빗 모형, #명목형 자료, #MCMC 알고리즘Y<NA><NA>한국데이터정보과학회지 제34권 제3호 443 * 458 (16page)980
9741077노인의 고령화에 따른 작업영역별 시간 사용과 여가·삶의 만족도 변화 분석김선우유두한건양대학교2023노인의 고령화에 따른 작업영역별 시간 사용과 여가·삶의 만족도 변화를 분석하고자 함#노인, #작업영역, #삶의 만족도, #여가 만족도Y<NA><NA>대한지역사회작업치료학회지 Vol.13 No.1 [2023]981
9751064비정규직의 일자리 이동성 분석유경준권태구대한민국 국회 국회의원(유경준), 한국기술교육대학교(권태구)2023비정규직 임금근로자의 경제활동상태 변동, 그중에서도 정규직으로의 근로형태 전환을 분석하고자 함#비정규직, #노동시장 분절, #일자리 이동성, #이행행렬Y<NA><NA>Journal of The Korean Data Analysis Society 2023, vol.25, no.2, pp. 599*614 (16 pages)968
9761065거시경제 환경 변화에 따른 가계부채 리스크 점검과 시사점김영일<NA>NICE평가정보2023최근 거시경제 환경의 변화가 가계부채 부실위험에 미칠 영향을 분석하기 위함#가계부채, #신용위험, #스트레스 테스트Y<NA><NA>한국경제포럼 2023, vol.16, no.1, pp. 1*37 (37 pages)969
9771066남녀 고령자의 고용현황 및 취업실태 분석김복태<NA>한국여성정책연구원2022고령층의 경제적 지위 향상 및 경제활동참여 촉진을 위한 정책적 기원을 강화하고자 함#남녀 고령자, #고용현황, #취업실태, #적극적 고용정책Y<NA><NA>한국여성정책연구원 연구보고서 Vol.2021 No.* [2022]970
9781074특허기반 개방형 혁신이 기업성과에 미치는 영향김혁준곽현한국지식재산연구원2023외부 R&D 지출액 규모를 개방형 혁신활동 참여 정 도로 보고 특허권 보유와 개방형 혁신이 기업의 매출액 증가에 미치는 영향을 분석하고자 함#개방형혁신, #특허, #기업경영, #외부 R&D지출액, #매출액Y<NA><NA>産業財産權(Journal of Industrial Property) Vol.* No.74 [2023]978
9791075팬데믹 시대 정부지원금이 가계소비에 미치는 효과분석: 제품군 및 가계특성 비교를 중심으로이제영<NA>충북대학교2023팬데믹 시대 정부지원금의 제품군 및 가계특성별 소비진작 효과를 분석하기 위함#팬데믹, #정부지원금, #가계동향조사, #SOR 모델, #성향점수매칭, #이중차분모형Y<NA><NA>유통연구 제28권 제2호 67 * 95 (29page)979
9801078다주택 중과세제 분석과 정책방향최진섭이슬이한국지방세연구원2022다주택 중과세제(취득세·보유세·양도소득세)를 분석하고 정책방향을 논의하는 데에 목적이 있음#다주택 중과세, #취득세, #보유세, #양도소득세, #주택가격Y<NA><NA><NA>982