Chapter 6 -- Research Design and Methods of Analysis for Change Over Time





COWI: Chapter 6
Last Modified 15 August 1998

Many of
the problems that social scientists study are dynamic in character. A sociologist
interested in the process of friendship formation would want to look at the
way friendship dyads are formed over time. A political scientist interested
in voting behavior would look at the ways in which people make up their minds
for whom to vote. An economist studying consumer optimism might want to trace
consumer sentiment over a period of years and see how that relates to other
changes in the economy and the society.

A survey that
looks at a cross-section of a group at one point in time (often called a cross-sectional
survey) is not well suited to a study of dynamic processes. The political
scientist interested in voting behavior might include questions that asked
respondents who they had intended to vote for at the beginning of the campaign
and who they plan to vote for now. This would allow an analysis of how voting
decisions have changed over the campaign, but at a severe cost. The respondents'
memory of who they preferred at the beginning of the campaign might be influenced
by who they prefer later. We need some way to adapt cross-sectional surveys
to the study of dynamic problems in which the focus is on change over time.

Cross-sectional
studies
focus on a group at one point in time. The Census is a good example
of a cross-sectional study. The 1990 Census describes the U.S. population
at one point in time -- April, 1990. Longitudinal studies focus on
the group at two or more points in time. We're going to look at three types
of longitudinal studies -- the trend study, the panel study, and the cohort
study.

TREND STUDIES

Trend studies
use cross-sections at two or more points in time to examine change over time.
The Virginia Slims Opinion Poll asked various questions about women at six
points in time (The Roper Center 1990). The first poll was conducted in 1970,
and it was repeated in 1972, 1974, 1980, 1985, and 1990. The Virginia Slims
polls are national probability samples of all adults living in the United
States. These six cross-sectional surveys can be compared to trace changes
in opinions and attitudes about women from 1970 to 1990.

Figure 6.1 shows
the percent of women who favor efforts to strengthen and change women's status
in society. The percent who favor such efforts has increased steadily from
1970, while the percent who oppose has decreased.

Figure 6.1

Figure 6.2 shows
the same information for men. The pattern is the same for men as it is for
women, but the percent of women favoring change is now larger (77%) than the
percent of men (74%).


Figure 6.2

In 1985, 1991,
and 1995, the Field Poll asked virtually the same question of a sample of
California residents age 18 and over. Figure 6.3 shows the percent of respondents
who favor such efforts. We got these percentages by crosstabulating the dependent
variable (i.e., favor or oppose efforts) by time (i.e., year). Clearly the
changes in opinion in California are similar to the national patterns.


Figure 6.3

The Virginia
Slims Polls showed the percentages separately for women and men. We can get
this information for the Field Polls by crosstabulating the dependent variable
by time and holding sex constant. This will give us two sets of figures, one
for women and one for men. Figure 6.4 shows the percent of women who favor
changes in women's roles, while Figure 6.5 shows the same for men. Notice
that the percentages increased for women from 72.1% in 1985 to 83.7% in 1991
and then to 85.1% in 1995, while the percentages for men increased from 75.0%
in 1985 to 83.7% in 1991 and then decreased to 79.3% in 1995.


Figure 6.4


Figure 6.5

Let's look at
the Field Polls for another example of change from 1985 to 1995. Figure 6.6
shows the percent who favor strengthening the status of women for three age
groups--those under 30, those 30 to 49, and those 50 and over. We can learn
several things from this table. First, the percent who favor such change increased
in all age groups from 1985 to 1991 and then stayed about the same in 1995.
Second, in all time periods, those 50 and over are least likely to favor such
change.


Figure 6.6

You might have
noticed one difference between the Virginia Slims example and the Field Poll
example. The Virginia Slims data are based on six time periods, while the
Field Poll data are based on three time periods. Does this make any difference?
Figure 6.7 shows the percent who favor efforts to strengthen the status of
women in 1985 and 1991 in the Field Poll. Clearly the percent who favor increased
from 1985 to 1991. Let's imagine what the pattern might look like if we had
data from four time periods prior to 1985. Figure 6.7 shows a long-term trend
of increasing support. Figure 6.8 shows a long-term trend of little change
with an increase from 1985 to 1991. Figure 6.9 shows a long-term trend of
decreasing support with a reversal in this trend from 1985 to 1991. Without
more time periods it is very difficult to determine what the long-term trend
actually looks like. The 1995 Field Poll indicates that there has been little
change from 1991 to 1995. This example suggests that many time periods are
better than few time periods. However, remember that every trend analysis
must start with two points in time and build from that to a longer series.


Figure 6.7


Figure 6.8

Figure 6.9

Figure 6.8 indicates
that there was a ten percentage point increase from 1985 to 1991 in the percent
of respondents who favor strengthening the status of women. Was this produced
by the shift of some individuals who opposed such changes in 1985 (or had
no opinion) to a position that favors these changes in 1991? While it might
seem tempting to accept this interpretation, it is not necessarily true. Table
6.1 shows a hypothetical example that is consistent with this interpretation.
All of the individuals who favored change in 1985 also favor change in 1991.
However, some of those who opposed change in 1985 now favor change in 1991
(n = 26), while more have shifted to a position of don't know (n = 76). Table
6.2 shows an example in which there has been a considerable shift in opinion
in both directions--from oppose to favor and from favor to oppose. A considerable
number of those who favored change in 1985 oppose change in 1991 (n = 100),
while many of those who opposed change in 1985 now favor change (n = 115)
or say they don't know (n = 87). Unfortunately, in a trend study we cannot
choose between these alternatives. The most we can do in a trend study is
to describe net changes between time periods. If we want to describe shifts
from favor to oppose or from oppose to favor, then we need a different type
of data called panel data.


PANEL STUDIES

Panel studies
describe information about the same cases at two or more points in time. In
a trend study we compare sample surveys describing the same population at
two or more points in time. These samples consist of different cases. In a
panel study we compare the same cases over time. If the Field Poll had been
able to reinterview the same individuals in 1991 and 1995 that were interviewed
in 1985, then it would have been a panel study. The advantage of panel studies
is that we can choose between alternatives such as those presented in Tables
6.1 and 6.2. Panel data allow us to go beyond describing net changes between
time periods. Panel data allow us to describe the types of shifts (e.g., from
favor to oppose or from oppose to favor) that occur between time periods.
We can also begin looking for factors that explain why some people change
in one direction, while other individuals change in another direction, and
still others do not change at all.

Table
6.1 -- Hypothetical Example Showing Opinion Shifts from 1985 to 1991
1985
1991
Favor 
Oppose 
Don't
Know 
Total 
Favor 
674 
26 
25 
725 
Oppose 
141 
141 
Don't
Know 
76 
58 
134 
Total 
674 
243 
83 
1000 


Table
6.2 -- Hypothetical Example Showing Opinion Shifts from 1985 to 1991
1985
1991
Favor 
Oppose 
Don't
Know 
Total 
Favor 
574 
115 
36 
725 
Oppose 
100 
41 
141 
Don't
Know 
87 
47 
134 
Total 
674 
243 
83 
1000 


However, there are
also problems with panel data. It is rare that all the cases are available in
later time periods. This is called panel mortality. When the case is
the individual, this may be because some individuals are not alive at a later
point in time. However, all panel mortality may not be due to the death of respondents.
Some respondents who cooperated initially may choose not to cooperate later.
If particular types of individuals choose not to cooperate at a later point
in time, then bias is introduced. For example, if low income respondents choose
not to cooperate at a later point in time and if low income respondents are
less likely to favor efforts to change the status of women, then part of the
shift in opinion might be due to panel mortality.

Another problem
with panel data is reactivity. If we ask people questions about the
status of women at two or more points in time, the questioning process itself
might produce opinion shifts. Perhaps the act of asking people about the status
of women makes them more sensitive to women's issues. This increased sensitivity
might mean they are more likely to favor or to oppose changes in the status
of women during later surveys. We call this reactivity because the respondents
are reacting to the initial questioning.


COHORT STUDIES

Table 6.3 shows
the percent who disagree that "women should take care of running their homes
and leave running the country to men" by age for three of the General Social
Surveys (1975, 1983, 1991). The General Social Survey is a national probability
sample of all adults living in the United States (Davis and Smith 1992). This
table can be analyzed in several ways.

Table
6.3. Percent of Respondents Who Disagreed that "Women should take care
of their homes and leave running the country up to men."
Age 1975
(n)
1983
(n)
1991
(n)
18-25 78.5
(251) 
87.2
(203) 
85.0
(120) 
26-33 75.6
(258) 
84.7
(360) 
90.1
(172) 
34-41 67.5
(194) 
83.0
(241) 
91.5
(201) 
42-49 68.3
(183) 
81.4
(177) 
86.5
(133) 
50-57 58.4
(173) 
76.3
(160) 
78.0
(82) 
58-65 56.1
(155) 
66.5
(167) 
80.2
(101) 
66-73 39.0
(136) 
57.6
(125) 
58.0
(81) 
74+ 42.9
(91) 
47.5
(99) 
48.9
(90) 
Total 64.4
(1441) 
76.9
(1532) 
80.8
(980) 

* The
data are from the General Social Survey. The values inside the parentheses
are the number of cases on which the percentages are based (i.e.,
the bases).


First, we can compare
opinions in the three time periods. We could compare the percent who disagree
for each time period. This would show that there has been growing disagreement
with this statement (64.4% of the total sample disagreed in 1975, 76.9% in 1983,
and 80.8% in 1991). We could also compare the percent who disagree within each
age group. For example, we could compare those age 34 to 41 in each of the three
time periods (67.5% of those 34 to 41 disagree in 1975, 83.0% in 1983, and 91.5%
in 1991) and repeat this for each of the age categories. This would involve
comparing percentages across each row and this would show that there has been
increasing disagreement even when we hold age constant.

Second, we could
compare age categories within each time period. This would involve comparing
percentages down within columns. This would show that the older respondents
are less likely to disagree in each time period. In each of the three time
periods, those age 66 to 73 and those 74 and over are considerably less likely
to disagree than the younger respondents. (Note: even they increase over time--42.9%
in 1975, 47.5% in 1983, 48.9% in 1991.)

Third, we could
compare birth cohorts. Groups of people born within the same time period are
called birth cohorts. The time period can be defined in any way that
is appropriate for your analysis. Here we are using eight-year periods--all
the people born within an eight-year period belong to the same birth cohort.
Those who are 18 to 25 in 1975 would be 26 to 33 in 1983 and 34 to 41 in 1991.
We could look at each of the birth cohorts in this table separately. This
would involve comparing percentages along the diagonals running from the upper
left part of the table to the lower right part. For example, for the birth
cohort who was 18 to 25 in 1975, the percentages would be 78.5 in 1975, 84.7
in 1983, and 91.5 in 1991. In general, the cohorts are more likely to disagree
with the statement in each successive time period. We could also compare other
birth cohorts. The pattern described above is particularly noticeable for
the four younger cohorts (i.e., those 18 to 25, 26 to 33, 34 to 41, and 42
to 49 in 1975).

Cohort studies
follow one or more cohorts (usually at least two) over a period of time. Cohort
studies are usually based on two or more cross-sectional studies. In the example
above, we have used three cross-sectional surveys (1975, 1983, 1991) and arranged
the data so that we can compare birth cohorts.

SUMMARY

The purpose of
this chapter has been to start you thinking about questions related to change
over time. The analysis of such questions require longitudinal data. We have
described three types of longitudinal studies -- the trend study, the panel
study, and the cohort study. The exercises in the next chapter will give you
some practice in exploring changes in opinions on women's issues by using
the Field Polls conducted in 1985, 1991, and 1995.